Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery

ABSTRACT Introduction Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experimental methods is likely to change the perspectives of protein-protein modulator research. Noteworthy, some novel low molecular weight (LMW) and short peptide modulators of PPIs are already in clinical trials for the treatment of relevant diseases. Areas covered This review focuses on the main molecular properties of protein-protein interfaces and on key concepts pertaining to the modulation of PPIs. The authors survey recently reported state-of-the-art methods dealing with the rational design of PPI modulators and highlight the role of several computer-based approaches. Expert opinion Interfering specifically with large protein interfaces is still an open challenge. The initial concerns about the unfavorable physicochemical properties of many of these modulators are nowadays less acute with several molecules lying beyond the rule of 5, orally available and successful in clinical trials. As the cost of biologics interfering with PPIs is very high, it would seem reasonable to put more effort, both in academia and the private sectors, on actively developing novel low molecular weight compounds and short peptides to perform this task.

[1]  G. Gao,et al.  De novo design of protein interactions with learned surface fingerprints , 2023, Nature.

[2]  P. Zhao,et al.  Hierarchical graph learning for protein–protein interaction , 2023, Nature Communications.

[3]  H. Ng,et al.  Computational approaches for the design of modulators targeting protein-protein interactions , 2023, Expert opinion on drug discovery.

[4]  K. Mizuguchi,et al.  DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein–protein interactions , 2023, Frontiers in Chemistry.

[5]  N. Amoroso,et al.  TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity , 2022, J. Chem. Inf. Model..

[6]  Abhay M. S. Aradhya,et al.  Discovery of small-molecule PD-1/PD-L1 antagonists through combined virtual screening and experimental validation , 2022, Comput. Biol. Chem..

[7]  R. McNulty,et al.  Cryo‐EM for Small Molecules , 2022, Current protocols.

[8]  Lydia Siragusa,et al.  An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening , 2022, J. Chem. Inf. Model..

[9]  J. Medina-Franco,et al.  Machine Learning Models to Predict Protein–Protein Interaction Inhibitors , 2022, Molecules.

[10]  B. Villoutreix,et al.  A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein–Protein Interfaces , 2022, International journal of molecular sciences.

[11]  M. Weiss,et al.  Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein-Protein Interaction Sites. , 2022, Journal of medicinal chemistry.

[12]  Lopamudra Dey,et al.  Up-Regulated Proteins Have More Protein–Protein Interactions than Down-Regulated Proteins , 2022, The Protein Journal.

[13]  M. Cornu,et al.  PROTAC technology: a new drug design modality for chemical biology with many challenges in drug discovery. , 2022, Drug discovery today.

[14]  P. Tufféry,et al.  Binding and Kinetic Analysis of Human Protein Phosphatase PP2A Interactions with Caspase 9 Protein and the Interfering Peptide C9h , 2022, Pharmaceutics.

[15]  S. Fletcher,et al.  Recent applications of covalent chemistries in protein-protein interaction inhibitors. , 2022, RSC medicinal chemistry.

[16]  P. Furet,et al.  The First Class of Small Molecules Potently Disrupting the YAP‐TEAD Interaction by Direct Competition , 2022, ChemMedChem.

[17]  E. Gavathiotis,et al.  Targeting protein conformations with small molecules to control protein complexes. , 2022, Trends in biochemical sciences.

[18]  Daizhou Zhang,et al.  A chemical perspective on the modulation of TEAD transcriptional activities: Recent progress, challenges, and opportunities. , 2022, European journal of medicinal chemistry.

[19]  Xinmei Wang,et al.  Molecular Glues: The Adhesive Connecting Targeted Protein Degradation to the Clinic , 2022, Biochemistry.

[20]  Kwang‐Hwi Cho,et al.  De novo molecular design with deep molecular generative models for PPI inhibitors , 2022, Briefings Bioinform..

[21]  C. Ottmann,et al.  Molecular glues to stabilise protein-protein interactions. , 2022, Current opinion in chemical biology.

[22]  Yasser B. Ruiz-Blanco,et al.  PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein–Peptide and Protein–Protein Binding Affinity , 2022, Journal of proteome research.

