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Tsishyn M, Cia G, Hermans P, Kwasigroch J, Rooman M, Pucci F. FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction. Hum Genomics 2024; 18:36. [PMID: 38627807 PMCID: PMC11020440 DOI: 10.1186/s40246-024-00605-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.
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Affiliation(s)
- Matsvei Tsishyn
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Pauline Hermans
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Jean Kwasigroch
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium.
- Interuniversity Institute of Bioinformatics in Brussels, Triumph Bvd, 1050, Brussels, Belgium.
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Yang Y, Wei Z, Cia G, Song X, Pucci F, Rooman M, Xue F, Hou Q. MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods. Front Immunol 2024; 15:1293706. [PMID: 38646540 PMCID: PMC11027168 DOI: 10.3389/fimmu.2024.1293706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/19/2024] [Indexed: 04/23/2024] Open
Abstract
Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.
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Affiliation(s)
- Yaqing Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Zhonghui Wei
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Xixi Song
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
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Cia G, Kwasigroch J, Stamatopoulos B, Rooman M, Pucci F. pyScoMotif: discovery of similar 3D structural motifs across proteins. Bioinform Adv 2023; 3:vbad158. [PMID: 38023327 PMCID: PMC10640396 DOI: 10.1093/bioadv/vbad158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/12/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023]
Abstract
Motivation The fast and accurate detection of similar geometrical arrangements of protein residues, known as 3D structural motifs, is highly relevant for many applications such as binding region and catalytic site detection, drug discovery and structure conservation analyses. With the recent publication of new protein structure prediction methods, the number of available protein structures is exploding, which makes efficient and easy-to-use tools for identifying 3D structural motifs essential. Results We present an open-source Python package that enables the search for both exact and mutated motifs with position-specific residue substitutions. The tool is efficient, flexible, accurate, and suitable to run both on computer clusters and personal laptops. Two successful applications of pyScoMotif for catalytic site identification are showcased. Availability and implementation The pyScoMotif package can be installed from the PyPI repository and is also available at https://github.com/3BioCompBio/pyScoMotif. It is free to use for non-commercial purposes.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triomflaan, Brussels,1050, Belgium
| | - Jean Kwasigroch
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium
| | - Basile Stamatopoulos
- Laboratory of Clinical Cell Therapy, Jules Bordet Institute, Université Libre de Bruxelles, Brussels, 1070, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triomflaan, Brussels,1050, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Triomflaan, Brussels,1050, Belgium
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Cia G, Pucci F, Rooman M. Critical review of conformational B-cell epitope prediction methods. Brief Bioinform 2023; 24:6972295. [PMID: 36611255 DOI: 10.1093/bib/bbac567] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 01/09/2023] Open
Abstract
Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, F. Roosevelt Avenue, 1050, Brussels, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Triumph Boulevard, 1050, Brussels, Belgium
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Cia G, Kwasigroch JM, Rooman M, Pucci F. SpikePro: a webserver to predict the fitness of SARS-CoV-2 variants. Bioinformatics 2022; 38:4418-4419. [PMID: 35861514 DOI: 10.1093/bioinformatics/btac517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 07/07/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The SARS-CoV-2 virus has shown a remarkable ability to evolve and spread across the globe through successive waves of variants since the original Wuhan lineage. Despite all the efforts of the last 2 years, the early and accurate prediction of variant severity is still a challenging issue which needs to be addressed to help, for example, the decision of activating COVID-19 plans long before the peak of new waves. Upstream preparation would indeed make it possible to avoid the overflow of health systems and limit the most severe cases. RESULTS We recently developed SpikePro, a structure-based computational model capable of quickly and accurately predicting the viral fitness of a variant from its spike protein sequence. It is based on the impact of mutations on the stability of the spike protein as well as on its binding affinity for the angiotensin-converting enzyme 2 (ACE2) and for a set of neutralizing antibodies. It yields a precise indication of the virus transmissibility, infectivity, immune escape and basic reproduction rate. We present here an updated version of the model that is now available on an easy-to-use webserver, and illustrate its power in a retrospective study of fitness evolution and reproduction rate of the main viral lineages. SpikePro is thus expected to be great help to assess the fitness of newly emerging SARS-CoV-2 variants in genomic surveillance and viral evolution programs. AVAILABILITY AND IMPLEMENTATION SpikePro webserver http://babylone.ulb.ac.be/SpikePro/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50, Brussels 1050, Belgium
| | - Jean Marc Kwasigroch
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50, Brussels 1050, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50, Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, Brussels 1050, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50, Brussels 1050, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Boulevard du Triomphe, Brussels 1050, Belgium
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Cia G, Pucci F, Rooman M. Analysis of the Neutralizing Activity of Antibodies Targeting Open or Closed SARS-CoV-2 Spike Protein Conformations. Int J Mol Sci 2022; 23:ijms23042078. [PMID: 35216194 PMCID: PMC8876721 DOI: 10.3390/ijms23042078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 12/20/2022] Open
Abstract
SARS-CoV-2 infection elicits a polyclonal neutralizing antibody (nAb) response that primarily targets the spike protein, but it is still unclear which nAbs are immunodominant and what distinguishes them from subdominant nAbs. This information would however be crucial to predict the evolutionary trajectory of the virus and design future vaccines. To shed light on this issue, we gathered 83 structures of nAbs in complex with spike protein domains. We analyzed in silico the ability of these nAbs to bind the full spike protein trimer in open and closed conformations, and predicted the change in binding affinity of the most frequently observed spike protein variants in the circulating strains. This led us to define four nAb classes with distinct variant escape fractions. By comparing these fractions with those measured from plasma of infected patients, we showed that the class of nAbs that most contributes to the immune response is able to bind the spike protein in its closed conformation. Although this class of nAbs only partially inhibits the spike protein binding to the host’s angiotensin converting enzyme 2 (ACE2), it has been suggested to lock the closed pre-fusion spike protein conformation and therefore prevent its transition to an open state. Furthermore, comparison of our predictions with mRNA-1273 vaccinated patient plasma measurements suggests that spike proteins contained in vaccines elicit a different nAb class than the one elicited by natural SARS-CoV-2 infection and suggests the design of highly stable closed-form spike proteins as next-generation vaccine immunogens.
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Affiliation(s)
- Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (G.C.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (G.C.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, 1050 Brussels, Belgium; (G.C.); (F.P.)
- Interuniversity Institute of Bioinformatics in Brussels, 1050 Brussels, Belgium
- Correspondence:
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Zhang Z, Jiang C, Cia G, Liao G. [Comparative study on baicalin contents in 4 traditional Chinese medicinal preparations of ying huang]. Zhongguo Zhong Yao Za Zhi 1995; 20:283-4, 319. [PMID: 7492358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The contents of baicalin in four different traditional Chinese medicinal preparations of Ying Huang were determined by HPLC. The contents of baicalin in Ying Huang tablets, Ying Huang oral liquid, Ying Huang injection and Ying Huang granules are respectively: 124.18 mg/g, 21.12 mg/ml, 12.39 mg/ml and 6.79 mg/g. The HPLC method suggested in this paper can be used for quality control in the production of Ying Huang preparations.
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Affiliation(s)
- Z Zhang
- School of Pharmacy, West China University of Medical Sciences, Chengdu
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Cia G, Durántez D, Crivell L, Dovale F. [Duplication of the appendix]. Rev Esp Enferm Apar Dig 1985; 67:191-3. [PMID: 3983456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Cia G, Durántez D, Crivell L, Dovale F. [Intestinal perforation caused by a fish bone]. Rev Esp Enferm Apar Dig 1985; 67:199-201. [PMID: 3885349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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