Evolution of protein structures and interactions from the perspective of residue contact networks

Aromatic-aromatic interactions drive fold switch of GA95 and GB95 with three residue difference

Proteins typically adopt a single fold to carry out their function, but metamorphic proteins, with multiple folding states, defy this norm. Deciphering the mechanism of conformational interconversion of metamorphic proteins is challenging. Herein, we employed nuclear magnetic resonance (NMR), circular dichroism (CD), and all-atom molecular dynamics (MD) simulations to elucidate the mechanism of fold switching in proteins GA95 and GB95, which share 95% sequence homology. The results reveal that long-range interactions, especially aromatic π-π interactions involving residues F52, Y45, F30, and Y29, are critical for the protein switching from a 3α to a 4β + α fold. This study contributes to understanding how proteins with highly similar sequences fold into distinct conformations and may provide valuable insights into the protein folding code.

Key interaction networks: Identifying evolutionarily conserved non-covalent interaction networks across protein families

Protein structure (and thus function) is dictated by non-covalent interaction networks. These can be highly evolutionarily conserved across protein families, the members of which can diverge in sequence and evolutionary history. Here we present KIN, a tool to identify and analyze conserved non-covalent interaction networks across evolutionarily related groups of proteins. KIN is available for download under a GNU General Public License, version 2, from https://www.github.com/kamerlinlab/KIN. KIN can operate on experimentally determined structures, predicted structures, or molecular dynamics trajectories, providing insight into both conserved and missing interactions across evolutionarily related proteins. This provides useful insight both into protein evolution, as well as a tool that can be exploited for protein engineering efforts. As a showcase system, we demonstrate applications of this tool to understanding the evolutionary-relevant conserved interaction networks across the class A β-lactamases.

Structural basis underlying the synergism of NADase and SLO during group A Streptococcus infection

Group A Streptococcus (GAS) is a strict human pathogen possessing a unique pathogenic trait that utilizes the cooperative activity of NAD+-glycohydrolase (NADase) and Streptolysin O (SLO) to enhance its virulence. How NADase interacts with SLO to synergistically promote GAS cytotoxicity and intracellular survival is a long-standing question. Here, the structure and dynamic nature of the NADase/SLO complex are elucidated by X-ray crystallography and small-angle scattering, illustrating atomic details of the complex interface and functionally relevant conformations. Structure-guided studies reveal a salt-bridge interaction between NADase and SLO is important to cytotoxicity and resistance to phagocytic killing during GAS infection. Furthermore, the biological significance of the NADase/SLO complex in GAS virulence is demonstrated in a murine infection model. Overall, this work delivers the structure-functional relationship of the NADase/SLO complex and pinpoints the key interacting residues that are central to the coordinated actions of NADase and SLO in the pathogenesis of GAS infection.

Allosteric pockets and dynamic residue network hubs of falcipain 2 in mutations including those linked to artemisinin resistance

Continually emerging resistant strains of malarial parasites to current drugs present challenges. Understanding the underlying resistance mechanisms, especially those linked to allostery is, thus, highly crucial for drug design. This forms the main concern of the paper through a case study of falcipain 2 (FP-2) and its mutations, some of which are linked to artemisinin (ART) drug resistance. Here, we applied a variety of in silico approaches and tools that we developed recently, together with existing computational tools. This included novel essential dynamics and dynamic residue network (DRN) analysis algorithms. We identified six pockets demonstrating dynamic differences in the presence of some mutations. We observed striking allosteric effects in two mutant proteins. In the presence of M245I, a cryptic pocket was detected via a unique mechanism in which Pocket 2 fused with Pocket 6. In the presence of the A353T mutation, which is located at Pocket 2, the pocket became the most rigid among all protein systems analyzed. Pocket 6 was also highly stable in all cases, except in the presence of M245I mutation. The effect of ART linked mutations was more subtle, and the changes were at residue level. Importantly, we identified an allosteric communication path formed by four unique averaged BC hubs going from the mutated residue to the catalytic site and passing through the interface of three identified pockets. Collectively, we established and demonstrated that we have robust tools and a pipeline that can be applicable to the analysis of mutations.