[23]  Ming Chen,et al.  Deep learning frameworks for protein–protein interaction prediction , 2022, Computational and structural biotechnology journal.

[24]  Jiayin Wang,et al.  Integration of Protein-Protein Interaction Networks and Gene Expression Profiles Helps Detect Pancreatic Adenocarcinoma Candidate Genes , 2022, Frontiers in Genetics.

[25]  Carlos H. M. Rodrigues,et al.  CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning , 2022, Nucleic Acids Res..

[26]  Mohammad A. Ghattas,et al.  Structure-based assessment and druggability classification of protein–protein interaction sites , 2022, Scientific Reports.

[27]  O. Nicolotti,et al.  PLATO: A Predictive Drug Discovery Web Platform for Efficient Target Fishing and Bioactivity Profiling of Small Molecules , 2022, International journal of molecular sciences.

[28]  Xingzhen Lao,et al.  HORDB a comprehensive database of peptide hormones , 2022, Scientific data.

[29]  P. Buchwald Developing Small-Molecule Inhibitors of Protein-Protein Interactions Involved in Viral Entry as Potential Antivirals for COVID-19 , 2022, Frontiers in Drug Discovery.

[30]  A. Awasthi,et al.  Traversing through the Dynamic Protein-Protein Interaction Landscape and Conformational Plasticity of PD-1 for Small-Molecule Discovery. , 2022, Journal of medicinal chemistry.

[31]  Lydia Siragusa,et al.  Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions , 2022, J. Chem. Inf. Model..

[32]  A. Bonvin,et al.  Using machine‐learning‐driven approaches to boost hot‐spot's knowledge , 2022, WIREs Computational Molecular Science.

[33]  Shayne D. Wierbowski,et al.  Deep learning methods for 3D structural proteome and interactome modeling. , 2022, Current opinion in structural biology.

[34]  D. Langley,et al.  PROTAC targeted protein degraders: the past is prologue , 2022, Nature Reviews Drug Discovery.

[35]  O. Keskin,et al.  Artificial intelligence based methods for hot spot prediction. , 2021, Current opinion in structural biology.

[36]  B. Oliva,et al.  Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures. , 2021, Methods in molecular biology.

[37]  P. Kastritis,et al.  Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons. , 2021, Chemical reviews.

[38]  P. Jayaraj,et al.  Protein–Protein Docking: Past, Present, and Future , 2021, The Protein Journal.

[39]  Jörg Menche,et al.  Network analysis reveals rare disease signatures across multiple levels of biological organization , 2021, Nature Communications.

[40]  G. Clore,et al.  NMR methods for exploring 'dark' states in ligand binding and protein-protein interactions. , 2021, Progress in nuclear magnetic resonance spectroscopy.

[41]  L. Belvisi,et al.  Editorial: Peptides Targeting Protein-Protein Interactions: Methods and Applications , 2021, Frontiers in Molecular Biosciences.

[42]  D. Hassabis,et al.  Protein complex prediction with AlphaFold-Multimer , 2021, bioRxiv.

[43]  M. Ohue,et al.  Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions , 2021, International journal of molecular sciences.

[44]  A. Elofsson,et al.  Improved prediction of protein-protein interactions using AlphaFold2 and extended multiple-sequence alignments , 2021, bioRxiv.

[45]  Jianyang Zeng,et al.  A deep-learning framework for multi-level peptide–protein interaction prediction , 2021, Nature Communications.

[46]  Alvaro Olivera-Nappa,et al.  Peptipedia: a user-friendly web application and a comprehensive database for peptide research supported by Machine Learning approach , 2021, Database J. Biol. Databases Curation.

[47]  Sook Mei Khor,et al.  Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis , 2021, Molecules.

[48]  S. Ovchinnikov,et al.  ColabFold: making protein folding accessible to all , 2022, Nature Methods.

[49]  M. Nilges,et al.  InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein–protein interactions , 2021, bioRxiv.

[50]  Jia Truong,et al.  Analysis of physicochemical properties of protein-protein interaction modulators suggests stronger alignment with the "rule of five". , 2021, RSC medicinal chemistry.

