1
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Sampson JM, Cannon DA, Duan J, Epstein JCK, Sergeeva AP, Katsamba PS, Mannepalli SM, Bahna FA, Adihou H, Guéret SM, Gopalakrishnan R, Geschwindner S, Rees DG, Sigurdardottir A, Wilkinson T, Dodd RB, De Maria L, Mobarec JC, Shapiro L, Honig B, Buchanan A, Friesner RA, Wang L. Robust Prediction of Relative Binding Energies for Protein-Protein Complex Mutations Using Free Energy Perturbation Calculations. J Mol Biol 2024; 436:168640. [PMID: 38844044 DOI: 10.1016/j.jmb.2024.168640] [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: 04/25/2024] [Accepted: 05/31/2024] [Indexed: 06/18/2024]
Abstract
Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how Protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.
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Affiliation(s)
- Jared M Sampson
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA
| | - Daniel A Cannon
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | - Jianxin Duan
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | | | - Alina P Sergeeva
- Columbia University, Department of Systems Biology, New York, NY, USA
| | | | - Seetha M Mannepalli
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA
| | - Fabiana A Bahna
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA
| | - Hélène Adihou
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden; Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stéphanie M Guéret
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden; Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Ranganath Gopalakrishnan
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden; Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stefan Geschwindner
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Gothenburg, Sweden
| | - D Gareth Rees
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | | | | | - Roger B Dodd
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | - Leonardo De Maria
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Juan Carlos Mobarec
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | - Lawrence Shapiro
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA; Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
| | - Barry Honig
- Columbia University, Department of Systems Biology, New York, NY, USA; Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA; Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA; Columbia University, Department of Medicine, New York, NY, USA
| | | | | | - Lingle Wang
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA.
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2
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Seneviratne S, Shi G, Szabo-Hever A, Zhang Z, Peters Haugrud AR, Running KLD, Singh G, Nandety RS, Fiedler JD, McClean PE, Xu SS, Friesen TL, Faris JD. Evolution, diversity, and function of the disease susceptibility gene Snn1 in wheat. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 119:1720-1736. [PMID: 38923651 DOI: 10.1111/tpj.16879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a disease of durum and common wheat initiated by the recognition of pathogen-produced necrotrophic effectors (NEs) by specific wheat genes. The wheat gene Snn1 was previously cloned, and it encodes a wall-associated kinase that directly interacts with the NE SnTox1 leading to programmed cell death and ultimately the development of SNB. Here, sequence analysis of Snn1 from 114 accessions including diploid, tetraploid, and hexaploid wheat species revealed that some wheat lines possess two copies of Snn1 (designated Snn1-B1 and Snn1-B2) approximately 120 kb apart. Snn1-B2 evolved relatively recently as a paralog of Snn1-B1, and both genes have undergone diversifying selection. Three point mutations associated with the formation of the first SnTox1-sensitive Snn1-B1 allele from a primitive wild wheat were identified. Four subsequent and independent SNPs, three in Snn1-B1 and one in Snn1-B2, converted the sensitive alleles to insensitive forms. Protein modeling indicated these four mutations could abolish Snn1-SnTox1 compatibility either through destabilization of the Snn1 protein or direct disruption of the protein-protein interaction. A high-throughput marker was developed for the absent allele of Snn1, and it was 100% accurate at predicting SnTox1-insensitive lines in both durum and spring wheat. Results of this study increase our understanding of the evolution, diversity, and function of Snn1-B1 and Snn1-B2 genes and will be useful for marker-assisted elimination of these genes for better host resistance.
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Affiliation(s)
- Sudeshi Seneviratne
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Gongjun Shi
- Department of Plant Pathology, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Agnes Szabo-Hever
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
| | - Zengcui Zhang
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
| | - Amanda R Peters Haugrud
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
| | - Katherine L D Running
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Gurminder Singh
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Raja Sekhar Nandety
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
| | - Jason D Fiedler
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
| | - Phillip E McClean
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, 58102, USA
| | - Steven S Xu
- USDA-ARS Crop Improvement and Genetics Research Unit, Western Regional Research Center, Albany, California, 94710, USA
| | - Timothy L Friesen
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
| | - Justin D Faris
- USDA-ARS Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Fargo, North Dakota, 58102, USA
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3
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Xu J, Gong J, Bo X, Tong Y, Ren Z, Ni M. A benchmark for evaluation of structure-based online tools for antibody-antigen binding affinity. Biophys Chem 2024; 311:107253. [PMID: 38768531 DOI: 10.1016/j.bpc.2024.107253] [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: 01/12/2024] [Revised: 04/08/2024] [Accepted: 04/28/2024] [Indexed: 05/22/2024]
Abstract
The prediction of binding affinity changes caused by missense mutations can elucidate antigen-antibody interactions. A few accessible structure-based online computational tools have been proposed. However, selecting suitable software for particular research is challenging, especially research on the SARS-CoV-2 spike protein with antibodies. Therefore, benchmarking of the mutation-diverse SARS-CoV-2 datasets is critical. Here, we collected the datasets including 1216 variants about the changes in binding affinity of antigens from 22 complexes for SARS-CoV-2 S proteins and 22 monoclonal antibodies as well as applied them to evaluate the performance of seven binding affinity prediction tools. The tested tools' Pearson correlations between predicted and measured changes in binding affinity were between -0.158 and 0.657, while accuracy in classification tasks on predicting increasing or decreasing affinity ranged from 0.444 to 0.834. These tools performed relatively better on predicting single mutations, especially at epitope sites, whereas poor performance on extremely decreasing affinity. The tested tools were relatively insensitive to the experimental techniques used to obtain structures of complexes. In summary, we constructed a list of datasets and evaluated a range of structure-based online prediction tools that will explicate relevant processes of antigen-antibody interactions and enhance the computational design of therapeutic monoclonal antibodies.
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Affiliation(s)
- Jiayi Xu
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jianting Gong
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yigang Tong
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
| | - Zilin Ren
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun 130122, China.
| | - Ming Ni
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China.
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4
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Li P, Liu ZP. MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2402918. [PMID: 38995072 DOI: 10.1002/advs.202402918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/04/2024] [Indexed: 07/13/2024]
Abstract
Assessing changes in protein-protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the complexity of the biological mechanisms involved. In the present work, a geometric deep learning framework called MuToN is introduced for quantifying protein binding affinity change upon residue mutations. The method, designed with geometric attention networks, is mechanism-aware. It captures changes in the protein binding interfaces of mutated complexes and assesses the allosteric effects of amino acids. Experimental results highlight MuToN's superiority compared to existing methods. Additionally, MuToN's flexibility and effectiveness are illustrated by its precise predictions of binding affinity changes between SARS-CoV-2 variants and the ACE2 complex.
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Affiliation(s)
- Pengpai Li
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China
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5
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Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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6
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Gurusinghe SNS, Wu Y, DeGrado W, Shifman JM. ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.600041. [PMID: 38979193 PMCID: PMC11230163 DOI: 10.1101/2024.06.21.600041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.
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Affiliation(s)
- Sagara N S Gurusinghe
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yibing Wu
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, CA, USA
| | - William DeGrado
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, CA, USA
| | - Julia M Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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7
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Nikam R, Jemimah S, Gromiha MM. DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation. Bioinformatics 2024; 40:btae309. [PMID: 38718170 PMCID: PMC11112046 DOI: 10.1093/bioinformatics/btae309] [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: 12/13/2023] [Revised: 04/08/2024] [Accepted: 05/08/2024] [Indexed: 05/24/2024] Open
Abstract
MOTIVATION Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes. RESULTS We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance. AVAILABILITY AND IMPLEMENTATION https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.
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Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Sherlyn Jemimah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biomedical Engineering, Khalifa University, P.O. Box: 127788 , Abu Dhabi, United Arab Emirates
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Computer Science, Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan
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8
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Sampson JM, Cannon DA, Duan J, Epstein JCK, Sergeeva AP, Katsamba PS, Mannepalli SM, Bahna FA, Adihou H, Guéret SM, Gopalakrishnan R, Geschwindner S, Rees DG, Sigurdardottir A, Wilkinson T, Dodd RB, De Maria L, Mobarec JC, Shapiro L, Honig B, Buchanan A, Friesner RA, Wang L. Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590325. [PMID: 38712280 PMCID: PMC11071377 DOI: 10.1101/2024.04.22.590325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.
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Affiliation(s)
| | | | - Jianxin Duan
- Schrödinger, GmbH, Life Sciences Software, Mannheim, Germany
| | | | - Alina P. Sergeeva
- Columbia University, Department of Systems Biology, New York, NY, USA
| | | | - Seetha M. Mannepalli
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
| | - Fabiana A. Bahna
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
| | - Hélène Adihou
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stéphanie M. Guéret
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Ranganath Gopalakrishnan
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
- Max Planck Institute of Molecular Physiology, AstraZeneca-MPI Satellite Unit, Dortmund, Germany
| | - Stefan Geschwindner
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | | | | | | | - Roger B. Dodd
- AstraZeneca, Biologics Engineering, R&D, Cambridge, UK
| | - Leonardo De Maria
- AstraZeneca, Medicinal Chemistry, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Juan Carlos Mobarec
- AstraZeneca, Mechanistic and Structural Biology, Discovery Sciences, R&D, Cambridge, UK
| | - Lawrence Shapiro
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
- Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
| | - Barry Honig
- Columbia University, Department of Systems Biology, New York, NY, USA
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, NY, USA, 10027
- Columbia University, Department of Biochemistry and Molecular Biophysics, New York, NY, USA
- Columbia University, Department of Medicine, New York, NY, USA
| | | | | | - Lingle Wang
- Schrödinger, Inc., Life Sciences Software, New York, NY, USA
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9
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Sharma D, Rawat P, Greiff V, Janakiraman V, Gromiha MM. Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation. Biochim Biophys Acta Mol Basis Dis 2024; 1870:166959. [PMID: 37967796 DOI: 10.1016/j.bbadis.2023.166959] [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: 09/18/2023] [Revised: 10/25/2023] [Accepted: 11/07/2023] [Indexed: 11/17/2023]
Abstract
COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.
