Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity. Despite the abundance of research on PPI modeling, the field of quantitative binding affinity prediction remains underexplored, mainly due to a lack of comprehensive data. This study seeks to address these needs by manually curating pairwise interaction labels on available 3D structures of protein complexes, with experimentally determined binding affinities, creating the largest data set for structure-based pairwise protein interaction with binding affinity to date. Subsequently, we introduce ProAffinity-GNN, a novel deep learning framework using protein language model and graph neural network (GNN) to improve the accuracy of prediction of structure-based protein-protein binding affinities. The evaluation results across several benchmark test sets and an additional case study demonstrate that ProAffinity-GNN not only outperforms existing models in terms of accuracy but also shows strong generalization capabilities.

Supramolecular complexes of GCAP1: implications for inherited retinal dystrophies

Guanylate Cyclase Activating Protein 1 (GCAP1) is a calcium sensor that regulates the enzymatic activity of retinal Guanylate Cyclase 1 (GC1) in photoreceptors in a Ca2+/Mg2+ dependent manner. While point mutations in GCAP1 have been associated with inherited retinal dystrophies (IRDs), their impact on protein dimerization or on the possible interaction with the potent GC1 inhibitor RD3 (retinal degeneration protein 3) has never been investigated. Here, we integrate exhaustive in silico investigations with biochemical assays to evaluate the effects of the p.(E111V) substitution, associated with a severe form of IRD, on GCAP1 homo- and hetero-dimerization, and demonstrate that wild type (WT) GCAP1 directly interacts with RD3. Although inducing constitutive activation in GC1, the E111V substitution only slightly affects the dimerization of GCAP1. Both WT- and E111V-GCAP1 are predominantly monomeric in the absence of the GC1 target, however E111V-GCAP1 shows a stronger tendency to be monomeric in the Ca2+-bound form, corresponding to GC1 inhibiting state. Reconstitution experiments performed in the co-presence of WT-GCAP1, E111V-GCAP1 and RD3 restored nearly physiological regulation of the GC1 enzymatic activity in terms of cGMP synthesis and Ca2+-sensitivity, suggesting new scenarios for biologics-mediated treatment of GCAP1-associated IRDs.

Disrupted protein interaction dynamics in a genetic neurodevelopmental disorder revealed by structural bioinformatics and genetic code expansion

Deciphering the structural effects of gene variants is essential for understanding the pathophysiological mechanisms of genetic diseases. Using a neurodevelopmental disorder called Bosch-Boonstra-Schaaf Optic Atrophy Syndrome (BBSOAS) as a genetic disease model, we applied structural bioinformatics and Genetic Code Expansion (GCE) strategies to assess the pathogenic impact of human NR2F1 variants and their binding with known and novel partners. While the computational analyses of the NR2F1 structure delineated the molecular basis of the impact of several variants on the isolated and complexed structures, the GCE enabled covalent and site-specific capture of transient supramolecular interactions in living cells. This revealed the variable quaternary conformations of NR2F1 variants and highlighted the disrupted interplay with dimeric partners and the newly identified co-factor, CRABP2. The disclosed consequence of the pathogenic mutations on the conformation, supramolecular interplay, and alterations in the cell cycle, viability, and sub-cellular localization of the different variants reflect the heterogeneous disease spectrum of BBSOAS and set up novel foundation for unveiling the complexity of neurodevelopmental diseases.

Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis

Predicting protein-protein interaction sites (PPI sites) can provide important clues for understanding biological activity. Using machine learning to predict PPI sites can mitigate the cost of running expensive and time-consuming biological experiments. Here we propose PPISP-XGBoost, a novel PPI sites prediction method based on eXtreme gradient boosting (XGBoost). First, the characteristic information of protein is extracted through the pseudo-position specific scoring matrix (PsePSSM), pseudo-amino acid composition (PseAAC), hydropathy index and solvent accessible surface area (ASA) under the sliding window. Next, these raw features are preprocessed to obtain more optimal representations in order to achieve better prediction. In particular, the synthetic minority oversampling technique (SMOTE) is used to circumvent class imbalance, and the kernel principal component analysis (KPCA) is applied to remove redundant characteristics. Finally, these optimal features are fed to the XGBoost classifier to identify PPI sites. Using PPISP-XGBoost, the prediction accuracy on the training dataset Dset186 reaches 85.4%, and the accuracy on the independent validation datasets Dtestset72, PDBtestset164, Dset_448 and Dset_355 reaches 85.3%, 83.9%, 85.8% and 85.4%, respectively, which all show an increase in accuracy against existing PPI sites prediction methods. These results demonstrate that the PPISP-XGBoost method can further enhance the prediction of PPI sites.

Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity

Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.

Correlation between the molecular effects of mutations at the dimer interface of alanine-glyoxylate aminotransferase leading to primary hyperoxaluria type I and the cellular response to vitamin B6

Primary hyperoxaluria type I (PH1) is a rare disease caused by the deficit of liver alanine-glyoxylate aminotransferase (AGT). AGT prevents oxalate formation by converting peroxisomal glyoxylate to glycine. When the enzyme is deficient, progressive calcium oxalate stones deposit first in the urinary tract and then at the systemic level. Pyridoxal 5'-phosphate (PLP), the AGT coenzyme, exerts a chaperone role by promoting dimerization, as demonstrated by studies at protein and cellular level. Thus, variants showing a destabilized dimeric structure should, in principle, be responsive to vitamin B₆, a precursor of PLP. However, models to predict the extent of responsiveness of each variant are missing. We examined the effects of pathogenic interfacial mutations by combining bioinformatic predictions with molecular and cellular studies on selected variants (R36H, G42E, I56N, G63R, and G216R), in both their holo- (i.e., with bound PLP) and apo- (i.e., without bound PLP) form. We found that all variants displayed structural alterations mainly related to the apoform and consisting of an altered tertiary and quaternary structure. G216R also shows a strongly reduced catalytic efficiency. Moreover, all but G216R respond to vitamin B₆, as shown by their increased specific activity and expression level in a cellular disease model. A global analysis of data unraveled a possible inverse correlation between the degree of destabilization/misfolding induced by a mutation and the extent of B6 responsiveness. These results provide a first explanation of factors influencing B6 response in PH1, a model possibly valuable for other rare diseases caused by protein deficits.

Self-Assembly of Human Serum Albumin: A Simplex Phenomenon

Spontaneous self-assemblies of biomolecules can generate geometrical patterns. Our findings provide an insight into the mechanism of self-assembled ring pattern generation by human serum albumin (HSA). The self-assembly is a process guided by kinetic and thermodynamic parameters. The generated protein ring patterns display a behavior which is geometrically related to a n-simplex model and is explained through thermodynamics and chemical kinetics.

Contacts-based prediction of binding affinity in protein-protein complexes

Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

Contacts-based prediction of binding affinity in protein-protein complexes

Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

Structural and sequential context of p53: A review of experimental and theoretical evidence

Approximately 27 million people are suffering from cancer that contains either an inactivating missense mutation of TP53 gene or partially abrogated p53 signaling pathway. Concerted action of folded and intrinsically disordered domains accounts for multi-faceted role of p53. The intricacy of dynamic p53 structure is believed to shed light on its cellular activity for developing new cancer therapies. In this review, insights into structural details of p53, diverse single point mutations affecting its core domain, thermodynamic understanding and therapeutic strategies for pharmacological rescue of p53 function has been illustrated. An effort has been made here to bridge the structural and sequential evidence of p53 from experimental to computational studies. First, we focused on the individual domains and the crucial protein-protein or DNA-protein contacts that determine conformation and dynamic behavior of p53. Next, the oncogenic mutations associated with cancer and its contribution to thermodynamic fluctuation has been discussed. Thus the emerging anti-cancer strategies include targeting of destabilized cancer mutants with selective inhibition of its negative regulators. Recent advances in development of small molecule inhibitors and peptides exploiting p53-MDM2 interaction has been included. In a nutshell, this review attempts to describe structural biology of p53 which provide new openings for structure-guided rescue.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects.

Directed self-assembly of proteins into discrete radial patterns

Unlike physical patterning of materials at nanometer scale, manipulating soft matter such as biomolecules into patterns is still in its infancy. Self-assembled monolayer (SAM) with surface density gradient has the capability to drive biomolecules in specific directions to create hierarchical and discrete structures. Here, we report on a two-step process of self-assembly of the human serum albumin (HSA) protein into discrete ring structures based on density gradient of SAM. The methodology involves first creating a 2-dimensional (2D) polyethylene glycol (PEG) islands with responsive carboxyl functionalities. Incubation of proteins on such pre-patterned surfaces results in direct self-assembly of protein molecules around PEG islands. Immobilization and adsorption of protein on such structures over time evolve into the self-assembled patterns.