[51]  Gyu Rie Lee,et al.  Accurate prediction of protein structures and interactions using a 3-track neural network , 2021, Science.

[52]  N. Kim,et al.  Exploring the chemical space of protein–protein interaction inhibitors through machine learning , 2021, Scientific Reports.

[53]  Michel F. Sanner,et al.  Improving Docking Power for Short Peptides Using Random Forest , 2021, J. Chem. Inf. Model..

[54]  W. Cabri,et al.  Therapeutic Peptides Targeting PPI in Clinical Development: Overview, Mechanism of Action and Perspectives , 2021, Frontiers in Molecular Biosciences.

[55]  A. Jain,et al.  Protein–protein interaction and in silico mutagenesis studies on IL17A and its peptide inhibitor , 2021, 3 Biotech.

[56]  A. Jahangiri,et al.  In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein–Protein Interactions , 2021, Frontiers in Molecular Biosciences.

[57]  K. Kehn-Hall,et al.  Targeting protein-protein interaction interfaces in COVID-19 drug discovery , 2021, Computational and Structural Biotechnology Journal.

[58]  Łukasz Nierzwicki,et al.  Molecular Dynamics to Predict Cryo-EM: Capturing Transitions and Short-Lived Conformational States of Biomolecules , 2021, Frontiers in Molecular Biosciences.

[59]  M. Walko,et al.  Peptide-based inhibitors of protein–protein interactions: biophysical, structural and cellular consequences of introducing a constraint , 2021, Chemical science.

[60]  Ziding Zhang,et al.  Current status and future perspectives of computational studies on human-virus protein-protein interactions , 2021, Briefings Bioinform..

[61]  Christopher R. Coxon,et al.  PepTherDia: database and structural composition analysis of approved peptide therapeutics and diagnostics. , 2021, Drug discovery today.

[62]  Christoph Wigge,et al.  The rapidly evolving role of cryo-EM in drug design , 2021 .

[63]  Qiaojun He,et al.  Recent advance of peptide-based molecules and nonpeptidic small-molecules modulating PD-1/PD-L1 protein-protein interaction or targeting PD-L1 protein degradation. , 2021, European journal of medicinal chemistry.

[64]  Maria G. A. Oliveira,et al.  Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm , 2021, BMC Bioinformatics.

[65]  M. Nilges,et al.  The iPPI-DB initiative: a community-centered database of protein–protein interaction modulators , 2021, Bioinform..

[66]  Alberto Pérez,et al.  Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations , 2021, Molecules.

[67]  Favour Danladi Makurvet,et al.  Biologics vs. small molecules: Drug costs and patient access , 2020 .

[68]  Jeffrey J. Gray,et al.  Advances to tackle backbone flexibility in protein docking. , 2020, Current opinion in structural biology.

[69]  Miguel Correa Marrero,et al.  Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms , 2020, Science.

[70]  Rongsheng Tong,et al.  Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials , 2020, Signal Transduction and Targeted Therapy.

[71]  Nicola Amoroso,et al.  De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization , 2020, J. Chem. Inf. Model..

[72]  Jiajun Qiu,et al.  Network-based protein-protein interaction prediction method maps perturbations of cancer interactome , 2020, PLoS genetics.

[73]  A. Giuliani,et al.  The Discovery of a Putative Allosteric Site in the SARS-CoV-2 Spike Protein Using an Integrated Structural/Dynamic Approach , 2020, Journal of proteome research.

[74]  Benjamin J. Polacco,et al.  A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing , 2020, Nature.

[75]  Olivier Sperandio,et al.  Fr-PPIChem: An academic compound library dedicated to protein-protein interactions. , 2020, ACS chemical biology.

[76]  Bruno O Villoutreix,et al.  Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace , 2020, Briefings Bioinform..

[77]  R. Bureau,et al.  Hot-Spots of Mcl-1 Protein. , 2020, Journal of medicinal chemistry.

[78]  Shailesh Kumar,et al.  PlantPepDB: A manually curated plant peptide database , 2020, Scientific Reports.

[79]  Mikael Trellet,et al.  Protein-Protein Modeling Using Cryo-EM Restraints. , 2020, Methods in molecular biology.

