Protein-protein Interaction Interface Research Articles

Abstract 5785: Shedding light on PARP1-E2F1 interaction by Cryo-EM

Abstract Cell division is orchestrated by a complex network of interactions between proteins involved in numerous signaling pathways that control cell proliferation. One of these main players is the E2F family of transcription factors. The role of these key players is to regulate the expression of several genes important in cell proliferation, particularly those involved in progression through G1 and into the S-phase of the cell cycle. In fact, cell cycle deregulation is a hallmark of cancer, and multiple studies have highlighted the critical role of E2F hyperactivation in tumor progression, angiogenesis, and metastasis. However, targeting a master regulator of cell growth will also have a pernicious effect on non-cancerous cells, thus limiting its applications. In order to overwhelm these restrictions, we propose a therapeutic approach in which E2F transcriptional activity is specifically reduced in cells with hyperactivation of the pathway, such as cancer cells.We and others have shown that poly (ADP-ribose) polymerase 1 (PARP1) is a transcriptional co-activator of E2F1 and, owing to this, loss of PARP results in cell cycle re-regulation and reduction of tumor growth. Interestingly, this effect of PARP1 does not depend on its enzymatic activity, thus opening a completely new strategic approach. Furthermore, considering that most oncogenic processes are associated with cell cycle deregulation, disruption of PARP1-E2F1 interaction could provide a new therapeutic target with a wide spectrum of indications in cancer. Currently, we lack comprehensive structural details regarding the PARP-1/E2F1 protein-protein interaction. To address this, we used cryo-EM to investigate both the standalone structure of PARP1 (full length) and its interaction with E2F1. This approach yielded high-resolution structural insights into the PARP1-E2F1 complex, enabling precise identification of the protein-protein interaction interface (PPI). Through this exploration of structural and molecular determinants governing this interaction, we aim to develop innovative strategies for designing drugs targeted at disrupting this complex. This work was supported by Agencia Estatal de Investigación (AEl/10.13039/501100011033), Xunta de Galicia (GPC GI-1862, ED431B 2020/26; ED431G 2019/02) and European Regional Development Fund-ERDF. Citation Format: Pablo Iglesias, Ester Casajus-Pelegay, Maria Moreno-Morcillo, Lara González-Rendo, Laura Porres-Ventin, Victor M. Arce, Rafael Fernandez-Leiro, Jose A. Costoya. Shedding light on PARP1-E2F1 interaction by Cryo-EM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5785.

Exploring the binding mechanism of a small molecular Hsp70-Bim PPI inhibitor through molecular dynamic simulation.

The interface of Hsp70-Bim protein-protein interaction (PPI) has been identified as a specific target for Chronic Myeloid Leukemia (CML) therapy and the specific inhibitors were developed to exhibit in vivo anti-leukemia activities. Herein, we explored the binding mechanism of a Hsp70-Bim inhibitor, 6-(cyclohexylthio)-3-((2-morpholinoethyl) amino)-1-oxo-1H-phenalene-2-carbonitrile (S1g-6), to Hsp70 at the atomic level by MD simulation. TYR-149, THR-222, ALA-223, and GLY-224 on Hsp70 were identified as four key residues that contribute to Hsp70/S1g-6 complex. Moreover, the site mutation validation demonstrated the TYR-149 of Hsp70 is a "hot-spot" in the Hsp70-Bim PPI interface. These results could benefit the design of further inhibitors to occupy the Bim binding site on the Hsp70 surface. The binding mechanism of S1g-6 and Hsp70 was predicted through the molecular dynamics (MD) method by Gromacs-2021.3. The MD simulation was performed with 100-ps NVT and 100-ps NPT ensemble, and the force field was chosen as the Charmm36 force field. The temperature was set as 300K, the time step was 2fs and the total MD simulation time was 500ns.

Discovery of (2-(4-Substituted phenyl)quinolin-4-yl)(4-isopropylpiperazin-1-yl)methanone Derivatives as Potent Proprotein Convertase Subtilisin/Kexin Type 9 Inhibitors.