Understanding the molecular basis of MK2-p38α signaling complex assembly: insights into protein-protein interaction by molecular dynamics and free energy studies

The formation of a p38 MAPK and MAPK-activated protein kinase 2 (MK2) signaling complex is physiologically relevant to cellular responses such as the proinflammatory cytokine production. The interaction between p38α isoform and MK2 is of great importance for this signaling. In this study, molecular dynamics simulation and binding free energy calculation were performed on the MK2-p38α signaling complex to investigate the protein-protein interaction between the two proteins. Dynamic domain motion analyses were performed to analyze the conformational changes between the unbound and bound states of proteins during the interaction. The activation loop, αF-I helices, and loops among α helices in the C-lobe of MK2 are found to be highly flexible and exhibit significant changes upon p38α binding. The results also show that after the binding of p38α, the N- and C-terminal domains of MK2 display an opening and twisting motion centered on the activation loop. The molecular mechanics Poisson-Boltzmann and generalized-Born surface area (MM-PB/GBSA) methods were used to calculate binding free energies between MK2 and p38α. The analysis of the components of binding free energy calculation indicates that the van der Waals interaction and the nonpolar solvation energy provide the driving force for the binding process, while the electrostatic interaction contributes critically to the specificity, rather than to MK2-p38α binding affinity. The contribution of each residue at the interaction interface to the binding affinity of MK2 with p38α was also analyzed by free energy decomposition. Several important residues responsible for the protein-protein interaction were also identified.

Kinetic rate constant prediction supports the conformational selection mechanism of protein binding

The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.

Protein-protein binding affinity prediction on a diverse set of structures

MOTIVATION

Accurate binding free energy functions for protein-protein interactions are imperative for a wide range of purposes. Their construction is predicated upon ascertaining the factors that influence binding and their relative importance. A recent benchmark of binding affinities has allowed, for the first time, the evaluation and construction of binding free energy models using a diverse set of complexes, and a systematic assessment of our ability to model the energetics of conformational changes.

RESULTS

We construct a large set of molecular descriptors using commonly available tools, introducing the use of energetic factors associated with conformational changes and disorder to order transitions, as well as features calculated on structural ensembles. The descriptors are used to train and test a binding free energy model using a consensus of four machine learning algorithms, whose performance constitutes a significant improvement over the other state of the art empirical free energy functions tested. The internal workings of the learners show how the descriptors are used, illuminating the determinants of protein-protein binding.

AVAILABILITY

The molecular descriptor set and descriptor values for all complexes are available in the Supplementary Material. A web server for the learners and coordinates for the bound and unbound structures can be accessed from the website: http://bmm.cancerresearchuk.org/~Affinity.

CONTACT

paul.bates@cancer.org.uk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Involvement of the recoverin C-terminal segment in recognition of the target enzyme rhodopsin kinase

NCS (neuronal Ca2+ sensor) proteins belong to a family of calmodulin-related EF-hand Ca2+-binding proteins which, in spite of a high degree of structural similarity, are able to selectively recognize and regulate individual effector enzymes in a Ca2+-dependent manner. NCS proteins vary at their C-termini, which could therefore serve as structural control elements providing specific functions such as target recognition or Ca2+ sensitivity. Recoverin, an NCS protein operating in vision, regulates the activity of rhodopsin kinase, GRK1, in a Ca2+-dependent manner. In the present study, we investigated a series of recoverin forms that were mutated at the C-terminus. Using pull-down assays, surface plasmon resonance spectroscopy and rhodopsin phosphorylation assays, we demonstrated that truncation of recoverin at the C-terminus significantly reduced the affinity of recoverin for rhodopsin kinase. Site-directed mutagenesis of single amino acids in combination with structural analysis and computational modelling of the recoverin–kinase complex provided insight into the protein–protein interface between the kinase and the C-terminus of recoverin. Based on these results we suggest that Phe3 from the N-terminal helix of rhodopsin kinase and Lys192 from the C-terminal segment of recoverin form a cation–π interaction pair which is essential for target recognition by recoverin. Taken together, the results of the present study reveal a novel rhodopsin-kinase-binding site within the C-terminal region of recoverin, and highlights its significance for target recognition and regulation.