[80]  Min Wu,et al.  Applications of genome editing technology in the targeted therapy of human diseases: mechanisms, advances and prospects , 2020, Signal Transduction and Targeted Therapy.

[81]  Peter K. Sorger,et al.  BIOPHYSICAL PREDICTION OF PROTEIN-PEPTIDE INTERACTIONS AND SIGNALING NETWORKS USING MACHINE LEARNING , 2019, Nature Methods.

[82]  A. Sharifi-Zarchi,et al.  Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis , 2019, BMC Medical Genomics.

[83]  Jesus A. Beltran,et al.  Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis , 2019, Bioinform..

[84]  Shaoyong Lu,et al.  Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery , 2019, Medicinal research reviews.

[85]  Sandor Vajda,et al.  Why Some Targets Benefit from beyond Rule of Five Drugs. , 2019, Journal of medicinal chemistry.

[86]  Brijesh K. Garg,et al.  Tandem Affinity Purification and Mass Spectrometry (TAP‐MS) for the Analysis of Protein Complexes , 2019, Current protocols in protein science.

[87]  Ozlem Keskin,et al.  Developments in integrative modeling with dynamical interfaces. , 2019, Current opinion in structural biology.

[88]  Andy Chi-Lung Lee,et al.  A Comprehensive Review on Current Advances in Peptide Drug Development and Design , 2019, International journal of molecular sciences.

[89]  M. Siddiqi,et al.  In silico identification and design of potent peptide inhibitors against PDZ-3 domain of Postsynaptic Density Protein (PSD-95) , 2019, Journal of biomolecular structure & dynamics.

[90]  B. Villoutreix,et al.  Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein-protein interaction inhibitors. , 2019, Drug discovery today.

[91]  Alberto J. M. Martin,et al.  RIP-MD: a tool to study residue interaction networks in protein molecular dynamics , 2018, PeerJ.

[92]  Paul A Bates,et al.  Refinement of protein‐protein complexes in contact map space with metadynamics simulations , 2018, Proteins.

[93]  Lei Deng,et al.  Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment , 2018, Molecules.

[94]  Longqin Hu,et al.  Immunomodulators targeting the PD‐1/PD‐L1 protein‐protein interaction: From antibodies to small molecules , 2018, Medicinal research reviews.

[95]  Stephani Joy Y Macalino,et al.  Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery , 2018, Molecules.

[96]  Zeyu Wen,et al.  PepBDB: a comprehensive structural database of biological peptide-protein interactions , 2018, Bioinform..

[97]  P. Buchwald,et al.  Toward Small-Molecule Inhibition of Protein-Protein Interactions: General Aspects and Recent Progress in Targeting Costimulatory and Coinhibitory (Immune Checkpoint) Interactions. , 2018, Current topics in medicinal chemistry.

[98]  J. Fernández-Recio,et al.  Hot-spot analysis for drug discovery targeting protein-protein interactions , 2018, Expert opinion on drug discovery.

[99]  Vincent Frappier,et al.  PixelDB: Protein–peptide complexes annotated with structural conservation of the peptide binding mode , 2018, Protein science : a publication of the Protein Society.

[100]  A. Hüser,et al.  Safety, pharmacokinetics and pharmacodynamics of single rising doses of BI 655064, an antagonistic anti-CD40 antibody in healthy subjects: a potential novel treatment for autoimmune diseases , 2017, European Journal of Clinical Pharmacology.

[101]  John W. Pinney,et al.  Protein–Protein Interactions in Virus–Host Systems , 2017, Front. Microbiol..

[102]  James C. Collins,et al.  The Current State of Peptide Drug Discovery: Back to the Future? , 2017, Journal of medicinal chemistry.

[103]  Michael Hann,et al.  Stabilization of protein-protein interactions in drug discovery , 2017, Expert opinion on drug discovery.

[104]  Jolene L. Lau,et al.  Therapeutic peptides: Historical perspectives, current development trends, and future directions. , 2017, Bioorganic & medicinal chemistry.

[105]  D. Mochly‐Rosen,et al.  Peptides and peptidomimetics as regulators of protein-protein interactions. , 2017, Current opinion in structural biology.