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Affiliation(s)
- Divya Sharma
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Puneet Rawat
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Vani Janakiraman
- Infection Biology Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - M Michael Gromiha
- Protein Bioinformatics Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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10
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Xu W, Zhang H, Guo W, Jiang L, Zhao Y, Peng Y. Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis. Brief Bioinform 2024; 25:bbad532. [PMID: 38329268 PMCID: PMC10851104 DOI: 10.1093/bib/bbad532] [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: 10/26/2023] [Revised: 11/30/2023] [Accepted: 12/23/2023] [Indexed: 02/09/2024] Open
Abstract
Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.
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Affiliation(s)
- Wang Xu
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China
| | - Houfang Zhang
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China
| | - Wenhan Guo
- Computational Science Program, University of Texas at El Paso, El Paso, TX 79902, USA
| | - Lijun Jiang
- Hubei Key Laboratory of Genetic Regulation & Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan 430079, China
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China
| | - Yunhui Peng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China
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11
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Shu C, Sun Q, Fan G, Peng K, Yu Z, Luo Y, Gao S, Ma J, Deng T, Hu S, Wu L. VarEPS-Influ:an risk evaluation system of occurred and virtual variations of influenza virus genomes. Nucleic Acids Res 2024; 52:D798-D807. [PMID: 37889020 PMCID: PMC10767863 DOI: 10.1093/nar/gkad912] [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: 08/05/2023] [Revised: 10/05/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
Influenza viruses undergo frequent genomic mutations, leading to potential cross-species transmission, phenotypic changes, and challenges in diagnostic reagents and vaccines. Accurately evaluating and predicting the risk of such variations remain significant challenges. To address this, we developed the VarEPS-Influ database, an influenza virus variations risk evaluation system (VarEPS-Influ). This database employs a 'multi-dimensional evaluation of mutations' strategy, utilizing various tools to assess the physical and chemical properties, primary, secondary, and tertiary structures, receptor affinity, antibody binding capacity, antigen epitopes, and other aspects of the variation's impact. Additionally, we consider space-time distribution, host species distribution, pedigree analysis, and frequency of mutations to provide a comprehensive risk evaluation of mutations and viruses. The VarEPS-Influ database evaluates both observed variations and virtual variations (variations that have not yet occurred), thereby addressing the time-lag issue in risk predictions. Our current one-stop evaluation system for influenza virus genomic variation integrates 1065290 sequences from 224 927 Influenza A, B and C isolates retrieved from public resources. Researchers can freely access the data at https://nmdc.cn/influvar/.
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Affiliation(s)
- Chang Shu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qinglan Sun
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Guomei Fan
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Kesheng Peng
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Zhengfei Yu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Yingfeng Luo
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shenghan Gao
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Juncai Ma
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Tao Deng
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- CAS Key Laboratory of Pathogenic Microbiology & Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Songnian Hu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Linhuan Wu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
- Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
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12
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Jarończyk M. Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants. Methods Mol Biol 2024; 2780:139-147. [PMID: 38987468 DOI: 10.1007/978-1-0716-3985-6_9] [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
Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.
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13
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Hung TI, Hsieh YJ, Lu WL, Wu KP, Chang CEA. What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks. J Mol Biol 2023; 435:168337. [PMID: 37918563 DOI: 10.1016/j.jmb.2023.168337] [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: 09/05/2023] [Revised: 10/13/2023] [Accepted: 10/26/2023] [Indexed: 11/04/2023]
Abstract
Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a ∼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.
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Affiliation(s)
- Ta I Hung
- Department of Chemistry, University of California, Riverside, United States; Department of Bioengineering, University of California, Riverside, United States
| | - Yun-Jung Hsieh
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan; Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan
| | - Wei-Lin Lu
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Kuen-Phon Wu
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan; Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan.
| | - Chia-En A Chang
- Department of Chemistry, University of California, Riverside, United States.
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14
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Tsishyn M, Pucci F, Rooman M. Quantification of biases in predictions of protein-protein binding affinity changes upon mutations. Brief Bioinform 2023; 25:bbad491. [PMID: 38197311 PMCID: PMC10777193 DOI: 10.1093/bib/bbad491] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/02/2023] [Accepted: 12/05/2023] [Indexed: 01/11/2024] Open
Abstract
Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.
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Affiliation(s)
- Matsvei Tsishyn
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
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15
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Liu Z, Qian W, Cai W, Song W, Wang W, Maharjan DT, Cheng W, Chen J, Wang H, Xu D, Lin GN. Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations. RESEARCH (WASHINGTON, D.C.) 2023; 6:0219. [PMID: 37701056 PMCID: PMC10494974 DOI: 10.34133/research.0219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/20/2023] [Indexed: 09/14/2023]
Abstract
Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein-protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.
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Affiliation(s)
- Zhe Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Qian
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenxiang Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Weidi Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
| | - Dhruba Tara Maharjan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhong Cheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jue Chen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Han Wang
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
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16
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Peka M, Balatsky V. Analysis of RBD-ACE2 interactions in livestock species as a factor in the spread of SARS-CoV-2 among animals. Vet Anim Sci 2023; 21:100303. [PMID: 37521409 PMCID: PMC10372456 DOI: 10.1016/j.vas.2023.100303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023] Open
Abstract
The high mutation rate of SARS-CoV-2, which has led to the emergence of a number of virus variants, creates risks of transmission from humans to animal species and the emergence of new animal reservoirs of COVID-19. This study aimed to identify animal species among livestock susceptible to infection and develop an approach that would be possible to use for assessing the hazards caused by new SARS-CoV-2 variants for animals. Bioinformatic analysis was used to evaluate the ability of receptor-binding domains (RBDs) of different SARS-CoV-2 variants to interact with ACE2 receptors of livestock species. The results indicated that the stability of RBD-ACE2 complexes depends on both amino acid residues in the ACE2 sequences of animal species and on mutations in the RBDs of SARS-CoV-2 variants, with the residues in the interface of the RBD-ACE2 complex being the most important. All studied SARS-CoV-2 variants had high affinity for ferret and American mink receptors, while the affinity for horse, donkey, and bird species' receptors significantly increased in the highly mutated Omicron variant. Hazards that future SARS-CoV-2 variants may acquire specificity to new animal species remain high given the mutability of the virus. The continued use and expansion of the bioinformatic approach presented in this study may be relevant for monitoring transmission risks and preventing the emergence of new reservoirs of COVID-19 among animals.
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Affiliation(s)
- Mykyta Peka
- V. N. Karazin Kharkiv National University, 4 Svobody Sq, Kharkiv, 61022, Ukraine
- Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013, Ukraine
| | - Viktor Balatsky
- V. N. Karazin Kharkiv National University, 4 Svobody Sq, Kharkiv, 61022, Ukraine
- Institute of Pig Breeding and Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, 1 Shvedska Mohyla St, Poltava, 36013, Ukraine
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17
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Berber I, Erten C, Kazan H. Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3163-3172. [PMID: 37030791 DOI: 10.1109/tcbb.2023.3262119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.
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18
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Naz S, Liu P, Farooq U, Ma H. Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method. Microb Cell Fact 2023; 22:161. [PMID: 37612753 PMCID: PMC10464499 DOI: 10.1186/s12934-023-02178-z] [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: 06/07/2023] [Accepted: 08/13/2023] [Indexed: 08/25/2023] Open
Abstract
Regulation of amino acid's biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology. Deregulation of feedback inhibition has been achieved for various enzymes using in vitro and in silico mutagenesis techniques. As enzyme's function, its substrate binding capacity, catalysis activity, regulation and stability are dependent on its structural characteristics, here, we provide detailed structural analysis of all feedback sensitive enzyme targets in amino acid biosynthetic pathways. Current review summarizes information regarding structural characteristics of various enzyme targets and effect of mutations on their structures and functions especially in terms of deregulation of feedback inhibition. Furthermore, applicability of various experimental as well as computational mutagenesis techniques to accomplish feedback resistance has also been discussed in detail to have an insight into various aspects of research work reported in this particular field of study.
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Affiliation(s)
- Sadia Naz
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Pi Liu
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Umar Farooq
- Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Islamabad, 22060, Pakistan
| | - Hongwu Ma
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
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19
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Pandey P, Panday SK, Rimal P, Ancona N, Alexov E. Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations. Int J Mol Sci 2023; 24:12073. [PMID: 37569449 PMCID: PMC10418460 DOI: 10.3390/ijms241512073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.
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Affiliation(s)
- Preeti Pandey
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Shailesh Kumar Panday
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Prawin Rimal
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
| | - Nicolas Ancona
- Department of Biological Sciences, Clemson University, Clemson, SC 29634, USA;
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (P.P.); (S.K.P.); (P.R.)
