1
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Fu H, Mo X, Ivanov AA. Decoding the functional impact of the cancer genome through protein-protein interactions. Nat Rev Cancer 2025; 25:189-208. [PMID: 39810024 DOI: 10.1038/s41568-024-00784-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/02/2024] [Indexed: 01/16/2025]
Abstract
Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.
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Affiliation(s)
- Haian Fu
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Emory University, Atlanta, GA, USA.
- Winship Cancer Institute of Emory University, Atlanta, GA, USA.
| | - Xiulei Mo
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Emory University, Atlanta, GA, USA
- Winship Cancer Institute of Emory University, Atlanta, GA, USA
| | - Andrey A Ivanov
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Emory University, Atlanta, GA, USA
- Winship Cancer Institute of Emory University, Atlanta, GA, USA
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2
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Zheng F, Jiang X, Wen Y, Yang Y, Li M. Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength. Comput Struct Biotechnol J 2024; 23:460-472. [PMID: 38235359 PMCID: PMC10792694 DOI: 10.1016/j.csbj.2023.12.018] [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: 10/03/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 01/19/2024] Open
Abstract
The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.
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Affiliation(s)
- Feifan Zheng
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Xin Jiang
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yuhao Wen
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yan Yang
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Minghui Li
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
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3
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Budi T, Kumnan N, Singchat W, Chalermwong P, Thong T, Wongloet W, Faniriharisoa Maxime Toky R, Pathomvanich P, Panthum T, Wattanadilokchatkun P, Farhan Ahmad S, Tanglertpaibul N, Vangnai K, Chaiyes A, Yokthongwattana C, Sinthuvanich C, Han K, Muangmai N, Koga A, Nunome M, Sawatdichaikul O, Duengkae P, Matsuda Y, Srikulnath K. Weak purifying selection in allelic diversity of the ADSL gene in indigenous and local chicken breeds and red junglefowl in Thailand. Gene 2024; 923:148587. [PMID: 38768877 DOI: 10.1016/j.gene.2024.148587] [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: 01/08/2024] [Revised: 05/01/2024] [Accepted: 05/16/2024] [Indexed: 05/22/2024]
Abstract
High levels of purine and uric acid, which are associated with health issues such as gout and cardiovascular disease, are found in the meat of fast-growing broiler chickens, which raises concerns about the quality of chicken meat and the health of the consumers who consume it. High genetic homogeneity and uniformity, particularly in genes involved in the synthesis of inosine monophosphate (IMP) and subsequent process of purine synthesis, which are associated with the meat quality, are exhibited in commercial broiler chickens owing to intensive inbreeding programs. Adenosine succinate lyase (ADSL) is a key enzyme involved in de novo purine biosynthetic pathway and its genetic polymorphisms affect IMP metabolism and purine content. In this study, we investigated the polymorphism of the ADSL gene in indigenous and local chicken breeds and red junglefowl in Thailand, using metabarcoding and genetic diversity analyses. Five alleles with 73 single nucleotide polymorphisms in exon 2, including missense and silent mutations, which may act on the synthesis efficiency of IMP and purine. Their protein structures revealed changes in amino acid composition that may affect ADSL enzyme activity. Weak purifying selection in these ADSL alleles was observed in the chicken population studied, implying that the variants have minor fitness impacts and a greater probability of fixation of beneficial mutations than strong purifying selection. A potential selective sweep was observed in Mae Hong Son chickens, whose purine content was lower than that in other breeds. This suggests a potential correlation between variations of the ADSL gene and reduced purine content and an impact of ADSL expression on the quality of chicken meat. However, further studies are required to validate its potential availability as a genetic marker for selecting useful traits that are beneficial to human health and well-being.
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Affiliation(s)
- Trifan Budi
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Nichakorn Kumnan
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Sciences for Industry, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Worapong Singchat
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Piangjai Chalermwong
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Sciences for Industry, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Thanyapat Thong
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Wongsathit Wongloet
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Rajaonarison Faniriharisoa Maxime Toky
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Prangyapawn Pathomvanich
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Thitipong Panthum
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Pish Wattanadilokchatkun
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Syed Farhan Ahmad
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Sciences for Industry, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Nivit Tanglertpaibul
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Sciences for Industry, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Kanithaporn Vangnai
- Department of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand
| | - Aingorn Chaiyes
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; School of Agriculture and Cooperatives, Sukhothai Thammathirat Open University, Nonthaburi 11120, Thailand
| | - Chotika Yokthongwattana
- Department of Biochemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
| | - Chomdao Sinthuvanich
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Department of Biochemistry, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
| | - Kyudong Han
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Department of Microbiology, Dankook University, Cheonan 31116, Republic of Korea; Bio-Medical Engineering Core Facility Research Center, Dankook University, Cheonan 31116, Republic of Korea
| | - Narongrit Muangmai
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Department of Fishery Biology, Faculty of Fisheries, Kasetsart University, Bangkok 10900, Thailand
| | - Akihiko Koga
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Mitsuo Nunome
- Department of Zoology, Faculty of Science, Okayama University of Science, Ridai-cho 1-1, Kita-ku, Okayama City, Okayama 700-0005, Japan
| | - Orathai Sawatdichaikul
- Department of Nutrition and Health, Institute of Food Research and Product Development, Kasetsart University, Bangkok 10900, Thailand
| | - Prateep Duengkae
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Yoichi Matsuda
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand
| | - Kornsorn Srikulnath
- Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Special Research Unit for Wildlife Genomics (SRUWG), Department of Forest Biology, Faculty of Forestry, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand; Laboratory of Animal Cytogenetics and Comparative Genomics (ACCG), Department of Genetics, Faculty of Science, Kasetsart University, 50 Ngamwongwan, Chatuchak, Bangkok 10900, Thailand.
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4
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Ridha F, Gromiha MM. MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes. Brief Bioinform 2024; 25:bbae598. [PMID: 39550225 PMCID: PMC11568875 DOI: 10.1093/bib/bbae598] [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: 07/04/2024] [Revised: 10/10/2024] [Accepted: 11/04/2024] [Indexed: 11/18/2024] Open
Abstract
Mutations in the interface of membrane protein (MP) complexes are key contributors to a broad spectrum of human diseases, primarily due to changes in their binding affinities. While various methods exist for predicting the mutation-induced changes in binding affinity (ΔΔG) in protein-protein complexes, none are specific to MP complexes. This study proposes a novel strategy for ΔΔG prediction in MP complexes, which combines linear and nonlinear models, to obtain a more robust model with improved prediction accuracy. We used multiple linear regression to extract informative features that influence the binding affinity in MP complexes, which included changes in the stability of the complex, conservation score, electrostatic interaction, relatively accessible surface area, and interface contacts. Further, using gradient boosting regressor on the selected features, we developed MPA-MutPred, a novel method specific for predicting the ΔΔG of membrane protein-protein complexes, and it is freely accessible at https://web.iitm.ac.in/bioinfo2/MPA-MutPred/. Our method achieved a correlation of 0.75 and a mean absolute error (MAE) of 0.73 kcal/mol in the jack-knife test conducted on a dataset of 770 mutants. We further validated the method using a blind test set of 86 mutations, obtaining a correlation of 0.85 and an MAE of 0.77 kcal/mol. We anticipate that this method can be used for large-scale studies to understand the influence of binding affinity change on disease-causing mutations in MP complexes, thereby aiding in the understanding of disease mechanisms and the identification of potential therapeutic targets.