Quantifying the impact of changes in effective population size and expression level on the rate of coding sequence evolution

Molecular sequences are shaped by selection, where the strength of selection relative to drift is determined by effective population size (N_e). Populations with high N_e are expected to undergo stronger purifying selection, and consequently to show a lower substitution rate for selected mutations relative to the substitution rate for neutral mutations (ω). However, computational models based on biophysics of protein stability have suggested that ω can also be independent of N_e. Together, the response of ω to changes in N_e depends on the specific mapping from sequence to fitness. Importantly, an increase in protein expression level has been found empirically to result in decrease of ω, an observation predicted by theoretical models assuming selection for protein stability. Here, we derive a theoretical approximation for the response of ω to changes in N_e and expression level, under an explicit genotype-phenotype-fitness map. The method is generally valid for additive traits and log-concave fitness functions. We applied these results to protein undergoing selection for their conformational stability and corroborate out findings with simulations under more complex models. We predict a weak response of ω to changes in either N_e or expression level, which are interchangeable. Based on empirical data, we propose that fitness based on the conformational stability may not be a sufficient mechanism to explain the empirically observed variation in ω across species. Other aspects of protein biophysics might be explored, such as protein-protein interactions, which can lead to a stronger response of ω to changes in N_e.

Reprogramming the Specificity of a Protein Interface by Computational and Data-Driven Design

The formation of specific protein complexes in a cell is a non-trivial problem given the co-existence of thousands of different polypeptide chains. A particularly difficult case are two glutamine amidotransferase complexes (anthranilate synthase [AS] and aminodeoxychorismate synthase [ADCS]), which are composed of homologous pairs of synthase and glutaminase subunits. We have attempted to identify discriminating interface residues of the glutaminase subunit TrpG from AS, which are responsible for its specific interaction with the synthase subunit TrpEx and prevent binding to the closely related synthase subunit PabB from ADCS. For this purpose, TrpG-specific interface residues were grafted into the glutaminase subunit PabA from ADCS by two different approaches, namely a computational and a data-driven one. Both approaches resulted in PabA variants that bound TrpEx with higher affinity than PabB. Hence, we have accomplished a reprogramming of protein-protein interaction specificity that provides insights into the evolutionary adaptation of protein interfaces.

Comparative Structural Analysis of 20S Proteasome Ortholog Protein Complexes by Native Mass Spectrometry

Ortholog protein complexes are responsible for equivalent functions in different organisms. However, during evolution, each organism adapts to meet its physiological needs and the environmental challenges imposed by its niche. This selection pressure leads to structural diversity in protein complexes, which are often difficult to specify, especially in the absence of high-resolution structures. Here, we describe a multilevel experimental approach based on native mass spectrometry (MS) tools for elucidating the structural preservation and variations among highly related protein complexes. The 20S proteasome, an essential protein degradation machinery, served as our model system, wherein we examined five complexes isolated from different organisms. We show that throughout evolution, from the Thermoplasma acidophilum archaeal prokaryotic complex to the eukaryotic 20S proteasomes in yeast (Saccharomyces cerevisiae) and mammals (rat - Rattus norvegicus, rabbit - Oryctolagus cuniculus and human - HEK293 cells), the proteasome increased both in size and stability. Native MS structural signatures of the rat and rabbit 20S proteasomes, which heretofore lacked high-resolution, three-dimensional structures, highly resembled that of the human complex. Using cryoelectron microscopy single-particle analysis, we were able to obtain a high-resolution structure of the rat 20S proteasome, allowing us to validate the MS-based results. Our study also revealed that the yeast complex, and not those in mammals, was the largest in size and displayed the greatest degree of kinetic stability. Moreover, we also identified a new proteoform of the PSMA7 subunit that resides within the rat and rabbit complexes, which to our knowledge have not been previously described. Altogether, our strategy enables elucidation of the unique structural properties of protein complexes that are highly similar to one another, a framework that is valid not only to ortholog protein complexes, but also for other highly related protein assemblies.

