Statistical analysis of predominantly transient protein-protein interfaces

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Imbalance Data Processing Strategy for Protein Interaction Sites Prediction

Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.

Thermodynamics and Free Energy Landscape of BAR-Domain Dimerization from Molecular Simulations

Proteins with BAR domains function to bind to and remodel biological membranes, where the dimerization of BAR domains is a key step in this function. These domains can dimerize in solution or after localizing to the membrane surface. Here, we characterize the binding thermodynamics of homodimerization between the LSP1 BAR domain proteins in solution, using molecular dynamics (MD) simulations. By combining the MARTINI coarse-grained protein models with enhanced sampling through metadynamics, we construct a two-dimensional free energy surface quantifying the bound versus unbound ensembles as a function of two distance variables. With this methodology, our simulations can simultaneously characterize the structures and relative stabilities of a range of sampled dimers, portraying a heterogeneous and extraordinarily stable bound ensemble, where the proper crystal structure dimer is the most stable in a 100 mM NaCl solution. Nonspecific dimers that are sampled involve contacts that are consistent with experimental structures of higher-order oligomers formed by the LSP1 BAR domain. Because the BAR dimers and oligomers can assemble on membranes, we characterize the relative alignment of the known membrane binding patches, finding that only the specific dimer is aligned to form strong interactions with the membrane. Hence, we would predict a strong selection of the specific dimer in binding to or assembling when on the membrane. Establishing the pairwise stabilities of homodimer contacts is difficult experimentally when the proteins form stable oligomers, but through the method used here, we can isolate these contacts, providing a foundation to study the same interactions on the membrane.

A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning

To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.

Could Dermaseptin Analogue be a Competitive Inhibitor for ACE2 Towards Binding with Viral Spike Protein Causing COVID19?: Computational Investigation

Initial phase of COVID-19 infection is associated with the binding of viral spike protein S1 receptor binding domain (RBD) with the host cell surface receptor, ACE2. Peptide inhibitors typically interact with spike proteins in order to block its interaction with ACE2, and this knowledge would promote the use of such peptides as therapeutic scaffolds. The present study examined the competitive inhibitor activity of a broad spectrum antimicrobial peptide, Dermaseptin-S4 (S4) and its analogues. Three structural S4 analogues viz., S4 (K₄), S4 (K₂₀) and S4 (K₄K₂₀) were modelled by substituting charged lysine for non-polar residues in S4 and subsequently, docked with S1. Further, the comparative analysis of inter-residue contacts and non-covalent intermolecular interactions among S1–S4 (K₄), S1–S4 (K₄K₂₀) and S1–ACE2 complexes were carried out to explore their mode of binding with S1. Interestingly, S1–S4 (K₄) established more inter-molecular interactions compared to S4 (K₄K₂₀) and S1–ACE2. In order to substantiate this study, the normal mode analysis (NMA) was conducted to show how the structural stability of the flexible loop region in S1 is affected by atomic displacements in unbound S1 and docked complexes. Markedly, the strong interactions consistently maintained by S1–S4 (K₄) complex revealed their conformational transition over the harmonic motion period. Moreover, S1–S4 (K₄) peptide complex showed a higher energy deformation profile compared to S1–S4 (K₄K₂₀), where the higher energy deformation suggests the rigidity of the docked complex and thus it’s harder deformability, which is also substantiated by molecular dynamics simulation. In conclusion, S1–S4 (K₄) complex has definitely exhibited a functionally significant dynamics compared to S1–ACE2 complex; this peptide inhibitor, S4 (K₄) will need to be considered as the best therapeutic scaffold to block SARS-CoV-2 infection.

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.

Identifying protein-protein interface via a novel multi-scale local sequence and structural representation

Background

Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface.

Results

In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average I_rmsd value of 3.28Å and overall F_nat value of 63%, which improves upon I_rmsd of 3.89Å and F_nat of 49% for ZRANK, and I_rmsd of 3.99Å and F_nat of 46% for ClusPro. On CAPRI targets, our method achieves average I_rmsd value of 3.45Å and overall F_nat value of 46%, which improves upon I_rmsd of 4.18Å and F_nat of 40% for ZRANK, and I_rmsd of 5.12Å and F_nat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively.

Conclusion

Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.

Semi-supervised prediction of protein interaction sites from unlabeled sample information

Background

The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today.

