Protein-peptide Recognition Research Articles

Propedia: a database for protein\u2013peptide identification based on a hybrid clustering algorithm

BackgroundProtein–peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein–peptide databases and tools that allow the retrieval, characterization and understanding of protein–peptide recognition and consequently support peptide design.ResultsWe introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein–peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein–peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation.ConclusionsPropedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein–peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia

Machine Learning in Quantitative Protein-peptide Affinity Prediction: Implications for Therapeutic Peptide Design.

Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein-peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein-peptide affinity and attempt to extend the content of generalized machine learning methods. Existing issues and future perspective on the statistical modeling and regression prediction of protein- peptide binding affinity are discussed. There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein-peptide affinity predictors.

The SIRAH 2.0 Force Field: Altius, Fortius, Citius.

A new version of the coarse-grained (CG) SIRAH force field for proteins has been developed. Modifications to bonded and non-bonded interactions on the existing molecular topologies significantly ameliorate the structural description and flexibility of a non-redundant set of proteins. The SIRAH 2.0 force field has also been ported to the popular simulation package AMBER, which along with the former implementation in GROMACS expands significantly the potential range of users and performance of this CG force field on CPU/GPU codes. As a non-trivial example of its application, we undertook the structural and dynamical analysis of the most abundant and conserved calcium-binding protein, calmodulin (CaM). CaM is composed of two calcium-binding motifs called EF-hands, which in the presence of calcium specifically recognize a cognate peptide by embracing it. CG simulations of CaM bound to four calcium ions in the presence or absence of a binding peptide (holo and apo forms, respectively) resulted in good and stable ion coordination. The simulation of the holo form starting from an experimental structure sampled near-native conformations, retrieving quasi-atomistic precision. Removing the binding peptide enabled the EF-hands to perform large reciprocal movements, comparable to those observed in NMR structures. On the other hand, the isolated peptide starting from the helical conformation experienced spontaneous unfolding, in agreement with previous experimental data. However, repositioning the peptide in the neighborhood of one EF-hand not only prevented the peptide from unfolding but also drove CaM to a fully bound conformation, with both EF-hands embracing the cognate peptide, resembling the experimental holo structure. Therefore, SIRAH 2.0 shows the capacity to handle a number of structurally and dynamically challenging situations, including metal ion coordination, unbiased conformational sampling, and specific protein-peptide recognition.

MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory.

Multispecificity–the ability of a single receptor protein molecule to interact with multiple substrates–is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.

Exhaustive search of linear information encoding protein-peptide recognition.

High-throughput in vitro methods have been extensively applied to identify linear information that encodes peptide recognition. However, these methods are limited in number of peptides, sequence variation, and length of peptides that can be explored, and often produce solutions that are not found in the cell. Despite the large number of methods developed to attempt addressing these issues, the exhaustive search of linear information encoding protein-peptide recognition has been so far physically unfeasible. Here, we describe a strategy, called DALEL, for the exhaustive search of linear sequence information encoded in proteins that bind to a common partner. We applied DALEL to explore binding specificity of SH3 domains in the budding yeast Saccharomyces cerevisiae. Using only the polypeptide sequences of SH3 domain binding proteins, we succeeded in identifying the majority of known SH3 binding sites previously discovered either in vitro or in vivo. Moreover, we discovered a number of sites with both non-canonical sequences and distinct properties that may serve ancillary roles in peptide recognition. We compared DALEL to a variety of state-of-the-art algorithms in the blind identification of known binding sites of the human Grb2 SH3 domain. We also benchmarked DALEL on curated biological motifs derived from the ELM database to evaluate the effect of increasing/decreasing the enrichment of the motifs. Our strategy can be applied in conjunction with experimental data of proteins interacting with a common partner to identify binding sites among them. Yet, our strategy can also be applied to any group of proteins of interest to identify enriched linear motifs or to exhaustively explore the space of linear information encoded in a polypeptide sequence. Finally, we have developed a webserver located at http://michnick.bcm.umontreal.ca/dalel, offering user-friendly interface and providing different scenarios utilizing DALEL.