Comprehensive analysis of all evolutionary paths between two divergent PDZ domain specificities

To understand the molecular evolution of functional diversity in protein families, we comprehensively investigated the consequences of all possible mutation combinations separating two peptide-binding domains with highly divergent specificities. We analyzed the Erbin PDZ domain (Erbin-PDZ), which exhibits canonical type I specificity, and a synthetic Erbin-PDZ variant (E-14) that differs at six positions and exhibits an atypical specificity that closely resembles that of the natural Pdlim4 PDZ domain (Pdlim4-PDZ). We constructed a panel of 64 PDZ domains covering all possible transitions between Erbin-PDZ and E-14 (i.e., the panel contained variants with all possible combinations of either the Erbin-PDZ or E-14 sequence at the six differing positions). We assessed the specificity profiles of the 64 PDZ domains using a C-terminal phage-displayed peptide library containing all possible genetically encoded heptapeptides. The specificity profiles clustered into six distinct groups, showing that intermediate domains can be nodes for the evolution of divergent functions. Remarkably, three substitutions were sufficient to convert the specificity of Erbin-PDZ to that of Pdlim4-PDZ, whereas Pdlim4-PDZ contains 71 differences relative to Erbin-PDZ. X-ray crystallography revealed the structural basis for specificity transition: a single substitution in the center of the binding site, supported by contributions from auxiliary substitutions, altered the main chain conformation of the peptide ligand to resemble that of ligands bound to Pdlim4-PDZ. Our results show that a very small set of mutations can dramatically alter protein specificity, and these findings support the hypothesis whereby complex protein functions evolve by gene duplication followed by cumulative mutations.

Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins Using Frequent Subgraph Mining

Modeling the interface region of a protein complex paves the way for understanding its dynamics and functionalities. Existing works model the interface region of a complex by using different approaches, such as, the residue composition at the interface region, the geometry of the interface residues, or the structural alignment of interface regions. These approaches are useful for ranking a set of docked conformation or for building scoring function for protein-protein docking, but they do not provide a generic and scalable technique for the extraction of interface patterns leading to functional motif discovery. In this work, we model the interface region of a protein complex by graphs and extract interface patterns of the given complex in the form of frequent subgraphs. To achieve this, we develop a scalable algorithm for frequent subgraph mining. We show that a systematic review of the mined subgraphs provides an effective method for the discovery of functional motifs that exist along the interface region of a given protein complex. In our experiments, we use three PDB protein structure datasets. The first two datasets are composed of PDB structures from different conformations of two dimeric protein complexes: HIV-1 protease (329 structures), and triosephosphate isomerase (TIM) (86 structures). The third dataset is a collection of different enzyme structures protein structures from the six top-level enzyme classes, namely: Oxydoreductase, Transferase, Hydrolase, Lyase, Isomerase, and Ligase. We show that for the first two datasets, our method captures the locking mechanism at the dimeric interface by taking into account the spatial positioning of the interfacial residues through graphs. Indeed, our frequent subgraph mining based approach discovers the patterns representing the dimerization lock which is formed at the base of the structure in 323 of the 329 HIV-1 protease structures. Similarly, for 86 TIM structures, our approach discovers the dimerization lock formation in 50 structures. For the enzyme structures, we show that we are able to capture the functional motifs (active sites) that are specific to each of the six top-level classes of enzymes through frequent subgraphs.

Evolutionary and structural analyses uncover a role for solvent interactions in the diversification of cocoonases in butterflies

Multi-omic approaches promise to supply the power to detect genes underlying disease and fitness-related phenotypes. Optimal use of the resulting profusion of data requires detailed investigation of individual candidate genes, a challenging proposition. Here, we combine transcriptomic and genomic data with molecular modelling of candidate enzymes to characterize the evolutionary history and function of the serine protease cocoonase. Heliconius butterflies possess the unique ability to feed on pollen; recent work has identified cocoonase as a candidate gene in pollen digestion. Cocoonase was first described in moths, where it aids in eclosure from the cocoon and is present as a single copy gene. In heliconiine butterflies it is duplicated and highly expressed in the mouthparts of adults. At least six copies of cocoonase are present in Heliconius melpomene and copy number varies across H. melpomene sub-populations. Most cocoonase genes are under purifying selection, however branch-site analyses suggest cocoonase 3 genes may have evolved under episodic diversifying selection. Molecular modelling of cocoonase proteins and examination of their predicted structures revealed that the active site region of each type has a similar structure to trypsin, with the same predicted substrate specificity across types. Variation among heliconiine cocoonases instead lies in the outward-facing residues involved in solvent interaction. Thus, the neofunctionalization of cocoonase duplicates appears to have resulted from the need for these serine proteases to operate in diverse biochemical environments. We suggest that cocoonase may have played a buffering role in feeding during the diversification of Heliconius across the neotropics by enabling these butterflies to digest protein from a range of biochemical milieux.