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Affiliation(s)
- Fathima Ridha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
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5
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Mechidi P, Holien J, Grando D, Huynh T, Lawrie AC. New Sources of Resistance to Terbinafine Revealed and Squalene Epoxidase Modelled in the Dermatophyte Fungus Trichophyton interdigitale From Australia. Mycoses 2024; 67:e13795. [PMID: 39304967 DOI: 10.1111/myc.13795] [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: 07/24/2024] [Accepted: 08/21/2024] [Indexed: 09/22/2024]
Abstract
BACKGROUND Terbinafine is widely used to treat onychomycosis caused by dermatophyte fungi. Terbinafine resistance in recent years is causing concern. Resistance has so far been associated with single-nucleotide substitutions in the DNA sequence of the enzyme squalene epoxidase (SQLE) but how this affects SQLE functionality is not understood. OBJECTIVES The aim of this study was to understand newly discovered resistance in two Australian strains of Trichophyton interdigitale. PATIENTS/METHODS Resistance to terbinafine was tested in four newly isolated strains. Three-dimensional SQLE models were prepared to investigate how the structure of their SQLE affected the binding of terbinafine. RESULTS This study found the first Australian occurrences of terbinafine resistance in two T. interdigitale strains. Both strains had novel deletion mutations in erg1 and frameshifts during translation. Three-dimensional models had smaller SQLE proteins and open reading frames as well as fewer C-terminal α-helices than susceptible strains. In susceptible strains, the lipophilic tail of terbinafine was predicted to dock stably into a hydrophobic pocket in SQLE lined by over 20 hydrophobic amino acids. In resistant strains, molecular dynamics simulations showed that terbinafine docking was unstable and so terbinafine did not block squalene metabolism and ultimately ergosterol production. The resistant reference strain ATCC MYA-4438 T. rubrum showed a single erg1 mutation that resulted in frameshift during translation, leading to C-terminal helix deletion. CONCLUSIONS Modelling their effects on their SQLE proteins will aid in the design of potential new treatments for these novel resistant strains, which pose clinical problems in treating dermatophyte infections with terbinafine.
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Affiliation(s)
- Phemelo Mechidi
- School of Science, RMIT University (Bundoora West Campus), Bundoora, Victoria, Australia
| | - Jessica Holien
- School of Science, RMIT University (Bundoora West Campus), Bundoora, Victoria, Australia
| | - Danilla Grando
- School of Science, RMIT University (Bundoora West Campus), Bundoora, Victoria, Australia
| | - Tien Huynh
- School of Science, RMIT University (Bundoora West Campus), Bundoora, Victoria, Australia
| | - Ann C Lawrie
- School of Science, RMIT University (Bundoora West Campus), Bundoora, Victoria, Australia
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6
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Guclu TF, Atilgan AR, Atilgan C. Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis. J Phys Chem B 2024; 128:7987-7996. [PMID: 39115184 PMCID: PMC11671028 DOI: 10.1021/acs.jpcb.4c04916] [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: 07/22/2024] [Revised: 08/02/2024] [Accepted: 08/02/2024] [Indexed: 08/23/2024]
Abstract
Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in silico mutational scanning for all possible single mutations, along with 2 μs-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. In sum, our findings highlight the potential of MuMi as a reliable and computationally efficient tool for predicting protein fitness landscapes, offering significant advantages over traditional methods. The methodologies and results presented in this study pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.
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Affiliation(s)
- Tandac F. Guclu
- Faculty of Natural Sciences
and Engineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
| | - Ali Rana Atilgan
- Faculty of Natural Sciences
and Engineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
| | - Canan Atilgan
- Faculty of Natural Sciences
and Engineering, Sabanci University, Tuzla, Istanbul 34956, Turkey
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7
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Tripathi V, Khare A, Shukla D, Bharadwaj S, Kirtipal N, Ranjan V. Genomic and computational-aided integrative drug repositioning strategy for EGFR and ROS1 mutated NSCLC. Int Immunopharmacol 2024; 139:112682. [PMID: 39029228 DOI: 10.1016/j.intimp.2024.112682] [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: 04/25/2024] [Revised: 07/02/2024] [Accepted: 07/11/2024] [Indexed: 07/21/2024]
Abstract
Non-small cell lung cancer (NSCLC) has been marked as the major cause of death in lung cancer patients. Due to tumor heterogeneity, mutation burden, and emerging resistance against the available therapies in NSCLC, it has been posing potential challenges in the therapy development. Hence, identification of cancer-driving mutations and their effective inhibition have been advocated as a potential approach in NSCLC treatment. Thereof, this study aims to employ the genomic and computational-aided integrative drug repositioning strategy to identify the potential mutations in the selected molecular targets and repurpose FDA-approved drugs against them. Accordingly, molecular targets and their mutations, i.e., EGFR (V843L, L858R, L861Q, and P1019L) and ROS1 (G1969E, F2046Y, Y2092C, and V2144I), were identified based on TCGA dataset analysis. Following, virtual screening and redocking analysis, Elbasvir, Ledipasvir, and Lomitapide drugs for EGFR mutants (>-10.8 kcal/mol) while Indinavir, Ledipasvir, Lomitapide, Monteleukast, and Isavuconazonium for ROS1 mutants (>-8.8 kcal/mol) were found as putative inhibitors. Furthermore, classical molecular dynamics simulation and endpoint binding energy calculation support the considerable stability of the selected docked complexes aided by substantial hydrogen bonding and hydrophobic interactions in comparison to the respective control complexes. Conclusively, the repositioned FDA-approved drugs might be beneficial alone or in synergy to overcome acquired resistance to EGFR and ROS1-positive lung cancers.
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Affiliation(s)
- Varsha Tripathi
- Department of Biochemistry, Dr. Ram Manohar Lohia Avadh University Ayodhya, Uttar Pradesh, India
| | - Aishwarya Khare
- Department of Biochemistry, Dr. Ram Manohar Lohia Avadh University Ayodhya, Uttar Pradesh, India
| | - Divyanshi Shukla
- Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India; Computational Chemistry & Drug Discovery Division, Quanta Calculus, Greater Noida, India.
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences, BIOCEV Research Center, Průmyslová 595, 252 50 Vestec, Czech Republic.
| | - Nikhil Kirtipal
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea.
| | - Vandana Ranjan
- Department of Biochemistry, Dr. Ram Manohar Lohia Avadh University Ayodhya, Uttar Pradesh, India.
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8
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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] [Download PDF] [Figures] [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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9
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Nandigrami P, Fiser A. Assessing the functional impact of protein binding site definition. Protein Sci 2024; 33:e5026. [PMID: 38757384 PMCID: PMC11099757 DOI: 10.1002/pro.5026] [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: 10/06/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.
