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Protein domain
Protein domain
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Pyruvate kinase, a protein with three domains (PDB: 1PKN​).

In molecular biology, a protein domain is a region of a protein's polypeptide chain that is self-stabilizing and that folds independently from the rest. Each domain forms a compact folded three-dimensional structure. Many proteins consist of several domains, and a domain may appear in a variety of different proteins. Molecular evolution uses domains as building blocks and these may be recombined in different arrangements to create proteins with different functions. In general, domains vary in length from between about 50 amino acids up to 250 amino acids in length.[1] The shortest domains, such as zinc fingers, are stabilized by metal ions or disulfide bridges. Domains often form functional units, such as the calcium-binding EF hand domain of calmodulin. Because they are independently stable, domains can be "swapped" by genetic engineering between one protein and another to make chimeric proteins.

Background

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The concept of the domain was first proposed in 1973 by Wetlaufer after X-ray crystallographic studies of hen lysozyme[2] and papain[3] and by limited proteolysis studies of immunoglobulins.[4][5] Wetlaufer defined domains as stable units of protein structure that could fold autonomously. In the past domains have been described as units of:

  • compact structure[6]
  • function and evolution[7]
  • folding.[8]

Each definition is valid and will often overlap, i.e. a compact structural domain that is found amongst diverse proteins is likely to fold independently within its structural environment. Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities.[9] In a multidomain protein, each domain may fulfill its own function independently, or in a concerted manner with its neighbours. Domains can either serve as modules for building up large assemblies such as virus particles or muscle fibres, or can provide specific catalytic or binding sites as found in enzymes or regulatory proteins.

Example: Pyruvate kinase

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An appropriate example is pyruvate kinase (see first figure), a glycolytic enzyme that plays an important role in regulating the flux from fructose-1,6-biphosphate to pyruvate. It contains an all-β nucleotide-binding domain (in blue), an α/β-substrate binding domain (in grey) and an α/β-regulatory domain (in olive green),[10] connected by several polypeptide linkers.[11] Each domain in this protein occurs in diverse sets of protein families.[12]

The central α/β-barrel substrate binding domain is one of the most common enzyme folds. It is seen in many different enzyme families catalysing completely unrelated reactions.[13] The α/β-barrel is commonly called the TIM barrel named after triose phosphate isomerase, which was the first such structure to be solved.[14] It is currently classified into 26 homologous families in the CATH domain database.[15] The TIM barrel is formed from a sequence of β-α-β motifs closed by the first and last strand hydrogen bonding together, forming an eight stranded barrel. There is debate about the evolutionary origin of this domain. One study has suggested that a single ancestral enzyme could have diverged into several families,[16] while another suggests that a stable TIM-barrel structure has evolved through convergent evolution.[17]

The TIM-barrel in pyruvate kinase is 'discontinuous', meaning that more than one segment of the polypeptide is required to form the domain. This is likely to be the result of the insertion of one domain into another during the protein's evolution. It has been shown from known structures that about a quarter of structural domains are discontinuous.[18][19] The inserted β-barrel regulatory domain is 'continuous', made up of a single stretch of polypeptide.[citation needed]

Units of protein structure

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The primary structure (string of amino acids) of a protein ultimately encodes its uniquely folded three-dimensional (3D) conformation.[20] The most important factor governing the folding of a protein into 3D structure is the distribution of polar and non-polar side chains.[21] Folding is driven by the burial of hydrophobic side chains into the interior of the molecule so to avoid contact with the aqueous environment. Generally proteins have a core of hydrophobic residues surrounded by a shell of hydrophilic residues. Since the peptide bonds themselves are polar they are neutralised by hydrogen bonding with each other when in the hydrophobic environment. This gives rise to regions of the polypeptide that form regular 3D structural patterns called secondary structure. There are two main types of secondary structure: α-helices and β-sheets.[citation needed]

Some simple combinations of secondary structure elements have been found to frequently occur in protein structure and are referred to as supersecondary structure or motifs. For example, the β-hairpin motif consists of two adjacent antiparallel β-strands joined by a small loop. It is present in most antiparallel β structures both as an isolated ribbon and as part of more complex β-sheets. Another common super-secondary structure is the β-α-β motif, which is frequently used to connect two parallel β-strands. The central α-helix connects the C-termini of the first strand to the N-termini of the second strand, packing its side chains against the β-sheet and therefore shielding the hydrophobic residues of the β-strands from the surface.[citation needed]

Covalent association of two domains represents a functional and structural advantage since there is an increase in stability when compared with the same structures non-covalently associated.[22] Other advantages are the protection of intermediates within inter-domain enzymatic clefts that may otherwise be unstable in aqueous environments, and a fixed stoichiometric ratio of the enzymatic activity necessary for a sequential set of reactions.[23]

Structural alignment is an important tool for determining domains.[citation needed]

Tertiary structure

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Several motifs pack together to form compact, local, semi-independent units called domains.[6] The overall 3D structure of the polypeptide chain is referred to as the protein's tertiary structure. Domains are the fundamental units of tertiary structure, each domain containing an individual hydrophobic core built from secondary structural units connected by loop regions. The packing of the polypeptide is usually much tighter in the interior than the exterior of the domain producing a solid-like core and a fluid-like surface.[24] Core residues are often conserved in a protein family, whereas the residues in loops are less conserved, unless they are involved in the protein's function. Protein tertiary structure can be divided into four main classes based on the secondary structural content of the domain.[25]

  • All-α domains have a domain core built exclusively from α-helices. This class is dominated by small folds, many of which form a simple bundle with helices running up and down.
  • All-β domains have a core composed of antiparallel β-sheets, usually two sheets packed against each other. Various patterns can be identified in the arrangement of the strands, often giving rise to the identification of recurring motifs, for example the Greek key motif.[26]
  • α+β domains are a mixture of all-α and all-β motifs. Classification of proteins into this class is difficult because of overlaps to the other three classes and therefore is not used in the CATH domain database.[15]
  • α/β domains are made from a combination of β-α-β motifs that predominantly form a parallel β-sheet surrounded by amphipathic α-helices. The secondary structures are arranged in layers or barrels.

Limits on size

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Domains have limits on size.[27] The size of individual structural domains varies from 36 residues in E-selectin to 692 residues in lipoxygenase-1,[18] but the majority, 90%, have fewer than 200 residues[28] with an average of approximately 100 residues.[29] Very short domains, less than 40 residues, are often stabilised by metal ions or disulfide bonds. Larger domains, greater than 300 residues, are likely to consist of multiple hydrophobic cores.[30]

Quaternary structure

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Many proteins have a quaternary structure, which consists of several polypeptide chains that associate into an oligomeric molecule. Each polypeptide chain in such a protein is called a subunit. Hemoglobin, for example, consists of two α and two β subunits. Each of the four chains has an all-α globin fold with a heme pocket.[citation needed]

Domain swapping

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Domain swapping is a mechanism for forming oligomeric assemblies.[31] In domain swapping, a secondary or tertiary element of a monomeric protein is replaced by the same element of another protein. Domain swapping can range from secondary structure elements to whole structural domains. It also represents a model of evolution for functional adaptation by oligomerisation, e.g. oligomeric enzymes that have their active site at subunit interfaces.[32]