Modeling allosteric signal propagation using protein structure networks

Allosteric communication in proteins can be induced by the binding of effective ligands, mutations or covalent modifications that regulate a site distant from the perturbed region. To understand allosteric regulation, it is important to identify the remote sites that are affected by the perturbation-induced signals and how these allosteric perturbations are transmitted within the protein structure. In this study, by constructing a protein structure network and modeling signal transmission with a Markov random walk, we developed a method to estimate the signal propagation and the resulting effects. In our model, the global perturbation effects from a particular signal initiation site were estimated by calculating the expected visiting time (EVT), which describes the signal-induced effects caused by signal transmission through all possible routes. We hypothesized that the residues with high EVT values play important roles in allosteric signaling. We applied our model to two protein structures as examples, and verified the validity of our model using various types of experimental data. We also found that the hot spots in protein binding interfaces have significantly high EVT values, which suggests that they play roles in mediating signal communication between protein domains.

Systems biochemistry approaches to vertebrate phototransduction: towards a molecular understanding of disease

Phototransduction in vertebrates represents a paradigm of signalling pathways, in particular those mediated by G-protein-coupled receptors. The variety of protein-protein, protein-ion and protein-nucleotide interactions makes up an intricate network which is finely regulated by activating-deactivating molecules and chemical modifications. The holistic systems properties of the network allow for typical adaptation mechanisms, which ultimately result in fine adjustments of sensitivity and electrical response of the photoreceptor cells to the broad range of light stimuli. In the present article, we discuss a novel bottom-up strategy to study the phototransduction cascade in rod cells starting from the underlying biochemistry. The resulting network model can be simulated and the predicted dynamic behaviour directly compared with data from electrophysiological experiments performed on a wide range of illumination conditions. The advantage of applying procedures typical of systems theory to a well-studied signalling pathway is also discussed. Finally, the potential application to the study of the molecular basis of retinal diseases is highlighted through a practical example, namely the simulation of conditions related to Leber congenital amaurosis.

Predicting kinetic constants of protein-protein interactions based on structural properties

Elucidating kinetic processes of protein-protein interactions (PPI) helps to understand how basic building blocks affect overall behavior of living systems. In this study, we used structure-based properties to build predictive models for kinetic constants of PPI. A highly diverse PPI dataset, protein-protein kinetic interaction data and structures (PPKIDS), was built. PPKIDS contains 62 PPI with complex structures and kinetic constants measured experimentally. The influence of structural properties on kinetics of PPI was studied using 35 structure-based features, describing different aspects of complex structures. Linear models for the prediction of kinetic constants were built by fitting with selected subsets of structure-based features. The models gave correlation coefficients of 0.801, 0.732, and 0.770 for k(off), k(on), and K(d), respectively, in leave-one-out cross validations. The predictive models reported here use only protein complex structures as input and can be generally applied in PPI studies as well as systems biology modeling. Our study confirmed that different properties play different roles in the kinetic process of PPI. For example, k(on) was affected by overall structural features of complexes, such as the composition of secondary structures, the change of translational and rotational entropy, and the electrostatic interaction; while k(off) was determined by interfacial properties, such as number of contacted atom pairs per 100 Ų. This information provides useful hints for PPI design.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.

Human cancer protein-protein interaction network: a structural perspective

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.

Protein-protein interaction networks provide a global picture of cellular function and biological processes. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. The structural details of interfaces are immensely useful in efforts to answer some fundamental questions such as: (i) what features of cancer-related protein interfaces make them act as hubs; (ii) how hub protein interfaces can interact with tens of other proteins with varying affinities; and (iii) which interactions can occur simultaneously and which are mutually exclusive. Addressing these questions, we propose a method to characterize interactions in a human protein-protein interaction network using three-dimensional protein structures and interfaces. Protein interface analysis shows that the strength and specificity of the interactions of hub proteins and cancer proteins are different than the interactions of non-hub and non-cancer proteins, respectively. In addition, distinguishing overlapping from non-overlapping interfaces, we illustrate how a fourth dimension, that of the sequence of processes, is integrated into the network with case studies. We believe that such an approach should be useful in structural systems biology.