[106]  Kenji Mizuguchi,et al.  Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery. , 2017, Current opinion in structural biology.

[107]  Devin K. Schweppe,et al.  Architecture of the human interactome defines protein communities and disease networks , 2017, Nature.

[108]  Dominique Douguet,et al.  Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors , 2017, Scientific Reports.

[109]  A. Ashkenazi,et al.  From basic apoptosis discoveries to advanced selective BCL-2 family inhibitors , 2017, Nature Reviews Drug Discovery.

[110]  Lee A. D. Cooper,et al.  The OncoPPi network of cancer-focused protein–protein interactions to inform biological insights and therapeutic strategies , 2017, Nature Communications.

[111]  J. Brenton,et al.  Development of Cell‐Permeable, Non‐Helical Constrained Peptides to Target a Key Protein–Protein Interaction in Ovarian Cancer , 2016, Angewandte Chemie.

[112]  Y. Shentu,et al.  Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. , 2016, The New England journal of medicine.

[113]  Philippe Roche,et al.  2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein–protein interactions , 2016, Database J. Biol. Databases Curation.

[114]  Guoqiang Dong,et al.  State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors. , 2015, Chemical Society reviews.

[115]  Olivier Sperandio,et al.  In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances. , 2015, Progress in biophysics and molecular biology.

[116]  Joerg Kallen,et al.  Discovery of a Dihydroisoquinolinone Derivative (NVP-CGM097): A Highly Potent and Selective MDM2 Inhibitor Undergoing Phase 1 Clinical Trials in p53wt Tumors. , 2015, Journal of medicinal chemistry.

[117]  Ernest Giralt,et al.  Modulating protein-protein interactions: the potential of peptides. , 2015, Chemical Communications.

[118]  Mateusz Kurcinski,et al.  CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site , 2015, Nucleic Acids Res..

[119]  Gabriele Cruciani,et al.  BioGPS: Navigating biological space to predict polypharmacology, off‐targeting, and selectivity , 2015, Proteins.

[120]  Jeffrey E. Lee,et al.  Measuring Protein‐Protein and Protein‐Nucleic Acid Interactions by Biolayer Interferometry , 2015, Current protocols in protein science.

[121]  J. Wells,et al.  Small-molecule inhibitors of protein-protein interactions: progressing toward the reality. , 2014, Chemistry & biology.

[122]  Lydia Siragusa,et al.  BioGPS: The Music for the Chemo‐ and Bioinformatics Walzer , 2014, Molecular informatics.

[123]  Jean-Luc Poyet,et al.  Drug-Like Protein–Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology , 2014, Molecular informatics.

[124]  Jerome Wielens,et al.  Oncogenic protein interfaces: small molecules, big challenges , 2014, Nature Reviews Cancer.

[125]  Y. Wang,et al.  Discovery of AMG 232, a potent, selective, and orally bioavailable MDM2-p53 inhibitor in clinical development. , 2014, Journal of medicinal chemistry.

[126]  Marc Baaden,et al.  Coarse-grain modelling of protein-protein interactions. , 2013, Current opinion in structural biology.

[127]  Nir London,et al.  Druggable protein-protein interactions--from hot spots to hot segments. , 2013, Current opinion in chemical biology.

[128]  Dima Kozakov,et al.  Detection of peptide‐binding sites on protein surfaces: The first step toward the modeling and targeting of peptide‐mediated interactions , 2013, Proteins.

[129]  J. Reichert,et al.  Future directions for peptide therapeutics development. , 2013, Drug discovery today.

[130]  Tom L. Blundell,et al.  TIMBAL v2: update of a database holding small molecules modulating protein–protein interactions , 2013, Database J. Biol. Databases Curation.

[131]  P. Kastritis,et al.  On the binding affinity of macromolecular interactions: daring to ask why proteins interact , 2013, Journal of The Royal Society Interface.

[132]  Philippe Roche,et al.  2P2Idb: a structural database dedicated to orthosteric modulation of protein–protein interactions , 2012, Nucleic Acids Res..

[133]  Alan Bridge,et al.  New and continuing developments at PROSITE , 2012, Nucleic Acids Res..