Exploring Protein-Peptide Recognition Pathways Using a Supervised Molecular Dynamics Approach

Peptides have gained increased interest as therapeutic agents during recent years. The high specificity and relatively low toxicity of peptide drugs derive from their extremely tight binding to their targets. Indeed, understanding the molecular mechanism of protein-peptide recognition has important implications in the fields of biology, medicine, and pharmaceutical sciences. Even if crystallography and nuclear magnetic resonance are offering valuable atomic insights into the assembling of the protein-peptide complexes, the mechanism of their recognition and binding events remains largely unclear. In this work we report, for the first time, the use of a supervised molecular dynamics approach to explore the possible protein-peptide binding pathways within a timescale reduced up to three orders ofmagnitude compared with classical molecular dynamics. The better and faster understating of the protein-peptide recognition pathways could be very beneficial in enlarging the applicability of peptide-based drug design approaches in several biotechnological and pharmaceutical fields.

Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery.

The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF). As for the investigated targets, the optimized MIEC-SVM models performed much better than the models based on the default SVM parameters and Autodock for the tested datasets. Then, the proposed strategy was utilized to screen the Specs database for discovering potential inhibitors of the ALK kinase. The experimental results showed that the optimized MIEC-SVM model, which identified 7 actives with IC50 < 10 μM from 50 purchased compounds (namely hit rate of 14%, and 4 in nM level) and performed much better than Autodock (3 actives with IC50 < 10 μM from 50 purchased compounds, namely hit rate of 6%, and 2 in nM level), suggesting that the proposed strategy is a powerful tool in structure-based virtual screening.

Self-binding peptides: folding or binding?

Self-binding peptides (SBPs) represent those short peptide segments within monomeric proteins to fulfill their biological functions by dynamically binding to/unbinding from their target domains in the same monomers. They are frequently found in disordered or unstructured regions of proteins that are now known to be not simply helices, loops, or linkers and adopt a well-defined structure upon binding. In this study, we systematically explored the structural basis, energy landscape, and thermodynamic properties of SBP-mediated biological mechanisms by dissecting dynamic interactions of SBPs with their cognate targets. In order to identify whether the formation of the SBP-target bound state is naturally a folding or a binding, we carried out atomistic molecular dynamics (MD) simulations to investigate both the native bound state of eight representative SBPs in complex with their cognate domains and the same bound state but where the SBPs were split artificially from the domains in primary sequence. Results showed that the splitting did not influence essentially the interaction behavior of SBPs with their target domains, suggesting that the SBP-domain interaction is almost a binding phenomenon, although the polypeptide linker between the SBP and the domain seems to facilitate the interaction. In addition, the sequence pattern of SBPs is very similar to those of short linear motifs (SLiM) found in many protein-binding peptides where few key residues contribute predominantly to protein-peptide recognition. All of the above comes together to suggest that the SBP is a novel biomolecular phenomenon spanning between folding and binding.

Indirect readout in protein-peptide recognition: a different story from classical biomolecular recognition.

Protein-peptide interactions are prevalent and play essential roles in many living activities. Peptides recognize their protein partners by direct nonbonded interactions and indirect adjustment of conformations. Although processes of protein-peptide recognition have been comprehensively studied in both sequences and structures recently, flexibility of peptides and the configuration entropy penalty in recognition did not get enough attention. In this study, 20 protein-peptide complexes and their corresponding unbound peptides were investigated by molecular dynamics simulations. Energy analysis revealed that configurational entropy penalty introduced by restriction of the degrees of freedom of peptides in indirect readout process of protein-peptide recognition is significant. Configurational entropy penalty has become the main content of the indirect readout energy in protein-peptide recognition instead of deformation energy which is the main source of the indirect readout energy in classical biomolecular recognition phenomena, such as protein-DNA binding. These results provide us a better understanding of protein-peptide recognition and give us some implications in peptide ligand design.

Modeling Protein–Peptide Recognition Based on Classical Quantitative Structure–Affinity Relationship Approach: Implication for Proteome-Wide Inference of Peptide-Mediated Interactions