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20
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Zhu Y, Xiong H, Liu S, Wu D, Zhang X, Shi X, Qu J, Chen L, Liu Z, Peng B, Zhang D. Combining MOE Bioinformatics Analysis and In Vitro Pseudovirus Neutralization Assays to Predict the Neutralizing Ability of CV30 Monoclonal Antibody on SARS-CoV-2 Variants. Viruses 2023; 15:1565. [PMID: 37515251 PMCID: PMC10386485 DOI: 10.3390/v15071565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Combining bioinformatics and in vitro cytology assays, a predictive method was established to quickly evaluate the protective effect of immunity acquired through SARS-CoV-2 infection against variants. Bioinformatics software was first used to predict the changes in the affinity of variant antigens to the CV30 monoclonal antibody by integrating bioinformatics and cytology assays. Then, the ability of the antibody to neutralize the variant antigen was further verified, and the ability of the CV30 to neutralize the new variant strain was predicted through pseudovirus neutralization experiments. The current study has demonstrated that when the Molecular Operating Environment (MOE) predicts |ΔBFE| ≤ 3.0003, it suggests that the CV30 monoclonal antibody exhibits some affinity toward the variant strain and can potentially neutralize it. However, if |ΔBFE| ≥ 4.1539, the CV30 monoclonal antibody does not display any affinity for the variant strain and cannot neutralize it. In contrast, if 3.0003 < |ΔBFE| < 4.1539, it is necessary to conduct a series of neutralization tests promptly with the CV30 monoclonal antibody and the variant pseudovirus to obtain results and supplement the existing method, which is faster than the typical procedures. This approach allows for a rapid assessment of the protective efficacy of natural immunity gained through SARS-CoV-2 infection against variants.
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Affiliation(s)
- Yajuan Zhu
- School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Husheng Xiong
- School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Shuang Liu
- School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Dawei Wu
- School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
| | - Xiaomin Zhang
- Department of Microbiology Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
| | - Xiaolu Shi
- Department of Microbiology Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
| | - Jing Qu
- Department of Microbiology Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
| | - Long Chen
- Department of Microbiology Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
| | - Zheng Liu
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, Chinese University of Hong Kong, Shenzhen 518172, China
| | - Bo Peng
- Department of Microbiology Laboratory, Shenzhen Center for Disease Control and Prevention, Shenzhen 518055, China
| | - Dingmei Zhang
- School of Public Health, Sun Yat-Sen University, Guangzhou 510080, China
- NMPA Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Products, Guangzhou 510080, China
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21
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Sergeeva AP, Katsamba PS, Liao J, Sampson JM, Bahna F, Mannepalli S, Morano NC, Shapiro L, Friesner RA, Honig B. Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity. J Mol Biol 2023:168187. [PMID: 37355034 DOI: 10.1016/j.jmb.2023.168187] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.
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Affiliation(s)
- Alina P Sergeeva
- Department of Systems Biology, Columbia University Medical Center, New York, NY 10032
| | - Phinikoula S Katsamba
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Junzhuo Liao
- Department of Chemistry, Columbia University, New York, NY 10027, USA
| | - Jared M Sampson
- Department of Chemistry, Columbia University, New York, NY 10027, USA; Schrödinger, Inc., New York, NY 10036, USA
| | - Fabiana Bahna
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Seetha Mannepalli
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Nicholas C Morano
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Lawrence Shapiro
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
| | | | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY 10032; Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA; Department of Medicine, Columbia University, New York, NY 10032.
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22
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Sierk M, Ratnayake S, Wagle MM, Chen B, Park B, Wang J, Youkharibache P, Meerzaman D. 3DVizSNP: a tool for rapidly visualizing missense mutations identified in high throughput experiments in iCn3D. BMC Bioinformatics 2023; 24:244. [PMID: 37296383 DOI: 10.1186/s12859-023-05370-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation. RESULTS We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations. CONCLUSIONS This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP .
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Affiliation(s)
- Michael Sierk
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
| | - Shashikala Ratnayake
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA
| | - Manoj M Wagle
- Faculty of Pharmacy, University of Grenoble Alpes, Grenoble, France
- Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
- School of Mathematics and Statistics, Faculty of Science, and Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
| | - Ben Chen
- Digital Services and Solutions Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA
| | - Brian Park
- Digital Services and Solutions Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA
| | - Jiyao Wang
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, 20894, USA
| | - Philippe Youkharibache
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Daoud Meerzaman
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA
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23
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Pandey P, Ghimire S, Wu B, Alexov E. On the linkage of thermodynamics and pathogenicity. Curr Opin Struct Biol 2023; 80:102572. [PMID: 36965249 PMCID: PMC10239362 DOI: 10.1016/j.sbi.2023.102572] [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: 01/25/2023] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 03/27/2023]
Abstract
This review outlines the effect of disease-causing mutations on proteins' thermodynamics. Two major thermodynamics quantities, which are essential for structural integrity, the folding and binding free energy changes caused by missense mutations, are considered. It is emphasized that disease effects in case of complex diseases may originate from several mutations over several genes, while monogenic diseases are caused by mutation is a single gene. Nevertheless, in both cases it is shown that pathogenic mutations cause larger perturbations of the above-mentioned thermodynamics quantities as compared with the benign mutations. Recent works demonstrating the effect of pathogenic mutations on the above-mentioned thermodynamics quantities, as well as on structural dynamics and allosteric pathways, are reviewed.
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Affiliation(s)
- Preeti Pandey
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | - Sanjeev Ghimire
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | - Bohua Wu
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
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24
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Sheng Z, Bimela JS, Wang M, Li Z, Guo Y, Ho DD. An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design. Front Immunol 2023; 14:1190416. [PMID: 37275896 PMCID: PMC10235760 DOI: 10.3389/fimmu.2023.1190416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 04/28/2023] [Indexed: 06/07/2023] Open
Abstract
Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations, but their accuracy leaves room for improvement. Thermodynamic integration (TI) is an alchemical free energy algorithm that offers an alternative technique for identifying beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies in predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through the stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study presents an accurate and efficient TI-based approach for optimizing antibodies and other biomolecules.
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Affiliation(s)
- Zizhang Sheng
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Jude S. Bimela
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Maple Wang
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Zhiteng Li
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Yicheng Guo
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - David D. Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
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25
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Peka M, Balatsky V. The impact of mutation sets in receptor-binding domain of SARS-CoV-2 variants on the stability of RBD–ACE2 complex. Future Virol 2023. [PMID: 37064325 PMCID: PMC10089296 DOI: 10.2217/fvl-2022-0152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/01/2023] [Indexed: 04/08/2023]
Abstract
Aim: Bioinformatic analysis of mutation sets in receptor-binding domain (RBD) of currently and previously circulating SARS-CoV-2 variants of concern (VOCs) and interest (VOIs) to assess their ability to bind the ACE2 receptor. Methods: In silico sequence and structure-oriented approaches were used to evaluate the impact of single and multiple mutations. Results: Mutations detected in VOCs and VOIs led to the reduction of binding free energy of the RBD–ACE2 complex, forming additional chemical bonds with ACE2, and to an increase of RBD–ACE2 complex stability. Conclusion: Mutation sets characteristic of SARS-CoV-2 variants have complex effects on the ACE2 receptor-binding affinity associated with amino acid interactions at mutation sites, as well as on the acquisition of other viral adaptive advantages.
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Affiliation(s)
- Mykyta Peka
- V. N. Karazin Kharkiv National University, Kharkiv, 61022, Ukraine
- Institute of Pig Breeding & Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, Poltava, 36013, Ukraine
| | - Viktor Balatsky
- V. N. Karazin Kharkiv National University, Kharkiv, 61022, Ukraine
- Institute of Pig Breeding & Agroindustrial Production, National Academy of Agrarian Sciences of Ukraine, Poltava, 36013, Ukraine
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26
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Batool Z, Qureshi U, Mushtaq M, Ahmed S, Nur-E-Alam M, Ul-Haq Z. Structural basis for the mutation-induced dysfunction of the human IL-15/IL-15α receptor complex. Phys Chem Chem Phys 2023; 25:3020-3030. [PMID: 36607223 DOI: 10.1039/d2cp03012h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In silico strategies offer a reliable, fast, and inexpensive, way compared to the clumsy in vitro approaches to boost understanding of the effect of amino acid substitution on the structure and consequently the associated function of proteins. In the present work, we report an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the Interleukin-15 (IL-15) protein and its receptor Interleukin-15α (IL-15α), consequently, providing qualitative and quantitative details of the key molecular determinants in ligand/receptor recognition. Molecular dynamics simulations were used to investigate the dynamic behavior of the specific binding between IL-15 and IL-15α followed by estimation of the free energies via molecular mechanics/generalized Born surface area (MM/GBSA). In particular, residues Y26, E46, E53, and E89 of the IL-15 protein receptor-binding domain are identified as main hot spots, shaping and governing the stability of the assembly. These results can be used for the development of neutralizing antibodies and the effective structure-based design of protein-protein interaction inhibitors against the so-called orphan disease, vitiligo.