Inhibition of proprotein convertase subtilisin/kexin type 9 (PCSK9) plays an increasingly important role in the treatment of hyperlipidemia. In pursuit of potent small molecules that block the PCSK9/low-density lipoprotein receptor (LDLR) protein-protein interaction (PPI), a series of 2-phenylquinoline-4-carboxylic acid derivatives were designed and synthesized based on previously derived molecules. In the in vitro PPI inhibition test, compounds M1, M12, M14, M18 and M27 exhibited potent activities with IC50 values of 6.25 μM, 0.91 μM, 2.81 μM, 4.26 μM and 0.76 μM, respectively, compared with SBC-115337 (IC50 value of 9.24 μM). Molecular docking and molecular dynamics simulations revealed the importance of hydrophobic interactions in the binding of inhibitors to the PPI interface of PCSK9. In LDLR expression and LDL uptake assays, the tested compounds M1, M12 and M14 were found to restore LDLR expression levels and to increase the extracellular LDL uptake capacity of HepG2 cells in the presence of exogenous PCSK9. Collectively, novel small-molecule PCSK9/LDLR PPI inhibitors (especially M12) with in vitro lipid lowering ability, were discovered as lead compounds for further development of hypolipidemic drugs.

Structure-guided screening of protein-protein interaction for the identification of Myc-Max heterodimer complex modulators

De-regulation of oncogenic myelocytomatosis (c-Myc or Myc) transcription factor is one of the most common molecular anomalies encountered in human cancers, and it is typically linked to many aggressive malignancies including breast, lung, cervix, colon glioblastomas, and other haematological organs. The Myc belongs to the basic helix-loop-helix zipper protein family (bHLH-ZIP), and its dimerization with another principal interactor protein partner Myc-associated factor X (Max) is essentially required for cellular transformation, cell growth and proliferation, and transcriptional activation. Intermolecular interactions have been evaluated between hetero-dimer Myc-Max protein, which identified protein-protein interaction (PPI) specific modulators using highly précised molecular docking study followed by long-range interaction stability analyzed through molecular dynamic (MD) simulation. Moreover, ADME profile analyses have been estimated for screened hit compounds. MM-GBSA-based binding free energy (ΔG) estimations have been performed for all screened hit compounds obtained from multi-step molecular docking-based virtual screening technique. According to the employed various rigorous multi-chemometric techniques, four identified inhibitors/modulators appear to have a considerable number of intermolecular contacts with hotspot residues in the hetero-dimer interface region of the Myc-Max PPI complex. However, identified hit compounds might need further structural optimization or extensive biophysical analyses for better understanding of the molecular mechanism for exhibiting the Myc-Max PPI interface binding stability. Communicated by Ramaswamy H. Sarma

Electrophile Scanning Reveals Reactivity Hotspots for the Design of Covalent Peptide Binders.

Protein-protein interactions (PPIs) are intriguing targets in drug discovery and development. Peptides are well suited to target PPIs, which typically present with large surface areas lacking distinct features and deep binding pockets. To improve binding interactions with these topologies and advance the development of PPI-focused therapeutics, potential ligands can be equipped with electrophilic groups to enable binding through covalent mechanisms of action. We report a strategy termed electrophile scanning to identify reactivity hotspots in a known peptide ligand and demonstrate its application in a model PPI. Cysteine mutants of a known ligand are used to install protein-reactive modifiers via a palladium oxidative addition complex (Pd-OAC). Reactivity hotspots are revealed by cross-linking reactions with the target protein under physiological conditions. In a model PPI with the 9-mer peptide antigen VL9 and major histocompatibility complex (MHC) class I protein HLA-E, we identify two reactivity hotspots that afford up to 87% conversion to the protein-peptide conjugate within 4 h. The reactions are specific to the target protein in vitro and dependent on the peptide sequence. Moreover, the cross-linked peptide successfully inhibits molecular recognition of HLA-E by CD94-NKG2A possibly due to structural changes enacted at the PPI interface. The results illustrate the potential application of electrophile scanning as a tool for rapid discovery and development of covalent peptide binders.