Results

In this work, three semi-supervised support vector machine–based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms.

Conclusion

The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.

Discovery of novel helix binding sites at protein-protein interfaces

Protein-protein interactions (PPIs) play a key role in numerous biological processes. Many efforts have been undertaken to develop PPI modulators for therapeutic applications; however, to date, most of the peptide binders designed to target PPIs are derived from native binding helices or using the native helix binding site, which has limited the applications of protein-protein interface binding peptide design. Here, we developed a general computational algorithm, HPer (Helix Positioner), that locates single-helix binding sites at protein-protein interfaces based on the structure of protein targets. HPer performed well on known single-helix-mediated PPIs and recaptured the key interactions and hot-spot residues of native helical binders. We also screened non-helical-mediated PPIs in the PDBbind database and identified 17 PPIs that were suitable for helical peptide binding, and the helical binding sites in these PPIs were also predicted for designing novel peptide ligands. The L2 domain of EGFR, which was the top ranked, was selected as an example to show the protocol and results of designing novel helical peptide ligands on the searched binding site. The binding stability of the designed sequences were further investigated using molecular dynamics simulations.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Å for these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

Collective Transformation of Water between Hyperactive Antifreeze Proteins: RiAFPs

We demonstrate, by molecular dynamics simulations, that water confined between a pair of insect hyperactive antifreeze proteins from the longhorn beetle Rhagium inquisitor is discontinuously expelled as the two proteins approach each other at a certain distance. The extensive striped hydrophobic–hydrophilic pattern on the surface, comprising arrays of threonine residues, enables water to form three independent ice channels through the assistance of hydroxyl groups, even at 300 K. The transformation is reminiscent of a freezing–melting transition rather than a drying transition and governs the stable protein–protein separation in the evaluation of the potential of mean force. The collectivity of water penetration or expulsion and the hysteresis in the time scale of ten nanoseconds predict a potential first-order phase transition at the limit of infinite size and provide a new framework for the water-mediated interaction between solutes.

Unbiased Atomistic Insight into the Mechanisms and Solvent Role for Globular Protein Dimer Dissociation

Association and dissociation of proteins are fundamental processes in nature. Although simple to understand conceptually, the details of the underlying mechanisms and role of the solvent are poorly understood. Here, we investigate the dissociation of the hydrophilic β-lactoglobulin dimer by employing transition path sampling. Analysis of the sampled path ensembles reveals a variety of mechanisms: (1) a direct aligned dissociation (2) a hopping and rebinding transition followed by unbinding, and (3) a sliding transition before unbinding. Reaction coordinate and transition-state analysis predicts that, besides native contact and neighboring salt-bridge interactions, solvent degrees of freedom play an important role in the dissociation process. Bridging waters, hydrogen-bonded to both proteins, support contacts in the native state and nearby lying transition-state regions, whereas they exhibit faster dynamics in further lying transition-state regions, rendering the proteins more mobile and assisting in rebinding. Analysis of the structure and dynamics of the solvent molecules reveals that the dry native interface induces enhanced populations of both disordered hydration water near hydrophilic residues and tetrahedrally ordered hydration water nearby hydrophobic residues. Although not exhaustive, our sampling of rare unbiased reactive molecular dynamics trajectories enhances the understanding of protein dissociation via complex pathways including (multiple) rebinding events.

Dynamics of Hydration Water Plays a Key Role in Determining the Binding Thermodynamics of Protein Complexes

Interfacial waters are considered to play a crucial role in protein–protein interactions, but in what sense and why are they important? Here, using molecular dynamics simulations and statistical thermodynamic analyses, we demonstrate distinctive dynamic characteristics of the interfacial water and investigate their implications for the binding thermodynamics. We identify the presence of extraordinarily slow (~1,000 times slower than in bulk water) hydrogen-bond rearrangements in interfacial water. We rationalize the slow rearrangements by introducing the “trapping” free energies, characterizing how strongly individual hydration waters are captured by the biomolecular surface, whose magnitude is then traced back to the number of water–protein hydrogen bonds and the strong electrostatic field produced at the binding interface. We also discuss the impact of the slow interfacial waters on the binding thermodynamics. We find that, as expected from their slow dynamics, the conventional approach to the water-mediated interaction, which assumes rapid equilibration of the waters’ degrees of freedom, is inadequate. We show instead that an explicit treatment of the extremely slow interfacial waters is critical. Our results shed new light on the role of water in protein–protein interactions, highlighting the need to consider its dynamics to improve our understanding of biomolecular bindings.