Domains as evolutionary modules

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Nature is a tinkerer and not an inventor,[33] new sequences are adapted from pre-existing sequences rather than invented. Domains are the common material used by nature to generate new sequences; they can be thought of as genetically mobile units, referred to as 'modules'. Often, the C and N termini of domains are close together in space, allowing them to easily be "slotted into" parent structures during the process of evolution. Many domain families are found in all three forms of life, Archaea, Bacteria and Eukarya.[34] Protein modules are a subset of protein domains which are found across a range of different proteins with a particularly versatile structure. Examples can be found among extracellular proteins associated with clotting, fibrinolysis, complement, the extracellular matrix, cell surface adhesion molecules and cytokine receptors.[35] Four concrete examples of widespread protein modules are the following domains: SH2, immunoglobulin, fibronectin type 3 and the kringle.[36]

Molecular evolution gives rise to families of related proteins with similar sequence and structure. However, sequence similarities can be extremely low between proteins that share the same structure. Protein structures may be similar because proteins have diverged from a common ancestor. Alternatively, some folds may be more favored than others as they represent stable arrangements of secondary structures and some proteins may converge towards these folds over the course of evolution. There are currently about 110,000 experimentally determined protein 3D structures deposited within the Protein Data Bank (PDB).[37] However, this set contains many identical or very similar structures. All proteins should be classified to structural families to understand their evolutionary relationships. Structural comparisons are best achieved at the domain level. For this reason many algorithms have been developed to automatically assign domains in proteins with known 3D structure (see § Domain definition from structural co-ordinates).[citation needed]

The CATH domain database classifies domains into approximately 800 fold families; ten of these folds are highly populated and are referred to as 'super-folds'. Super-folds are defined as folds for which there are at least three structures without significant sequence similarity.[38] The most populated is the α/β-barrel super-fold, as described previously.

Multidomain proteins

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The majority of proteins, two-thirds in unicellular organisms and more than 80% in metazoa, are multidomain proteins.[39] However, other studies concluded that 40% of prokaryotic proteins consist of multiple domains while eukaryotes have approximately 65% multi-domain proteins.[40]

Many domains in eukaryotic multidomain proteins can be found as independent proteins in prokaryotes,[41] suggesting that domains in multidomain proteins have once existed as independent proteins. For example, vertebrates have a multi-enzyme polypeptide containing the GAR synthetase, AIR synthetase and GAR transformylase domains (GARs-AIRs-GARt; GAR: glycinamide ribonucleotide synthetase/transferase; AIR: aminoimidazole ribonucleotide synthetase). In insects, the polypeptide appears as GARs-(AIRs)2-GARt, in yeast GARs-AIRs is encoded separately from GARt, and in bacteria each domain is encoded separately.[42]

(scrollable image) Attractin-like protein 1 (ATRNL1) is a multi-domain protein found in animals, including humans.[43][44] Each unit is one domain, e.g. the EGF or Kelch domains.

Origin

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Multidomain proteins are likely to have emerged from selective pressure during evolution to create new functions. Various proteins have diverged from common ancestors by different combinations and associations of domains. Modular units frequently move about, within and between biological systems through mechanisms of genetic shuffling:

  • transposition of mobile elements including horizontal transfers (between species);[45]
  • gross rearrangements such as inversions, translocations, deletions and duplications;
  • homologous recombination;
  • slippage of DNA polymerase during replication.

Types of organization

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Insertions of similar PH domain modules (maroon) into two different proteins.

The simplest multidomain organization seen in proteins is that of a single domain repeated in tandem.[46] The domains may interact with each other (domain-domain interaction) or remain isolated, like beads on string. The giant 30,000 residue muscle protein titin comprises about 120 fibronectin-III-type and Ig-type domains.[47] In the serine proteases, a gene duplication event has led to the formation of a two β-barrel domain enzyme.[48] The repeats have diverged so widely that there is no obvious sequence similarity between them. The active site is located at a cleft between the two β-barrel domains, in which functionally important residues are contributed from each domain. Genetically engineered mutants of the chymotrypsin serine protease were shown to have some proteinase activity even though their active site residues were abolished and it has therefore been postulated that the duplication event enhanced the enzyme's activity.[48]

Modules frequently display different connectivity relationships, as illustrated by the kinesins and ABC transporters. The kinesin motor domain can be at either end of a polypeptide chain that includes a coiled-coil region and a cargo domain.[49] ABC transporters are built with up to four domains consisting of two unrelated modules, ATP-binding cassette and an integral membrane module, arranged in various combinations.

Not only do domains recombine, but there are many examples of a domain having been inserted into another. Sequence or structural similarities to other domains demonstrate that homologues of inserted and parent domains can exist independently. An example is that of the 'fingers' inserted into the 'palm' domain within the polymerases of the Pol I family.[50] Since a domain can be inserted into another, there should always be at least one continuous domain in a multidomain protein. This is the main difference between definitions of structural domains and evolutionary/functional domains. An evolutionary domain will be limited to one or two connections between domains, whereas structural domains can have unlimited connections, within a given criterion of the existence of a common core. Several structural domains could be assigned to an evolutionary domain.[citation needed]

A superdomain consists of two or more conserved domains of nominally independent origin, but subsequently inherited as a single structural/functional unit.[51] This combined superdomain can occur in diverse proteins that are not related by gene duplication alone. An example of a superdomain is the protein tyrosine phosphataseC2 domain pair in PTEN, tensin, auxilin and the membrane protein TPTE2. This superdomain is found in proteins in animals, plants and fungi. A key feature of the PTP-C2 superdomain is amino acid residue conservation in the domain interface.

Domains are autonomous folding units

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Folding

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Protein folding - the unsolved problem : Since the seminal work of Anfinsen in the early 1960s,[20] the goal to completely understand the mechanism by which a polypeptide rapidly folds into its stable native conformation remains elusive. Many experimental folding studies have contributed much to our understanding, but the principles that govern protein folding are still based on those discovered in the very first studies of folding. Anfinsen showed that the native state of a protein is thermodynamically stable, the conformation being at a global minimum of its free energy.[citation needed]

Folding is a directed search of conformational space allowing the protein to fold on a biologically feasible time scale. The Levinthal paradox states that if an averaged sized protein would sample all possible conformations before finding the one with the lowest energy, the whole process would take billions of years.[52] Proteins typically fold within 0.1 and 1000 seconds. Therefore, the protein folding process must be directed some way through a specific folding pathway. The forces that direct this search are likely to be a combination of local and global influences whose effects are felt at various stages of the reaction.[53]

Advances in experimental and theoretical studies have shown that folding can be viewed in terms of energy landscapes,[54][55] where folding kinetics is considered as a progressive organisation of an ensemble of partially folded structures through which a protein passes on its way to the folded structure. This has been described in terms of a folding funnel, in which an unfolded protein has a large number of conformational states available and there are fewer states available to the folded protein. A funnel implies that for protein folding there is a decrease in energy and loss of entropy with increasing tertiary structure formation. The local roughness of the funnel reflects kinetic traps, corresponding to the accumulation of misfolded intermediates. A folding chain progresses toward lower intra-chain free-energies by increasing its compactness. The chain's conformational options become increasingly narrowed ultimately toward one native structure.