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Affiliation(s)
- Zahida Batool
- H.E.J Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan.
| | - Urooj Qureshi
- H.E.J Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan.
| | - Mamona Mushtaq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan
| | - Sarfaraz Ahmed
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box. 2457, Riyadh 11451, Kingdom of Saudi Arabia
| | - Mohammad Nur-E-Alam
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box. 2457, Riyadh 11451, Kingdom of Saudi Arabia
| | - Zaheer Ul-Haq
- H.E.J Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan. .,Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi-75270, Pakistan
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27
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Shea A, Bartz J, Zhang L, Dong X. Predicting mutational function using machine learning. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 791:108457. [PMID: 36965820 PMCID: PMC10239318 DOI: 10.1016/j.mrrev.2023.108457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 03/27/2023]
Abstract
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such as diabetes and heart diseases. Mutations occurring in somatic cells are a main cause of cancer and likely cause age-related phenotypes and other age-related diseases. Because of the high abundance of genetic variations in the human genome, i.e., millions of germline variations per human subject and thousands of additional somatic mutations per cell, it is technically challenging to experimentally verify the function of every possible mutation and their interactions. Significant progress has been made to solve this problem using computational approaches, especially machine learning (ML). Here, we review the progress and achievements made in recent years in this field of research. We classify the computational models in two ways: one according to their prediction goals including protein structural alterations, gene expression changes, and disease risks, and the other according to their methodologies, including non-machine learning methods, classical machine learning methods, and deep neural network methods. For models in each category, we discuss their architecture, prediction accuracy, and potential limitations. This review provides new insights into the applications and future directions of computational approaches in understanding the role of mutations in aging and disease.
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Affiliation(s)
- Anthony Shea
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Josh Bartz
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
| | - Lei Zhang
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Xiao Dong
- Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN 55455, USA; Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA.
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28
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Papadimitriou S, Gravel B, Nachtegael C, De Baere E, Loeys B, Vikkula M, Smits G, Lenaerts T. Toward reporting standards for the pathogenicity of variant combinations involved in multilocus/oligogenic diseases. HGG ADVANCES 2022; 4:100165. [PMID: 36578772 PMCID: PMC9791921 DOI: 10.1016/j.xhgg.2022.100165] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Although standards and guidelines for the interpretation of variants identified in genes that cause Mendelian disorders have been developed, this is not the case for more complex genetic models including variant combinations in multiple genes. During a large curation process conducted on 318 research articles presenting oligogenic variant combinations, we encountered several recurring issues concerning their proper reporting and pathogenicity assessment. These mainly concern the absence of strong evidence that refutes a monogenic model and the lack of a proper genetic and functional assessment of the joint effect of the involved variants. With the increasing accumulation of such cases, it has become essential to develop standards and guidelines on how these oligogenic/multilocus variant combinations should be interpreted, validated, and reported in order to provide high-quality data and supporting evidence to the scientific community.
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Affiliation(s)
- Sofia Papadimitriou
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium,Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium,Artificial Intelligence Laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium,Corresponding author
| | - Barbara Gravel
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium,Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium,Artificial Intelligence Laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Charlotte Nachtegael
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium,Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium
| | - Elfride De Baere
- Center for Medical Genetics, Ghent University Hospital, Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Bart Loeys
- Center for Medical Genetics, Antwerp University Hospital/University of Antwerp, 2650 Antwerp, Belgium
| | - Miikka Vikkula
- Human Molecular Genetics, de Duve Institute, UCLouvain, Brussels, Belgium
| | - Guillaume Smits
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium,Center of Human Genetics, Hôpital Erasme, Université Libre de Bruxelles, 1070 Brussels, Belgium,Hôpital Universitaire des Enfants Reine Fabiola, Université Libre de Bruxelles, 1020 Brussels, Belgium
| | - Tom Lenaerts
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, 1050 Brussels, Belgium,Machine Learning Group, Université Libre de Bruxelles, 1050 Brussels, Belgium,Artificial Intelligence Laboratory, Vrije Universiteit Brussel, 1050 Brussels, Belgium,Corresponding author
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29
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Javed H, Singh S, Urs SUR, Oldenburg J, Biswas A. Genetic landscape in coagulation factor XIII associated defects – Advances in coagulation and beyond. Blood Rev 2022; 59:101032. [PMID: 36372609 DOI: 10.1016/j.blre.2022.101032] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022]
Abstract
Coagulation factor XIII (FXIII) acts as a fine fulcrum in blood plasma that maintains the balance between bleeding and thrombosis by covalently crosslinking the pre-formed fibrin clot into an insoluble one that is resistant to premature fibrinolysis. In plasma, FXIII circulates as a pro-transglutaminase complex composed of the dimeric catalytic FXIII-A encoded by the F13A1 gene and dimeric carrier/regulatory FXIII-B subunits encoded by the F13B gene. Growing evidence accumulated over decades of exhaustive research shows that not only does FXIII play major roles in both pathological extremes of hemostasis i.e. bleeding and thrombosis, but that it is, in fact, a pleiotropic protein with physiological roles beyond coagulation. However, the current FXIII genetic-epidemiological literature is overwhelmingly derived from the bleeding pathology associated with its deficiency. In this article we review the current clinical, functional, and molecular understanding of this fascinating multifaceted protein, especially putting into the same perspective its genetic landscape.
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30
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Hidalgo Mayoral I, Martínez-Salio A, Llamas-Velasco S, Gómez-Majón I, Arteche-López A, Quesada-Espinosa JF, Palma Milla C, Lezana Rosales JM, Pérez de la Fuente R, Juárez Rufián A, Sierra Tomillo O, Sánchez Calvín MT, Gómez Rodríguez MJ, Ramos Gómez P, Villarejo-Galende A, Díaz-Guzmán J, Ortega-Casarrubios MÁ, Calleja-Castaño P, Moreno-García M. Hereditary cerebral small vessel disease: Assessment of a HTRA1 variant using protein stability predictors and 3D modelling. Eur J Med Genet 2022; 65:104539. [PMID: 35705147 DOI: 10.1016/j.ejmg.2022.104539] [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: 10/24/2021] [Revised: 02/28/2022] [Accepted: 06/06/2022] [Indexed: 11/03/2022]
Abstract
Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL) is an autosomal recessive vascular disorder caused by biallellic variants in HTRA1. Recently, it has been reported that several heterozygous mutations in HTRA1 are responsible for a milder late-onset cerebral small vessel disease (CSVD) with an autosomal dominant pattern of inheritance. The majority of them are missense that affects the Htr1A protease activity due to a dominant-negative effect caused by defective trimerization or monomer activation. The molecular mechanism related to the structural destabilization of the protein supports the practical utility of integrating computational stability predictors to prioritize candidate variants in this gene. In this work, we report a family with several members diagnosed with subcortical ischemic events and progressive cognitive impairment caused by the novel c.820C > G, p.(Arg274Gly) heterozygous variant in HTRA1 segregating in an autosomal dominant manner and propose its molecular mechanism by a three-dimensional model of the protein's structure.
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Affiliation(s)
| | | | - Sara Llamas-Velasco
- Neurology Service, Hospital Universitario 12 de Octubre, Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Group of Neurodegenerative Diseases, Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain
| | - Irene Gómez-Majón
- Genetics Department, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Ana Arteche-López
- Genetics Department, Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | - Carmen Palma Milla
- Genetics Department, Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | | | | | | | | | | | | | - Alberto Villarejo-Galende
- Neurology Service, Hospital Universitario 12 de Octubre, Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Group of Neurodegenerative Diseases, Instituto de Investigación Hospital 12 de Octubre (i+12), Department of Medicine, Universidad Complutense, 28041, Madrid, Spain
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31
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Khan AS, Parvez N, Ahsan T, Shoily SS, Sajib AA. A comprehensive in silico exploration of the impacts of missense variants on two different conformations of human pirin protein. BULLETIN OF THE NATIONAL RESEARCH CENTRE 2022; 46:225. [PMID: 35967515 PMCID: PMC9362109 DOI: 10.1186/s42269-022-00917-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Pirin, a member of the cupin superfamily, is an iron-binding non-heme protein. It acts as a coregulator of several transcription factors, especially the members of NFκB transcription factor family. Based on the redox state of its iron cofactor, it can assume two different conformations and thereby act as a redox sensor inside the nucleus. Previous studies suggested that pirin may be associated with cancer, inflammatory diseases as well as COVID-19 severities. Hence, it is important to explore the pathogenicity of its missense variants. In this study, we used a number of in silico tools to investigate the effects of missense variants of pirin on its structure, stability, metal cofactor binding affinity and interactions with partner proteins. In addition, we used protein dynamics simulation to elucidate the effects of selected variants on its dynamics. Furthermore, we calculated the frequencies of haplotypes containing pirin missense variants across five major super-populations (African, Admixed American, East Asian, European and South Asian). RESULTS Among a total of 153 missense variants of pirin, 45 were uniformly predicted to be pathogenic. Of these, seven variants can be considered for further experimental studies. Variants R59P and L116P were predicted to significantly destabilize and damage pirin structure, substantially reduce its affinity to its binding partners and alter pirin residue fluctuation profile via changing the flexibility of several key residues. Additionally, variants R59Q, F78V, G98D, V151D and L220P were found to impact pirin structure and function in multiple ways. As no haplotype was identified to be harboring more than one missense variant, further interrogation of the individual effects of these seven missense variants is highly recommended. CONCLUSIONS Pirin is involved in the transcriptional regulation of several genes and can play an important role in inflammatory responses. The variants predicted to be pathogenic in this study may thus contribute to a better understanding of the underlying molecular mechanisms of various inflammatory diseases. Future studies should be focused on clarifying if any of these variants can be used as disease biomarkers. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s42269-022-00917-7.