[134]  B. Villoutreix,et al.  A leap into the chemical space of protein-protein interaction inhibitors. , 2012, Current pharmaceutical design.

[135]  Jason E. Gestwicki,et al.  Features of protein–protein interactions that translate into potent inhibitors: topology, surface area and affinity , 2012, Expert Reviews in Molecular Medicine.

[136]  David C. Smith,et al.  Phase II study of Cilengitide (EMD 121974, NSC 707544) in patients with non-metastatic castration resistant prostate cancer, NCI-6735. A study by the DOD/PCF prostate cancer clinical trials consortium , 2012, Investigational New Drugs.

[137]  Henning Hermjakob,et al.  Analyzing protein-protein interaction networks. , 2012, Journal of proteome research.

[138]  T. Blundell,et al.  Structural biology and drug discovery of difficult targets: the limits of ligandability. , 2012, Chemistry & biology.

[139]  D. Kelsell,et al.  Cell–cell connectivity: desmosomes and disease , 2012, The Journal of pathology.

[140]  Brooke N. Bullock,et al.  Assessing helical protein interfaces for inhibitor design. , 2011, Journal of the American Chemical Society.

[141]  L. Watkins,et al.  An MD2 Hot‐Spot‐Mimicking Peptide that Suppresses TLR4‐Mediated Inflammatory Response in vitro and in vivo , 2011, Chembiochem : a European journal of chemical biology.

[142]  Philippe Roche,et al.  Chemical and structural lessons from recent successes in protein-protein interaction inhibition (2P2I). , 2011, Current opinion in chemical biology.

[143]  Nir London,et al.  Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors , 2011, PloS one.

[144]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[145]  E. Levy A simple definition of structural regions in proteins and its use in analyzing interface evolution. , 2010, Journal of molecular biology.

[146]  Jonathan G. Lees,et al.  Transient protein-protein interactions: structural, functional, and network properties. , 2010, Structure.

[147]  Nir London,et al.  Sub‐angstrom modeling of complexes between flexible peptides and globular proteins , 2010, Proteins.

[148]  Olivier Sperandio,et al.  Rationalizing the chemical space of protein-protein interaction inhibitors. , 2010, Drug discovery today.

[149]  Olivier Sperandio,et al.  Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods , 2010, PLoS Comput. Biol..

[150]  Nir London,et al.  The structural basis of peptide-protein binding strategies. , 2010, Structure.

[151]  Alan R. Fersht,et al.  Awakening guardian angels: drugging the p53 pathway , 2009, Nature Reviews Cancer.

[152]  Jonathan C. Fuller,et al.  Predicting druggable binding sites at the protein-protein interface. , 2009, Drug discovery today.

[153]  Cheng-Yan Kao,et al.  Ortholog-based protein-protein interaction prediction and its application to inter-species interactions , 2008, BMC Bioinformatics.

[154]  Duncan Patrick McGregor,et al.  Discovering and improving novel peptide therapeutics. , 2008, Current opinion in pharmacology.

[155]  Hyeong Jun An,et al.  Estimating the size of the human interactome , 2008, Proceedings of the National Academy of Sciences.

[156]  J. Janin,et al.  Protein–protein interaction and quaternary structure , 2008, Quarterly Reviews of Biophysics.

[157]  Christopher L. McClendon,et al.  Reaching for high-hanging fruit in drug discovery at protein–protein interfaces , 2007, Nature.

[158]  Pedro A Fernandes,et al.  Hot spots—A review of the protein–protein interface determinant amino‐acid residues , 2007, Proteins.

[159]  R. Hartmann,et al.  Prediction of protein-protein interaction inhibitors by chemoinformatics and machine learning methods. , 2007, Journal of medicinal chemistry.

[160]  Timur Shtatland,et al.  PepBank - a database of peptides based on sequence text mining and public peptide data sources , 2007, BMC Bioinformatics.

[161]  Andrew Chatr-aryamontri,et al.  DOMINO: a database of domain–peptide interactions , 2006, Nucleic Acids Res..

[162]  B. Villoutreix,et al.  A Formylated Hexapeptide Ligand Mimics the Ability of Wnt-5a to Impair Migration of Human Breast Epithelial Cells* , 2006, Journal of Biological Chemistry.