Advantage of domains in protein folding

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The organisation of large proteins by structural domains represents an advantage for protein folding, with each domain being able to individually fold, accelerating the folding process and reducing a potentially large combination of residue interactions. Furthermore, given the observed random distribution of hydrophobic residues in proteins,[56] domain formation appears to be the optimal solution for a large protein to bury its hydrophobic residues while keeping the hydrophilic residues at the surface.[57][58]

However, the role of inter-domain interactions in protein folding and in energetics of stabilisation of the native structure, probably differs for each protein. In T4 lysozyme, the influence of one domain on the other is so strong that the entire molecule is resistant to proteolytic cleavage. In this case, folding is a sequential process where the C-terminal domain is required to fold independently in an early step, and the other domain requires the presence of the folded C-terminal domain for folding and stabilisation.[59]

It has been found that the folding of an isolated domain can take place at the same rate or sometimes faster than that of the integrated domain,[60] suggesting that unfavourable interactions with the rest of the protein can occur during folding. Several arguments suggest that the slowest step in the folding of large proteins is the pairing of the folded domains.[30] This is either because the domains are not folded entirely correctly or because the small adjustments required for their interaction are energetically unfavourable,[61] such as the removal of water from the domain interface.

Domains and protein flexibility

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Protein domain dynamics play a key role in a multitude of molecular recognition and signaling processes. Protein domains, connected by intrinsically disordered flexible linker domains, induce long-range allostery via protein domain dynamics. The resultant dynamic modes cannot be generally predicted from static structures of either the entire protein or individual domains.

Domain definition from structural co-ordinates

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The importance of domains as structural building blocks and elements of evolution has brought about many automated methods for their identification and classification in proteins of known structure. Automatic procedures for reliable domain assignment is essential for the generation of the domain databases, especially as the number of known protein structures is increasing. Although the boundaries of a domain can be determined by visual inspection, construction of an automated method is not straightforward. Problems occur when faced with domains that are discontinuous or highly associated.[62] The fact that there is no standard definition of what a domain really is has meant that domain assignments have varied enormously, with each researcher using a unique set of criteria.[63]

A structural domain is a compact, globular sub-structure with more interactions within it than with the rest of the protein.[64] Therefore, a structural domain can be determined by two visual characteristics: its compactness and its extent of isolation.[65] Measures of local compactness in proteins have been used in many of the early methods of domain assignment[66][67][68][69] and in several of the more recent methods.[28][70][71][72][73]

Methods

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One of the first algorithms[66] used a Cα-Cα distance map together with a hierarchical clustering routine that considered proteins as several small segments, 10 residues in length. The initial segments were clustered one after another based on inter-segment distances; segments with the shortest distances were clustered and considered as single segments thereafter. The stepwise clustering finally included the full protein. Go[69] also exploited the fact that inter-domain distances are normally larger than intra-domain distances; all possible Cα-Cα distances were represented as diagonal plots in which there were distinct patterns for helices, extended strands and combinations of secondary structures.[citation needed]

The method by Sowdhamini and Blundell clusters secondary structures in a protein based on their Cα-Cα distances and identifies domains from the pattern in their dendrograms.[62] As the procedure does not consider the protein as a continuous chain of amino acids there are no problems in treating discontinuous domains. Specific nodes in these dendrograms are identified as tertiary structural clusters of the protein, these include both super-secondary structures and domains. The DOMAK algorithm is used to create the 3Dee domain database.[71] It calculates a 'split value' from the number of each type of contact when the protein is divided arbitrarily into two parts. This split value is large when the two parts of the structure are distinct.[citation needed]

The method of Wodak and Janin[74] was based on the calculated interface areas between two chain segments repeatedly cleaved at various residue positions. Interface areas were calculated by comparing surface areas of the cleaved segments with that of the native structure. Potential domain boundaries can be identified at a site where the interface area was at a minimum. Other methods have used measures of solvent accessibility to calculate compactness.[28][75][76]

The PUU algorithm[19] incorporates a harmonic model used to approximate inter-domain dynamics. The underlying physical concept is that many rigid interactions will occur within each domain and loose interactions will occur between domains. This algorithm is used to define domains in the FSSP domain database.[70]

Swindells (1995) developed a method, DETECTIVE, for identification of domains in protein structures based on the idea that domains have a hydrophobic interior. Deficiencies were found to occur when hydrophobic cores from different domains continue through the interface region.

RigidFinder is a novel method for identification of protein rigid blocks (domains and loops) from two different conformations. Rigid blocks are defined as blocks where all inter residue distances are conserved across conformations.

The method RIBFIND developed by Pandurangan and Topf identifies rigid bodies in protein structures by performing spacial clustering of secondary structural elements in proteins.[77] The RIBFIND rigid bodies have been used to flexibly fit protein structures into cryo electron microscopy density maps.[78]

A general method to identify dynamical domains, that is protein regions that behave approximately as rigid units in the course of structural fluctuations, has been introduced by Potestio et al.[79] and, among other applications was also used to compare the consistency of the dynamics-based domain subdivisions with standard structure-based ones. The method, termed PiSQRD, is publicly available in the form of a webserver.[80] The latter allows users to optimally subdivide single-chain or multimeric proteins into quasi-rigid domains[79][80] based on the collective modes of fluctuation of the system. By default the latter are calculated through an elastic network model;[81] alternatively pre-calculated essential dynamical spaces can be uploaded by the user.

Example domains

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  • Armadillo repeats: named after the β-catenin-like Armadillo protein of the fruit fly Drosophila melanogaster.
  • Basic leucine zipper domain (bZIP domain): found in many DNA-binding eukaryotic proteins. One part of the domain contains a region that mediates sequence-specific DNA-binding properties and the Leucine zipper that is required for the dimerization of two DNA-binding regions. The DNA-binding region comprises a number of basic aminoacids such as arginine and lysine.
  • Cadherin repeats: Cadherins function as Ca2+-dependent cell–cell adhesion proteins. Cadherin domains are extracellular regions which mediate cell-to-cell homophilic binding between cadherins on the surface of adjacent cells.
  • Death effector domain (DED): allows protein–protein binding by homotypic interactions (DED-DED). Caspase proteases trigger apoptosis via proteolytic cascades. Pro-caspase-8 and pro-caspase-9 bind to specific adaptor molecules via DED domains, which leads to autoactivation of caspases.
  • EF hand: a helix-turn-helix structural motif found in each structural domain of the signaling protein calmodulin and in the muscle protein troponin-C.
  • Foldon domain: A small protein domain from fibritin in T4 bacteriophage that can cause proteins to trimerize.
  • Immunoglobulin-like domains: found in proteins of the immunoglobulin superfamily (IgSF).[82] They contain about 70-110 amino acids and are classified into different categories (IgV, IgC1, IgC2 and IgI) according to their size and function. They possess a characteristic fold in which two beta sheets form a "sandwich" that is stabilized by interactions between conserved cysteines and other charged amino acids. They are important for protein–protein interactions in processes of cell adhesion, cell activation, and molecular recognition. These domains are commonly found in molecules with roles in the immune system.
  • Phosphotyrosine-binding domain (PTB): PTB domains usually bind to phosphorylated tyrosine residues. They are often found in signal transduction proteins. PTB-domain binding specificity is determined by residues to the amino-terminal side of the phosphotyrosine. Examples: the PTB domains of both SHC and IRS-1 bind to a NPXpY sequence. PTB-containing proteins such as SHC and IRS-1 are important for insulin responses of human cells.
  • Pleckstrin homology domain (PH): PH domains bind phosphoinositides with high affinity. Specificity for PtdIns(3)P, PtdIns(4)P, PtdIns(3,4)P2, PtdIns(4,5)P2, and PtdIns(3,4,5)P3 have all been observed. Given the fact that phosphoinositides are sequestered to various cell membranes (due to their long lipophilic tail) the PH domains usually causes recruitment of the protein in question to a membrane where the protein can exert a certain function in cell signalling, cytoskeletal reorganization or membrane trafficking.
  • Src homology 2 domain (SH2): SH2 domains are often found in signal transduction proteins. SH2 domains confer binding to phosphorylated tyrosine (pTyr). Named after the phosphotyrosine binding domain of the src viral oncogene, which is itself a tyrosine kinase. See also: SH3 domain.
  • Zinc finger DNA-binding domain (ZnF_GATA): ZnF_GATA domain-containing proteins are typically transcription factors that usually bind to the DNA sequence [AT]GATA[AG] of promoters.