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Affiliation(s)
- Auroni Semonti Khan
- Department of Genetic Engineering and Biotechnology, Jagannath University, Dhaka, 1100 Bangladesh
| | - Nahid Parvez
- Department of Genetic Engineering and Biotechnology, Jagannath University, Dhaka, 1100 Bangladesh
| | - Tamim Ahsan
- Molecular Biotechnology Division, National Institute of Biotechnology, Savar, Dhaka, 1349 Bangladesh
| | - Sabrina Samad Shoily
- Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, 1000 Bangladesh
| | - Abu Ashfaqur Sajib
- Department of Genetic Engineering and Biotechnology, University of Dhaka, Dhaka, 1000 Bangladesh
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32
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Rezaei S, Sefidbakht Y, Uskoković V. Comparative molecular dynamics study of the receptor-binding domains in SARS-CoV-2 and SARS-CoV and the effects of mutations on the binding affinity. J Biomol Struct Dyn 2022; 40:4662-4681. [PMID: 33331243 PMCID: PMC7784839 DOI: 10.1080/07391102.2020.1860829] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Here, we report on a computational comparison of the receptor-binding domains (RBDs) on the spike proteins of severe respiratory syndrome coronavirus-2 (SARS-CoV-2) and SARS-CoV in free forms and as complexes with angiotensin-converting enzyme 2 (ACE2) as their receptor in humans. The impact of 42 mutations discovered so far on the structure and thermodynamics of SARS-CoV-2 RBD was also assessed. The binding affinity of SARS-CoV-2 RBD for ACE2 is higher than that of SARS-CoV RBD. The binding of COVA2-04 antibody to SARS-CoV-2 RBD is more energetically favorable than the binding of COVA2-39, but also less favorable than the formation of SARS-CoV-2 RBD-ACE2 complex. The net charge, the dipole moment and hydrophilicity of SARS-CoV-2 RBD are higher than those of SARS-CoV RBD, producing lower solvation and surface free energies and thus lower stability. The structure of SARS-CoV-2 RBD is also more flexible and more open, with a larger solvent-accessible surface area than that of SARS-CoV RBD. Single-point mutations have a dramatic effect on distribution of charges, most prominently at the site of substitution and its immediate vicinity. These charge alterations alter the free energy landscape, while X→F mutations exhibit a stabilizing effect on the RBD structure through π stacking. F456 and W436 emerge as two key residues governing the stability and affinity of the spike protein for its ACE2 receptor. These analyses of the structural differences and the impact of mutations on different viral strains and members of the coronavirus genera are an essential aid in the development of effective therapeutic strategies. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shokouh Rezaei
- Protein Research Center, Shahid Behesti University, Tehran, Iran
| | - Yahya Sefidbakht
- Protein Research Center, Shahid Behesti University, Tehran, Iran
| | - Vuk Uskoković
- Advanced Materials and Nanobiotechnology Laboratory, TardigradeNano, Irvine, CA, USA
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33
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Substituted Syndecan-2-Derived Mimetic Peptides Show Improved Antitumor Activity over the Parent Syndecan-2-Derived Peptide. Int J Mol Sci 2022; 23:ijms23115888. [PMID: 35682569 PMCID: PMC9180903 DOI: 10.3390/ijms23115888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
We previously showed that a synthetic peptide (S2-P) corresponding to a portion of the human syndecan-2 (SDC2) sequence can bind to the pro-domain of matrix metalloproteinase-7 (MMP-7) to inhibit colon cancer activities. Since S2-P had a relatively weak binding affinity for the MMP-7 pro-domain, we herein modified the amino acid sequence of S2-P to improve the anticancer potential. On the basis of the interaction structure of S2-P and MMP-7, four peptides were generated by replacing amino acids near Tyr 51, which is critical for the interaction. The SDC2-mimetic peptides harboring an Ala-to-Asp substitution at the C-terminal side of Tyr 51 (S2-D) or with an Ala-to-Phe substitution at the N-terminal side of Tyr 51 and an Ala-to-Asp substitution at the C-terminal side of Tyr 51 (S2-FE) showed improved interaction affinities for the MMP-7 pro-domain. Compared to S2-P, S2-FE was better able to inhibit the SDC2-MMP-7 interaction, the cell surface localization of MMP-7, the gelatin degradation activity of MMP-7, and the cancer activities (cell migration, invasion, and colony-forming activity) of human HCT116 colon cancer cells in vitro. In vivo, S2-FE inhibited the primary tumor growth and lung metastasis of CT26 mouse colon cancer cells in a xenograft mouse model. Together, these data suggest that S2-FE could be useful therapeutic anticancer peptides for colon cancer.
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Welch BA, Cho HJ, Ucakturk SA, Farmer SM, Cetinkaya S, Abaci A, Akkus G, Simsek E, Kotan LD, Turan I, Gurbuz F, Yuksel B, Wray S, Topaloglu AK. PLXNB1 mutations in the etiology of idiopathic hypogonadotropic hypogonadism. J Neuroendocrinol 2022; 34:e13103. [PMID: 35170806 DOI: 10.1111/jne.13103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/15/2022] [Accepted: 01/28/2022] [Indexed: 11/27/2022]
Abstract
Idiopathic hypogonadotropic hypogonadism (IHH) comprises a group of rare genetic disorders characterized by pubertal failure caused by gonadotropin-releasing hormone (GnRH) deficiency. Genetic factors involved in semaphorin/plexin signaling have been identified in patients with IHH. PlexinB1, a member of the plexin family receptors, serves as the receptor for semaphorin 4D (Sema4D). In mice, perturbations in Sema4D/PlexinB1 signaling leads to improper GnRH development, highlighting the importance of investigating PlexinB1 mutations in IHH families. In total, 336 IHH patients (normosmic IHH, n = 293 and Kallmann syndrome, n = 43) from 290 independent families were included in the present study. Six PLXNB1 rare sequence variants (p.N361S, p.V608A, p.R636C, p.V672A, p.R1031H, and p.C1318R) are described in eight normosmic IHH patients from seven independent families. These variants were examined using bioinformatic modeling and compared to mutants reported in PLXNA1. Based on these analyses, the variant p.R1031H was assayed for alterations in cell morphology, PlexinB1 expression, and migration using a GnRH cell line and Boyden chambers. Experiments showed reduced membrane expression and impaired migration in cells expressing this variant compared to the wild-type. Our results provide clinical, genetic, molecular/cellular, and modeling evidence to implicate variants in PLXNB1 in the etiology of IHH.
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Affiliation(s)
- Bradley A Welch
- Department of Neurobiology and Anatomical Sciences, University of Mississippi Medical Center, Jackson, MS, USA
| | - Hyun-Ju Cho
- Cellular and Developmental Neurobiology Section, National Institute of Neurological Disorders and Stroke/National Institutes of Health, Bethesda, MD, USA
| | - Seyit Ahmet Ucakturk
- Division of Pediatric Endocrinology, Ankara Training and Research Hospital, Ankara, Turkey
| | - Stephen Matthew Farmer
- Cellular and Developmental Neurobiology Section, National Institute of Neurological Disorders and Stroke/National Institutes of Health, Bethesda, MD, USA
| | - Semra Cetinkaya
- Division of Pediatric Endocrinology, Dr. Sami Ulus Obstetrics and Gynecology, Pediatric Health and Disease Training and Research Hospital, University of Health Sciences, Ankara, Turkey
| | - Ayhan Abaci
- Division of Pediatric Endocrinology, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey
| | - Gamze Akkus
- Division of Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Enver Simsek
- Division of Pediatric Endocrinology, Faculty of Medicine, Eskisehir Osman Gazi University, Eskisehir, Turkey
| | - Leman Damla Kotan
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Ihsan Turan
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Fatih Gurbuz
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Bilgin Yuksel
- Division of Pediatric Endocrinology, Faculty of Medicine, Cukurova University, Adana, Turkey
| | - Susan Wray
- Cellular and Developmental Neurobiology Section, National Institute of Neurological Disorders and Stroke/National Institutes of Health, Bethesda, MD, USA
| | - A Kemal Topaloglu
- Department of Neurobiology and Anatomical Sciences, University of Mississippi Medical Center, Jackson, MS, USA
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
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Fayez AG, Eldeen GN, Zarouk WA, Hamed K, Ramadan A, Foda BM, Kobesiy MM, Zekrie ME, Lotfy RS, Sokkar MF, El-Bassyouni HT. Dynamic disequilibrium-based pathogenicity model in mutated pyrin’s B30.2 domain—Casp1/p20 complex. J Genet Eng Biotechnol 2022; 20:31. [PMID: 35190906 PMCID: PMC8861233 DOI: 10.1186/s43141-022-00300-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 01/14/2022] [Indexed: 01/04/2023]
Abstract
Background The B30.2 variants lead to most relevant severity forms of familial Mediterranean fever (FMF) manifestations. The B30.2 domain plays a key role in protein-protein interaction (PPI) of pyrin with other apoptosis proteins and in regulation the cascade of inflammatory reactions. Pyrin-casp1 interaction is mainly responsible for the dysregulation of the inflammatory responses in FMF. Lower binding affinity was observed between the mutant B30.2 pyrin and casp1 without the release of the complete pathogenicity mechanism. The aim of this study was to identify the possible effects of the interface pocked residues in B30.2/SPRY-Casp1/p20 complex using molecular mechanics simulation and in silico analysis. Results It was found that Lys671Met, Ser703Ile, and Ala744Ser variants led mainly to shift of the binding affinity (∆G), dissociation constant (Kd), and root mean square deviation (RMSD) in B30.2/SPRY-Casp1/p20 complex leading to dynamic disequilibrium of the p20-B30.2/SPRY complex toward its complex form. The current pathogenicity model and its predicted implementation in the relevant colchicine dosage were delineated. Conclusion The molecular mechanics analysis of B30.2/SPRY-p20 complex harboring Lys671Met, Ser703Ile, and Ala744Ser variants showed dynamic disequilibrium of B30.2/SPRY-casp1/p20complex in context of the studied variants that could be a new computational model for FMF pathogenicity. This study also highlighted the specific biochemical markers that could be useful to adjust the colchicine dose in FMF patients.