[163]  David A. Price,et al.  Maraviroc (UK-427,857), a Potent, Orally Bioavailable, and Selective Small-Molecule Inhibitor of Chemokine Receptor CCR5 with Broad-Spectrum Anti-Human Immunodeficiency Virus Type 1 Activity , 2005, Antimicrobial Agents and Chemotherapy.

[164]  Jinfa Ying,et al.  Structure-activity studies of peptides from the "hot-spot" region of human CD2 protein: development of peptides for immunomodulation. , 2005, Journal of medicinal chemistry.

[165]  Zhen Liu,et al.  Refined phylogenetic profiles method for predicting protein-protein interactions , 2005, Bioinform..

[166]  R. Ladner,et al.  Phage display-derived peptides as therapeutic alternatives to antibodies. , 2004, Drug discovery today.

[167]  Michelle R. Arkin,et al.  Small-molecule inhibitors of protein–protein interactions: progressing towards the dream , 2004, Nature Reviews Drug Discovery.

[168]  J. Thornton,et al.  Diversity of protein–protein interactions , 2003, The EMBO journal.

[169]  J. Janin,et al.  Dissecting protein–protein recognition sites , 2002, Proteins.

[170]  A. Valencia,et al.  In silico two‐hybrid system for the selection of physically interacting protein pairs , 2002, Proteins.

[171]  Kurt S. Thorn,et al.  ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions , 2001, Bioinform..

[172]  A. Bogan,et al.  Anatomy of hot spots in protein interfaces. , 1998, Journal of molecular biology.

[173]  S. Jones,et al.  Principles of protein-protein interactions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[174]  T. Clackson,et al.  A hot spot of binding energy in a hormone-receptor interface , 1995, Science.

[175]  R J Lynch,et al.  Non-peptide fibrinogen receptor antagonists. 1. Discovery and design of exosite inhibitors. , 1992, Journal of medicinal chemistry.

[176]  C. Chothia,et al.  The structure of protein-protein recognition sites. , 1990, The Journal of biological chemistry.

[177]  P. Goodford A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[178]  OUP accepted manuscript , 2022, Nucleic Acids Research.

[179]  OUP accepted manuscript , 2021, Nucleic Acids Research.

[180]  J. Cole,et al.  Use of molecular docking computational tools in drug discovery. , 2021, Progress in medicinal chemistry.

[181]  Juan Fernández-Recio,et al.  Structural Characterization of Protein-Protein Interactions with pyDockSAXS. , 2020, Methods in molecular biology.

[182]  Yu Zhu,et al.  Role of protein-protein interactions in allosteric drug design for DNA methyltransferases. , 2020, Advances in protein chemistry and structural biology.

[183]  D. Ni,et al.  Allosteric Modulators of Protein-Protein Interactions (PPIs). , 2019, Advances in experimental medicine and biology.

[184]  J. Fernández-Recio,et al.  Structural Prediction of Protein-Protein Interactions by Docking: Application to Biomedical Problems. , 2018, Advances in protein chemistry and structural biology.

[185]  Dmitri A. Nusinow,et al.  Mapping Protein-Protein Interactions Using Affinity Purification and Mass Spectrometry. , 2017, Methods in molecular biology.

[186]  B. Douzi,et al.  Protein-Protein Interactions: Surface Plasmon Resonance. , 2017, Methods in molecular biology.

[187]  A. De,et al.  Use of BRET to Study Protein-Protein Interactions In Vitro and In Vivo. , 2016, Methods in molecular biology.

[188]  R. Neubig,et al.  Small Molecule Protein–Protein Interaction Inhibitors as CNS Therapeutic Agents: Current Progress and Future Hurdles , 2009, Neuropsychopharmacology.

[189]  A. Whitty,et al.  Between a rock and a hard place? , 2006, Nature chemical biology.

[190]  John P. Miller,et al.  Using the yeast two-hybrid system to identify interacting proteins. , 2004, Methods in molecular biology.

[191]  J. Janin,et al.  Wet and dry interfaces: the role of solvent in protein-protein and protein-DNA recognition. , 1999, Structure.