Identification of a short sequence in the HCMV terminase pUL56 essential for interaction with pUL89 subunit

The human cytomegalovirus (HCMV) terminase complex consists of several components acting together to cleave viral DNA into unit length genomes and translocate them into capsids, a critical process in the production of infectious virions subsequent to DNA replication. Previous studies suggest that the carboxyl-terminal portion of the pUL56 subunit interacts with the pUL89 subunit. However, the specific interacting residues of pUL56 remain unknown. We identified a conserved sequence in the C-terminal moiety of pUL56 (671WMVVKYMGFF680). Overrepresentation of conserved aromatic amino acids through 20 herpesviruses homologues of pUL56 suggests an involvement of this short peptide into the interaction between the larger pUL56 terminase subunit and the smaller pUL89 subunit. Use of Alpha technology highlighted an interaction between pUL56 and pUL89 driven through the peptide 671WMVVKYMGFF680. A deletion of these residues blocks viral replication. We hypothesize that it is the consequence of the disruption of the pUL56-pUL89 interaction. These results show that this motif is essential for HCMV replication and could be a target for development of new small antiviral drugs or peptidomimetics.

Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids

Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis.

Anomalous Dynamics of Water Confined in Protein-Protein and Protein-DNA Interfaces

Confined water often exhibits anomalous properties not observable in the bulk phase. Although water in hydrophobic confinement has been the focus of intense investigation, the behavior of water confined between hydrophilic surfaces, which are more frequently found in biological systems, has not been fully explored. Here, we investigate using molecular dynamics simulations dynamical properties of the water confined in hydrophilic protein-protein and protein-DNA interfaces. We find that the interfacial water exhibits glassy slow relaxations even at 300 K. In particular, the rotational dynamics show a logarithmic decay that was observed in glass-forming liquids at deeply supercooled states. We argue that such slow water dynamics are indeed induced by the hydrophilic binding surfaces, which is in opposition to the picture that the hydration water slaves protein motions. Our results will significantly impact the view on the role of water in biomolecular interactions.

Surface energetics and protein-protein interactions: analysis and mechanistic implications

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.

Energetics of Hydrophilic Protein-Protein Association and the Role of Water

Hydrophilic protein-protein interfaces constitute a major part of all protein-protein interfaces and are thus of great importance. However, the quantitative characterization of their association is still an ongoing challenge and the driving force behind their association remains poorly characterized. Here, we have addressed the association of hydrophilic proteins and the role of water by means of extensive molecular dynamics simulations in explicit water using three well studied protein complexes; Barnase-Barstar, cytochrome c-cytochrome c peroxidase, and the N-terminal domain of enzyme I-histidine-containing phosphocarrier. The one-dimensional free energy profiles obtained from umbrella sampling simulations are downhill or, in other words, barrierless. Using these one-dimensional free energy profiles, the computed standard free energies of binding are -12.7 ± 1.1 kcal/mol, -9.4 ± 0.7 kcal/mol, and -8.4 ± 1.9 kcal/mol that are in reasonable to very good agreement with the experimental values of -19.6 kcal/mol, -8.8 kcal/mol, and -7.8 kcal/mol. As expected, analysis of the confined water between the hydrophilic complex partners shows that the density and the orientational order parameter deviate noticeably from the bulk values, especially at close separations of the confining proteins.

Antibody Binding Selectivity: Alternative Sets of Antigen Residues Entail High-Affinity Recognition