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Ahsan T, Sajib AA. Missense variants in the TNFA epitopes and their effects on interaction with therapeutic antibodies-in silico analysis. J Genet Eng Biotechnol 2022; 20:7. [PMID: 35006391 PMCID: PMC8748575 DOI: 10.1186/s43141-021-00288-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 12/13/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND Tumor necrosis factor alpha (TNFA) is an important cytokine that influences multiple biological processes. It is one of the key mediators of acute and chronic systemic inflammatory reactions and plays a central role in several autoimmune diseases. A number of approved monoclonal antibodies (mAbs) are widely used to subside these autoimmune diseases. However, there is a high rate of non-responsiveness to treatments with these mAbs. Therefore, it is important to be able to predict responses of the patients in an individualistic manner to these therapeutic antibodies before administration. In the present study, we used in silico tools to explore the effects of missense variants in the respective epitopes of four therapeutic anti-TNFA mAbs-adalimumab (ADA), certolizumab pegol (CZP), golimumab (GLM), and infliximab (IFX)-on their interactions with TNFA. RESULTS The binding affinities of CZP and ADA to corresponding epitopes appear to be reduced by four (TNFAR131Q, TNFAE135G, TNFAR138Q, and TNFAR138W) and two (TNFAG66C and TNFAG66S) variants, respectively. The binding of GLM and IFX appears to be affected by TNFAR141S and TNFAR138W, respectively. TNFAG66C and TNFAG66S may be associated with autoimmune diseases, whereas TNFAE135G, TNFAR138W, and TNFAR141S may be pathogenic per se. CONCLUSION These variants may contribute to the observed inter-individual variability in response to anti-TNFA mAbs treatments and be used as markers to predict responses, and thus optimize therapeutic benefits to the patients.
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Affiliation(s)
- Tamim Ahsan
- Molecular Biotechnology Division, National Institute of Biotechnology, Dhaka, 1349 Bangladesh
| | - Abu Ashfaqur Sajib
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, 1000 Bangladesh
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Schardt JS, Jhajj HS, O’Meara RL, Lwo TS, Smith MD, Tessier PM. Agonist antibody discovery: Experimental, computational, and rational engineering approaches. Drug Discov Today 2022; 27:31-48. [PMID: 34571277 PMCID: PMC8714685 DOI: 10.1016/j.drudis.2021.09.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/19/2021] [Accepted: 09/20/2021] [Indexed: 01/03/2023]
Abstract
Agonist antibodies that activate cellular signaling have emerged as promising therapeutics for treating myriad pathologies. Unfortunately, the discovery of rare antibodies with the desired agonist functions is a major bottleneck during drug development. Nevertheless, there has been important recent progress in discovering and optimizing agonist antibodies against a variety of therapeutic targets that are activated by diverse signaling mechanisms. Herein, we review emerging high-throughput experimental and computational methods for agonist antibody discovery as well as rational molecular engineering methods for optimizing their agonist activity.
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Affiliation(s)
- John S. Schardt
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Harkamal S. Jhajj
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ryen L. O’Meara
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Timon S. Lwo
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew D. Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Peter M. Tessier
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA,Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
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Structure-function relationships of the disease-linked A218T oxytocin receptor variant. Mol Psychiatry 2022; 27:907-917. [PMID: 34980886 PMCID: PMC9054668 DOI: 10.1038/s41380-021-01241-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/24/2021] [Accepted: 07/15/2021] [Indexed: 12/20/2022]
Abstract
Various single nucleotide polymorphisms (SNPs) in the oxytocin receptor (OXTR) gene have been associated with behavioral traits, autism spectrum disorder (ASD) and other diseases. The non-synonymous SNP rs4686302 results in the OXTR variant A218T and has been linked to core characteristics of ASD, trait empathy and preterm birth. However, the molecular and intracellular mechanisms underlying those associations are still elusive. Here, we uncovered the molecular and intracellular consequences of this mutation that may affect the psychological or behavioral outcome of oxytocin (OXT)-treatment regimens in clinical studies, and provide a mechanistic explanation for an altered receptor function. We created two monoclonal HEK293 cell lines, stably expressing either the wild-type or A218T OXTR. We detected an increased OXTR protein stability, accompanied by a shift in Ca2+ dynamics and reduced MAPK pathway activation in the A218T cells. Combined whole-genome and RNA sequencing analyses in OXT-treated cells revealed 7823 differentially regulated genes in A218T compared to wild-type cells, including 429 genes being associated with ASD. Furthermore, computational modeling provided a molecular basis for the observed change in OXTR stability suggesting that the OXTR mutation affects downstream events by altering receptor activation and signaling, in agreement with our in vitro results. In summary, our study provides the cellular mechanism that links the OXTR rs4686302 SNP with genetic dysregulations associated with aspects of ASD.
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Abstract
Directed evolution, a strategy for protein engineering, optimizes protein properties (i.e., fitness) by expensive and time-consuming screening or selection of large mutational sequence space. Machine learning-assisted directed evolution (MLDE), which screens sequence properties in silico, can accelerate the optimization and reduce the experimental burden. This work introduces a MLDE framework, cluster learning-assisted directed evolution (CLADE), that combines hierarchical unsupervised clustering sampling and supervised learning to guide protein engineering. The clustering sampling selectively picks and screens variants in targeted subspaces, which guides the subsequent generation of diverse training sets. In the last stage, accurate predictions via supervised learning models improve final outcomes. By sequentially screening 480 sequences out of 160,000 in a four-site combinatorial library with five equal experimental batches, CLADE achieves the global maximal fitness hit rate up to 91.0% and 34.0% for GB1 and PhoQ datasets, respectively, improved from 18.6% and 7.2% obtained by random-sampling-based MLDE.
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Affiliation(s)
- Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
| | - Jian Hu
- Department of Chemistry, Michigan State University, MI, 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, MI, 48824, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA
- Department of Biochemistry and Molecular Biology, Michigan State University, MI, 48824, USA
- Department of Electrical and Computer Engineering, Michigan State University, MI 48824, USA
- Corresponding author:
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Sun T, Chen Y, Wen Y, Zhu Z, Li M. PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions. Commun Biol 2021; 4:1311. [PMID: 34799678 PMCID: PMC8604987 DOI: 10.1038/s42003-021-02826-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/26/2021] [Indexed: 02/07/2023] Open
Abstract
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning. Sun et al. present PremPLI, a machine learning approach and web tool to predict how missense mutations in a drug’s target protein will affect the drug’s binding affinity. PremPLI can be applied to identify drug resistance mechanisms in cancer cells and microorganisms and develop drugs to combat drug resistance.
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Affiliation(s)
- Tingting Sun
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Yuting Chen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Yuhao Wen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Zefeng Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China
| | - Minghui Li
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, 215123, Suzhou, China.
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Mendis J, Kaya E, Kucukkal TG. Identification of Hotspot Residues in Binding of SARS-CoV-2 Spike and Human ACE2 Proteins. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2021. [DOI: 10.1142/s2737416521500447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Coronaviruses are a large family of viruses that can cause respiratory infections with varying severity from common cold to severe diseases such as novel coronavirus disease (COVID-19). COVID-19 has been declared as a global pandemic by the World Health Organization on March 11, 2020 and with the development of vaccines it slowed down as of this date. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses its spike glycoprotein (Sgp) to bind human angiotensin-converting enzyme 2 (hACE2) receptor, and mediates membrane fusion and virus entry. The recognition of Sgp to human ACE2 and its high affinity for it has been of great importance since this provides the first step in viral entry to human cells. Therefore, it is crucial to identify key residues (hotspots) in this process. In this study, computational alanine scanning has been performed for Sgp and hACE2. The residues identified with significance in binding and other residues in close proximity were studied further through molecular mechanics-based protein binding free energy change prediction methods. Moreover, the interfacial residues in both proteins were investigated for their cooperative binding. Additionally, folding free energy changes upon mutation to Ala were calculated to assess their effect on stability of Sgp and hACE2. Our results taken together with findings from previous studies revealed the residues that are most significant and are relatively significant in binding of Sgp to human ACE2 protein.