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Affiliation(s)
- Prithviraj Nandigrami
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Andras Fiser
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
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10
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Hussin A, Nathan S, Shahidan MA, Nor Rahim MY, Zainun MY, Khairuddin NAN, Ibrahim N. Identification and mechanism determination of the efflux pump subunit amrB gene mutations linked to gentamicin susceptibility in clinical Burkholderia pseudomallei from Malaysian Borneo. Mol Genet Genomics 2024; 299:12. [PMID: 38381232 DOI: 10.1007/s00438-024-02105-w] [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: 07/18/2023] [Accepted: 12/29/2023] [Indexed: 02/22/2024]
Abstract
The bacterium Burkholderia pseudomallei is typically resistant to gentamicin but rare susceptible strains have been isolated in certain regions, such as Thailand and Sarawak, Malaysia. Recently, several amino acid substitutions have been reported in the amrB gene (a subunit of the amrAB-oprA efflux pump gene) that confer gentamicin susceptibility. However, information regarding the mechanism of the substitutions conferring the susceptibility is lacking. To understand the mechanism of amino acid substitution that confers susceptibility, this study identifies the corresponding mutations in clinical gentamicin-susceptible B. pseudomallei isolates from the Malaysian Borneo (n = 46; Sarawak: 5; Sabah: 41). Three phenotypically confirmed gentamicin-susceptible (GENs) strains from Sarawak, Malaysia, were screened for mutations in the amrB gene using gene sequences of gentamicin-resistant (GENr) strains (QEH 56, QEH 57, QEH20, and QEH26) and publicly available sequences (AF072887.1 and BX571965.1) as the comparator. The effect of missense mutations on the stability of the AmrB protein was determined by calculating the average energy change value (ΔΔG). Mutagenesis analysis identified a polymorphism-associated mutation, g.1056 T > G, a possible susceptible-associated in-frame deletion, Delta V412, and a previously confirmed susceptible-associated amino acid substitution, T368R, in each of the three GENs isolates. The contribution of Delta V412 needs further confirmation by experimental mutagenesis analysis. The mechanism by which T368R confers susceptibility, as elucidated by in silico mutagenesis analysis using AmrB-modeled protein structures, is proposed to be due to the location of T368R in a highly conserved region, rather than destabilization of the AmrB protein structure.
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Affiliation(s)
- Ainulkhir Hussin
- Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
- Department of Pathology, Queen Elizabeth Hospital, Ministry of Health Malaysia, Kota Kinabalu, Sabah, Malaysia
| | - Sheila Nathan
- Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Muhammad Ashraf Shahidan
- Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Mohd Yusof Nor Rahim
- Department of Pathology, Queen Elizabeth Hospital, Ministry of Health Malaysia, Kota Kinabalu, Sabah, Malaysia
| | - Mohamad Yusof Zainun
- Department of Pathology, Queen Elizabeth Hospital, Ministry of Health Malaysia, Kota Kinabalu, Sabah, Malaysia
| | | | - Nazlina Ibrahim
- Department of Biological Sciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
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11
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Mills KR, Torabifard H. Computational approaches to investigate fluoride binding, selectivity and transport across the membrane. Methods Enzymol 2024; 696:109-154. [PMID: 38658077 DOI: 10.1016/bs.mie.2024.01.006] [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: 04/26/2024]
Abstract
The use of molecular dynamics (MD) simulations to study biomolecular systems has proven reliable in elucidating atomic-level details of structure and function. In this chapter, MD simulations were used to uncover new insights into two phylogenetically unrelated bacterial fluoride (F-) exporters: the CLCF F-/H+ antiporter and the Fluc F- channel. The CLCF antiporter, a member of the broader CLC family, has previously revealed unique stoichiometry, anion-coordinating residues, and the absence of an internal glutamate crucial for proton import in the CLCs. Through MD simulations enhanced with umbrella sampling, we provide insights into the energetics and mechanism of the CLCF transport process, including its selectivity for F- over HF. In contrast, the Fluc F- channel presents a novel architecture as a dual topology dimer, featuring two pores for F- export and a central non-transported sodium ion. Using computational electrophysiology, we simulate the electrochemical gradient necessary for F- export in Fluc and reveal details about the coordination and hydration of both F- and the central sodium ion. The procedures described here delineate the specifics of these advanced techniques and can also be adapted to investigate other membrane protein systems.
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Affiliation(s)
- Kira R Mills
- Department of Chemistry & Biochemistry, The University of Texas at Dallas, Richardson, TX, United States
| | - Hedieh Torabifard
- Department of Chemistry & Biochemistry, The University of Texas at Dallas, Richardson, TX, United States.
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12
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Rana MM, Nguyen DD. Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation. J Phys Chem Lett 2023; 14:10870-10879. [PMID: 38032742 DOI: 10.1021/acs.jpclett.3c02679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.
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Affiliation(s)
- Md Masud Rana
- Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Duc Duy Nguyen
- Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States
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13
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Islam S, Pantazes RJ. Developing similarity matrices for antibody-protein binding interactions. PLoS One 2023; 18:e0293606. [PMID: 37883504 PMCID: PMC10602319 DOI: 10.1371/journal.pone.0293606] [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: 05/16/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.
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Affiliation(s)
- Sumaiya Islam
- Department of Chemical Engineering, Auburn University, Auburn, Alabama, United States of America
| | - Robert J. Pantazes
- Department of Chemical Engineering, Auburn University, Auburn, Alabama, United States of America
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14
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Shirvanizadeh N, Vihinen M. VariBench, new variation benchmark categories and data sets. FRONTIERS IN BIOINFORMATICS 2023; 3:1248732. [PMID: 37795169 PMCID: PMC10546188 DOI: 10.3389/fbinf.2023.1248732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/08/2023] [Indexed: 10/06/2023] Open
Affiliation(s)
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
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15
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Yang Z, Ye Z, Qiu J, Feng R, Li D, Hsieh C, Allcock J, Zhang S. A mutation-induced drug resistance database (MdrDB). Commun Chem 2023; 6:123. [PMID: 37316673 DOI: 10.1038/s42004-023-00920-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/02/2023] [Indexed: 06/16/2023] Open
Abstract
Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.
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Affiliation(s)
- Ziyi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Zhaofeng Ye
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Jiezhong Qiu
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Rongjun Feng
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Danyu Li
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | - Changyu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China
| | | | - Shengyu Zhang
- Tencent Quantum Laboratory, Shenzhen, 518057, Guangdong, China.
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16
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PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity. J Cheminform 2023; 15:31. [PMID: 36864534 PMCID: PMC9983232 DOI: 10.1186/s13321-023-00701-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/17/2023] [Indexed: 03/04/2023] Open
Abstract
Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.
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17
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Nandigrami P, Fiser A. Assessing the functional impact of protein binding site definition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525812. [PMID: 36747792 PMCID: PMC9900911 DOI: 10.1101/2023.01.26.525812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data this approach generates, it also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also provides guidance on the minimum expected accuracy of interface definition that is required to capture the biological function of a protein.
