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Baeza J, Bedoya M, Cruz P, Ojeda P, Adasme-Carreño F, Cerda O, González W. Main methods and tools for peptide development based on protein-protein interactions (PPIs). Biochem Biophys Res Commun 2025; 758:151623. [PMID: 40121967 DOI: 10.1016/j.bbrc.2025.151623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2024] [Revised: 03/05/2025] [Accepted: 03/10/2025] [Indexed: 03/25/2025]
Abstract
Protein-protein interactions (PPIs) regulate essential physiological and pathological processes. Due to their large and shallow binding surfaces, PPIs are often considered challenging drug targets for small molecules. Peptides offer a viable alternative, as they can bind these targets, acting as regulators or mimicking interaction partners. This review focuses on competitive peptides, a class of orthosteric modulators that disrupt PPI formation. We provide a concise yet comprehensive overview of recent advancements in in-silico peptide design, highlighting computational strategies that have improved the efficiency and accuracy of PPI-targeting peptides. Additionally, we examine cutting-edge experimental methods for evaluating PPI-based peptides. By exploring the interplay between computational design and experimental validation, this review presents a structured framework for developing effective peptide therapeutics targeting PPIs in various diseases.
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Affiliation(s)
- Javiera Baeza
- Centro de Bioinformática, Simulación y Modelado (CBSM), Facultad de Ingeniería. Universidad de Talca, Talca, Chile; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Chile
| | - Mauricio Bedoya
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca, Chile; Laboratorio de Bioinformática y Química Computacional (LBQC), Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca, Chile.
| | - Pablo Cruz
- Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Chile; Programa de Biología Celular y Molecular, Instituto de Ciencias Biomédicas (ICBM), Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Paola Ojeda
- Carrera de Química y Farmacia, Facultad de Medicina y Ciencia, Universidad San Sebastián, General Lagos 1163, 5090000, Valdivia, Chile
| | - Francisco Adasme-Carreño
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca, Chile; Laboratorio de Bioinformática y Química Computacional (LBQC), Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca, Chile
| | - Oscar Cerda
- Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Chile; Programa de Biología Celular y Molecular, Instituto de Ciencias Biomédicas (ICBM), Facultad de Medicina, Universidad de Chile, Santiago, Chile.
| | - Wendy González
- Centro de Bioinformática, Simulación y Modelado (CBSM), Facultad de Ingeniería. Universidad de Talca, Talca, Chile; Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), Chile.
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Xiong D, Qiu Y, Zhao J, Zhou Y, Lee D, Gupta S, Torres M, Lu W, Liang S, Kang JJ, Eng C, Loscalzo J, Cheng F, Yu H. A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations. Nat Biotechnol 2024:10.1038/s41587-024-02428-4. [PMID: 39448882 DOI: 10.1038/s41587-024-02428-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 09/11/2024] [Indexed: 10/26/2024]
Abstract
To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.
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Grants
- R01GM124559 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01GM125639 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01GM130885 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- RM1GM139738 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01DK115398 U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases)
- U01HG007691 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
- R01HL155107 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL155096 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL166137 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- U54HL119145 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- AHA957729 American Heart Association (American Heart Association, Inc.)
- 24MERIT1185447 American Heart Association (American Heart Association, Inc.)
- R01AG084250 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R56AG074001 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- U01AG073323 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R01AG066707 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R01AG076448 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R01AG082118 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- RF1AG082211 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- R21AG083003 U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
- RF1NS133812 U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
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Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY, USA
| | - Yunguang Qiu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY, USA
| | - Yadi Zhou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Dongjin Lee
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Shobhita Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY, USA
- Biophysics Program, Cornell University, Ithaca, NY, USA
| | - Mateo Torres
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Jin Joo Kang
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY, USA
| | - Charis Eng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Feixiong Cheng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA.
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY, USA.
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
- Center for Innovative Proteomics, Cornell University, Ithaca, NY, USA.
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Alam R, Mahbub S, Bayzid MS. Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models. Bioinformatics 2024; 40:btae588. [PMID: 39360982 PMCID: PMC11495673 DOI: 10.1093/bioinformatics/btae588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 09/03/2024] [Accepted: 10/01/2024] [Indexed: 10/05/2024] Open
Abstract
MOTIVATION Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. RESULTS We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pretrained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the 3D structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. AVAILABILITY AND IMPLEMENTATION Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET.
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Affiliation(s)
- Ramisa Alam
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
| | - Sazan Mahbub
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Md Shamsuzzoha Bayzid
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
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4
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Lin P, Li H, Huang SY. Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches. Curr Opin Struct Biol 2024; 85:102789. [PMID: 38402744 DOI: 10.1016/j.sbi.2024.102789] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/16/2024] [Accepted: 02/06/2024] [Indexed: 02/27/2024]
Abstract
Protein-protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein-protein docking. With the rapid development of artificial intelligence and its great success in monomer protein structure prediction, deep learning has widely been applied to modeling protein-protein complex structures through inter-protein contact prediction and end-to-end approaches in the past few years. This article reviews the recent advances of deep-learning-based approaches in modeling protein-protein complex structures as well as their advantages and limitations. Challenges and possible future directions are also briefly discussed in applying deep learning for the prediction of protein complex structures.
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Affiliation(s)
- Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China.
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5
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Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
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Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
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6
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Xiong D, Qiu Y, Zhao J, Zhou Y, Lee D, Gupta S, Torres M, Lu W, Liang S, Kang JJ, Eng C, Loscalzo J, Cheng F, Yu H. Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.24.538110. [PMID: 37162909 PMCID: PMC10168245 DOI: 10.1101/2023.04.24.538110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.
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Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Junfei Zhao
- Department of Systems Biology, Herbert Irving Comprehensive Center, Columbia University, New York, NY 10032, USA
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dongjin Lee
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Shobhita Gupta
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
- Biophysics Program, Cornell University, Ithaca, NY 14853, USA
| | - Mateo Torres
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Jin Joo Kang
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
- Center for Innovative Proteomics, Cornell University, Ithaca, NY 14853, USA
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Michalik I, Kuder KJ. Machine Learning Methods in Protein-Protein Docking. Methods Mol Biol 2024; 2780:107-126. [PMID: 38987466 DOI: 10.1007/978-1-0716-3985-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.
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Affiliation(s)
- Ilona Michalik
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland
| | - Kamil J Kuder
- Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College, Kraków, Poland.
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Jin Z, Wei Z. Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends. Compr Rev Food Sci Food Saf 2024; 23:e13280. [PMID: 38284571 DOI: 10.1111/1541-4337.13280] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 01/30/2024]
Abstract
In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.
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Affiliation(s)
- Zihan Jin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, China
| | - Zihao Wei
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, China
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9
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Computational Resources for Molecular Biology 2022. J Mol Biol 2022; 434:167625. [PMID: 35569508 DOI: 10.1016/j.jmb.2022.167625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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