Domains of unknown function

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A large fraction of domains are of unknown function. A domain of unknown function (DUF) is a protein domain that has no characterized function. These families have been collected together in the Pfam database using the prefix DUF followed by a number, with examples being DUF2992 and DUF1220. There are now over 3,000 DUF families within the Pfam database representing over 20% of known families.[83] Surprisingly, the number of DUFs in Pfam has increased from 20% (in 2010) to 22% (in 2019), mostly due to an increasing number of new genome sequences. Pfam release 32.0 (2019) contained 3,961 DUFs.[84]

See also

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References

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Key papers

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A protein domain is a distinct, structurally compact of a protein sequence that folds independently into a stable three-dimensional structure and often performs a specific biological function. These domains typically consist of 50 to 350 and are characterized by a hydrophobic core surrounded by a hydrophilic surface, enabling them to exist as autonomous units or combine to form multi-domain proteins. While most domains are continuous in the protein sequence, some are discontinuous, with segments separated in the primary structure but brought together in the folded state. Protein domains serve as the fundamental building blocks of protein , determining both the overall and functional properties of proteins. In single-domain proteins, the domain encompasses the entire molecule, whereas larger proteins often feature multiple domains linked by flexible linker regions, allowing for modular assembly that enhances functional diversity. This modularity is evident in databases like , which catalog thousands of domain families conserved across species, highlighting their role in protein classification and annotation. Evolutionarily, protein domains are considered the primary units of protein evolution, capable of , duplication, and recombination to generate novel proteins and functions over time. Through mechanisms such as domain fusion and exon , domains have facilitated the diversification of proteomes, enabling complex cellular processes in eukaryotes and prokaryotes alike. For instance, many signaling pathways rely on specific domain interactions, underscoring their conservation and adaptability across evolutionary timescales. The study of protein domains is crucial for understanding protein function, predicting structures, and annotating genomes, with applications in and . Hierarchical classifications like and CATH group domains by fold and homology, revealing patterns in protein evolution and aiding in the transfer of functional knowledge. Disruptions in domain integrity, such as through mutations, can lead to diseases, emphasizing their biomedical significance.

Introduction and Fundamentals

Definition and Overview

A protein domain is defined as a conserved portion of a protein and structure that can fold, function, and evolve independently of the rest of the protein. These units typically consist of 50 to 250 , with an average length of around 100 to 150 residues, enabling them to form stable, self-contained structures within larger polypeptides. This independent folding capability allows domains to maintain integrity even when isolated from the full protein context. Key characteristics of protein domains include their compact, globular architecture, featuring hydrophobic cores and hydrophilic surfaces that contribute to stability in aqueous environments. They often contain motifs that underpin specific functions, such as binding, enzymatic , or protein-protein interactions. Domains serve as modular building blocks, permitting proteins to assemble diverse functionalities through combinations of these units. Protein domains differ from motifs and folds in scale and autonomy: while motifs are short, linear sequence patterns (typically 10-20 amino acids) that confer functional roles without independent stability, domains are larger, structurally autonomous entities. In contrast, folds refer to recurring three-dimensional architectures shared across domains, reflecting evolutionary conservation but not necessarily defining independent units. Representative examples include the SH2 domain, which mediates signal transduction by binding phosphorylated tyrosine residues, and the zinc finger domain, involved in DNA binding through zinc ion coordination.

Historical Development

The concept of protein domains originated in the early amid growing insights from into protein three-dimensional structures, which began with the determination of in 1958 and in 1960. These early structures revealed that larger proteins often comprised distinct, compact regions, challenging the view of proteins as monolithic folding units. In 1973, Donald B. Wetlaufer proposed domains as independent folding units, or "nucleation centers," based on a survey of 18 available protein structures, noting their role in facilitating rapid and stable folding through globular intrachain regions. Building on this, Jane S. Richardson advanced the visualization and analysis of domain topologies in 1977, using ribbon diagrams to illustrate β-sheet arrangements in proteins such as immunoglobulins, where she identified recurring fold patterns that suggested structural modularity and evolutionary relatedness among proteins. Her work emphasized how these domains maintained distinct topologies despite sequence variations, influencing subsequent classifications. Advances in crystallographic techniques during the 1960s and , which increased the number of solved structures from a handful to over 100 by the late 1970s, further drove the conceptual shift from treating proteins as single cooperative units to recognizing them as assemblies of semi-autonomous domains. In the 1980s, accumulating structural data solidified domain modularity, with analyses of over 200 protein structures highlighting repeated domain architectures across unrelated proteins. Cyrus Chothia and colleagues contributed significantly by developing methods for structural alignments to define domain boundaries, as seen in their 1986 study correlating sequence divergence with structural conservation within and across domains, which demonstrated that core domain scaffolds remain stable even as surface loops vary. This era's efforts, including Richardson's 1981 comprehensive taxonomy of protein structures into hierarchical domain classes, established domains as fundamental building blocks observable through folding patterns like α/β barrels and β-sandwiches. By the , the explosion of sequence data from projects enabled confirmation of domain conservation beyond structures, revealing modular units shared across diverse proteins via database-driven analyses. Seminal work, such as the 1995 launch of the database by Murzin, Brenner, Hubbard, and Chothia, classified over 500 protein domains structurally while integrating sequence similarities to underscore their evolutionary persistence. Sequence-based tools like the database, initiated in 1996 and formalized in the late , analyzed thousands of alignments to detect conserved domain signatures in non-structural proteins, affirming modularity as a universal principle of protein .

Structural Organization

Tertiary Structure Integration

Protein domains serve as the fundamental building blocks of a protein's tertiary structure, which refers to the overall three-dimensional of a single polypeptide chain resulting from interactions between side chains. Each domain typically adopts a compact, globular conformation that contributes to the native tertiary architecture, enabling the protein to perform its biological function. These domains are evolutionarily conserved units that can fold independently yet integrate seamlessly into the larger structure. The stability of individual domains within the tertiary structure is primarily maintained by non-covalent interactions, including a hydrophobic core that buries nonpolar residues away from the aqueous environment, hydrogen bonds between polar groups, and, in some cases, covalent bridges that link residues. These forces collectively minimize the free energy of the folded state, with hydrophobic interactions providing the dominant energetic contribution to stability, often accounting for a significant portion of the overall folding free energy. bonds further enhance stability, particularly in extracellular proteins, by constraining the polypeptide chain and reducing the of the unfolded state. Boundaries between domains in multi-domain proteins are often delineated by flexible linkers or hinge regions, which consist of short, unstructured polypeptide segments that connect the globular domains and permit relative movement while allowing each domain to fold autonomously within the tertiary context. These interfaces facilitate domain-domain communication and functional modulation without disrupting the overall fold. For instance, hinge regions identified through sequence and structural analysis help define domain edges, enabling independent stability and dynamics. Within domains, common structural motifs such as alpha helices, beta sheets, and connecting loops form the secondary structural elements that assemble into the tertiary fold. A representative example is the Rossmann fold, a beta-alpha-beta motif prevalent in nucleotide-binding domains of dehydrogenases, where alternating beta strands and alpha helices create a dinucleotide-binding pocket that exemplifies how motifs integrate into functional tertiary units. This fold, one of the most common in enzymes, highlights the modular nature of domain architecture. In structural databases like the (PDB), protein domains are visualized as distinct, compact globular regions within the three-dimensional model of the full protein, often highlighted by their separation via linkers or by computational domain assignment tools that reveal their independent folding units through iterative dissection of the tertiary structure. This representation underscores the anatomical modularity of proteins, where domains appear as self-contained lobes or subunits in ribbon diagrams or surface models.