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Affiliation(s)
- Jenny Mendis
- School of Science, Technology, Accessibility, Mathematics and Public Health, Gallaudet University, Washington, D.C. 20002, USA
| | - Ekrem Kaya
- Quest Student Research Institute, Chantilly, VA 20151, USA
- Freedom High School, Chantilly, VA 20152, USA
| | - Tugba G. Kucukkal
- School of Science, Technology, Accessibility, Mathematics and Public Health, Gallaudet University, Washington, D.C. 20002, USA
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Chen C, Boorla VS, Banerjee D, Chowdhury R, Cavener VS, Nissly RH, Gontu A, Boyle NR, Vandegrift K, Nair MS, Kuchipudi SV, Maranas CD. Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2. Proc Natl Acad Sci U S A 2021; 118:e2106480118. [PMID: 34588290 PMCID: PMC8594574 DOI: 10.1073/pnas.2106480118] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 01/22/2023] Open
Abstract
The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single-amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single- and multiple-amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.
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Affiliation(s)
- Chen Chen
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Deepro Banerjee
- The Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Victoria S Cavener
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Ruth H Nissly
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Abhinay Gontu
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Nina R Boyle
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Kurt Vandegrift
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802
| | - Meera Surendran Nair
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Suresh V Kuchipudi
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802;
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802;
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Kollati Y, Akella RRD, Naushad SM, Patel RK, Reddy GB, Dirisala VR. Molecular insights into the role of genetic determinants of congenital hypothyroidism. Genomics Inform 2021; 19:e29. [PMID: 34638176 PMCID: PMC8510868 DOI: 10.5808/gi.21034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/22/2021] [Indexed: 12/01/2022] Open
Abstract
In our previous studies, we have demonstrated the association of certain variants of the thyroid-stimulating hormone receptor (TSHR), thyroid peroxidase (TPO), and thyroglobulin (TG) genes with congenital hypothyroidism. Herein, we explored the mechanistic basis for this association using different in silico tools. The mRNA 3'-untranslated region (3'-UTR) plays key roles in gene expression at the post-transcriptional level. In TSHR variants (rs2268477, rs7144481, and rs17630128), the binding affinity of microRNAs (miRs) (hsa-miR-154-5p, hsa-miR-376a-2-5p, hsa-miR-3935, hsa-miR-4280, and hsa-miR-6858-3p) to the 3'-UTR is disrupted, affecting post-transcriptional gene regulation. TPO and TG are the two key proteins necessary for the biosynthesis of thyroid hormones in the presence of iodide and H2O2. Reduced stability of these proteins leads to aberrant biosynthesis of thyroid hormones. Compared to the wild-type TPO protein, the p.S398T variant was found to exhibit less stability and significant rearrangements of intra-atomic bonds affecting the stoichiometry and substrate binding (binding energies, ΔG of wild-type vs. mutant: ‒15 vs. ‒13.8 kcal/mol; and dissociation constant, Kd of wild-type vs. mutant: 7.2E-12 vs. 7.0E-11 M). The missense mutations p.G653D and p.R1999W on the TG protein showed altered ΔG (0.24 kcal/mol and 0.79 kcal/mol, respectively). In conclusion, an in silico analysis of TSHR genetic variants in the 3'-UTR showed that they alter the binding affinities of different miRs. The TPO protein structure and mutant protein complex (p.S398T) are less stable, with potentially deleterious effects. A structural and energy analysis showed that TG mutations (p.G653D and p.R1999W) reduce the stability of the TG protein and affect its structure-functional relationship.
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Affiliation(s)
- Yedukondalu Kollati
- Department of Biotechnology, Vignan's University, Vadlamudi, Guntur, Andhra Pradesh 522213, India
| | - Radha Rama Devi Akella
- Department of Genetics, Rainbow Children's Hospital, Banjara Hills, Hyderabad, Telangana 500009, India.,Department of Biochemical Genetics and Pharmacogenomics, Sandor Speciality Diagnostics Pvt. Ltd, Banjara Hills, Hyderabad, Telangana 500034, India
| | - Shaik Mohammad Naushad
- Department of Biochemical Genetics and Pharmacogenomics, Sandor Speciality Diagnostics Pvt. Ltd, Banjara Hills, Hyderabad, Telangana 500034, India
| | - Rajesh K Patel
- Department of Genetics, Genetic Group of Gujarat Diagnostic Centre, Mehsana, Gujarat 384002, India
| | - G Bhanuprakash Reddy
- Biochemistry Division, National Institute of Nutrition, Hyderabad, Telangana 500007, India
| | - Vijaya R Dirisala
- Department of Biotechnology, Vignan's University, Vadlamudi, Guntur, Andhra Pradesh 522213, India
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Sun Q, Shu C, Shi W, Luo Y, Fan G, Nie J, Bi Y, Wang Q, Qi J, Lu J, Zhou Y, Shen Z, Meng Z, Zhang X, Yu Z, Gao S, Wu L, Ma J, Hu S. VarEPS: an evaluation and prewarning system of known and virtual variations of SARS-CoV-2 genomes. Nucleic Acids Res 2021; 50:D888-D897. [PMID: 34634813 PMCID: PMC8728250 DOI: 10.1093/nar/gkab921] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/18/2021] [Accepted: 09/28/2021] [Indexed: 12/17/2022] Open
Abstract
The genomic variations of SARS-CoV-2 continue to emerge and spread worldwide. Some mutant strains show increased transmissibility and virulence, which may cause reduced protection provided by vaccines. Thus, it is necessary to continuously monitor and analyze the genomic variations of SARS-COV-2 genomes. We established an evaluation and prewarning system, SARS-CoV-2 variations evaluation and prewarning system (VarEPS), including known and virtual mutations of SARS-CoV-2 genomes to achieve rapid evaluation of the risks posed by mutant strains. From the perspective of genomics and structural biology, the database comprehensively analyzes the effects of known variations and virtual variations on physicochemical properties, translation efficiency, secondary structure, and binding capacity of ACE2 and neutralizing antibodies. An AI-based algorithm was used to verify the effectiveness of these genomics and structural biology characteristic quantities for risk prediction. This classifier could be further used to group viral strains by their transmissibility and affinity to neutralizing antibodies. This unique resource makes it possible to quickly evaluate the variation risks of key sites, and guide the research and development of vaccines and drugs. The database is freely accessible at www.nmdc.cn/ncovn.
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Affiliation(s)
- Qinglan Sun
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Chang Shu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Wenyu Shi
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Yingfeng Luo
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guomei Fan
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Jingyi Nie
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuhai Bi
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qihui Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jianxun Qi
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jian Lu
- State Key Laboratory of Protein and Plant Gene Research, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Yuanchun Zhou
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhihong Shen
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhen Meng
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Xinjiao Zhang
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Zhengfei Yu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Shenghan Gao
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
| | - Linhuan Wu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Juncai Ma
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,Chinese National Microbiology Data Center (NMDC), Beijing 100101, China
| | - Songnian Hu
- Microbial Resource and Big Data Center, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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45
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Wu B, Hand W, Alexov E. Opioid Addiction and Opioid Receptor Dimerization: Structural Modeling of the OPRD1 and OPRM1 Heterodimer and Its Signaling Pathways. Int J Mol Sci 2021; 22:ijms221910290. [PMID: 34638633 PMCID: PMC8509015 DOI: 10.3390/ijms221910290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/17/2021] [Accepted: 09/21/2021] [Indexed: 12/25/2022] Open
Abstract
Opioid addiction is a complex phenomenon with genetic, social, and other components. Due to such complexity, it is difficult to interpret the outcome of clinical studies, and thus, mutations found in individuals with these addictions are still not indisputably classified as opioid addiction-causing variants. Here, we computationally investigated two such mutations, A6V and N40D, found in the mu opioid receptor gene OPRM1. The mutations are located in the extracellular domain of the corresponding protein, which is important to the hetero-dimerization of OPRM1 with the delta opioid receptor protein (OPRD1). The hetero-dimerization of OPRD1-OPRM1 affects the signaling pathways activated by opioids and natural peptides and, thus, could be considered a factor contributing to addiction. In this study, we built four 3D structures of molecular pathways, including the G-protein signaling pathway and the β-arrestin signaling pathway of the heterodimer of OPRD1-OPRM1. We also analyzed the effect of mutations of A6V and N40D on the stability of individual OPRM1/OPRD1 molecules and the OPRD1-OPRM1 heterodimer with the goal of inferring their plausible linkage with opioid addiction. It was found that both mutations slightly destabilize OPRM1/OPRD1 monomers and weaken their association. Since hetero-dimerization is a key step for signaling processes, it is anticipated that both mutations may be causing increased addiction risk.
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Affiliation(s)
- Bohua Wu
- Department of Physics, College of Science, Clemson University, Clemson, SC 29634, USA;
| | - William Hand
- Department of Anesthesiology and Perioperative Medicine, Prisma Health, Greenville, SC 29605, USA;
| | - Emil Alexov
- Department of Physics, College of Science, Clemson University, Clemson, SC 29634, USA;
- Correspondence:
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46
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Singh AN, Sharma N. In-silico identification of frequently mutated genes and their co-enriched metabolic pathways associated with Prostate cancer progression. Andrologia 2021; 53:e14236. [PMID: 34468989 DOI: 10.1111/and.14236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/04/2021] [Accepted: 08/15/2021] [Indexed: 11/27/2022] Open
Abstract
Prostate cancer (PCa) has emerged as a significant health burden in men globally. Several genetic anomalies such as mutations and also epigenetic aberrations are responsible for the heterogeneity of this disease. This study identified the 20 most frequently mutated genes reported in PCa based on literature and database survey. Further gene ontology and functional enrichment analysis were conducted to determine their co-modulated molecular and biological pathways. A protein-protein interaction network was used for the identification of hub genes. These hub genes identified were then subjected to survival analysis. The prognostic values of these identified genes were investigated using GEPIA and HPA. Gene Ontology analysis of the identified genes depicted that these genes significantly contributed to the cell cycle, apoptosis, angiogenesis and TGF-β receptor signalling. Further, the research showed that high expressions of identified mutated genes led to a reduction in the long-term survival of PCa patients, which was supported by immunohistochemical and mRNA expression level data. Our results suggest that identified panel of mutated genes viz., CTNNB1, TP53, ATM, AR and KMT2D play crucial roles in the onset and progression of PCa, thereby providing candidate diagnostic markers for PCa for individualised treatment in the future.