Peptide-mediated interactions are crucial to a variety of functions in the living cell and are estimated to be involved in up to 40 % of all cellular processes. Fast and reliable inference of such interactions is fundamentally important for our understanding and, then, reconstruction of complete virtual interactomics involved in a specific cell, tissue or organism. In the current study, we performed structure-level characterization, modeling and prediction of protein-peptide recognition specificity and stability in a high-throughput manner. To achieve this, the classical chemometrics methodology quantitative structure-activity relationship (QSAR), which is traditionally applied to small-molecule entities such as drug compounds and environmental chemicals, was employed to statistically correlate structure features with binding affinities for a panel of structure-solved, affinity-known protein-peptide complexes compiled from the PDB database and literatures. In the standard QSAR procedure, various structural descriptors including physicochemical, geometrical and constitutional parameters that characterize diverse aspects of protein-peptide interaction property were derived from the biomacromolecular complex structure architecture, and these descriptors were then correlated with experimentally measured affinities by using the partial least squares (PLS) regression and Gaussian process (GP) in conjunction with genetic algorithm (GA) variable selection. The nonlinear GA/GP method was found to perform much well as compared to linear GA/PLS modeling, suggesting that the protein-peptide interaction system is highly complicated that may involve strong noise and interactive effect. The optimal GA/GP model revealed that the interface size and solvent effect play a critical role in protein-peptide binding, and other properties such as peptide length and flexibility also contribute significantly to the binding. A further test on 2,018 human amphiphysin SH3 domain-binding peptides demonstrated that the purposed QSAR modeling procedure is very fast and effective, which can thus be readily used to perform proteome-wide inference of peptide-mediated interactions.

Epitope‐specificity of recombinant antibodies reveals promiscuous peptide‐binding properties

Protein-peptide interactions are a common occurrence and essential for numerous cellular processes, and frequently explored in broad applications within biology, medicine, and proteomics. Therefore, understanding the molecular mechanism(s) of protein-peptide recognition, specificity, and binding interactions will be essential. In this study, we report the first detailed analysis of antibody-peptide interaction characteristics, by combining large-scale experimental peptide binding data with the structural analysis of eight human recombinant antibodies and numerous peptides, targeting tryptic mammalian and eukaryote proteomes. The results consistently revealed that promiscuous peptide-binding interactions, that is, both specific and degenerate binding, were exhibited by all antibodies, and the discovery was corroborated by orthogonal data, indicating that this might be a general phenomenon for low-affinity antibody-peptide interactions. The molecular mechanism for the degenerate peptide-binding specificity appeared to be executed through the use of 2-3 semi-conserved anchor residues in the C-terminal part of the peptides, in analogue to the mechanism utilized by the major histocompatibility complex-peptide complexes. In the long-term, this knowledge will be instrumental for advancing our fundamental understanding of protein-peptide interactions, as well as for designing, generating, and applying peptide specific antibodies, or peptide-binding proteins in general, in various biotechnical and medical applications.

Fine tuning of the active site modulates specificity in the interaction of O-acetylserine sulfhydrylase isozymes with serine acetyltransferase

O-acetylserine sulfhydrylase (OASS) catalyzes the synthesis of l-cysteine in the last step of the reductive sulfate assimilation pathway in microorganisms. Its activity is inhibited by the interaction with serine acetyltransferase (SAT), the preceding enzyme in the metabolic pathway. Inhibition is exerted by the insertion of SAT C-terminal peptide into the OASS active site. This action is effective only on the A isozyme, the prevalent form in enteric bacteria under aerobic conditions, but not on the B-isozyme, the form expressed under anaerobic conditions. We have investigated the active site determinants that modulate the interaction specificity by comparing the binding affinity of thirteen pentapeptides, derived from the C-terminal sequences of SAT of the closely related species Haemophilus influenzae and Salmonella typhimurium, towards the corresponding OASS-A, and towards S. typhimurium OASS-B. We have found that subtle changes in protein active sites have profound effects on protein–peptide recognition. Furthermore, affinity is strongly dependent on the pentapeptide sequence, signaling the relevance of P3–P4–P5 for the strength of binding, and P1–P2 mainly for specificity. The presence of an aromatic residue at P3 results in high affinity peptides with Kdiss in the micromolar and submicromolar range, regardless of the species. An acidic residue, like aspartate at P4, further strengthens the interaction and results in the higher affinity ligand of S. typhimurium OASS-A described to date. Since OASS knocked-out bacteria exhibit a significantly decreased fitness, this investigation provides key information for the development of selective OASS inhibitors, potentially useful as novel antibiotic agents.