Size Constraints and Limits

Protein domains exhibit a characteristic size range that balances , folding efficiency, and functional . Typically, they span 40 to 200 , with an average length of about 100 to 150 observed in both single-domain proteins and domains within multidomain architectures. Smaller domains, such as those in motifs, measure 20 to 40 and often rely on metal coordination for stability rather than a fully autonomous hydrophobic core. Larger domains, up to approximately 200 , occur in multifunctional units where extended structures support additional binding sites or catalytic elements without compromising overall fold integrity. Biophysical constraints impose strict limits on domain size to ensure viable folding pathways. At the lower end, a minimum of approximately 40 is required to form a hydrophobic core, as this length allows sufficient burial of nonpolar residues to drive collapse and minimize exposure, with core volumes around 1000 ų supporting globular stability. Below this threshold, insufficient hydrophobic packing leads to unstable or disordered states. The upper limit, generally around 200 , arises from the need for efficient folding; beyond this, chains encounter kinetic traps due to exponentially increasing conformational search spaces, where suboptimal hydrophobic interactions hinder the native state. Experimental data from 1,236 folded domains confirm that 90% are shorter than 200 , aligning with folding timescales feasible within cellular constraints (e.g., τ_folding ≈ 10^{-6} to 10^3 seconds). Several interconnected factors govern these size constraints. Chain , which scales with length and promotes unfolded states, must be overcome by enthalpic gains from hydrophobic burial, favoring compact domains that efficiently pack nonpolar residues into a core while exposing polar ones to . Evolutionary pressures further enforce compactness, as optimizes domains for rapid folding and functional reuse, reducing the risk of misfolding in longer sequences and enabling modular architectures in larger proteins. Trade-offs in hydrophobicity and stability also play a role, with longer domains exhibiting reduced core hydrophobicity to maintain foldability. Statistical analyses of structural databases underscore these patterns. In the database, which classifies sequence-based families, the overall average domain length is 96 , while in the database, focusing on structural domains, it is 174 ; for directly comparable one-to-one mappings, these averages are 164 for and 183 for . Across both, roughly 80% to 90% of domains fall within 50 to 250 , reflecting the biophysical and evolutionary biases toward this range for autonomous folding units, though some exceptions up to ~300 exist in certain superfamilies.

Quaternary Assembly and Interactions

In quaternary protein structures, domains from distinct subunits associate to form higher-order complexes that enable cooperative functions, such as oxygen transport in tetrameric protein composed of two α-globin and two β-globin subunits, each containing a heme-binding domain that assembles via non-covalent interfaces to create a functional oxygen-binding unit. This assembly exemplifies how domain-level interactions across subunits stabilize the overall quaternary architecture, allowing where binding at one site modulates affinity at others. Domain-domain interfaces in these quaternary assemblies are primarily stabilized by non-covalent interactions, including hydrogen bonds, salt bridges, and der Waals forces, which collectively bury significant surface area—often exceeding 1,000 Ų per interface—to ensure specificity and stability without covalent linkages. These contacts typically involve complementary charged and hydrophobic residues from adjacent domains, as seen in the α-β interfaces of , where electrostatic interactions between helix residues contribute to the tetramer's dimer-of-dimers configuration. Such interfaces are evolutionarily conserved to maintain functional integrity, with disruptions often leading to pathological dissociation. A specialized mechanism in assembly is three-dimensional (3D) domain swapping, where subunits exchange identical structural elements—such as α-helices or β-sheets—via regions, forming inter-subunit bonds that enhance stability and mimic monomeric conformations at the interface. In , the monomeric form features three domains (catalytic C, transmembrane T, and receptor-binding R), but dimerization involves swapping of the T-domain's α-helical segments between subunits, triggered by low to facilitate insertion and toxicity. Evolutionarily, domain swapping promotes oligomerization by minimizing in folding landscapes, allowing rapid adaptation from monomeric ancestors to multimeric forms that confer advantages like increased or regulated activity, as evidenced in over 100 protein families. In antibodies, the Fab (fragment antigen-binding) regions illustrate domain interactions across heavy and light chains in a context, where the variable heavy (VH) and variable light (VL) domains pair via conserved hydrophobic cores and hydrogen-bonded β-strands to form the antigen-binding site, while constant domains (CH1 and CL) provide additional stabilizing contacts through salt bridges. This heterodimeric assembly within each Fab arm of the IgG tetramer ensures precise recognition, with interface variations influencing binding affinity across and light chain types.

Evolutionary Significance

Domains as Modular Units

Protein domains function as modular units in evolution, serving as conserved, semi-independent building blocks that can be rearranged to generate novel protein architectures and functions. This arises primarily through mechanisms such as exon shuffling, where intronic recombination allows encoding individual domains to be exchanged between genes, and gene fusion, in which adjacent genes merge to combine domains into multidomain proteins. These processes facilitate functional innovation by enabling the assembly of new combinations without disrupting existing core functions, as seen in the expansion of signaling and regulatory proteins in eukaryotes. The conservation of domains across diverse species underscores their modular stability, with sequence and structural homology preserved over vast evolutionary distances. For instance, ATP-binding cassette (ABC) domains, responsible for in transporters, exhibit high conservation in their nucleotide-binding domains (NBDs), including signature motifs like Walker A and B, from prokaryotes such as to eukaryotes like humans. This homology reflects an ancient origin predating the divergence of , , and eukaryotes, allowing these domains to maintain core transport mechanisms while adapting to species-specific substrates. Gene duplication further promotes modularity by generating tandem copies within the same , often leading to multi-domain proteins where one copy retains the original function while the other diverges. Tandem duplications are particularly common in prokaryotes and early eukaryotes, contributing to the proliferation of environmental response proteins like transporters. In non-functional regions, such as inter-domain linkers, neutral allows sequence variation without selective pressure, facilitating structural flexibility and eventual functional specialization in the duplicated domains. Phylogenetic analyses of domain families provide compelling evidence for this modularity, revealing deep evolutionary roots through trees constructed from structural classifications like CATH or . For example, the Rossmann fold, a β-α-β motif common in nucleotide-binding enzymes, traces back to a last universal Rossmann ancestor (LURA) predating the (LUCA), with conserved motifs retained for over 3.7 billion years. Such trees illustrate how domain and duplication have driven the diversification of metabolic pathways since early life.