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Affiliation(s)
- Prithviraj Nandigrami
- Departments of Systems & Computational Biology, and Biochemistry, Albert Einstein College of Medicine 1300 Morris Park Ave, Bronx, NY 10461, USA
| | - Andras Fiser
- Departments of Systems & Computational Biology, and Biochemistry, Albert Einstein College of Medicine 1300 Morris Park Ave, Bronx, NY 10461, USA
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18
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Identification of NRAS Diagnostic Biomarkers and Drug Targets for Endometrial Cancer-An Integrated in Silico Approach. Int J Mol Sci 2022; 23:ijms232214285. [PMID: 36430761 PMCID: PMC9692821 DOI: 10.3390/ijms232214285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 11/19/2022] Open
Abstract
The diagnosis of endometrial cancer involves sequential, invasive tests to assess the thickness of the endometrium by a transvaginal ultrasound scan. In 6−33% of cases, endometrial biopsy results in inadequate tissue for a conclusive pathological diagnosis and 6% of postmenopausal women with non-diagnostic specimens are later discovered to have severe endometrial lesions. Thus, identifying diagnostic biomarkers could offer a non-invasive diagnosis for community or home-based triage of symptomatic or asymptomatic women. Herein, this study identified high-risk pathogenic nsSNPs in the NRAS gene. The nsSNPs of NRAS were retrieved from the NCBI database. PROVEAN, SIFT, PolyPhen-2, SNPs&GO, PhD-SNP and PANTHER were used to predict the pathogenicity of the nsSNPs. Eleven nsSNPs were identified as “damaging”, and further stability analysis using I-Mutant 2.0 and MutPred 2 indicated eight nsSNPs to cause decreased stability (DDG scores < −0.5). Post-translational modification and protein−protein interactions (PPI) analysis showed putative phosphorylation sites. The PPI network indicated a GFR-MAPK signalling pathway with higher node degrees that were further evaluated for drug targets. The P34L, G12C and Y64D showed significantly lower binding affinity towards GTP than wild-type. Furthermore, the Kaplan−Meier bioinformatics analyses indicated that the NRAS gene deregulation affected the overall survival rate of patients with endometrial cancer, leading to prognostic significance. Findings from this could be considered novel diagnostic and therapeutic markers.
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19
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Yu Y, Wang Z, Wang L, Tian S, Hou T, Sun H. Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses. J Cheminform 2022; 14:56. [PMID: 35987841 PMCID: PMC9392442 DOI: 10.1186/s13321-022-00639-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/08/2022] [Indexed: 12/04/2022] Open
Abstract
Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.
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20
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On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations. Antibodies (Basel) 2022; 11:antib11030051. [PMID: 35997345 PMCID: PMC9397028 DOI: 10.3390/antib11030051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/23/2022] [Accepted: 08/01/2022] [Indexed: 02/05/2023] Open
Abstract
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.
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21
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Li Y, Zhang R, Wang C, Forouhar F, Clarke OB, Vorobiev S, Singh S, Montelione GT, Szyperski T, Xu Y, Hunt JF. Oligomeric interactions maintain active-site structure in a noncooperative enzyme family. EMBO J 2022; 41:e108368. [PMID: 35801308 DOI: 10.15252/embj.2021108368] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 04/07/2022] [Accepted: 04/16/2022] [Indexed: 11/09/2022] Open
Abstract
The evolutionary benefit accounting for widespread conservation of oligomeric structures in proteins lacking evidence of intersubunit cooperativity remains unclear. Here, crystal and cryo-EM structures, and enzymological data, demonstrate that a conserved tetramer interface maintains the active-site structure in one such class of proteins, the short-chain dehydrogenase/reductase (SDR) superfamily. Phylogenetic comparisons support a significantly longer polypeptide being required to maintain an equivalent active-site structure in the context of a single subunit. Oligomerization therefore enhances evolutionary fitness by reducing the metabolic cost of enzyme biosynthesis. The large surface area of the structure-stabilizing oligomeric interface yields a synergistic gain in fitness by increasing tolerance to activity-enhancing yet destabilizing mutations. We demonstrate that two paralogous SDR superfamily enzymes with different specificities can form mixed heterotetramers that combine their individual enzymological properties. This suggests that oligomerization can also diversify the functions generated by a given metabolic investment, enhancing the fitness advantage provided by this architectural strategy.
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Affiliation(s)
- Yaohui Li
- Key Laboratory of Industrial Biotechnology of Ministry of Education and School of Biotechnology, Jiangnan University, Wuxi, China.,Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| | - Rongzhen Zhang
- Key Laboratory of Industrial Biotechnology of Ministry of Education and School of Biotechnology, Jiangnan University, Wuxi, China
| | - Chi Wang
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA.,Cryo-Electron Microscopy Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Farhad Forouhar
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA.,Macromolecular Crystallography Shared Resource, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Oliver B Clarke
- Department of Physiology and Cellular Biophysics and Department of Anesthesiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Vorobiev
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| | - Shikha Singh
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
| | - Gaetano T Montelione
- Department of Chemistry & Chemical Biology and Center for Biotechnology & Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Thomas Szyperski
- Department of Chemistry, State University of New York at Buffalo, Buffalo, NY, USA
| | - Yan Xu
- Key Laboratory of Industrial Biotechnology of Ministry of Education and School of Biotechnology, Jiangnan University, Wuxi, China
| | - John F Hunt
- Department of Biological Sciences, 702 Sherman Fairchild Center, MC2434, Columbia University, New York, NY, USA
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22
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Pabbathi A, Coleman L, Godar S, Paul A, Garlapati A, Spencer M, Eller J, Alper JD. Long-range electrostatic interactions significantly modulate the affinity of dynein for microtubules. Biophys J 2022; 121:1715-1726. [PMID: 35346642 PMCID: PMC9117880 DOI: 10.1016/j.bpj.2022.03.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/13/2022] [Accepted: 03/24/2022] [Indexed: 11/02/2022] Open
Abstract
The dynein family of microtubule minus-end-directed motor proteins drives diverse functions in eukaryotic cells, including cell division, intracellular transport, and flagellar beating. Motor protein processivity, which characterizes how far a motor walks before detaching from its filament, depends on the interaction between its microtubule-binding domain (MTBD) and the microtubule. Dynein's MTBD switches between high- and low-binding affinity states as it steps. Significant structural and functional data show that specific salt bridges within the MTBD and between the MTBD and the microtubule govern these affinity state shifts. However, recent computational work suggests that nonspecific, long-range electrostatic interactions between the MTBD and the microtubule may also play an important role in the processivity of dynein. To investigate this hypothesis, we mutated negatively charged amino acids remote from the dynein MTBD-microtubule-binding interface to neutral residues and measured the binding affinity using microscale thermophoresis and optical tweezers. We found a significant increase in the binding affinity of the mutated MTBDs for microtubules. Furthermore, we found that charge screening by free ions in solution differentially affected the binding and unbinding rates of MTBDs to microtubules. Together, these results demonstrate a significant role for long-range electrostatic interactions in regulating dynein-microtubule affinity. Moreover, these results provide insight into the principles that potentially underlie the biophysical differences between molecular motors with various processivities and protein-protein interactions more generally.
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Affiliation(s)
- Ashok Pabbathi
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina
| | - Lawrence Coleman
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina
| | - Subash Godar
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina
| | - Apurba Paul
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina; Eukaryotic Pathogen Innovations Center, Clemson, University, Clemson, South Carolina
| | - Aman Garlapati
- School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina
| | - Matheu Spencer
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina
| | - Jared Eller
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina; Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina
| | - Joshua Daniel Alper
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina; Eukaryotic Pathogen Innovations Center, Clemson, University, Clemson, South Carolina; Department of Biological Sciences, Clemson University, Clemson, South Carolina.