Understanding the relationship between protein sequence and molecular recognition selectivity remains a major challenge. The antibody fragment scFv1F4 recognizes with sub nM affinity a decapeptide (sequence 6TAMFQDPQER15) derived from the N-terminal end of human papilloma virus E6 oncoprotein. Using this decapeptide as antigen, we had previously shown that only the wild type amino-acid or conservative replacements were allowed at positions 9 to 12 and 15 of the peptide, indicating a strong binding selectivity. Nevertheless phenylalanine (F) was equally well tolerated as the wild type glutamine (Q) at position 13, while all other amino acids led to weaker scFv binding. The interfaces of complexes involving either Q or F are expected to diverge, due to the different physico-chemistry of these residues. This would imply that high-affinity binding can be achieved through distinct interfacial geometries. In order to investigate this point, we disrupted the scFv-peptide interface by modifying one or several peptide positions. We then analyzed the effect on binding of amino acid changes at the remaining positions, an altered susceptibility being indicative of an altered role in complex formation. The 23 starting variants analyzed contained replacements whose effects on scFv1F4 binding ranged from minor to drastic. A permutation analysis (effect of replacing each peptide position by all other amino acids except cysteine) was carried out on the 23 variants using the PEPperCHIP® Platform technology. A comparison of their permutation patterns with that of the wild type peptide indicated that starting replacements at position 11, 12 or 13 modified the tolerance to amino-acid changes at the other two positions. The interdependence between the three positions was confirmed by SPR (Biacore® technology). Our data demonstrate that binding selectivity does not preclude the existence of alternative high-affinity recognition modes.

Weak conservation of structural features in the interfaces of homologous transient protein-protein complexes

Residue types at the interface of protein-protein complexes (PPCs) are known to be reasonably well conserved. However, we show, using a dataset of known 3-D structures of homologous transient PPCs, that the 3-D location of interfacial residues and their interaction patterns are only moderately and poorly conserved, respectively. Another surprising observation is that a residue at the interface that is conserved is not necessarily in the interface in the homolog. Such differences in homologous complexes are manifested by substitution of the residues that are spatially proximal to the conserved residue and structural differences at the interfaces as well as differences in spatial orientations of the interacting proteins. Conservation of interface location and the interaction pattern at the core of the interfaces is higher than at the periphery of the interface patch. Extents of variability of various structural features reported here for homologous transient PPCs are higher than the variation in homologous permanent homomers. Our findings suggest that straightforward extrapolation of interfacial nature and inter-residue interaction patterns from template to target could lead to serious errors in the modeled complex structure. Understanding the evolution of interfaces provides insights to improve comparative modeling of PPC structures.

Structural neighboring property for identifying protein-protein binding sites

Background

The protein-protein interaction plays a key role in the control of many biological functions, such as drug design and functional analysis. Determination of binding sites is widely applied in molecular biology research. Therefore, many efficient methods have been developed for identifying binding sites. In this paper, we calculate structural neighboring property through Voronoi diagram. Using 6,438 complexes, we study local biases of structural neighboring property on interface.

Results

We propose a novel statistical method to extract interacting residues, and interacting patches can be clustered as predicted interface residues. In addition, structural neighboring property can be adopted to construct a new energy function, for evaluating docking solutions. It includes new statistical property as well as existing energy items. Comparing to existing methods, our approach improves overall F_nat value by at least 3%. On Benchmark v4.0, our method has average I_rmsd value of 3.31Å and overall F_nat value of 63%, which improves upon I_rmsd of 3.89 Å and F_nat of 49% for ZRANK, and I_rmsd of 3.99Å and F_nat of 46% for ClusPro. On the CAPRI targets, our method has average I_rmsd value of 3.46 Å and overall F_nat value of 45%, which improves upon I_rmsd of 4.18 Å and F_nat of 40% for ZRANK, and I_rmsd of 5.12 Å and F_nat of 32% for ClusPro.

Conclusions

Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein binding sites, with the prediction quality improved in terms of CAPRI evaluation criteria.

Dehydration-mediated cluster formation of nanoparticles

Drying procedure is a powerful method to modulate the bottom-up assembly of basic building component. The initially weak attraction between the components screened in a solution strengthens as the solvent evaporates, organizing the components into structures. Drying is process-dependent, irreversible, and nonequilibrated, thus the mechanism and the dynamics are influenced by many factors. Therefore, the interaction of the solvent and the elements during the drying procedure as well as the resulting pattern formations are strongly related. Nonetheless still many things are open in questions in terms of their dynamics. In this study, nanoscale dehydration procedure is experimentally investigated using a nanoparticle (NP) model system. The role of water is verified in a single NP scale and the patterns of collective NP clusters are determined. Stepwise drying procedures are proposed based on the location from which water is removed. Effective water exodus from a unit NP surface enhances the attractive interaction in nanoscale and induces heterogeneous distribution in microscale. This study provides fundamental proof of systematic relation between the dehydration process and the resultant cluster patterns in hierarchical multiscales.