Epistasis in a Fitness Landscape Defined by Antibody-Antigen Binding Free Energy

Epistasis is the phenomenon by which the effect of a mutation depends on its genetic background. While it is usually defined in terms of organismal fitness, for single proteins, it must reflect physical interactions among residues. Here, we systematically extract the specific contribution pairwise epistasis makes to the physical affinity of antibody-antigen binding relevant to affinity maturation, a process of accelerated Darwinian evolution. We find that, among competing definitions of affinity, the binding free energy is the most appropriate to describe epistasis. We show that epistasis is pervasive, accounting for 25%-35% of variability, of which a large fraction is beneficial. This work suggests that epistasis both constrains, through negative epistasis, and enlarges, through positive epistasis, the set of possible evolutionary paths that can produce high-affinity sequences during repeated rounds of mutation and selection.

Visualization and analysis of non-covalent contacts using the Protein Contacts Atlas

Visualizations of biomolecular structures empower us to gain insights into biological functions, generate testable hypotheses, and communicate biological concepts. Typical visualizations (such as ball and stick) primarily depict covalent bonds. In contrast, non-covalent contacts between atoms, which govern normal physiology, pathogenesis, and drug action, are seldom visualized. We present the Protein Contacts Atlas, an interactive resource of non-covalent contacts from over 100,000 PDB crystal structures. We developed multiple representations for visualization and analysis of non-covalent contacts at different scales of organization: atoms, residues, secondary structure, subunits, and entire complexes. The Protein Contacts Atlas enables researchers from different disciplines to investigate diverse questions in the framework of non-covalent contacts, including the interpretation of allostery, disease mutations and polymorphisms, by exploring individual subunits, interfaces, and protein-ligand contacts and by mapping external information. The Protein Contacts Atlas is available at http://www.mrc-lmb.cam.ac.uk/pca/ and also through PDBe.

Docking, thermodynamics and molecular dynamics (MD) studies of a non-canonical protease inhibitor, MP-4, from Mucuna pruriens

Sequence and structural homology suggests that MP-4 protein from Mucuna pruriens belongs to Kunitz-type protease inhibitor family. However, biochemical assays showed that this protein is a poor inhibitor of trypsin. To understand the basis of observed poor inhibition, thermodynamics and molecular dynamics (MD) simulation studies on binding of MP-4 to trypsin were carried out. Molecular dynamics simulations revealed that temperature influences the spectrum of conformations adopted by the loop regions in the MP-4 structure. At an optimal temperature, MP-4 achieves maximal binding while above and below the optimum temperature, its functional activity is hampered due to unfavourable flexibility and relative rigidity, respectively. The low activity at normal temperature is due to the widening of the conformational spectrum of the Reactive Site Loop (RSL) that reduces the probability of formation of stabilizing contacts with trypsin. The unique sequence of the RSL enhances flexibility at ambient temperature and thus reduces its ability to inhibit trypsin. This study shows that temperature influences the function of a protein through modulation in the structure of functional domain of the protein. Modulation of function through appearance of new sequences that are more sensitive to temperature may be a general strategy for evolution of new proteins.