Structural and Dynamic Determinants of Protein-Peptide Recognition

Protein-peptide interactions play important roles in many cellular processes, including signal transduction, trafficking, and immune recognition. Protein conformational changes upon binding, an ill-defined peptide binding surface, and the large number of peptide degrees of freedom make the prediction of protein-peptide interactions particularly challenging. To address these challenges, we perform rapid molecular dynamics simulations in order to examine the energetic and dynamic aspects of protein-peptide binding. We find that, in most cases, we recapitulate the native binding sites and native-like poses of protein-peptide complexes. Inclusion of electrostatic interactions in simulations significantly improves the prediction accuracy. Our results also highlight the importance of protein conformational flexibility, especially side-chain movement, which allows the peptide to optimize its conformation. Our findings not only demonstrate the importance of sufficient sampling of the protein and peptide conformations, but also reveal the possible effects of electrostatics and conformational flexibility on peptide recognition.

Structural and Dynamic Determinants of Protein-Peptide Recognition

Structural and Dynamic Determinants of Protein-Peptide Recognition

Plasticity in Protein-Peptide Recognition: Crystal Structures of Two Different Peptides Bound to Concanavalin A

The structures of concanavalin A (ConA) in complex with two carbohydrate-mimicking peptides, 10-mer (MYWYPYASGS) and 15-mer (RVWYPYGSYLTASGS) have been determined at 2.75 Å resolution. In both crystal structures four independent peptide molecules bind to each of the crystallographically independent subunits of ConA tetramer. The peptides exhibit small but significant variability in conformations and interactions while binding to ConA. The crystal structure of another similar peptide, 12-mer (DVFYPYPYASGS), in complex with ConA has been determined (Jain, D., K. J. Kaur, B. Sundaravadivel, and D. M. Salunke. 2000. Structural and functional consequences of peptide-carbohydrate mimicry. J. Biol. Chem. 275:16098–16102). Comparison of the three complexes shows that the peptides bind to ConA at a common binding site, using different contacting residues and interactions depending on their sequence and the local environment at the binding site. The binding is also optimized by corresponding plasticity of the peptide binding site on ConA. The diversity in conformation and interactions observed here are in agreement with the structural leeway concerning plasticity of specific molecular recognition in biological processes. The adaptability of peptide-ConA interactions may also be correlated with the carbohydrate-mimicking property of these peptides.

Contribution of Solvation Energy in Protein‐Peptide Recognition Systems

AbstractThe contribution of solvation energy to guiding molecular recognition for six rigid protein‐peptide systems bad been evaluated by the variation in the number of the identified native‐like configurations and in the driving force of specific interaction resulting from the addition of the explicit solvation term in the force field function. The AMBER force field energy and the total energy including the force field energy and the WZS solvation energy were calculated for sampled configurations. The results obtained by the calculations of both force field and total energies were compared with each other. It suggests that the contribution of solvation energy is important to guiding the specific recognition of the systems in which the ligands possess larger hydrophobic or aromatic residues while the protein receptors provide the active surfaces with hydrophobic property.

Backbone entropy of loops as a measure of their flexibility: Application to a Ras protein simulated by molecular dynamics

The flexibility of surface loops plays an important role in protein–protein and protein–peptide recognition; it is commonly studied by Molecular Dynamics or Monte Carlo simulations. We propose to measure the relative backbone flexibility of loops by the difference in their backbone conformational entropies, which are calculated here with the local states (LS) method of Meirovitch. Thus, one can compare the entropies of loops of the same protein or, under certain simulation conditions, of different proteins. These loops should be equal in size but can differ in their sequence of amino acids residues. This methodology is applied successfully to three segments of 10 residues of a Ras protein simulated by the stochastic boundary molecular dynamics procedure. For the first time estimates of backbone entropy differences are obtained, and their correlation with B factors is pointed out; for example, the segments which consist of residues 60–65 and 112–117 have average B factors of 67 and 18 Å2, respectively, and entropy difference T ΔS = 5.4 ± 0.1 kcal/mol at T = 300 K. In a large number of recent publications the entropy due to the fast motions (on the ps-ns time scale) of N–H and C–H vectors has been obtained from their order parameter, measured in nuclear magnetic resonance spin relaxation experiments. This enables one to estimate differences in the entropy of protein segments due to folding–unfolding transitions, for example. However, the vectors are assumed to be independent, and the effect of the neglected correlations is unknown; our method is expected to become an important tool for assessing this approximation. The present calculations, obtained with the LS method, suggest that the errors involved in experimental entropy differences might not be large; however, this should be verified in each case. Potential applications of entropy calculations to rational drug design are discussed. Proteins 29:127–140, 1997. © 1997 Wiley-Liss, Inc.