Origins of Multidomain Architectures

In the earliest stages of life, approximately 4.2 billion years ago, the (LUCA) possessed a that included both single-domain and multidomain proteins, particularly in core metabolic and translational functions. These primordial domains formed the foundational repertoire from which more complex architectures evolved. The emergence of multidomain proteins was driven by environmental pressures favoring functional innovation, with (HGT) playing a pivotal role by disseminating pre-existing domains across microbial lineages and enabling the rapid assembly of novel combinations without relying solely on vertical inheritance. Key mechanisms driving the origins of multidomain architectures include followed by fusion, intronic recombination, and retrotransposition. provided raw material for domain tandem repeats, often creating efficient bifunctional enzymes, as seen in ancient metabolic pathways where duplicated domains fused to streamline sequential reactions. In eukaryotes, which emerged around 2 billion years ago, intronic recombination—facilitating shuffling—allowed for the modular rearrangement of domains, inserting new ones between existing sequences to enhance regulatory complexity. Retrotransposition contributed less frequently but notably in generating de novo domain insertions, particularly in lineage-specific adaptations, by reverse-transcribing and reintegrating intermediates that incorporated domain-encoding exons. These processes resulted in tandem (adjacent) or inserted (intercalated) domain arrangements, with fusion events predominating in early multidomain proteins to minimize interdomain interference during folding. Prokaryotes and eukaryotes diverged in their approaches to multidomain evolution due to genomic differences. In and , lacking introns, domain fusions often arose from the juxtaposition of adjacent genes in operons, promoting efficiency in compact genomes by linking functionally related domains without the need for post-transcriptional processing. This mechanism favored streamlined architectures, with a substantial proportion of prokaryotic proteins—estimated at around 67% in some analyses—being multidomain and emphasizing metabolic and stress-response adaptations. Eukaryotes, in contrast, leveraged intron-rich genomes for greater domain shuffling, enabling diverse insertions and rearrangements that supported multicellularity and signaling complexity; eukaryotic proteins are longer and more multidomain on average, reflecting this flexibility. Metagenomic analyses of ancient microbial communities provide fossil-like evidence for the increasing complexity of domain combinations over time. Studies of from diverse habitats reveal that simple single-domain proteins dominate in prokaryotic-dominated assemblages from ~3 billion-year-old proxies, while multidomain architectures proliferate in more complex ecosystems, correlating with organismal diversification and . For instance, metagenomes from and microbiomes show a gradient where domain fusion rates rise with phylogenetic depth, underscoring how HGT and recombination amplified architectural diversity in response to ecological pressures.

Types of Domain Arrangements

Protein domains within multi-domain proteins can be arranged in several distinct configurations, each contributing to the functional versatility and structural complexity of the protein. These arrangements include tandem repeats, inserted domains, and discontinuous domains, which reflect diverse evolutionary strategies for assembling modular units. Tandem repeats consist of consecutive copies of the same or similar domains aligned in a linear fashion along the polypeptide chain. This arrangement often forms elongated structures that facilitate scaffolding or binding interactions. A prominent example is the ankyrin repeat domain, where multiple 33-residue ankyrin repeats stack to create a curved that mediates protein-protein interactions in cytoskeletal and signaling complexes. Inserted domains occur when one domain is embedded within the sequence of another "parent" domain, typically in a surface loop, resulting in a non-contiguous in the primary but a compact assembly in the folded protein. Such insertions are relatively rare, comprising about 9% of multi-domain proteins in structural databases, and usually involve a single insert domain. For instance, in the thermosome from Thermoplasma acidophilum, an apical domain is nested as an insertion within the domain, enhancing chaperone function. Discontinuous domains arise when segments of a domain are separated in the linear sequence by unrelated intervening sequences but converge in during folding to form a cohesive unit. This configuration is present in approximately 28% of multi-domain proteins and can enable . A well-characterized example is the N-terminal domain of , where non-contiguous segments assemble to create the functional domain involved in sugar transport. Multi-domain arrangements are prevalent in eukaryotic proteomes, with approximately 65% of proteins containing multiple domains according to annotations, and they are particularly common in signaling proteins where modular architectures allow for integrated .

Folding and Biophysical Properties

Autonomous Folding Mechanisms

Protein domains resolve —the challenge of rapidly navigating vast conformational spaces to achieve native structure—through a funnel-shaped landscape that guides folding via thermodynamically favorable pathways, minimizing kinetic traps. This model posits that domains evolve rugged yet minimally frustrated landscapes, enabling efficient folding from denatured states without exhaustive random search. For protein domains, this funnel facilitates hierarchical assembly, where local secondary structures, such as alpha-helices or beta-strands, form first as compact sites, subsequently coalescing into the tertiary fold through cooperative interactions.01346-2) Most small protein domains, typically under 150 residues, exhibit two-state folding kinetics, transitioning directly from unfolded to native states without intermediates, as opposed to multi-state folding observed in larger or more complex systems.00033-9) This ensures all-or-none transitions, with folding rates often in the to millisecond range, measurable via that monitors burial or extrinsic probe during refolding. Such kinetics highlight the intrinsic stability of domain architectures, allowing autonomous refolding under physiological conditions. Unlike multi-domain proteins, which frequently require molecular chaperones to prevent interdomain misfolding or aggregation during synthesis, individual protein domains typically fold chaperone-independently due to their compact size and minimized exposure of hydrophobic surfaces. This autonomy stems from evolved sequences that prioritize intra-domain contacts, reducing off-pathway traps. Experimental validation comes from nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopy, which demonstrate rapid refolding of isolated domains like the Src homology 3 (SH3) domain or protein G B1 domain to native-like spectra upon dilution from denaturants, confirming independent tertiary structure recovery without external assistance.

Advantages in Protein Folding Efficiency

In multi-domain proteins, the autonomy of individual domains enables parallel folding pathways, where multiple domains can fold simultaneously rather than sequentially as a single large unit. This parallelism reduces the overall folding time for large polypeptides and lowers the risk of misfolding, as entangled conformations across the entire chain are less likely to form compared to a monolithic folding process in equivalent-sized single-domain proteins. For instance, in the bacterial phosphoglycerate kinase (bsPGK), the N- and C-terminal domains fold independently through similar intermediates without a strict order, facilitating efficient maturation even in the presence of interdomain interactions. The modular nature of domains further minimizes errors during folding by localizing potential misfolding events to individual units, preventing propagation across the protein. If one domain encounters a kinetic trap or transient misfold, the prefolded neighboring domains can stabilize it through interdomain contacts, allowing sequential maturation without derailing the entire structure. This isolation of folding failures is evident in spectrin tandem repeats like R15-R16, where sequence divergence between domains suppresses stable misfolded states, resolving transients rapidly (within ~0.5 seconds) rather than persisting for days as seen in identical repeats. Such mechanisms enhance overall folding yield, particularly where crowding exacerbates aggregation risks. From an evolutionary perspective, domain modularity in folding confers a selective advantage by permitting rapid through shuffling without necessitating a complete redesign of folding pathways for the whole protein. Multi-domain architectures evolved preferentially over large single domains because tethered domains maintain stability and folding via interdomain interactions, compensating for intrinsically unstable isolated units and enabling functional diversification. Quantitative studies underscore these benefits: in spectrin R15-R16, the folding rate of the R16 domain accelerates approximately 30-fold when tethered to the prefolded R15, compared to its isolated counterpart, highlighting how multi-domain contexts can boost rates by orders of magnitude relative to large single domains. Similarly, full-length multi-domain chains exhibit stabilities (-6.57 kcal/mol) comparable to single-domain homologs, driven by domain interfaces that enhance folding kinetics.