Bioinformatics insights into CENP-T and CENP-W protein-protein interaction disruptive amino acid substitution in the CENP-T-W complex.

Kinetochores are multi-protein assemblies present at the centromere of the human chromosome and play a crucial role in cellular mitosis. The CENP-T and CENP-W chains form a heterodimer, which is an integral part of the inner kinetochore, interacting with the linker DNA on one side and the outer kinetochore on the other. Additionally, the CENP-T-W dimer interacts with other regulatory proteins involved in forming inner kinetochores. The specific roles of different amino acids in the CENP-W at the protein-protein interaction (PPI) interface during the CENP-T-W dimer formation remain incompletely understood. Since cell division goes awry in diseases like cancer, this CENP-T-W partnership is a potential target for new drugs that could restore healthy cell division. We employed molecular docking, binding free energy calculations, and molecular dynamics (MD) simulations to investigate the disruptive effects of amino acids substitutions in the CENP-W chain on CENP-T-W dimer formation. By conducting a molecular docking study and analysing hydrogen bonding interactions, we identified key residues in CENP-W (ASN-46, ARG-53, LEU-83, SER-86, ARG-87, and GLY-88) for further investigation. Through site-directed mutagenesis and subsequent binding free energy calculations, we refined the selection of mutant. We chose four mutants (N46K, R53K, L83K, and R87E) of CENP-W to assess their comparative potential in forming CENP-T-W dimer. Our analysis from 250 ns long revealed that the substitution of LEU83 and ARG53 residues in CENP-W with the LYS significantly disrupts the formation of CENP-T-W dimer. In conclusion, LEU83 and ARG53 play a critical role in CENP-T and CENP-W dimerization which is ultimately required for cellular mitosis. Our findings not only deepen our understanding of cell division but also hint at exciting drug-target possibilities.

Linking Chemical Fragments, “Gluing” Protein Complexes, Connecting across Cultures

AbstractThis invited Team Profile was created by Michelle Arkin and Adam Renslo from the University of California, San Francisco in the USA and Luc Brunsveld and Christian Ottmann from the Eindhoven University of Technology in the Netherlands. They recently published an article on designing molecular glues for the 14‐3‐3/estrogen receptor (ER) protein‐protein interaction (PPI). Molecular glues increase the binding between two proteins by binding at the PPI interface. While they hold exciting promise to induce new biology and treat disease, systematic approaches to discover glues are just becoming available. Fragment‐based drug discovery has been used to discover inhibitors of PPI; here, the team demonstrated a fragment discovery and linking strategy to create a new molecular glue for 14‐3‐3/ER, an anticancer target. “From Tethered to Freestanding Stabilizers of 14‐3‐3 Protein‐Protein Interactions though Fragment Linking”, E. J. Visser, P. Jaishankar, E. Sijbesma, M. A. M. Pennings, E. M. F. Vandenboorn, X. Guillory, R. J. Neitz, J. Morrow, S. Dutta, A. R. Renslo, L. Brunsveld, M. R. Arkin, C. Ottmann, Angew. Chem. Int. Ed. 2023, 62, e202308004.

Peptidomimetics for CVD screened via TRADD-TRAF2 complex interface assessments.