Backbone entropy of loops as a measure of their flexibility: Application to a Ras protein simulated by molecular dynamics

The flexibility of surface loops plays an important role in protein-protein and protein-peptide recognition; it is commonly studied by Molecular Dynamics or Monte Carlo stimulations. We propose to measure the relative backbone flexibility of loops by the difference in their backbone conformational entropies, which are calculated here with the local states (LS) method of Meirovitch. Thus, one can compare the entropies of loops of the same protein or, under certain simulation conditions, of different proteins. These loops should be equal in size but can differ in their sequence of amino acids residues. This methodology is applied successfully to three segments of 10 residues of a Ras protein simulated by the stochastic boundary molecular dynamics procedure. For the first time estimates of backbone entropy differences are obtained, and their correlation with B factors is pointed out; for example, the segments which consist of residues 60-65 and 112-117 have average B factors of 67 and 18 A2, respectively, and entropy difference T delta S = 5.4 +/- 0.1 kcal/mol at T = 300 K. In a large number of recent publications the entropy due to the fast motions (on the ps-ns time scale) of N-H and C-H vectors has been obtained from their order parameter, measured in nuclear magnetic resonance spin relaxation experiments. This enables one to estimate differences in the entropy of protein segments due to folding-unfolding transitions, for example. However, the vectors are assumed to be independent, and the effect of the neglected correlations is unknown; our method is expected to become an important tool for assessing this approximation. The present calculations, obtained with the LS method, suggest that the errors involved in experimental entropy differences might not be large; however, this should be verified in each case. Potential applications of entropy calculations to rational drug design are discussed.

Consistency in structural energetics of protein folding and peptide recognition.

We report a new free energy decomposition that includes structure-derived atomic contact energies for the desolvation component, and show that it applies equally well to the analysis of single-domain protein folding and to the binding of flexible peptides to proteins. Specifically, we selected the 17 single-domain proteins for which the three-dimensional structures and thermodynamic unfolding free energies are available. By calculating all terms except the backbone conformational entropy change and comparing the result to the experimentally measured free energy, we estimated that the mean entropy gain by the backbone chain upon unfolding (delta Sbb) is 5.3 cal/K per mole of residue, and that the average backbone entropy for glycine is 6.7 cal/K. Both numbers are in close agreement with recent estimates made by entirely different methods, suggesting a promising degree of consistency between data obtained from disparate sources. In addition, a quantitative analysis of the folding free energy indicates that the unfavorable backbone entropy for each of the proteins is balanced predominantly by favorable backbone interactions. Finally, because the binding of flexible peptides to receptors is physically similar to folding, the free energy function should, in principle, be equally applicable to flexible docking. By combining atomic contact energies, electrostatics, and sequence-dependent backbone entropy, we calculated a priori the free energy changes associated with the binding of four different peptides to HLA-A2, 1 MHC molecule and found agreement with experiment to within 10% without parameter adjustment.

In vitro models that support adhesion specificity in biofilms of oral bacteria.

Adhesion to adsorbed pellicles and interspecies co-adhesion to form plaque biofilms involve selective interactions of bacterial adhesins with specific receptors. Our laboratory has devised in vitro assays for co-adhesion between Actinomyces naeslundii and Streptococcus oralis or Porphyromonas gingivalis on saliva-coated mineral and hexadecane droplet substrata. P. gingivalis structures significant for co-adhesion with A. naeslundii include surface vesicles and fimbriae. A family of arginine-specific cysteine proteinases in vesicles may be involved in adherence to bacteria, to host cells, and to matrix proteins. New research from several laboratories has found that such proteinases are processed from genes encoding polyproteins containing both proteinase and hemagglutinin domains. In addition to enzyme-substrate recognition, bacterial adhesion is often determined by specific protein-peptide and lectincarbohydrate recognition. A. naeslundii--salivary prolinerich protein, S. gordonii--salivary alpha-amylase, and Treponema denticola--matrix protein recognition are examples of the former. Co-adhesion of A. naeslundii and S. oralis is an example of the latter. Lactose can selectively desorb A. naeslundii cells from mixed biofilms with S. oralis, a demonstration of the significance of specificity. Although non-specific forces are probably secondary to stereochemical fit in determining the selective range of surfaces that bacteria have evolved to recognize and bind, they probably help stabilize non-covalent bonds within aligned, complementary domains.