Modular Organization of Residue-Level Contacts Shapes the Selection Pressure on Individual Amino Acid Sites of Ribosomal Proteins

Understanding the molecular evolution of macromolecular complexes in the light of their structure, assembly, and stability is of central importance. Here, we address how the modular organization of native molecular contacts shapes the selection pressure on individual residue sites of ribosomal complexes. The bacterial ribosomal complex is represented as a residue contact network where nodes represent amino acid/nucleotide residues and edges represent their van der Waals interactions. We find statistically overrepresented native amino acid-nucleotide contacts (OaantC, one amino acid contacts one or multiple nucleotides, internucleotide contacts are disregarded). Contact number is defined as the number of nucleotides contacted. Involvement of individual amino acids in OaantCs with smaller contact numbers is more random, whereas only a few amino acids significantly contribute to OaantCs with higher contact numbers. An investigation of structure, stability, and assembly of bacterial ribosome depicts the involvement of these OaantCs in diverse biophysical interactions stabilizing the complex, including high-affinity protein-RNA contacts, interprotein cooperativity, intersubunit bridge, packing of multiple ribosomal RNA domains, etc. Amino acid-nucleotide constituents of OaantCs with higher contact numbers are generally associated with significantly slower substitution rates compared with that of OaantCs with smaller contact numbers. This evolutionary rate heterogeneity emerges from the strong purifying selection pressure that conserves the respective amino acid physicochemical properties relevant to the stabilizing interaction with OaantC nucleotides. An analysis of relative molecular orientations of OaantC residues and their interaction energetics provides the biophysical ground of purifying selection conserving OaantC amino acid physicochemical properties.

Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

Combining ancestral sequence reconstruction with protein design to identify an interface hotspot in a key metabolic enzyme complex

It is important to identify hotspot residues that determine protein-protein interactions in interfaces of macromolecular complexes. We have applied a combination of ancestral sequence reconstruction and protein design to identify hotspots within imidazole glycerol phosphate synthase (ImGPS). ImGPS is a key metabolic enzyme complex, which links histidine and de novo purine biosynthesis and consists of the cyclase subunit HisF and the glutaminase subunit HisH. Initial fluorescence titration experiments showed that HisH from Zymomonas mobilis (zmHisH) binds with high affinity to the reconstructed HisF from the last universal common ancestor (LUCA-HisF) but not to HisF from Pyrobaculum arsenaticum (paHisF), which differ by 103 residues. Subsequent titration experiments with a reconstructed evolutionary intermediate linking LUCA-HisF and paHisF and inspection of the subunit interface of a contemporary ImGPS allowed us to narrow down the differences crucial for zmHisH binding to nine amino acids of HisF. Homology modeling and in silico mutagenesis studies suggested that at most two of these nine HisF residues are crucial for zmHisH binding. These computational results were verified by experimental site-directed mutagenesis, which finally enabled us to pinpoint a single amino acid residue in HisF that is decisive for high-affinity binding of zmHisH. Our work shows that the identification of protein interface hotspots can be very efficient when reconstructed proteins with different binding properties are included in the analysis. Proteins 2017; 85:312-321. © 2016 Wiley Periodicals, Inc.

Protein-Protein Interface and Disease: Perspective from Biomolecular Networks

Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.

AlloRep: A Repository of Sequence, Structural and Mutagenesis Data for the LacI/GalR Transcription Regulators

Protein families evolve functional variation by accumulating point mutations at functionally important amino acid positions. Homologs in the LacI/GalR family of transcription regulators have evolved to bind diverse DNA sequences and allosteric regulatory molecules. In addition to playing key roles in bacterial metabolism, these proteins have been widely used as a model family for benchmarking structural and functional prediction algorithms. We have collected manually curated sequence alignments for >3000 sequences, in vivo phenotypic and biochemical data for >5750 LacI/GalR mutational variants, and noncovalent residue contact networks for 65 LacI/GalR homolog structures. Using this rich data resource, we compared the noncovalent residue contact networks of the LacI/GalR subfamilies to design and experimentally validate an allosteric mutant of a synthetic LacI/GalR repressor for use in biotechnology. The AlloRep database (freely available at www.AlloRep.org) is a key resource for future evolutionary studies of LacI/GalR homologs and for benchmarking computational predictions of functional change.

Universal allosteric mechanism for Gα activation by GPCRs

G protein-coupled receptors (GPCRs) allosterically activate heterotrimeric G proteins and trigger GDP release. Given that there are ∼800 human GPCRs and 16 different Gα genes, this raises the question of whether a universal allosteric mechanism governs Gα activation. Here we show that different GPCRs interact with and activate Gα proteins through a highly conserved mechanism. Comparison of Gα with the small G protein Ras reveals how the evolution of short segments that undergo disorder-to-order transitions can decouple regions important for allosteric activation from receptor binding specificity. This might explain how the GPCR-Gα system diversified rapidly, while conserving the allosteric activation mechanism.

