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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] [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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Proteus: An algorithm for proposing stabilizing mutation pairs based on interactions observed in known protein 3D structures. BMC Bioinformatics 2020; 21:275. [PMID: 32611389 PMCID: PMC7330979 DOI: 10.1186/s12859-020-03575-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 05/28/2020] [Indexed: 11/10/2022] Open
Abstract
Background Protein engineering has many applications for industry, such as the development of new drugs, vaccines, treatment therapies, food, and biofuel production. A common way to engineer a protein is to perform mutations in functionally essential residues to optimize their function. However, the discovery of beneficial mutations for proteins is a complex task, with a time-consuming and high cost for experimental validation. Hence, computational approaches have been used to propose new insights for experiments narrowing the search space and reducing the costs. Results In this study, we developed Proteus (an acronym for Protein Engineering Supporter), a new algorithm for proposing mutation pairs in a target 3D structure. These suggestions are based on contacts observed in other known structures from Protein Data Bank (PDB). Proteus’ basic assumption is that if a non-interacting pair of amino acid residues in the target structure is exchanged to an interacting pair, this could enhance protein stability. This trade is only allowed if the main-chain conformation of the residues involved in the contact is conserved. Furthermore, no steric impediment is expected between the proposed mutations and the surrounding protein atoms. To evaluate Proteus, we performed two case studies with proteins of industrial interests. In the first case study, we evaluated if the mutations suggested by Proteus for four protein structures enhance the number of inter-residue contacts. Our results suggest that most mutations proposed by Proteus increase the number of interactions into the protein. In the second case study, we used Proteus to suggest mutations for a lysozyme protein. Then, we compared Proteus’ outcomes to mutations with available experimental evidence reported in the ProTherm database. Four mutations, in which our results agree with the experimental data, were found. This could be initial evidence that changes in the side-chain of some residues do not cause disturbances that harm protein structure stability. Conclusion We believe that Proteus could be used combined with other methods to give new insights into the rational development of engineered proteins. Proteus user-friendly web-based tool is available at <http://proteus.dcc.ufmg.br>.
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Mariano D, Pantuza N, Santos LH, Rocha REO, de Lima LHF, Bleicher L, de Melo-Minardi RC. Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases. BMC Mol Cell Biol 2020; 21:50. [PMID: 32611314 PMCID: PMC7329481 DOI: 10.1186/s12860-020-00293-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 06/22/2020] [Indexed: 11/22/2022] Open
Abstract
Β-glucosidases are key enzymes used in second-generation biofuel production. They act in the last step of the lignocellulose saccharification, converting cellobiose in glucose. However, most of the β-glucosidases are inhibited by high glucose concentrations, which turns it a limiting step for industrial production. Thus, β-glucosidases have been targeted by several studies aiming to understand the mechanism of glucose tolerance, pH and thermal resistance for constructing more efficient enzymes. In this paper, we present a database of β-glucosidase structures, called Glutantβase. Our database includes 3842 GH1 β-glucosidase sequences collected from UniProt. We modeled the sequences by comparison and predicted important features in the 3D-structure of each enzyme. Glutantβase provides information about catalytic and conserved amino acids, residues of the coevolution network, protein secondary structure, and residues located in the channel that guides to the active site. We also analyzed the impact of beneficial mutations reported in the literature, predicted in analogous positions, for similar enzymes. We suggested these mutations based on six previously described mutants that showed high catalytic activity, glucose tolerance, or thermostability (A404V, E96K, H184F, H228T, L441F, and V174C). Then, we used molecular docking to verify the impact of the suggested mutations in the affinity of protein and ligands (substrate and product). Our results suggest that only mutations based on the H228T mutant can reduce the affinity for glucose (product) and increase affinity for cellobiose (substrate), which indicates an increment in the resistance to product inhibition and agrees with computational and experimental results previously reported in the literature. More resistant β-glucosidases are essential to saccharification in industrial applications. However, thermostable and glucose-tolerant β-glucosidases are rare, and their glucose tolerance mechanisms appear to be related to multiple and complex factors. We gather here, a set of information, and made predictions aiming to provide a tool for supporting the rational design of more efficient β-glucosidases. We hope that Glutantβase can help improve second-generation biofuel production. Glutantβase is available at http://bioinfo.dcc.ufmg.br/glutantbase .
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Affiliation(s)
- Diego Mariano
- Laboratory of Bioinformatics and Systems. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil.
| | - Naiara Pantuza
- Laboratory of Bioinformatics and Systems. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Lucianna H Santos
- Laboratory of Bioinformatics and Systems. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Rafael E O Rocha
- Laboratory of Bioinformatics and Systems. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Leonardo H F de Lima
- Laboratory of Molecular Modelling and Bioinformatics (LAMMB), Department of Physical and Biological Sciences, Universidade Federal de São João Del-Rei, Campus Sete Lagoas, Sete Lagoas, 35701-970, Brazil
| | - Lucas Bleicher
- Protein Computational Biology Laboratory, Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Raquel Cardoso de Melo-Minardi
- Laboratory of Bioinformatics and Systems. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil.
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Queiroz FC, Vargas AMP, Oliveira MGA, Comarela GV, Silveira SA. ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces. BMC Bioinformatics 2020; 21:143. [PMID: 32293241 PMCID: PMC7158050 DOI: 10.1186/s12859-020-3474-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 03/27/2020] [Indexed: 12/15/2022] Open
Abstract
Background Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. Results ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. Conclusions ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.
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Affiliation(s)
- Felippe C Queiroz
- Department of Computer Science, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.
| | - Adriana M P Vargas
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil
| | - Maria G A Oliveira
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.,Instituto de Biotecnologia aplicada a Agropecuaria, BIOAGRO-UFV, Av Peter Henry Rolfs, Viçosa MG, Brazil
| | - Giovanni V Comarela
- Department of Computer Science, Universidade Federal do Espírito Santo, Av Fernando Ferrari, Vitória, ES, Brazil
| | - Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, 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] [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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Aydınkal RM, Serçinoğlu O, Ozbek P. ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism. Nucleic Acids Res 2019; 47:W471-W476. [PMID: 31114881 PMCID: PMC6602423 DOI: 10.1093/nar/gkz390] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/17/2019] [Accepted: 05/09/2019] [Indexed: 01/14/2023] Open
Abstract
ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.
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Affiliation(s)
- Rasim Murat Aydınkal
- Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul 34722, Turkey
- Ali Nihat Gokyigit Foundation, Etiler, Istanbul 34340, Turkey
| | - Onur Serçinoğlu
- Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul 34722, Turkey
- Department of Bioengineering, Faculty of Engineering, Recep Tayyip Erdoğan University, Rize 53100, Turkey
| | - Pemra Ozbek
- Department of Bioengineering, Faculty of Engineering, Marmara University, Kadikoy, Istanbul 34722, Turkey
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