Role in Protein Flexibility and Dynamics

Protein domains play a crucial role in enabling the flexibility and dynamic behavior of proteins, allowing them to adopt multiple conformations essential for biological function. Through inter-domain linkers and regions, domains facilitate large-scale movements that respond to environmental cues, such as binding, thereby modulating activity without requiring wholesale structural disassembly. This dynamic contrasts with the relative rigidity of individual domains, which maintain core folds while permitting collective motions that enhance adaptability. Hinge motions, often mediated by flexible inter-domain linkers, allow domains to bend relative to one another, facilitating substrate access and functional transitions. In enzymes like , hinge bending between the small and large domains opens the for glucose binding and closes it to position catalytic residues, a characterized by rotation axes identified through structural comparisons. Similarly, in protein kinases such as ERK2, enhanced hinge flexibility at residues like LMETD (positions 106-110) promotes domain closure upon , altering binding and enabling substrate without major secondary structure changes. These motions are typically on the order of 10-20° rotations, underscoring their efficiency in regulatory mechanisms. Allosteric regulation in multidomain proteins relies on domain rearrangements to transmit signals across distant sites, altering affinity or activity. In heterotrimeric G-proteins, GTP binding to the Gα subunit induces conformational changes in switch I, II, and III regions within the Ras-like and helical domains, disrupting the Gα-Gβγ interface via a conserved Gly-Arg-Glu motif that generates torsional strain. This allosteric linkage propagates the signal from the nucleotide-binding site to the effector-binding domain, facilitating Gβγ dissociation and downstream signaling with high fidelity. Such rearrangements exemplify how domain interfaces serve as conduits for long-range communication, often captured at atomic resolution in cryo-EM structures. Conformational ensembles describe the population of states accessible to proteins, where domain flexibility contributes to entropy-driven dynamics observable in molecular dynamics (MD) simulations. These simulations reveal that inter-domain linkers exhibit higher root-mean-square fluctuations (RMSF > 1 Å), enabling a broader ensemble of poses that buffer against perturbations and support functional plasticity. For instance, the dynamic flexibility index (dfi) from elastic network models shows low-dfi residues (<20%) in domain cores maintaining stability, while high-dfi regions (>80%) in linkers drive entropic contributions to binding free energy (up to -TΔS ≈ 2-5 kcal/mol). This entropy facilitates adaptive responses, as seen in multi-domain proteins where domain motions populate catalytically competent states. The functional implications of domain-mediated flexibility are profound, particularly in catalysis and structural roles. In enzymes, domain dynamics lower activation barriers by aligning substrates in transition states; for example, flexible loops in triosephosphate isomerase (TIM) close rapidly (k ≈ 10^5 s⁻¹) to exclude water and enhance rate accelerations (10^6-fold). Conversely, structural domains, such as those in scaffold proteins like spectrin, prioritize rigidity to maintain mechanical integrity under stress, with minimal fluctuations (RMSF < 0.5 Å) ensuring load-bearing without deformation. This balance—flexibility for enzymatic adaptability and rigidity for stability—optimizes overall protein performance in cellular contexts.

Identification and Characterization

Structural Coordinate-Based Methods

Structural coordinate-based methods for identifying protein domains rely on three-dimensional atomic coordinates derived from experimental techniques such as and . These methods generate high-resolution structures deposited in the , enabling the segmentation of proteins into domains by analyzing spatial arrangements of atoms, particularly Cα atoms. provides precise coordinates for crystallized proteins, often resolving domains as compact globular units, while cryo-EM excels in visualizing large multidomain complexes in near-native states, though at varying resolutions that influence domain delineation accuracy. Key algorithms exploit these coordinates to detect domain boundaries and alignments. The DALI (Distance-matrix ALIgnment) algorithm, a seminal tool for structural comparison, aligns protein structures by matching intra-molecular distance matrices derived from 3D coordinates, facilitating the identification of recurrent domains across proteins with low root-mean-square deviation (RMSD) values, typically below 3 for homologous domains. DomainParser, another graph-theoretic approach, partitions structures by modeling proteins as networks of residues and optimizing for compactness scores, where domains are defined as subgraphs with minimal inter-subgraph connectivity and high intra-subgraph density, achieving accurate boundary detection in over 90% of test cases when the number of domains is specified. These methods prioritize evolutionary conservation of structural motifs, using coordinate data to cluster residues into semi-independent folding units. Domain identification criteria emphasize intra-domain cohesion and inter-domain separation. Within a domain, residues exhibit low RMSD (often <2 Å) upon superposition, indicating structural rigidity, while inter-domain regions show high discontinuity, such as large gaps in Cα-Cα distances (>10 Å) or abrupt changes in secondary structure packing. Compactness is quantified by metrics like the domain's relative to its size, ensuring domains form globular, independently stable entities. These criteria, applied to PDB coordinates, have enabled the curation of domain databases like and CATH, which classify millions of structures based on such geometric properties. Despite their strengths, these methods face limitations from structural ambiguities and . Flexible linkers between domains, often unstructured loops, can obscure boundaries by allowing variable conformations, leading to inconsistent partitioning across related structures. Additionally, resolution dependencies—cryo-EM maps below 4 may lack atomic detail for precise RMSD calculations—can result in over- or under-segmentation, particularly in dynamic multidomain proteins. Advances in higher-resolution cryo-EM have mitigated some issues, but manual curation remains necessary for ambiguous cases.

Sequence and Computational Prediction Methods

Sequence-based prediction of protein domains relies on analyzing sequences to identify modular regions without requiring experimental structural data. These methods are essential for annotating uncharacterized proteins, particularly in large-scale efforts. Key approaches include homology-based detection using statistical models and techniques that infer domains from intrinsic sequence properties. Recent advancements in have further enhanced prediction accuracy, especially for boundary delineation. Hidden Markov model (HMM)-based profiles represent a of sequence-based domain prediction, enabling sensitive detection of conserved domains across divergent sequences. The database compiles thousands of protein families, each characterized by multiple sequence alignments (MSAs) and corresponding HMM profiles built using tools like . These profiles capture position-specific conservation and variability, allowing searches against query sequences to identify domain matches with reported sensitivities exceeding 80% for known domains in eukaryotic proteomes. Similarly, the database focuses on signaling and extracellular domains, employing manually curated HMMs to minimize false positives while achieving high specificity in domain annotation. Both databases integrate with broader resources like for comprehensive coverage, facilitating the assignment of domains in novel sequences through probabilistic scoring. Ab initio prediction methods, which do not rely on homology to known domains, integrate tools and to infer domain boundaries from physicochemical features and evolutionary signals. The DomPred server exemplifies this approach by combining PSI-BLAST for homology-assisted alignment against non-redundant databases with neural network-based predictors like DPS (Domain Prediction using Secondary Structure Assignments) to estimate boundaries. This integration allows DomPred to achieve approximately 70-85% accuracy in predicting the number of domains and boundary positions for single- and multi-domain proteins on benchmark datasets. Such tools are particularly valuable for orphan proteins lacking close homologs, though they may underperform in highly disordered regions. Post-2020 advances have leveraged deep neural architectures to improve domain boundary precision, often drawing inspiration from protein models and successes like . Methods such as Res-Dom employ residual networks combined with bidirectional (Bi-LSTM) units to process sequence embeddings, yielding a normalized domain overlap score of 0.849 on datasets—about 5% higher than prior state-of-the-art. Similarly, DistDom uses multi-head U-Nets on one-dimensional sequence features and predicted contact maps, attaining average per-target F1 scores of 0.26-0.47 for boundary across benchmarks like 14 and Topdomain. These AI-driven models enhance in low-homology scenarios by learning subtle sequence motifs, with boundary accuracies approaching 85-90% for well-folded domains when trained on expanded datasets including -derived structural priors. As of 2025, tools like DI-TASSER leverage 3 for improved multidomain , enhancing domain boundary accuracy in complex proteins. Validation of sequence-based predictions typically involves benchmarking against experimentally determined structural domains from the (PDB), using metrics such as boundary distance error (e.g., predicted vs. observed linkers within 30 residues) and domain overlap scores. For low-homology cases, where sequence identity drops below 30%, methods outperform traditional HMMs by incorporating evolutionary couplings and secondary structure predictions, though challenges persist in multi-domain proteins with short linkers, where false merger rates can exceed 20%. Cross-validation on curated sets like CATH or ensures robustness, with recent tools demonstrating improved handling of such cases through ensemble predictions.