The main aim of this study is to screen and develop Peptidomimetics to treat atherosclerosis (AS) which is a Cardio Vascular Disease (CVD). Peptidomimetics were obtained from the protein-protein interaction interface of TRADD (Tumor necrosis factor receptor type 1-associated DEATH domain protein) and TRAF2 (TNF receptor-associated factor 2) complex. TRADD-TRAF2 interaction is critical in AS pathogenesis since it assists a series of signal transducers that activate NF-κB. Conceptually, the triggered NF-κB makes an extensive amount of nitric oxide (NO) synthesized by inducible nitric oxide synthase (iNOS), which boons the progress of AS. The examined TRADD-TRAF2 complex (PDB ID: 1F3V) and its interaction details revealed that the sequence range W11-G165 of TRADD highly interacts with TRAF2. The sequence range W11-G165 was selected for the design and preparation of the inhibitory peptide in silico. The selected sequence was mutated with the alanine scanning method to have a range of inhibitory peptides. With the help of different in silico tools, the top three, namely MIP11-25L, MIP131-143h, and MIP149-164m peptides showed the best interaction with the critical residues of TRAF2. Thus, these three peptides were used for generating peptidomimetics using pepMMsMIMIC, a peptidomimetics virtual screening tool. Around 600 peptidomimetics were identified & and retrieved for further screening by employing molecular docking tools and MD (Molecular Dynamics) simulations. Density Functional Theory (DFT) and ADMET predictions were applied to validate the screened peptidomimetics druggability. In the results, peptidomimic compounds MMs03918858 and MMs03927281 with binding energy values of -9.6kcal/mol and - 9.1kcal/mol respectively were screened as the best and are proposed for further pre-clinical studies.

Discovering covalent inhibitors of protein-protein interactions from trillions of sulfur(VI) fluoride exchange-modified oligonucleotides.

Molecules that covalently engage target proteins are widely used as activity-based probes and covalent drugs. The performance of these covalent inhibitors is, however, often compromised by the paradox of efficacy and risk, which demands a balance between reactivity and selectivity. The challenge is more evident when targeting protein-protein interactions owing to their low ligandability and undefined reactivity. Here we report sulfur(VI) fluoride exchange (SuFEx) in vitro selection, a general platform for high-throughput discovery of covalent inhibitors from trillions of SuFEx-modified oligonucleotides. With SuFEx in vitro selection, we identified covalent inhibitors that cross-link distinct residues of the SARS-CoV-2 spike protein at its protein-protein interaction interface with the human angiotensin-converting enzyme 2. A separate suite of covalent inhibitors was isolated for the human complement C5 protein. In both cases, we observed a clear disconnection between binding affinity and cross-linking reactivity, indicating that direct search for the aimed reactivity-as enabled by SuFEx in vitro selection-is vital for discovering covalent inhibitors of high selectivity and potency.

Delineation of the CENP-LN sub-complex dissociation mechanism upon multisite phosphorylation during mitosis

Phosphorylation is the most prevalent form of regulation in cells, organizing virtually all cellular functions, including survival, motility, differentiation, proliferation, and metabolism. This regulatory function has been largely conserved from the primitive single-cell to the more complex multicellular organisms. More than a third of proteins in eukaryotes are phosphorylated, and essentially every class of protein undergoes regulation by phosphorylation. A decline in the cellular level of CENP-L and CENP-N (components of the constitutive centromere associated network) has earlier been reported and linked to cyclin-dependent kinase (CDK) phosphorylation upon transition into mitosis. Given the importance of posttranslational modifications in cell cycle regulation, mechanistic comprehension of the impact of phosphorylation on both proteins (CENP-L and CENP-N) is of high significance. Through the application of diverse computational analytical techniques, including atomistic molecular dynamics simulations, the mechanism of kinetochore mis-localization and dissociation of the CENP-LN sub-complex in mitosis was delineated. We showed that the phosphorylation of both components of the sub-complex induces global conformational destabilizing effects on the proteins, combined with changes in the electrostatic potential and increase in steric clashes around the protein-protein interaction interface. This, consistent with earlier experimental reports, suggest that the multisite phosphorylation of the CENP-LN sub-complex plays a crucial role in the regulation of cell division. Communicated by Ramaswamy H. Sarma

Prediction of transient and permanent protein interactions using AI methods.

Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.