How do disordered regions achieve comparable functions to structured domains?

The traditional structure to function paradigm conceives of a protein's function as emerging from its structure. In recent years, it has been established that unstructured, intrinsically disordered regions (IDRs) in proteins are equally crucial elements for protein function, regulation and homeostasis. In this review, we provide a brief overview of how IDRs can perform similar functions to structured proteins, focusing especially on the formation of protein complexes and assemblies and the mediation of regulated conformational changes. In addition to highlighting instances of such functional equivalence, we explain how differences in the biological and physicochemical properties of IDRs allow them to expand the functional and regulatory repertoire of proteins. We also discuss studies that provide insights into how mutations within functional regions of IDRs can lead to human diseases.

NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.

Evolution of oligomeric state through allosteric pathways that mimic ligand binding

Evolution and design of protein complexes are almost always viewed through the lens of amino acid mutations at protein interfaces. We showed previously that residues not involved in the physical interaction between proteins make important contributions to oligomerization by acting indirectly or allosterically. In this work, we sought to investigate the mechanism by which allosteric mutations act, using the example of the PyrR family of pyrimidine operon attenuators. In this family, a perfectly sequence-conserved helix that forms a tetrameric interface is exposed as solvent-accessible surface in dimeric orthologs. This means that mutations must be acting from a distance to destabilize the interface. We identified 11 key mutations controlling oligomeric state, all distant from the interfaces and outside ligand-binding pockets. Finally, we show that the key mutations introduce conformational changes equivalent to the conformational shift between the free versus nucleotide-bound conformations of the proteins.

Protomers of protein hetero-oligomers tend to resemble each other more than expected

A large fraction of the proteome is made by proteins that are not permanently monomeric but form oligomeric assemblies, which can be either homo- or hetero-oligomeric. Here it is described that protomers of hetero-oligomeric proteins tend to resemble each other more than expected. This is verified by comparing the level of similarity of pairs of hetero-oligomeric protein protomers and of pairs of proteins that do not interact with each other. This observation, interesting per se, might reflect the evolution of hetero-oligomers from ancestral homo-oligomers, through gene duplication and paralogs divergence. However, other hypotheses cannot be excluded and the observed structural similarity might result from several causes.

The robustness and innovability of protein folds

Assignment of protein folds to functions indicates that >60% of folds carry out one or two enzymatic functions, while few folds, for example, the TIM-barrel and Rossmann folds, exhibit hundreds. Are there structural features that make a fold amenable to functional innovation (innovability)? Do these features relate to robustness--the ability to readily accumulate sequence changes? We discuss several hypotheses regarding the relationship between the architecture of a protein and its evolutionary potential. We describe how, in a seemingly paradoxical manner, opposite properties, such as high stability and rigidity versus conformational plasticity and structural order versus disorder, promote robustness and/or innovability. We hypothesize that polarity--differentiation and low connectivity between a protein's scaffold and its active-site--is a key prerequisite for innovability.

Use of host-like peptide motifs in viral proteins is a prevalent strategy in host-virus interactions

Viruses interact extensively with host proteins, but the mechanisms controlling these interactions are not well understood. We present a comprehensive analysis of eukaryotic linear motifs (ELMs) in 2,208 viral genomes and reveal that viruses exploit molecular mimicry of host-like ELMs to possibly assist in host-virus interactions. Using a statistical genomics approach, we identify a large number of potentially functional ELMs and observe that the occurrence of ELMs is often evolutionarily conserved but not uniform across virus families. Some viral proteins contain multiple types of ELMs, in striking similarity to complex regulatory modules in host proteins, suggesting that ELMs may act combinatorially to assist viral replication. Furthermore, a simple evolutionary model suggests that the inherent structural simplicity of ELMs often enables them to tolerate mutations and evolve quickly. Our findings suggest that ELMs may allow fast rewiring of host-virus interactions, which likely assists rapid viral evolution and adaptation to diverse environments.

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.