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23
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Koshlan T, Kulikov K. Analysis to determine the effect of mutations on binding to small chemical molecules. J Bioinform Comput Biol 2022; 20:2240003. [DOI: 10.1142/s0219720022400030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, the authors present and describe, in detail, an original software-implemented numerical methodology used to determine the effect of mutations on binding to small chemical molecules, on the example of gefitinib, AMPPNP, CO-1686, ASP8273, erlotinib binding with EGFR protein, and imatinib binding with PPARgamma. Furthermore, the developed numerical approach makes it possible to determine the stability of a molecular complex, which consists of a protein and a small chemical molecule. The description of the software package that implements the presented algorithm is given in the website: https://binomlabs.com/ .
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Affiliation(s)
- T. V. Koshlan
- Department of Molecular Biophysics, St. Petersburg State University, 199034 St. Petersburg, Russia
| | - K. G. Kulikov
- Department of Medical Physics, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
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24
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Lai J, Yang J, Gamsiz Uzun ED, Rubenstein BM, Sarkar IN. LYRUS: a machine learning model for predicting the pathogenicity of missense variants. BIOINFORMATICS ADVANCES 2021; 2:vbab045. [PMID: 35036922 PMCID: PMC8754197 DOI: 10.1093/bioadv/vbab045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 12/08/2021] [Accepted: 12/21/2021] [Indexed: 01/27/2023]
Abstract
SUMMARY Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53. AVAILABILITY AND IMPLEMENTATION LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Jiaying Lai
- Center for Biomedical Informatics, Brown University, Providence, RI 02903, USA,Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA
| | - Jordan Yang
- Department of Chemistry, Brown University, Providence, RI 02906, USA
| | - Ece D Gamsiz Uzun
- Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA,Department of Pathology and Laboratory Medicine, Brown University Alpert Medical School, Providence, RI 02903, USA,Department of Pathology, Rhode Island Hospital, Providence, RI 02903, USA
| | - Brenda M Rubenstein
- Center for Computational Molecular Biology, Brown University, Providence, RI 02906, USA,Department of Chemistry, Brown University, Providence, RI 02906, USA,To whom correspondence should be addressed. and
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Brown University, Providence, RI 02903, USA,Rhode Island Quality Institute, Providence, RI 02908, USA,To whom correspondence should be addressed. and
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25
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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: 4.3] [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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26
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Li G, Pahari S, Murthy AK, Liang S, Fragoza R, Yu H, Alexov E. SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity. Bioinformatics 2021; 37:992-999. [PMID: 32866236 PMCID: PMC8128451 DOI: 10.1093/bioinformatics/btaa761] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 08/17/2020] [Accepted: 08/24/2020] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. RESULTS Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37-0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein-protein interactions. AVAILABILITY AND IMPLEMENTATION SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. 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
| | - Swagata Pahari
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | | | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Robert Fragoza
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
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27
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Patel D, Patel JS, Ytreberg FM. Implementing and Assessing an Alchemical Method for Calculating Protein-Protein Binding Free Energy. J Chem Theory Comput 2021; 17:2457-2464. [PMID: 33709712 PMCID: PMC8044032 DOI: 10.1021/acs.jctc.0c01045] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Protein-protein binding is fundamental to most biological processes. It is important to be able to use computation to accurately estimate the change in protein-protein binding free energy due to mutations in order to answer biological questions that would be experimentally challenging, laborious, or time-consuming. Although nonrigorous free-energy methods are faster, rigorous alchemical molecular dynamics-based methods are considerably more accurate and are becoming more feasible with the advancement of computer hardware and molecular simulation software. Even with sufficient computational resources, there are still major challenges to using alchemical free-energy methods for protein-protein complexes, such as generating hybrid structures and topologies, maintaining a neutral net charge of the system when there is a charge-changing mutation, and setting up the simulation. In the current study, we have used the pmx package to generate hybrid structures and topologies, and a double-system/single-box approach to maintain the net charge of the system. To test the approach, we predicted relative binding affinities for two protein-protein complexes using a nonequilibrium alchemical method based on the Crooks fluctuation theorem and compared the results with experimental values. The method correctly identified stabilizing from destabilizing mutations for a small protein-protein complex, and a larger, more challenging antibody complex. Strong correlations were obtained between predicted and experimental relative binding affinities for both protein-protein systems.
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Affiliation(s)
- Dharmeshkumar Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States
- Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - F Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States
- Department of Physics, University of Idaho, Moscow, Idaho 83844, United States
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28
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Ochoa R, Laskowski RA, Thornton JM, Cossio P. Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders. Front Mol Biosci 2021; 8:636562. [PMID: 34222328 PMCID: PMC8253603 DOI: 10.3389/fmolb.2021.636562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/15/2021] [Indexed: 11/23/2022] Open
Abstract
The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
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29
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Chen Y, Lu H, Zhang N, Zhu Z, Wang S, Li M. PremPS: Predicting the impact of missense mutations on protein stability. PLoS Comput Biol 2020; 16:e1008543. [PMID: 33378330 PMCID: PMC7802934 DOI: 10.1371/journal.pcbi.1008543] [Citation(s) in RCA: 136] [Impact Index Per Article: 27.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 01/12/2021] [Accepted: 11/16/2020] [Indexed: 12/12/2022] Open
Abstract
Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.
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Affiliation(s)
- Yuting Chen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Haoyu Lu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Ning Zhang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Zefeng Zhu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Shuqin Wang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Minghui Li
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
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30
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Huang X, Zheng W, Pearce R, Zhang Y. SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function. Bioinformatics 2020; 36:2429-2437. [PMID: 31830252 DOI: 10.1093/bioinformatics/btz926] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Revised: 11/08/2019] [Accepted: 12/09/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein-protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. RESULTS We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities. AVAILABILITY AND IMPLEMENTATION Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics
| | - Robin Pearce
- Department of Computational Medicine and Bioinformatics
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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31
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Meseguer A, Dominguez L, Bota PM, Aguirre‐Plans J, Bonet J, Fernandez‐Fuentes N, Oliva B. Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions. Protein Sci 2020; 29:2112-2130. [PMID: 32797645 PMCID: PMC7513729 DOI: 10.1002/pro.3930] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 12/24/2022]
Abstract
Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods.
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Affiliation(s)
- Alberto Meseguer
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Lluis Dominguez
- Integrative Biomedical Informatics Group (GRIB‐IMIM). Department of Experimental and Life SciencesUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Patricia M. Bota
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
- Department of BiosciencesUniversitat de Vic‐Universitat Central de CatalunyaVicCataloniaSpain
| | - Joaquim Aguirre‐Plans
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Jaume Bonet
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Narcis Fernandez‐Fuentes
- Department of BiosciencesUniversitat de Vic‐Universitat Central de CatalunyaVicCataloniaSpain
- Institute of Biological, Environmental and Rural SciencesAberystwyth UniversityAberystwythUK
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
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32
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Zhang N, Lu H, Chen Y, Zhu Z, Yang Q, Wang S, Li M. PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions. Int J Mol Sci 2020; 21:ijms21155560. [PMID: 32756481 PMCID: PMC7432928 DOI: 10.3390/ijms21155560] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 07/28/2020] [Accepted: 07/30/2020] [Indexed: 12/23/2022] Open
Abstract
Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol−1, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.
