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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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2
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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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3
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Zhuang Q, Holt BA, Kwong GA, Qiu P. Deconvolving multiplexed protease signatures with substrate reduction and activity clustering. PLoS Comput Biol 2019; 15:e1006909. [PMID: 31479443 PMCID: PMC6743790 DOI: 10.1371/journal.pcbi.1006909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 09/13/2019] [Accepted: 07/29/2019] [Indexed: 12/16/2022] Open
Abstract
Proteases are multifunctional, promiscuous enzymes that degrade proteins as well as peptides and drive important processes in health and disease. Current technology has enabled the construction of libraries of peptide substrates that detect protease activity, which provides valuable biological information. An ideal library would be orthogonal, such that each protease only hydrolyzes one unique substrate, however this is impractical due to off-target promiscuity (i.e., one protease targets multiple different substrates). Therefore, when a library of probes is exposed to a cocktail of proteases, each protease activates multiple probes, producing a convoluted signature. Computational methods for parsing these signatures to estimate individual protease activities primarily use an extensive collection of all possible protease-substrate combinations, which require impractical amounts of training data when expanding to search for more candidate substrates. Here we provide a computational method for estimating protease activities efficiently by reducing the number of substrates and clustering proteases with similar cleavage activities into families. We envision that this method will be used to extract meaningful diagnostic information from biological samples. The activity of enzymatic proteins, which are called proteases, drives numerous important processes in health and disease: including cancer, immunity, and infectious disease. Many labs have developed useful diagnostics by designing sensors that measure the activity of these proteases. However, if we want to detect multiple proteases at the same time, it becomes impractical to design sensors that only detect one protease. This is due to a phenomenon called protease promiscuity, which means that proteases will activate multiple different sensors. Computational methods have been created to solve this problem, but the challenge is that these often require large amounts of training data. Further, completely different proteases may be detected by the same subset of sensors. In this work, we design a computational method to overcome this problem by clustering similar proteases into "subfamilies", which increases estimation accuracy. Further, our method tests multiple combinations of sensors to maintain accuracy while minimizing the number of sensors used. Together, we envision that this work will increase the amount of useful information we can extract from biological samples, which may lead to better clinical diagnostics.
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Affiliation(s)
- Qinwei Zhuang
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Brandon Alexander Holt
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of Medicine, Atlanta, Georgia, United States of America
| | - Gabriel A. Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of Medicine, Atlanta, Georgia, United States of America
- Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Integrated Cancer Research Center, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Georgia ImmunoEngineering Consortium, Georgia Tech and Emory University, Atlanta, Georgia, United States of America
- * E-mail: (GAK); (PQ)
| | - Peng Qiu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech College of Engineering and Emory School of Medicine, Atlanta, Georgia, United States of America
- Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail: (GAK); (PQ)
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4
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Fassio AV, Martins PM, Guimarães SDS, Junior SSA, Ribeiro VS, de Melo-Minardi RC, Silveira SDA. Vermont: a multi-perspective visual interactive platform for mutational analysis. BMC Bioinformatics 2017; 18:403. [PMID: 28929973 PMCID: PMC5606220 DOI: 10.1186/s12859-017-1789-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2.0, a visual interactive platform that combines sequence and structural parameters with interactive visualizations to make the impact of protein point mutations more understandable. RESULTS We aimed to contribute a novel visual analytics oriented method to analyze and gain insight on the impact of protein point mutations. To assess the ability of VERMONT to do this, we visually examined a set of mutations that were experimentally characterized to determine if VERMONT could identify damaging mutations and why they can be considered so. CONCLUSIONS VERMONT allowed us to understand mutations by interpreting position-specific structural and physicochemical properties. Additionally, we note some specific positions we believe have an impact on protein function/structure in the case of mutation.
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Affiliation(s)
- Alexandre V Fassio
- Department of Computer Science, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil. .,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil.
| | - Pedro M Martins
- Department of Computer Science, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil
| | - Samuel da S Guimarães
- Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil
| | - Sócrates S A Junior
- Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil
| | - Vagner S Ribeiro
- Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, 6627, Antônio Carlos avenue, Pampulha, Belo Horizonte, 31270-901, Brazil
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil
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5
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He W, Liang Z, Teng M, Niu L. LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition. FEBS Open Bio 2016; 6:1331-1340. [PMID: 28255540 PMCID: PMC5324770 DOI: 10.1002/2211-5463.12150] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 10/26/2016] [Accepted: 10/27/2016] [Indexed: 11/23/2022] Open
Abstract
Identifying conserved binding motifs is an efficient way to study protein–ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand‐binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its analogs are highly similar to well‐known motifs reported by previous studies. The superiority of our method to handle flexible ligands was also demonstrated using isocitric acid as an example. Finally, we show that these motifs, together with their visual exhibition, permit better investigating and understanding of protein–ligand recognition process.