On the packing density of the unbound protein-protein interaction interface and its implications in dynamics

Background

Characterizing the interface residues will help shed light on protein-protein interactions, which are involved in many important biological processes. Many studies focus on characterizing sequence or structure features of protein interfaces, but there are few studies characterizing the dynamics of interfaces. Therefore, we would like to know whether there is any specific dynamics pattern in the protein-protein interaction interfaces. Thermal fluctuation is an important dynamical property for a residue, and could be quickly estimated by local packing density without large computation since studies have showen closely relationship between these two properties. Therefore, we divided surface of an unbound subunit (free protein subunits before they are involved in forming the protein complexes) into several separate regions, and compared their average thermal fluctuations of different regions in order to characterize the dynamics pattern in unbound protein-protein interaction interfaces.

Results

We used weighted contact numbers (WCN), a parameter-free method to quantify packing density, to estimate the thermal fluctuations of residues in the interfaces. By analyzing the WCN distributions of interfaces in unbound subunits from 1394 non-homologous protein complexes, we show that the residues in the central regions of interfaces have higher packing density (i.e. more rigid); on the other hand, residues surrounding the central regions have smaller packing density (i.e. more flexible). The distinct distributions of packing density, suggesting distinct thermal fluctuation, reveals specific dynamics pattern in the interface of unbound protein subunits.

Conclusions

We found general trend that the unbound protein-protein interaction interfaces consist of rigid residues in the central regions, which are surrounded by flexible residues. This finding suggests that the dynamics might be one of the important features for the formation of protein complexes.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

Prediction of protein interaction types based on sequence and network features

BACKGROUND

Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range of structural adaptations that determine their binding affinity, the number of interaction sites, and the lifetime. In terms of stability it has become customary to distinguish between obligate and non-obligate interactions dependent on whether or not the protomers can exist independently. In terms of spatio-temporal control protein interactions can be either simultaneously possible (SP) or mutually exclusive (ME). In the former case a network hub interacts with several proteins at the same time, offering each of them a separate interface, while in the latter case the hub interacts with its partners one at a time via the same binding site. So far different types of interactions were distinguished based on the properties of the corresponding binding interfaces derived from known three-dimensional structures of protein complexes.

RESULTS

Here we present PiType, an accurate 3D structure-independent computational method for classifying protein interactions into simultaneously possible (SP) and mutually exclusive (ME) as well as into obligate and non-obligate. Our classifier exploits features of the binding partners predicted from amino acid sequence, their functional similarity, and network topology. We find that the constituents of non-obligate complexes possess a higher degree of structural disorder, more short linear motifs, and lower functional similarity compared to obligate interaction partners while SP and ME interactions are characterized by significant differences in network topology. Each interaction type is associated with a distinct set of biological functions. Moreover, interactions within multi-protein complexes tend to be enriched in one type of interactions.

CONCLUSION

PiType does not rely on atomic structures and is thus suitable for characterizing proteome-wide interaction datasets. It can also be used to identify sub-modules within protein complexes. PiType is available for download as a self-installing package from http://webclu.bio.wzw.tum.de/PiType/PiType.zip.

Predicting permanent and transient protein-protein interfaces

Protein-protein interactions (PPIs) are involved in diverse functions in a cell. To optimize functional roles of interactions, proteins interact with a spectrum of binding affinities. Interactions are conventionally classified into permanent and transient, where the former denotes tight binding between proteins that result in strong complexes, whereas the latter compose of relatively weak interactions that can dissociate after binding to regulate functional activity at specific time point. Knowing the type of interactions has significant implications for understanding the nature and function of PPIs. In this study, we constructed amino acid substitution models that capture mutation patterns at permanent and transient type of protein interfaces, which were found to be different with statistical significance. Using the substitution models, we developed a novel computational method that predicts permanent and transient protein binding interfaces (PBIs) in protein surfaces. Without knowledge of the interacting partner, the method uses a single query protein structure and a multiple sequence alignment of the sequence family. Using a large dataset of permanent and transient proteins, we show that our method, BindML+, performs very well in protein interface classification. A very high area under the curve (AUC) value of 0.957 was observed when predicted protein binding sites were classified. Remarkably, near prefect accuracy was achieved with an AUC of 0.991 when actual binding sites were classified. The developed method will be also useful for protein design of permanent and transient PBIs.