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33
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Shringari SR, Giannakoulias S, Ferrie JJ, Petersson EJ. Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning. Chem Commun (Camb) 2020; 56:6774-6777. [PMID: 32441721 DOI: 10.1039/d0cc01959c] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify "hotspots" have remained elusive despite the myriad of modeling and machine learning methods tested. Here, for the first time, we demonstrate that nonlinear reweighting of energy terms from Rosetta, through the use of machine learning, exhibits improved predictability of ΔΔG values associated with interfacial mutations.
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Affiliation(s)
- Sumant R Shringari
- Department of Chemistry, University of Pennsylvania, 231 South 34th Street, Philadelphia, PA 19104, USA.
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34
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Pahari S, Li G, Murthy AK, Liang S, Fragoza R, Yu H, Alexov E. SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions. Int J Mol Sci 2020; 21:E2563. [PMID: 32272725 PMCID: PMC7177817 DOI: 10.3390/ijms21072563] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/04/2020] [Accepted: 04/05/2020] [Indexed: 12/26/2022] Open
Abstract
Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.
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Affiliation(s)
- Swagata Pahari
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
| | - Gen Li
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
| | - Adithya Krishna Murthy
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
| | - Siqi Liang
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA; (S.L.); (R.F.); (H.Y.)
| | - Robert Fragoza
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA; (S.L.); (R.F.); (H.Y.)
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA; (S.L.); (R.F.); (H.Y.)
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA; (S.P.); (G.L.); (A.K.M.)
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35
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Zhang N, Chen Y, Lu H, Zhao F, Alvarez RV, Goncearenco A, Panchenko AR, Li M. MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions. iScience 2020; 23:100939. [PMID: 32169820 PMCID: PMC7068639 DOI: 10.1016/j.isci.2020.100939] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/21/2019] [Accepted: 02/20/2020] [Indexed: 01/17/2023] Open
Abstract
Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.
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Affiliation(s)
- Ning Zhang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Yuting Chen
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Haoyu Lu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Feiyang Zhao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Roberto Vera Alvarez
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | - Alexander Goncearenco
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
| | - Anna R Panchenko
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA.
| | - Minghui Li
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.
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36
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Ahmed Awan Z, Bima A, Rashidi OM, Jamil K, Khan IA, Almukadi HS, Bilgrami AL, Ahmad Shaik N, Banaganapalli B. Low resolution protein mapping and KB-R7943 drug-protein molecular interaction analysis of long-QT syndrome linked KCNH2 mutations. ALL LIFE 2020. [DOI: 10.1080/26895293.2020.1737249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Affiliation(s)
- Zuhier Ahmed Awan
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulhadi Bima
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Omran M. Rashidi
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Kaiser Jamil
- Deptartment of Genetics, Bhagwan Mahavir Medical Research Centre, Hyderabad, India
| | - Imran A. Khan
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Haifa S. Almukadi
- Department of Pharmacology & Toxicology, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Anwar L. Bilgrami
- Department of Entomology, Rutgers University, New Brunswick, NJ, USA
- Deanship of Scientific Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Noor Ahmad Shaik
- Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Princess Al-Jawhara Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
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Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem Rev 2019; 119:9478-9508. [DOI: 10.1021/acs.chemrev.9b00055] [Citation(s) in RCA: 578] [Impact Index Per Article: 96.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Ercheng Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Huiyong Sun
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhe Wang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hui Liu
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - John Z. H. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU−ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200122, China
- Department of Chemistry, New York University, New York, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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Junaid M, Li CD, Shah M, Khan A, Guo H, Wei DQ. Extraction of molecular features for the drug discovery targeting protein-protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2. Proteins 2019; 87:837-849. [PMID: 31134671 DOI: 10.1002/prot.25748] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 05/04/2019] [Accepted: 05/22/2019] [Indexed: 12/13/2022]
Abstract
Half of the world population is infected by the Gram-negative bacterium Helicobacter pylori (H. pylori). It colonizes in the stomach and is associated with severe gastric pathologies including gastric cancer and peptic ulceration. The most virulent factor of H. pylori is the cytotoxin-associated gene A (CagA) that is injected into the host cell. CagA interacts with several host proteins and alters their function, thereby causing several diseases. The most well-known target of CagA is the tumor suppressor protein ASPP2. The subdomain I at the N-terminus of CagA interacts with the proline-rich motif of ASPP2. Here, in this study, we carried out alanine scanning mutagenesis and an extensive molecular dynamics simulation summing up to 3.8 μs to find out hot spot residues and discovered some new protein-protein interaction (PPI)-modulating molecules. Our findings are in line with previous biochemical studies and further suggested new residues that are crucial for binding. The alanine scanning showed that mutation of Y207 and T211 residues to alanine decreased the binding affinity. Likewise, dynamics simulation and molecular mechanics with generalized Born surface area (MMGBSA) analysis also showed the importance of these two residues at the interface. A four-feature pharmacophore model was developed based on these two residues, and top 10 molecules were filtered from ZINC, NCI, and ChEMBL databases. The good binding affinity of the CHEMBL17319 and CHEMBL1183979 molecules shows the reliability of our adopted protocol for binding hot spot residues. We believe that our study provides a new insight for using CagA as the therapeutic target for gastric cancer treatment and provides a platform for a future experimental study.
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Affiliation(s)
- Muhammad Junaid
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Cheng-Dong Li
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Masaud Shah
- Department of Molecular Science and Technology, Ajou University, Suwon, South Korea
| | - Abbas Khan
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Haoyue Guo
- Department of Physiology, McGill University, Montreal, Quebec, Canada
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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Clark AJ, Negron C, Hauser K, Sun M, Wang L, Abel R, Friesner RA. Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein-Protein Interfaces. J Mol Biol 2019; 431:1481-1493. [PMID: 30776430 PMCID: PMC6453258 DOI: 10.1016/j.jmb.2019.02.003] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 01/17/2019] [Accepted: 02/04/2019] [Indexed: 01/16/2023]
Abstract
Building on the substantial progress that has been made in using free energy perturbation (FEP) methods to predict the relative binding affinities of small molecule ligands to proteins, we have previously shown that results of similar quality can be obtained in predicting the effect of mutations on the binding affinity of protein–protein complexes. However, these results were restricted to mutations which did not change the net charge of the side chains due to known difficulties with modeling perturbations involving a change in charge in FEP. Various methods have been proposed to address this problem. Here we apply the co-alchemical water approach to study the efficacy of FEP calculations of charge changing mutations at the protein–protein interface for the antibody–gp120 system investigated previously and three additional complexes. We achieve an overall root mean square error of 1.2 kcal/mol on a set of 106 cases involving a change in net charge selected by a simple suitability filter using side-chain predictions and solvent accessible surface area to be relevant to a biologic optimization project. Reasonable, although less precise, results are also obtained for the 44 more challenging mutations that involve buried residues, which may in some cases require substantial reorganization of the local protein structure, which can extend beyond the scope of a typical FEP simulation. We believe that the proposed prediction protocol will be of sufficient efficiency and accuracy to guide protein engineering projects for which optimization and/or maintenance of a high degree of binding affinity is a key objective.