A potent phosphodiester Keap1-Nrf2 protein-protein interaction inhibitor as the efficient treatment of Alzheimer's disease

The Keap1-Nrf2 pathway has been established as a therapeutic target for Alzheimer's disease (AD). Directly inhibiting the protein-protein interaction (PPI) between Keap1 and Nrf2 has been reported as an effective strategy for treating AD. Our group has validated this in an AD mouse model for the first time using the inhibitor 1,4-diaminonaphthalene NXPZ-2 with high concentrations. In the present study, we reported a new phosphodiester containing diaminonaphthalene compound, POZL, designed to target the PPI interface using a structure-based design strategy to combat oxidative stress in AD pathogenesis. Our crystallographic verification confirms that POZL shows potent Keap1-Nrf2 inhibition. Remarkably, POZL showed its high in vivo anti-AD efficacy at a much lower dosage compared to NXPZ-2 in the transgenic APP/PS1 AD mouse model. POZL treatment in the transgenic mice could effectively ameliorate learning and memory dysfunction by promoting the Nrf2 nuclear translocation. As a result, the oxidative stress and AD biomarker expression such as BACE1 and hyperphosphorylation of Tau were significantly reduced, and the synaptic function was recovered. HE and Nissl staining confirmed that POZL improved brain tissue pathological changes by enhancing neuron quantity and function. Furthermore, it was confirmed that POZL could effectively reverse Aβ-caused synaptic damage by activating Nrf2 in primary cultured cortical neurons. Collectively, our findings demonstrated that the phosphodiester diaminonaphthalene Keap1-Nrf2 PPI inhibitor could be regarded as a promising preclinical candidate of AD.

Performance of the Classification of Critical Residues at the Interface of BMPs Complexes Pondered with the Ground-State Energy Feature using Random Forest Classifier

This work is focused on implementing and evaluating the Random Forest Classifier (RFC), among other classical machine learning models, on predicting the residues at the interface of protein-protein interactions (PPI) that contribute most of the binding free energy (called hot spots and hot regions). The dataset comprises twenty-nine bone morphogenetic proteins (BMPs) complexes from the Protein Data Bank (PDB). We used just six features such as B-factor, hydrophobicity index, prevalence score, accessible surface area (ASA), conservation score, and the ground-state energy of the amino acids, which were calculated using the Density Functional Theory (DFT). Proving and testing several machine learning methods, we selected the RCF because of its better performance using classical classification metrics and tests. An optimal parameter selection of the RFC reached a better performance using this dataset with around 90 % with the correct class assigned (hot spot & hot region / non-hot spot hot region) residues.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

Prediction of the tetramer protein complex interaction based on CNN and SVM.

Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs.

PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries.

Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary data are available at Bioinformatics Advances online.

Peptide-based covalent inhibitors of protein-protein interactions.

Protein-protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.

A Comprehensive Study of De Novo Mutations on the Protein-Protein Interaction Interfaces Provides New Insights into Developmental Delay.

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

YAPPIS-Finder: A novel method for protein-protein interaction site predictions.

We describe a multi parametric-approach, YAPPIS-Finder, for predicting the PPI sites on protein surface. A non-redundant database of comprised of 2,265 protein-protein interaction interfaces (PPIIs) involving 4,530 protein-protein interacting partners (PPIPs) and depicting the interaction between protein-chains of experimentally determined PPCs was used in designing the YAPPIS-Finder. Parametric score obtained on analyzing these 4,530 PPIPs with respect to their residue interface propensity, their hydrophobic content, and amount of solvation free energy associated with them provided the basis of YAPPIS-Finder. By applying YAPPIS-Finder on another dataset 4,290 PPIPs from 2,145 PPIIs, the optimal range of the parametric scores and protein-probe van der Waals energy of interaction was determined. Subsequently, taking the optimal range of PPIP parametric scores and threshold for protein-probe van der Waals energy of interaction into the consideration, the YAPPIS-Finder was tested on a blind dataset of 554 protein-chains and it was found predicting 69.67% sites correctly. On predicting only one PPI site on each protein-chain, the YAPPIS-Finder found covering 22.91% of actually sites in the predicted site. Contrary to this, the sites predicted by SPPIDER covered 22.7% of actual sites. However, on predicting two PPI sites for each protein-chain, the percentage coverage of actual sites in the predicted sites by YAPPIS-Finder exceeded two-fold (i.e. 41.81%), thus making the YAPPIS-Finder a better method.

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein–protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker’s yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.