Examples of Well-Characterized Domains

One prominent example of a multidomain protein is (PK), an critical to that catalyzes the transfer of a group from phosphoenolpyruvate to ADP, producing ATP and pyruvate. Each subunit of the homotetrameric PK typically consists of four structural domains: an N-terminal domain (present in mammalian isoforms), a central A domain with a (β/α)₈ barrel fold forming part of the , an intervening B domain characterized by a β-barrel , and a C-terminal domain with an α/β architecture. The of rabbit muscle pyruvate kinase (PDB: 1PKN), resolved at 2.9 Å resolution in complex with Mn²⁺, K⁺, and pyruvate, illustrates these modular units, where the N-terminal and C-terminal domains flank the catalytic core and contribute to interdomain interfaces. In allosteric isoforms such as PKM2 or liver PK, the C-terminal domain harbors the binding site for effectors like fructose-1,6-bisphosphate, which induces conformational changes across domains to shift the enzyme from a low-affinity T-state to a high-affinity R-state, enhancing catalytic under varying metabolic conditions. The SH3 (Src homology 3) domain exemplifies a compact, independently folding module specialized for protein-protein interactions in signaling pathways. Comprising approximately 60 , the SH3 domain adopts a compact β-barrel structure consisting of five β-strands arranged in two antiparallel sheets packed against each other, stabilized by a conserved hydrophobic core. This fold creates a shallow binding groove that specifically recognizes -rich peptide ligands, often with the PxxP (where P is and x is any ), enabling SH3 domains to mediate transient associations in cytoskeletal regulation, , and cascades. Over 300 SH3 domains are encoded in the , underscoring their modular versatility in assembling multiprotein complexes. The immunoglobulin (Ig) domain represents a highly conserved central to function and beyond, forming the building blocks of immunoglobulins and many proteins. Each Ig domain features a β-sandwich fold, typically with seven to nine antiparallel β-strands organized into two β-sheets (one with three or four strands and the other with four strands) that pack face-to-face via a conserved bond and hydrophobic interactions. In antibodies like IgG, variable (V) and constant (C) Ig domains in the heavy and light chains create the antigen-binding Fab region and effector Fc region, respectively, with the β-sandwich providing stability and flexibility for immune recognition. This fold's evolutionary conservation spans billions of years, appearing in diverse proteins from vertebrates to and even some , reflecting its ancient origin and adaptability for binding and domain swapping. The of a mouse IgG1 (PDB: 1IGY), determined at 3.2 Å resolution, highlights the tandem arrangement of these domains in maintaining antibody bivalency and solubility.

Current Challenges

Domains of Unknown Function

Domains of unknown function (DUFs) represent a substantial portion of annotated protein families in major databases, highlighting the extent of uncharacterized elements in the . In the database, approximately 24% of families are classified as DUFs, with over 4,700 such families documented as of 2024. These domains are systematically named with the prefix "DUF" followed by a unique identifier, reflecting their lack of assigned biological roles despite widespread occurrence across genomes. Characterizing DUFs presents significant hurdles due to the absence of close structural homologs to known domains and limited experimental data, which complicates functional inference. Many DUFs are prevalent in non-model organisms, such as and , where genetic manipulation and biochemical assays are technically demanding. This scarcity of often leaves these domains annotated solely based on sequence conservation, perpetuating a cycle of uncertainty in their roles within protein architectures. The uncharacterized nature of DUFs carries profound implications for understanding disease mechanisms and biotechnological applications, as they may harbor novel activities with therapeutic or industrial potential. For instance, variations in copy number of the DUF1220 domain have been linked to development and cognitive disorders, including and , suggesting roles in neural proliferation and evolution. Similarly, microbial DUFs could encode unique enzymes for metabolic pathways exploitable in or . Ongoing research gaps underscore the need for advanced approaches to unravel DUF functions, particularly as metagenomic studies continue to uncover vast numbers of microbial DUFs in environmental samples. These efforts reveal thousands of novel DUF variants in uncultured microbes, expanding the known diversity but intensifying the challenge of functional assignment without targeted experimental validation.

Advances in Domain Discovery

Significant advances in and have revolutionized protein domain discovery by enabling accurate de novo structure prediction without relying on homologous templates. AlphaFold2, introduced in 2021, achieves atomic-level accuracy in predicting protein structures, including individual domains, even for sequences lacking close homologs, with a median backbone r.m.s.d.95 of 0.96 Å on challenging CASP14 targets. Similarly, RoseTTAFold, fine-tuned into the RFdiffusion model in 2023, generates novel protein backbones de novo through denoising diffusion processes, successfully designing diverse domain topologies such as TIM barrels and NTF2 folds with high fidelity (up to 54.1% success rate). Recent updates from 2023 to 2025 have enhanced multi-domain modeling capabilities, allowing integrated predictions of domain interactions within full-length proteins. AlphaFold3 (2024) employs a diffusion-based architecture to predict joint structures of multi-domain complexes, outperforming prior methods in protein-protein interfaces (DockQ > 0.23) and accurately modeling large assemblies like the 7,663-residue human 40S ribosomal subunit (LDDT 87.7). Complementing this, the RoseTTAFold All-Atom model (2024) extends de novo design to include ligands and post-translational modifications, facilitating the creation of functional multi-domain architectures with atomic precision. These tools have democratized domain discovery by providing reliable structural insights for previously intractable proteins. High-throughput techniques have further accelerated in situ domain visualization, capturing domains in their native cellular contexts. Cryo-electron tomography (cryo-ET), advanced through improved pipelines, now resolves macromolecular complexes at near-atomic resolution, enabling the mapping of domain arrangements within cellular environments. A notable 2024 development, DomainSeeker, integrates cryo-ET maps with 2 predictions for de novo domain identification in protein complexes, bypassing the need for prior structural knowledge and revealing novel domain organizations in tomograms. Supporting these efforts, the Protein Structure Database, updated in 2025, hosts predictions for over 200 million proteins, serving as a comprehensive for high-throughput domain analysis across proteomes. Integrative approaches combining computational predictions with experimental have begun to annotate functions for domains of unknown function (DUFs). By overlaying AlphaFold-derived structures with data on protein interactions and modifications, researchers can infer functional roles; for instance, structural models of DUF507 guided experimental validation via , revealing its α-helical architecture and potential enzymatic implications. Tools like AnnoDUF (2024) leverage such integrations to propagate annotations across DUF families, using sequence and structural alignments to assign putative functions to uncharacterized domains. Looking ahead, is poised to resolve a substantial portion of DUFs by integrating multimodal data for functional prediction, with tools like AnnoDUF exemplifying scalable annotation pipelines. Projections suggest that AI-driven methods could characterize functions for thousands of remaining DUFs within the decade, enhancing coverage. However, advances in synthetic domain design raise ethical concerns, including risks of misuse for harmful biomolecules; in response, over 100 researchers endorsed principles in for responsible AI development, emphasizing , , and equitable access in .

References

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