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Affiliation(s)
- Anshika N Singh
- School of Engineering, Ajeenkya DY Patil University (ADYPU), Pune, India
| | - Neeti Sharma
- School of Engineering, Ajeenkya DY Patil University (ADYPU), Pune, India
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47
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Li G, Panday SK, Peng Y, Alexov E. SAMPDI-3D: predicting the effects of protein and DNA mutations on protein-DNA interactions. Bioinformatics 2021; 37:3760-3765. [PMID: 34343273 DOI: 10.1093/bioinformatics/btab567] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/28/2021] [Accepted: 07/31/2021] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION Mutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein-DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein-DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet. RESULTS Here, we report a new version of SAMPDI, the SAMPDI-3D, which is a gradient boosting decision tree machine learning method to predict the change of the protein-DNA binding free energy caused by mutations in both the binding protein and the bases of the corresponding DNA. The method is shown to achieve Pearson correlation coefficient of 0.76 and 0.80 in a benchmarking test against experimentally determined change of the binding free energy caused by mutations in the binding protein or DNA, respectively. Furthermore, three datasets collected from literature were used to do blind benchmark for SAMPDI-3D and it is shown that it outperforms all existing state-of-the-art methods. The method is very fast allowing for genome-scale investigations. AVAILABILITY It is available as a web server and a stand-code at http://compbio.clemson.edu/SAMPDI-3D/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gen Li
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | | | - Yunhui Peng
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
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48
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Du Y, Wang H, Chen L, Fang Q, Zhang B, Jiang L, Wu Z, Yang Y, Zhou Y, Chen B, Lyu J, Wang Z. Non-RBM Mutations Impaired SARS-CoV-2 Spike Protein Regulated to the ACE2 Receptor Based on Molecular Dynamic Simulation. Front Mol Biosci 2021; 8:614443. [PMID: 34386518 PMCID: PMC8353372 DOI: 10.3389/fmolb.2021.614443] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 07/16/2021] [Indexed: 12/28/2022] Open
Abstract
The emergence of novel coronavirus mutants is a main factor behind the deterioration of the epidemic situation. Further studies into the pathogenicity of these mutants are thus urgently needed. Binding of the spinous protein receptor binding domain (RBD) of SARS-CoV-2 to the angiotensin-converting enzyme 2 (ACE2) receptor was shown to initiate coronavirus entry into host cells and lead to their infection. The receptor-binding motif (RBM, 438-506) is a region that directly interacts with ACE2 receptor in the RBD and plays a crucial role in determining affinity. To unravel how mutations in the non-RBM regions impact the interaction between RBD and ACE2, we selected three non-RBM mutant systems (N354D, D364Y, and V367F) from the documented clinical cases, and the Q498A mutant system located in the RBM region served as the control. Molecular dynamics simulation was conducted on the mutant systems and the wild-type (WT) system, and verified experiments also performed. Non-RBM mutations have been shown not only to change conformation of the RBM region but also to significantly influence its hydrogen bonding and hydrophobic interactions. In particular, the D364Y and V367F systems showed a higher affinity for ACE2 owing to their electrostatic interactions and polar solvation energy changes. In addition, although the binding free energy at this point increased after the mutation of N354D, the conformation of the random coil (Pro384-Asp389) was looser than that of other systems, and the combined effect weakened the binding free energy between RBD and ACE2. Interestingly, we also found a random coil (Ala475-Gly485). This random coil is very sensitive to mutations, and both types of mutations increase the binding free energy of residues in this region. We found that the binding loop (Tyr495-Tyr505) in the RBD domain strongly binds to Lys353, an important residue of the ACE2 domain previously identified. The binding free energy of the non-RBM mutant group at the binding loop had positive and negative changes, and these changes were more obvious than that of the Q498A system. The results of this study elucidate the effect of non-RBM mutation on ACE2-RBD binding, and provide new insights for SARS-CoV-2 mutation research.
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Affiliation(s)
- Yaoqiang Du
- Allergy Center, Department of Transfusion Medicine, Ministry of Education Key Laboratory of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Hao Wang
- National Clinical Research Center for Child Health, National Children's Regional Medical Center, Department of Clinical Laboratory, The Children's Hospital, Zhejiang University Shcool of Medicine, Hangzhou, China
| | - Linjie Chen
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Quan Fang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Biqin Zhang
- Department of Hematology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Luxi Jiang
- Allergy Center, Department of Transfusion Medicine, Ministry of Education Key Laboratory of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Zhaoyu Wu
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Yexiaoqing Yang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Ying Zhou
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Bingyu Chen
- Allergy Center, Department of Transfusion Medicine, Ministry of Education Key Laboratory of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
| | - Jianxin Lyu
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Zhen Wang
- Allergy Center, Department of Transfusion Medicine, Ministry of Education Key Laboratory of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
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49
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Riahi S, Lee JH, Wei S, Cost R, Masiero A, Prades C, Olfati-Saber R, Wendt M, Park A, Qiu Y, Zhou Y. Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers. Antib Ther 2021; 4:109-122. [PMID: 34396040 PMCID: PMC8344454 DOI: 10.1093/abt/tbab011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 01/07/2023] Open
Abstract
As the COVID-19 pandemic continues to spread, hundreds of new initiatives including
studies on existing medicines are running to fight the disease. To deliver a potentially
immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new
collaborations and ways of sharing are required to create as many paths forward as
possible. Here, we leverage our expertise in computational antibody engineering to
rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and
share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing
antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified
epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except
for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain
(RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody
engineering platform to improve their binding affinity to SARS-CoV-2 and developability
profiles. The selected top mutations were ensembled into a focused library for each
antibody for further screening. In addition, we convert the selected binders with
different epitopes into the trispecific format, aiming to increase potency and to prevent
mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we
suggest application of NNAS and DQ mutations to the Fc region to eliminate effector
functions and extend half-life.
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Affiliation(s)
- Saleh Riahi
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Jae Hyeon Lee
- Data & Data Science, Sanofi, Cambridge, MA, United States
| | - Shuai Wei
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Robert Cost
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | | | | | | | - Maria Wendt
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Anna Park
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Yu Qiu
- Large Molecule Research, Sanofi, Framingham, MA, United States
| | - Yanfeng Zhou
- Large Molecule Research, Sanofi, Framingham, MA, United States
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50
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Chen J, Wu F, Lin D, Kong W, Cai X, Yang J, Sun X, Cao P. Rational optimization of a human neutralizing antibody of SARS-CoV-2. Comput Biol Med 2021; 135:104550. [PMID: 34147856 PMCID: PMC8196228 DOI: 10.1016/j.compbiomed.2021.104550] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/02/2021] [Accepted: 06/02/2021] [Indexed: 01/06/2023]
Abstract
SARS-CoV-2 has caused a worldwide epidemic of coronavirus disease 19 (COVID-19). Antibody drugs present an effective weapon for tens of millions of COVID-19 patients. Antibodies disrupting the interactions between the receptor-binding domain (RBD) of SARS-CoV-2 S protein and the angiotensin converting enzyme 2 (ACE2) effectively block SARS-CoV-2 cell entry into host cells. In order to rapidly develop more potent neutralizing antibodies, we utilized virtual scanning mutageneses and molecular dynamics simulations to optimize the antibody of P2B-2F6 isolated from single B cells of SARS-CoV-2 infected patients. Two potent P2B-2F6 mutants, namely H:V106R and H:V106R/H:P107Y, were found to possess higher binding affinities with the RBD domain of SARS-CoV-2 than others. Polar interactions are preferred near 106 and 107 paratope residues of the heavy chain. The mutations also increase the hydrogen-bonding network formed between the antibody and the RBD. Notably, the optimized antibodies possess potential neutralizing activity against the alarming SARS-CoV-2 variant of N501Y. This study provides insights into structure-based optimization of antibodies with higher affinity to the antigen. We hope that our proposed antibody mutants could contribute to the development of improved therapies against COVID-19.
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Affiliation(s)
- Jiao Chen
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, China; Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, 210028, China
| | - Fei Wu
- Engineering Research Center of Modern Preparation Technology of TCM of Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Dan Lin
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, China
| | - Weikang Kong
- Sir Run Run Hospital, NanJing Medical University, Nanjing, 211100, China
| | - Xueting Cai
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, China; Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, 210028, China
| | - Jie Yang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, China; Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, 210028, China
| | - Xiaoyan Sun
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, China; Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, 210028, China.
| | - Peng Cao
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, 210028, China; Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, 210028, China; School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
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