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Paiva VA, Mendonça MV, Silveira SA, Ascher DB, Pires DEV, Izidoro SC. GASS-Metal: identifying metal-binding sites on protein structures using genetic algorithms. Brief Bioinform 2022; 23:6590153. [PMID: 35595534 DOI: 10.1093/bib/bbac178] [Citation(s) in RCA: 2] [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: 01/24/2022] [Revised: 04/18/2022] [Accepted: 04/20/2022] [Indexed: 12/12/2022] Open
Abstract
Metals are present in >30% of proteins found in nature and assist them to perform important biological functions, including storage, transport, signal transduction and enzymatic activity. Traditional and experimental techniques for metal-binding site prediction are usually costly and time-consuming, making computational tools that can assist in these predictions of significant importance. Here we present Genetic Active Site Search (GASS)-Metal, a new method for protein metal-binding site prediction. The method relies on a parallel genetic algorithm to find candidate metal-binding sites that are structurally similar to curated templates from M-CSA and MetalPDB. GASS-Metal was thoroughly validated using homologous proteins and conservative mutations of residues, showing a robust performance. The ability of GASS-Metal to identify metal-binding sites was also compared with state-of-the-art methods, outperforming similar methods and achieving an MCC of up to 0.57 and detecting up to 96.1% of the sites correctly. GASS-Metal is freely available at https://gassmetal.unifei.edu.br. The GASS-Metal source code is available at https://github.com/sandroizidoro/gassmetal-local.
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Affiliation(s)
- Vinícius A Paiva
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Murillo V Mendonça
- Institute of Technological Sciences, Campus Theodomiro Carneiro Santiago, Universidade Federal de Itajubá, Itabira, Brazil
| | - Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Sandro C Izidoro
- Institute of Technological Sciences, Campus Theodomiro Carneiro Santiago, Universidade Federal de Itajubá, Itabira, Brazil
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Santana CA, Izidoro SC, de Melo-Minardi RC, Tyzack JD, Ribeiro AJM, Pires DEV, Thornton JM, de A Silveira S. GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs. Nucleic Acids Res 2022; 50:W392-W397. [PMID: 35524575 PMCID: PMC9252730 DOI: 10.1093/nar/gkac323] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/14/2022] [Accepted: 04/22/2022] [Indexed: 11/14/2022] Open
Abstract
Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.
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Affiliation(s)
- Charles A Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil.,Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Sandro C Izidoro
- Institute of Technological Sciences (ICT), Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - Raquel C de Melo-Minardi
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil.,Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Jonathan D Tyzack
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - António J M Ribeiro
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Parkville 3052, Australia
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
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Santana CA, Silveira SDA, Moraes JPA, Izidoro SC, de Melo-Minardi RC, Ribeiro AJM, Tyzack JD, Borkakoti N, Thornton JM. GRaSP: a graph-based residue neighborhood strategy to predict binding sites. Bioinformatics 2020; 36:i726-i734. [DOI: 10.1093/bioinformatics/btaa805] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2020] [Indexed: 01/22/2023] Open
Abstract
Abstract
Motivation
The discovery of protein–ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein–ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost.
Results
We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10–20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2–5 h on average.
Availability and implementation
The source code and datasets are available at https://github.com/charles-abreu/GRaSP.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Charles A Santana
- Department of Biochemistry and Immunology
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
- Institute of Technological Sciences (ICT), Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - João P A Moraes
- Institute of Technological Sciences (ICT), Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - Sandro C Izidoro
- Institute of Technological Sciences (ICT), Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira 35903-087, Brazil
| | - Raquel C de Melo-Minardi
- Department of Biochemistry and Immunology
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - António J M Ribeiro
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jonathan D Tyzack
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Neera Borkakoti
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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Ribeiro VS, Santana CA, Fassio AV, Cerqueira FR, da Silveira CH, Romanelli JPR, Patarroyo-Vargas A, Oliveira MGA, Gonçalves-Almeida V, Izidoro SC, de Melo-Minardi RC, Silveira SDA. visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level. BMC Bioinformatics 2020; 21:80. [PMID: 32164574 PMCID: PMC7068867 DOI: 10.1186/s12859-020-3347-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces. Results To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets. Conclusions We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.
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Affiliation(s)
- Vagner S Ribeiro
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Charles A Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Alexandre V Fassio
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Fabio R Cerqueira
- Department of Production Engineering, Universidade Federal Fluminense, Petrópolis, 25650-050, Brazil
| | - Carlos H da Silveira
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - João P R Romanelli
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - Adriana Patarroyo-Vargas
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Maria G A Oliveira
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.,Instituto de Biotecnologia aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Valdete Gonçalves-Almeida
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sandro C Izidoro
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil. .,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
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Moraes JPA, Pappa GL, Pires DEV, Izidoro SC. GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms. Nucleic Acids Res 2019; 45:W315-W319. [PMID: 28459991 PMCID: PMC5570142 DOI: 10.1093/nar/gkx337] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 04/27/2017] [Indexed: 02/01/2023] Open
Abstract
Enzyme active sites are important and conserved functional regions of proteins whose identification can be an invaluable step toward protein function prediction. Most of the existing methods for this task are based on active site similarity and present limitations including performing only exact matches on template residues, template size restraints, despite not being capable of finding inter-domain active sites. To fill this gap, we proposed GASS-WEB, a user-friendly web server that uses GASS (Genetic Active Site Search), a method based on an evolutionary algorithm to search for similar active sites in proteins. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures. The method has shown to be very effective on a range of experiments and was able to correctly identify >90% of the catalogued active sites from the Catalytic Site Atlas. It also managed to achieve a Matthew correlation coefficient of 0.63 using the Critical Assessment of protein Structure Prediction (CASP 10) dataset. In our analysis, GASS was ranking fourth among 18 methods. GASS-WEB is freely available at http://gass.unifei.edu.br/.
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Affiliation(s)
- João P A Moraes
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá - UNIFEI, Itabira, 35903-087, Brazil
| | - Gisele L Pappa
- Department of Computer Science, Universidade Federal de Minas Gerais - UFMG, Belo Horizonte, 31270-901, Brazil
| | - Douglas E V Pires
- Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, 30190-002, Brazil
| | - Sandro C Izidoro
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá - UNIFEI, Itabira, 35903-087, Brazil
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Abstract
MOTIVATION Currently, 25% of proteins annotated in Pfam have their function unknown. One way of predicting proteins function is by looking at their active site, which has two main parts: the catalytic site and the substrate binding site. The active site is more conserved than the other residues of the protein and can be a rich source of information for protein function prediction. This article presents a new heuristic method, named genetic active site search (GASS), which searches for given active site 3D templates in unknown proteins. The method can perform non-exact amino acid matches (conservative mutations), is able to find amino acids in different chains and does not impose any restrictions on the active site size. RESULTS GASS results were compared with those catalogued in the catalytic site atlas (CSA) in four different datasets and compared with two other methods: amino acid pattern search for substructures and motif and catalytic site identification. The results show GASS can correctly identify >90% of the templates searched. Experiments were also run using data from the substrate binding sites prediction competition CASP 10, and GASS is ranked fourth among the 18 methods considered.
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Affiliation(s)
- Sandro C Izidoro
- Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Raquel C de Melo-Minardi
- Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
| | - Gisele L Pappa
- Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil Advanced Campus at Itabira, Universidade Federal de Itajubá, Itajubá, MG 35903-087, Brazil and Department of Computer Science and Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, MG 31270-901, Brazil
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