Binding efficiency of protein-protein complexes

We examine the relationship between binding affinity and interface size for reversible protein-protein interactions (PPIs), using cytokines from the tumor necrosis factor (TNF) superfamily and their receptors as a test case. Using surface plasmon resonance, we measured single-site binding affinities for binding of the large receptor TNFR1 to its ligands TNFα (K(D) = 1.4 ± 0.4 nM) and lymphotoxin-α (K(D) = 50 ± 10 nM), and also for binding of the small receptor Fn14 to TWEAK (K(D) = 70 ± 10 nM). We additionally assembled data for all other TNF-TNFR family complexes for which reliable single-site binding affinities have been reported. We used these values to calculate the binding efficiencies, defined as binding energy per square angstrom of surface area buried at the contact interface, for nine of these complexes for which cocrystal structures are available, and compared the results to those for a set of 144 protein-protein complexes with published affinities. The results show that the most efficient PPI complexes generate ~20 cal mol(-1) Å(-2) of binding energy. A minimal contact area of ~500 Å(2) is required for a stable complex, required to generate sufficient interaction energy to pay the entropic cost of colocalizing two proteins from 1 M solution. The most compact and efficient TNF-TNFR complex was the BAFF-BR3 complex, which achieved ~80% of the maximal achievable binding efficiency. Other small receptors also gave high binding efficiencies, while the larger receptors generated only 44-49% of this limit despite interacting primarily through just a single small domain. The results provide new insight into how much binding energy can be generated by a PPI interface of a given size, and establish a quantitative method for predicting how large a natural or engineered contact interface must be to achieve a given level of binding affinity.

Comparison of tertiary structures of proteins in protein-protein complexes with unbound forms suggests prevalence of allostery in signalling proteins

BACKGROUND

Most signalling and regulatory proteins participate in transient protein-protein interactions during biological processes. They usually serve as key regulators of various cellular processes and are often stable in both protein-bound and unbound forms. Availability of high-resolution structures of their unbound and bound forms provides an opportunity to understand the molecular mechanisms involved. In this work, we have addressed the question "What is the nature, extent, location and functional significance of structural changes which are associated with formation of protein-protein complexes?"

RESULTS

A database of 76 non-redundant sets of high resolution 3-D structures of protein-protein complexes, representing diverse functions, and corresponding unbound forms, has been used in this analysis. Structural changes associated with protein-protein complexation have been investigated using structural measures and Protein Blocks description. Our study highlights that significant structural rearrangement occurs on binding at the interface as well as at regions away from the interface to form a highly specific, stable and functional complex. Notably, predominantly unaltered interfaces interact mainly with interfaces undergoing substantial structural alterations, revealing the presence of at least one structural regulatory component in every complex.Interestingly, about one-half of the number of complexes, comprising largely of signalling proteins, show substantial localized structural change at surfaces away from the interface. Normal mode analysis and available information on functions on some of these complexes suggests that many of these changes are allosteric. This change is largely manifest in the proteins whose interfaces are altered upon binding, implicating structural change as the possible trigger of allosteric effect. Although large-scale studies of allostery induced by small-molecule effectors are available in literature, this is, to our knowledge, the first study indicating the prevalence of allostery induced by protein effectors.

CONCLUSIONS

The enrichment of allosteric sites in signalling proteins, whose mutations commonly lead to diseases such as cancer, provides support for the usage of allosteric modulators in combating these diseases.

Roles of residues in the interface of transient protein-protein complexes before complexation

Transient protein-protein interactions play crucial roles in all facets of cellular physiology. Here, using an analysis on known 3-D structures of transient protein-protein complexes, their corresponding uncomplexed forms and energy calculations we seek to understand the roles of protein-protein interfacial residues in the unbound forms. We show that there are conformationally near invariant and evolutionarily conserved interfacial residues which are rigid and they account for ∼65% of the core interface. Interestingly, some of these residues contribute significantly to the stabilization of the interface structure in the uncomplexed form. Such residues have strong energetic basis to perform dual roles of stabilizing the structure of the uncomplexed form as well as the complex once formed while they maintain their rigid nature throughout. This feature is evolutionarily well conserved at both the structural and sequence levels. We believe this analysis has general bearing in the prediction of interfaces and understanding molecular recognition.

