[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118901-en":3,"doc-seo-118901-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118901,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction - Review","Peptides mediate a large fraction of protein-protein interactions in cellular processes and are attractive candidates for drug development due to their specificity and efficacy. Traditional peptide-protein complex prediction methods such as docking and molecular dynamics remain difficult because of high computational cost, peptide flexibility, and limited availability of peptide-protein complex structures. Growing biological data has accelerated machine learning and deep learning approaches that provide efficient, accurate, robust, and more interpretable prediction of peptide-protein interactions. This review surveys recent ML and DL models for peptide-protein interaction prediction.","arXiv :2310 . 18249v2 [ q-bio .BM] 7 Feb 2024  \nLeveraging Machine Learning Models for Peptide-Protein Interaction Prediction  \nSong Yin,†, § Xuenan Mi,‡, § and Diwakar Shukla∗ ,†,‡,¶  \n†Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States  \n‡Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign,  \nUrbana, IL 61801, United States  \n¶Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL  \n61801, United States  \n§These authors contributed to the work equally.  \nE-mail: [diwakar@illinois.edu](diwakar@illinois.edu)  \nAbstract  \nPeptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational  \napproaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.  \nIntroduction  \nPeptides consist of short chains of amino acids connected by peptide bonds, typically comprising 2 to 50 amino acids. One of the most critical functions of peptides is their mediation of 15-40% of protein-protein interactions (PPIs) . 1 PPIs play essential roles in various biological processes within living organisms, including DNA replication, DNA transcription, catalyzing metabolic reactions and regulating cellular signal. 2 Peptides have become promising drug candidates due to their ability to modulate PPIs. Over the past century, Food and Drug Administration (FDA) has approved more than 80 peptide drugs, 3 with insulin being the pioneering therapeutic peptide used extensively in diabetes treatment. Compared with the small molecules, peptide drugs demonstrate high specificity and efficacy. 4 Additionally, compared with other classes of drug candidates, peptides have more flexible backbones, enabling their better membrane permeability. 4  \nRational design of peptide drugs is challenging and costly, due to the lack of stability and the big pool of potential target candidates. Therefore, computational methodologies that have proven effective in small molecule drug design have been adapted for modelling peptideprotein interactions (PepPIs) . These computational techniques include Docking, Molecular Dynamics (MD) simulations, and machine learning (ML) and deep learning (DL) models. Docking approaches enable exploration of peptide binding positions and poses in atomistic details, facilitating the prediction of binding affinities. 5–9 However, peptides are inherently flexible and they can interact with proteins in various conformations. These conformations often change during the binding process. 10 MD simulation is another approach to model  \nthe peptide-protein interaction. The peptide-protein binding and unbinding process can be  \nstudied thermodynamically and kinetically through MD simulations. 10–18 But sampling the complex energy landscapes associated with peptide-protein interactions typically requires intensive computational resources and time. The accuracy of Docking and MD simulations both rely on the knowledge of protein ","cbCaiu1ohT6BDSl6","https://ap.wps.com/l/cbCaiu1ohT6BDSl6","pdf",9017690,1,51,"English","en",105,"# Abstract\n# Introduction\n## Challenges of traditional computational approaches\n## Motivation for machine learning and deep learning\n## Scope and organization of the review","[{\"question\":\"Why is peptide-protein interaction prediction challenging with traditional methods?\",\"answer\":\"Docking and molecular dynamics are hindered by high computational cost, the inherent flexibility of peptides that leads to multiple conformations during binding, and limited structural information for peptide-protein complexes.\"},{\"question\":\"How do machine learning and deep learning models help address these challenges?\",\"answer\":\"ML/DL models can infer sequence–function relationships from large biological datasets and make predictions efficiently in a single pass. Interpretable DL approaches also provide residue-level contributions to binding predictions.\"},{\"question\":\"What is the coverage and classification logic of the review?\",\"answer\":\"The review summarizes progress in predicting peptide-protein interactions, starting from traditional divisions of PPI prediction methods into sequence-based and structure-based approaches, and then highlighting newer models that integrate both sequence and structure information.\"}]","Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction - Review | PDF",1785720876,129,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"leveraging-machine-learning-models-for-peptide-protein-interaction-prediction-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/leveraging-machine-learning-models-for-peptide-protein-interaction-prediction-review/118901/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is peptide-protein interaction prediction challenging with traditional methods?","Question",{"text":76,"@type":77},"Docking and molecular dynamics are hindered by high computational cost, the inherent flexibility of peptides that leads to multiple conformations during binding, and limited structural information for peptide-protein complexes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do machine learning and deep learning models help address these challenges?",{"text":81,"@type":77},"ML/DL models can infer sequence–function relationships from large biological datasets and make predictions efficiently in a single pass. Interpretable DL approaches also provide residue-level contributions to binding predictions.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the coverage and classification logic of the review?",{"text":85,"@type":77},"The review summarizes progress in predicting peptide-protein interactions, starting from traditional divisions of PPI prediction methods into sequence-based and structure-based approaches, and then highlighting newer models that integrate both sequence and structure information.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]