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Affiliation(s)
- Anthony J Clark
- Schrodinger Inc., 120 W 45th Street, New York, NY 10036, USA.
| | | | - Kevin Hauser
- Schrodinger Inc., 120 W 45th Street, New York, NY 10036, USA
| | - Mengzhen Sun
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3178, New York, NY 10027, USA
| | - Lingle Wang
- Schrodinger Inc., 120 W 45th Street, New York, NY 10036, USA
| | - Robert Abel
- Schrodinger Inc., 120 W 45th Street, New York, NY 10036, USA
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3178, New York, NY 10027, USA
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40
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Peng Y, Alexov E, Basu S. Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases. Int J Mol Sci 2019; 20:ijms20030548. [PMID: 30696058 PMCID: PMC6386852 DOI: 10.3390/ijms20030548] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 01/25/2019] [Accepted: 01/26/2019] [Indexed: 12/25/2022] Open
Abstract
Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.
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Affiliation(s)
- 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.
| | - Sankar Basu
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
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41
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Geng C, Xue LC, Roel‐Touris J, Bonvin AMJJ. Finding the ΔΔ
G
spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1410] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Li C. Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Jorge Roel‐Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
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42
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Maheepala DC, Emerling CA, Rajewski A, Macon J, Strahl M, Pabón-Mora N, Litt A. Evolution and Diversification of FRUITFULL Genes in Solanaceae. FRONTIERS IN PLANT SCIENCE 2019; 10:43. [PMID: 30846991 PMCID: PMC6394111 DOI: 10.3389/fpls.2019.00043] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Accepted: 01/11/2019] [Indexed: 05/12/2023]
Abstract
Ecologically and economically important fleshy edible fruits have evolved from dry fruit numerous times during angiosperm diversification. However, the molecular mechanisms that underlie these shifts are unknown. In the Solanaceae there has been a major shift to fleshy fruits in the subfamily Solanoideae. Evidence suggests that an ortholog of FRUITFULL (FUL), a transcription factor that regulates cell proliferation and limits the dehiscence zone in the silique of Arabidopsis, plays a similar role in dry-fruited Solanaceae. However, studies have shown that FUL orthologs have taken on new functions in fleshy fruit development, including regulating elements of tomato ripening such as pigment accumulation. FUL belongs to the core eudicot euFUL clade of the angiosperm AP1/FUL gene lineage. The euFUL genes fall into two paralogous clades, euFULI and euFULII. While most core eudicots have one gene in each clade, Solanaceae have two: FUL1 and FUL2 in the former, and MBP10 and MBP20 in the latter. We characterized the evolution of the euFUL genes to identify changes that might be correlated with the origin of fleshy fruit in Solanaceae. Our analyses revealed that the Solanaceae FUL1 and FUL2 clades probably originated through an early whole genome multiplication event. By contrast, the data suggest that the MBP10 and MBP20 clades are the result of a later tandem duplication event. MBP10 is expressed at weak to moderate levels, and its atypical short first intron lacks putative transcription factor binding sites, indicating possible pseudogenization. Consistent with this, our analyses show that MBP10 is evolving at a faster rate compared to MBP20. Our analyses found that Solanaceae euFUL gene duplications, evolutionary rates, and changes in protein residues and expression patterns are not correlated with the shift in fruit type. This suggests deeper analyses are needed to identify the mechanism underlying the change in FUL ortholog function.
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Affiliation(s)
- Dinusha C. Maheepala
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Christopher A. Emerling
- Institut des Sciences de l’Évolution de Montpellier, Université de Montpellier, Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, École Pratique des Hautes Études, Montpellier, France
| | - Alex Rajewski
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Jenna Macon
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
| | - Maya Strahl
- The New York Botanical Garden, Bronx, NY, United States
| | | | - Amy Litt
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
- *Correspondence: Amy Litt,
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43
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PremPDI estimates and interprets the effects of missense mutations on protein-DNA interactions. PLoS Comput Biol 2018; 14:e1006615. [PMID: 30533007 PMCID: PMC6303081 DOI: 10.1371/journal.pcbi.1006615] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 12/21/2018] [Accepted: 11/01/2018] [Indexed: 01/01/2023] Open
Abstract
Protein-DNA interactions play important roles in regulations of many vital cellular processes, including transcription, translation, DNA replication and recombination. Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function, potentially leading to diseases. Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need. To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change. The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes. PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0.71 and root-mean-square error of 0.86 kcal mol-1. The PremPDI server could map mutations on a structural protein-DNA complex, calculate the associated changes in binding affinity, determine the deleterious effect of a mutation, and produce a mutant structural model for download. PremPDI can be applied to many tasks, such as determination of potential damaging mutations in cancer and other diseases. PremPDI is available at http://lilab.jysw.suda.edu.cn/research/PremPDI/. Developing methods for accurate prediction of effects of amino acid substitutions on protein-DNA interactions is important for a wide range of biomedical applications such as understanding disease-causing mechanism of missense mutations and guiding protein engineering. Very few methods have been developed for predicting the effects of mutations on protein-DNA binding affinity. Here we report a new computational method, PRedicts the Effects of single Mutations on Protein-DNA Interactions (PremPDI). The core of the PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms that makes the PremPDI algorithm efficient and being fast enough to handle large number of cases. The performance of the PremPDI protocol was tested against experimentally determined binding free energy changes of 219 mutations from 49 protein-DNA complexes and yields very good correlation coefficient. The PremPDI webserver is available to the community at http://lilab.jysw.suda.edu.cn/research/PremPDI/.
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44
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Geng C, Vangone A, Folkers GE, Xue LC, Bonvin AMJJ. iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins 2018; 87:110-119. [PMID: 30417935 PMCID: PMC6587874 DOI: 10.1002/prot.25630] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 10/19/2018] [Accepted: 11/05/2018] [Indexed: 02/06/2023]
Abstract
Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning‐based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy‐based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein‐protein complexes. It competes with existing state‐of‐the‐art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.