Comprehensive, atomic-level characterization of structurally characterized protein-protein interactions: the PICCOLO database

Background

Structural studies are increasingly providing huge amounts of information on multi-protein assemblies. Although a complete understanding of cellular processes will be dependent on an explicit characterization of the intermolecular interactions that underlie these assemblies and mediate molecular recognition, these are not well described by standard representations.

Results

Here we present PICCOLO, a comprehensive relational database capturing the details of structurally characterized protein-protein interactions. Interactions are described at the level of interacting pairs of atoms, residues and polypeptide chains, with the physico-chemical nature of the interactions being characterized. Distance and angle terms are used to distinguish 12 different interaction types, including van der Waals contacts, hydrogen bonds and hydrophobic contacts. The explicit aim of PICCOLO is to underpin large-scale analyses of the properties of protein-protein interfaces. This is exemplified by an analysis of residue propensity and interface contact preferences derived from a much larger data set than previously reported. However, PICCOLO also supports detailed inspection of particular systems of interest.

Conclusions

The current PICCOLO database comprises more than 260 million interacting atom pairs from 38,202 protein complexes. A web interface for the database is available at http://www-cryst.bioc.cam.ac.uk/piccolo.

Application of information theory to feature selection in protein docking

In the era of structural genomics, the prediction of protein interactions using docking algorithms is an important goal. The success of this method critically relies on the identification of good docking solutions among a vast excess of false solutions. We have adapted the concept of mutual information (MI) from information theory to achieve a fast and quantitative screening of different structural features with respect to their ability to discriminate between physiological and nonphysiological protein interfaces. The strategy includes the discretization of each structural feature into distinct value ranges to optimize its mutual information. We have selected 11 structural features and two datasets to demonstrate that the MI is dimensionless and can be directly compared for diverse structural features and between datasets of different sizes. Conversion of the MI values into a simple scoring function revealed that those features with a higher MI are actually more powerful for the identification of good docking solutions. Thus, an MI-based approach allows the rapid screening of structural features with respect to their information content and should therefore be helpful for the design of improved scoring functions in future. In addition, the concept presented here may also be adapted to related areas that require feature selection for biomolecules or organic ligands.

Macromolecular complexes in crystals and solutions

This paper presents a discussion of existing methods for the analysis of macromolecular interactions and complexes in crystal packing. Typical situations and conditions where wrong answers may be obtained in the course of ordinary procedures are presented and discussed. The more general question of what the relationship is between natural (in-solvent) and crystallized assemblies is discussed and researched. A computational analysis suggests that weak interactions with K(d) ≥ 100 µM have a considerable chance of being lost during the course of crystallization. In such instances, crystal packing misrepresents macromolecular complexes and interactions. For as many as 20% of protein dimers in the PDB the likelihood of misrepresentation is estimated to be higher than 50%. Given that weak macromolecular interactions play an important role in many biochemical processes, these results suggest that a complementary noncrystallographic study should be always conducted when inferring structural aspects of weakly bound complexes.

Transient protein-protein interactions: structural, functional, and network properties

Transient interactions, which involve protein interactions that are formed and broken easily, are important in many aspects of cellular function. Here we describe structural and functional properties of transient interactions between globular domains and between globular domains, short peptides, and disordered regions. The importance of posttranslational modifications in transient interactions is also considered. We review techniques used in the detection of the different types of transient protein-protein interactions. We also look at the role of transient interactions within protein-protein interaction networks and consider their contribution to different aspects of these networks.

Exposing the co-adaptive potential of protein-protein interfaces through computational sequence design

MOTIVATION

In nature, protein-protein interactions are constantly evolving under various selective pressures. Nonetheless, it is expected that crucial interactions are maintained through compensatory mutations between interacting proteins. Thus, many studies have used evolutionary sequence data to extract such occurrences of correlated mutation. However, this research is confounded by other evolutionary pressures that contribute to sequence covariance, such as common ancestry.

RESULTS

Here, we focus exclusively on the compensatory mutations deriving from physical protein interactions, by performing large-scale computational mutagenesis experiments for >260 protein-protein interfaces. We investigate the potential for co-adaptability present in protein pairs that are always found together in nature (obligate) and those that are occasionally in complex (transient). By modeling each complex both in bound and unbound forms, we find that naturally transient complexes possess greater relative capacity for correlated mutation than obligate complexes, even when differences in interface size are taken into account.