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Affiliation(s)
- Wei He
- Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences University of Science and Technology of China Anhui China
| | - Zhi Liang
- Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences University of Science and Technology of China Anhui China
| | - MaiKun Teng
- Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences University of Science and Technology of China Anhui China
| | - LiWen Niu
- Hefei National Laboratory for Physical Sciences at Microscale and School of Life Sciences University of Science and Technology of China Anhui China
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6
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An Intriguing Correlation Based on the Superimposition of Residue Pairs with Inhibitors that Target Protein-Protein Interfaces. Sci Rep 2016; 6:18543. [PMID: 26730437 PMCID: PMC4698585 DOI: 10.1038/srep18543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 11/19/2015] [Indexed: 11/26/2022] Open
Abstract
Druggable sites on protein-protein interfaces are difficult to predict. To survey inhibitor-binding sites onto which residues are superimposed at protein-protein interfaces, we analyzed publicly available information for 39 inhibitors that target the protein-protein interfaces of 8 drug targets. By focusing on the differences between residues that were superimposed with inhibitors and non-superimposed residues, we observed clear differences in the distances and changes in the solvent-accessible surface areas (∆SASA). Based on the observation that two or more residues were superimposed onto inhibitors in 37 (95%) of 39 protein-inhibitor complexes, we focused on the two-residue relationships. Application of a cross-validation procedure confirmed a linear negative correlation between the absolute value of the dihedral angle and the sum of the ∆SASAs of the residues. Finally, we applied the regression equation of this correlation to four inhibitors that bind to new sites not bound by the 39 inhibitors as well as additional inhibitors of different targets. Our results shed light on the two-residue correlation between the absolute value of the dihedral angle and the sum of the ∆SASA, which may be a useful relationship for identifying the key two-residues as potential targets of protein-protein interfaces.
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7
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Gonçalves WRS, Gonçalves-Almeida VM, Arruda AL, Meira W, da Silveira CH, Pires DEV, de Melo-Minardi RC. PDBest: a user-friendly platform for manipulating and enhancing protein structures. Bioinformatics 2015; 31:2894-6. [PMID: 25910698 DOI: 10.1093/bioinformatics/btv223] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 04/19/2015] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility. AVAILABILITY AND IMPLEMENTATION PDBest installation packages are freely available for several platforms at http://www.pdbest.dcc.ufmg.br CONTACT wellisson@dcc.ufmg.br, dpires@dcc.ufmg.br, raquelcm@dcc.ufmg.br SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Aleksander L Arruda
- Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
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8
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Silveira SA, Fassio AV, Gonçalves-Almeida VM, de Lima EB, Barcelos YT, Aburjaile FF, Rodrigues LM, Meira W, de Melo-Minardi RC. VERMONT: Visualizing mutations and their effects on protein physicochemical and topological property conservation. BMC Proc 2014; 8:S4. [PMID: 25237391 PMCID: PMC4155615 DOI: 10.1186/1753-6561-8-s2-s4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
In this paper, we propose an interactive visualization called VERMONT which tackles the problem of visualizing mutations and infers their possible effects on the conservation of physicochemical and topological properties in protein families. More specifically, we visualize a set of structure-based sequence alignments and integrate several structural parameters that should aid biologists in gaining insight into possible consequences of mutations. VERMONT allowed us to identify patterns of position-specific properties as well as exceptions that may help predict whether specific mutations could damage protein function.
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Affiliation(s)
- Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Alexandre V Fassio
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Valdete M Gonçalves-Almeida
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Elisa B de Lima
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Yussif T Barcelos
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Flávia F Aburjaile
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Laerte M Rodrigues
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
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9
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Ebina T, Umezawa Y, Kuroda Y. IS-Dom: a dataset of independent structural domains automatically delineated from protein structures. J Comput Aided Mol Des 2013; 27:419-26. [PMID: 23715893 DOI: 10.1007/s10822-013-9654-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2012] [Accepted: 05/07/2013] [Indexed: 11/25/2022]
Abstract
Protein domains that can fold in isolation are significant targets in diverse area of proteomics research as they are often readily analyzed by high-throughput methods. Here, we report IS-Dom, a dataset of Independent Structural Domains (ISDs) that are most likely to fold in isolation. IS-Dom was constructed by filtering domains from SCOP, CATH, and DomainParser using quantitative structural measures, which were calculated by estimating inter-domain hydrophobic clusters and hydrogen bonds from the full length protein's atomic coordinates. The ISD detection protocol is fully automated, and all of the computed interactions are stored in the server which enables rapid update of IS-Dom. We also prepared a standard IS-Dom using parameters optimized by maximizing the Youden's index. The standard IS-Dom, contained 54,860 ISDs, of which 25.5 % had high sequence identity and termini overlap with a Protein Data Bank (PDB) cataloged sequence and are thus experimentally shown to fold in isolation [coined autonomously folded domain (AFDs)]. Furthermore, our ISD detection protocol missed less than 10 % of the AFDs, which corroborated our protocol's ability to define structural domains that are able to fold independently. IS-Dom is available through the web server ( http://domserv.lab.tuat.ac.jp/IS-Dom.html ), and users can either, download the standard IS-Dom dataset, construct their own IS-Dom by interactively varying the parameters, or assess the structural independence of newly defined putative domains.
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Affiliation(s)
- Teppei Ebina
- Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 12-24-16 Nakamachi, Koganei-shi, Tokyo 184-8588, Japan.
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10
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Pires DEV, de Melo-Minardi RC, da Silveira CH, Campos FF, Meira W. aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction. ACTA ACUST UNITED AC 2013; 29:855-61. [PMID: 23396119 DOI: 10.1093/bioinformatics/btt058] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design. RESULTS We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitor's techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario. AVAILABILITY AND IMPLEMENTATION Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Douglas E V Pires
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Pampulha Belo Horizonte - MG, 31270-901, Brazil.
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