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Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands.,Roche Pharmaceutical Research and Early Development, Large Molecule Research, Roche Innovation Center Penzberg, Penzberg, Germany
| | - Gert E Folkers
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, The Netherlands
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45
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Dobson L, Mészáros B, Tusnády GE. Structural Principles Governing Disease-Causing Germline Mutations. J Mol Biol 2018; 430:4955-4970. [DOI: 10.1016/j.jmb.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/11/2018] [Indexed: 01/03/2023]
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46
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Liu Q, Chen P, Wang B, Zhang J, Li J. dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions. BMC Bioinformatics 2018; 19:455. [PMID: 30482172 PMCID: PMC6260753 DOI: 10.1186/s12859-018-2493-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 11/13/2018] [Indexed: 02/06/2023] Open
Abstract
Background Protein-protein interactions (PPIs) play important roles in biological functions. Studies of the effects of mutants on protein interactions can provide further understanding of PPIs. Currently, many databases collect experimental mutants to assess protein interactions, but most of these databases are old and have not been updated for several years. Results To address this issue, we manually curated a kinetic and thermodynamic database of mutant protein interactions (dbMPIKT) that is freely accessible at our website. This database contains 5291 mutants in protein interactions collected from previous databases and the literature published within the last three years. Furthermore, some data analysis, such as mutation number, mutation type, protein pair source and network map construction, can be performed online. Conclusion Our work can promote the study on PPIs, and novel information can be mined from the new database. Our database is available in http://DeepLearner.ahu.edu.cn/web/dbMPIKT/ for use by all, including both academics and non-academics. Electronic supplementary material The online version of this article (10.1186/s12859-018-2493-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Quanya Liu
- Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Peng Chen
- Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
| | - Bing Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 243032, Anhui, China
| | - Jun Zhang
- School of Electronic Engineering & Automation, Anhui University, Hefei, 230601, Anhui, China
| | - Jinyan Li
- Advanced Analytics Institute and Centre for Health Technologies, University of Technology, Broadway, Sydney, NSW, 2007, Australia
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Šoštarić N, O'Reilly FJ, Giansanti P, Heck AJR, Gavin AC, van Noort V. Effects of Acetylation and Phosphorylation on Subunit Interactions in Three Large Eukaryotic Complexes. Mol Cell Proteomics 2018; 17:2387-2401. [PMID: 30181345 DOI: 10.1074/mcp.ra118.000892] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/27/2018] [Indexed: 01/18/2023] Open
Abstract
Protein post-translational modifications (PTMs) have an indispensable role in living cells as they expand chemical diversity of the proteome, providing a fine regulatory layer that can govern protein-protein interactions in changing environmental conditions. Here we investigated the effects of acetylation and phosphorylation on the stability of subunit interactions in purified Saccharomyces cerevisiae complexes, namely exosome, RNA polymerase II and proteasome. We propose a computational framework that consists of conformational sampling of the complexes by molecular dynamics simulations, followed by Gibbs energy calculation by MM/GBSA. After benchmarking against published tools such as FoldX and Mechismo, we could apply the framework for the first time on large protein assemblies with the aim of predicting the effects of PTMs located on interfaces of subunits on binding stability. We discovered that acetylation predominantly contributes to subunits' interactions in a locally stabilizing manner, while phosphorylation shows the opposite effect. Even though the local binding contributions of PTMs may be predictable to an extent, the long range effects and overall impact on subunits' binding were only captured because of our dynamical approach. Employing the developed, widely applicable workflow on other large systems will shed more light on the roles of PTMs in protein complex formation.
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Affiliation(s)
- Nikolina Šoštarić
- KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium
| | - Francis J O'Reilly
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany; Technical University of Berlin, Berlin, Germany
| | - Piero Giansanti
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Science4Life, Utrecht University, Utrecht, The Netherlands; Netherlands Proteomics Centre, Utrecht, The Netherlands; Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Science4Life, Utrecht University, Utrecht, The Netherlands; Netherlands Proteomics Centre, Utrecht, The Netherlands
| | - Anne-Claude Gavin
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Vera van Noort
- KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium; Leiden University, Institute of Biology Leiden, Leiden, The Netherlands.
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48
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Computational Approaches to Prioritize Cancer Driver Missense Mutations. Int J Mol Sci 2018; 19:ijms19072113. [PMID: 30037003 PMCID: PMC6073793 DOI: 10.3390/ijms19072113] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 07/02/2018] [Accepted: 07/05/2018] [Indexed: 12/31/2022] Open
Abstract
Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.
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49
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Jia Z, Li L, Peng Y, Ding F, Alexov E. The capricious electrostatic force: Revealing the signaling pathway in integrin α2-I domain. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2018. [DOI: 10.1142/s0219633618400011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Integrins are cellular adhesion proteins located on cell surface. They are known to have open and closed conformations that correspond to high and low binding affinity to ligands, respectively. Integrin [Formula: see text]2 binds to the ligands via the ligand binding domain, [Formula: see text]2-I domain, which also has open and closed conformations. Experimentally, the closed to open conformation change is shown to be triggered by pulling the C-terminal away from the ligand binding site, but how the signal propagates from the distant C-terminal to the binding site is unknown. To explain the mechanisms of the conformation change, we built models of the [Formula: see text]2-I domain open and closed conformations in ligand free and ligand bound states, respectively. We found that the signaling pathway consists of F313-I280-V252 residues that connect the C-terminal and the ligand binding site. The pathway is highly conserved as revealed by a protein sequence analysis among 55 species. Furthermore, MM/PBSA energy calculations on the stabilities and ligand binding affinities of the closed and open conformations are consistent with experimental measurements. The open conformation is more favorable for ligand binding, and the closed conformation is more stable in unbound state. Energy analysis also revealed the “hot spots” for ligand binding, and most residues that contribute strongly to ligand binding free energy are highly conserved in evolution. In addition, the electrostatic analysis showed that the closed conformation has stronger long-range electrostatic attraction to the ligand compared with the open conformation. The difference is caused by the rearrangement of several charged residues during the binding. These observations make us suggest that the integrin [Formula: see text]2-I domain binding process involves the two-step “dock-lock” mechanism. The closed conformation first attracts the ligand from a long distance and afterwards, the open conformation locks the ligand at the binding site with high binding affinity.
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Affiliation(s)
- Zhe Jia
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina 29634, United States
| | - Lin Li
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina 29634, United States
| | - Yunhui Peng
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina 29634, United States
| | - Feng Ding
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina 29634, United States
| | - Emil Alexov
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, South Carolina 29634, United States
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Barlow KA, Ó Conchúir S, Thompson S, Suresh P, Lucas JE, Heinonen M, Kortemme T. Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation. J Phys Chem B 2018; 122:5389-5399. [PMID: 29401388 DOI: 10.1021/acs.jpcb.7b11367] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Computationally modeling changes in binding free energies upon mutation (interface ΔΔ G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using "backrub" to generate an ensemble of models and then applies torsion minimization, side chain repacking, and averaging across this ensemble to estimate interface ΔΔ G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting nonlinear reweighting model improved the agreement with experimentally determined interface ΔΔ G values but also highlighted the necessity of future energy function improvements.
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Affiliation(s)
- Kyle A Barlow
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America
| | - Shane Ó Conchúir
- California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America
| | - Samuel Thompson
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - Pooja Suresh
- Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America
| | - James E Lucas
- Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America
| | - Markus Heinonen
- Department of Computer Science , Aalto University , Espoo , Finland.,Helsinki Institute for Information Technology (HIIT) , Helsinki , Finland
| | - Tanja Kortemme
- Graduate Program in Bioinformatics , University of California San Francisco , San Francisco , California , United States of America.,California Institute for Quantitative Biosciences , University of California San Francisco , San Francisco , California , United States of America.,Department of Bioengineering and Therapeutic Sciences , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Biophysics , University of California San Francisco , San Francisco , California , United States of America.,Graduate Program in Bioengineering , University of California San Francisco , San Francisco , California , United States of America.,Chan Zuckerberg Biohub , San Francisco , California 94158 , United States
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