Insight into the structure of the pUL89 C-terminal domain of the human cytomegalovirus terminase complex

In a previous study, we identified 12 conserved domains within pUL89, the small terminase subunit of the human cytomegalovirus. A latter study showed that the fragment pUL89(580-600) plays an important role in the formation of the terminase complex by interacting with the large terminase subunit pUL56. In this study, analysis was performed to solve the structure of pUL89(568-635) in 50% H2O/50% acetonitrile (v/v). We showed that pUL89(568-635) consists of four alpha helices, but we did not identify any tertiary structure. The fragment 580-600 formed an amphipathic alpha helix, which had a hydrophobic side highly conserved among herpesviral homologs of pUL89; this was not observed for its hydrophilic side. The modeling of pUL89(457-612) using the recognition fold method allowed us to position pUL89(580-600) within this domain. The theoretical structure highlighted three important features. First, we identified a metal-binding pocket containing residues Asp(463), Glu(534), and Glu(588), which are highly conserved among pUL89 homologs. Second, the model predicted a positively charged surface able to interact with the DNA duplex during the nicking event. Third, a hydrophobic patch on the top of the catalytic site suggested that this may constitute part of the pUL89 site recognized by pUL56 potentially involved in DNA binding.

Energy based approach for understanding the recognition mechanism in protein-protein complexes

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

PIE-efficient filters and coarse grained potentials for unbound protein-protein docking

Identifying correct binding modes in a large set of models is an important step in protein-protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two-chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near-native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near-native model on the top in 46 cases and within top 10 in 58 cases. Our filter+potential is well suited for selecting a small set of models to be refined to atomic resolution.

Prediction of protein-protein interaction sites using an ensemble method

Background

Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved.

Results

In this paper, an ensemble method is proposed, which combines bootstrap resampling technique, SVM-based fusion classifiers and weighted voting strategy, to overcome the imbalanced problem and effectively utilize a wide variety of features. We evaluate the ensemble classifier using a dataset extracted from 99 polypeptide chains with 10-fold cross validation, and get a AUC score of 0.86, with a sensitivity of 0.76 and a specificity of 0.78, which are better than that of the existing methods. To improve the usefulness of the proposed method, two special ensemble classifiers are designed to handle the cases of missing homologues and structural information respectively, and the performance is still encouraging. The robustness of the ensemble method is also evaluated by effectively classifying interaction sites from surface residues as well as from all residues in proteins. Moreover, we demonstrate the applicability of the proposed method to identify interaction sites from the non-structural proteins (NS) of the influenza A virus, which may be utilized as potential drug target sites.

Conclusion

Our experimental results show that the ensemble classifiers are quite effective in predicting protein interaction sites. The Sub-EnClassifiers with resampling technique can alleviate the imbalanced problem and the combination of Sub-EnClassifiers with a wide variety of feature groups can significantly improve prediction performance.

Conserved domains and structure prediction of human cytomegalovirus UL27 protein

Background

The human cytomegalovirus (HCMV) nuclear UL27 protein (pUL27) could be involved at the stage of nuclear egress. Maribavir is a new anti-HCMV drug that targets nuclear egress through direct inhibition of the HCMV serine–threonine kinase, UL97 protein (pUL97). Because maribavir- resistance-related mutations are observed in both proteins, pUL27 is thought to interfere with pUL97 activity; however, its mechanism of action remains unclear.

Methods

As there is no available crystal structure for pUL27 or any known structures of its homologous proteins, we attempted to identify pUL27 functional domains by sequence analysis, identification of conserved domains, structure prediction and matching with previously known maribavir resistance mutations.

Results

The UL27 sequence analysis of 20 HCMV wild-type strains and 8 ganciclovir-resistant HCMV strains allowed us to describe four conserved domains, to localize the putative phosphorylation sites and to identify protein–protein interface domains, suggesting that pUL27 could interact with either pUL97 or itself.

Conclusions

Although the function of pUL27 is still unknown in the HCMV replication cycle, our approach identified target domains that appeared to be essential to the function of pUL27. This work provides a better understanding on the relative importance of each pUL27 mutation and could form the basis of later comparison analyses, when a three-dimensional structure of a pUL27 homologue will be available.