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Yang Q, Liu Z, Xu X, Wang J, Du B, Zhang P, Liu B, Mu X, Tong Z. Virtual Screening and Validation of Affinity DNA Functional Ligands for IgG Fc Segment. Int J Mol Sci 2024; 25:8681. [PMID: 39201368 PMCID: PMC11354668 DOI: 10.3390/ijms25168681] [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: 08/05/2024] [Accepted: 08/07/2024] [Indexed: 09/02/2024] Open
Abstract
The effective attachment of antibodies to the immune sensing interface is a crucial factor that determines the detection performance of immunosensors. Therefore, this study aims to investigate a novel antibody immobilization material with low molecular weight, high stability, and excellent directional immobilization effect. In this study, we employed molecular docking technology based on the ZDOCK algorithm to virtually screen DNA functional ligands (DNAFL) for the Fc segment of antibodies. Through a comprehensive analysis of the key binding sites and contact propensities at the interface between DNAFL and IgG antibody, we have gained valuable insights into the affinity relationship, as well as the principles governing amino acid and nucleotide interactions at this interface. Furthermore, molecular affinity experiments and competitive binding experiments were conducted to validate both the binding ability of DNAFL to IgG antibody and its actual binding site. Through affinity experiments using multi-base sequences, we identified bases that significantly influence antibody-DNAFL binding and successfully obtained DNAFL with an enhanced affinity towards the IgG Fc segment. These findings provide a theoretical foundation for the targeted design of higher-affinity DNAFLs while also presenting a new technical approach for immunosensor preparation with potential applications in biodetection.
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Affiliation(s)
| | - Zhiwei Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (Q.Y.); (X.X.); (J.W.); (B.D.); (P.Z.); (B.L.); (X.M.)
| | | | | | | | | | | | | | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China; (Q.Y.); (X.X.); (J.W.); (B.D.); (P.Z.); (B.L.); (X.M.)
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2
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Harini K, Sekijima M, Gromiha MM. PRA-Pred: Structure-based prediction of protein-RNA binding affinity. Int J Biol Macromol 2024; 259:129490. [PMID: 38224813 DOI: 10.1016/j.ijbiomac.2024.129490] [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: 12/10/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Sekijima
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8501, Japan; Department of Computer Science, National University of Singapore, Singapore.
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3
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Angles R, Arenas-Salinas M, García R, Ingram B. An optimized relational database for querying structural patterns in proteins. Database (Oxford) 2024; 2024:baad093. [PMID: 38236197 PMCID: PMC10939390 DOI: 10.1093/database/baad093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/16/2023] [Accepted: 12/19/2023] [Indexed: 01/19/2024]
Abstract
A database is an essential component in almost any software system, and its creation involves more than just data modeling and schema design. It also includes query optimization and tuning. This paper focuses on a web system called GSP4PDB, which is used for searching structural patterns in proteins. The system utilizes a normalized relational database, which has proven to be inefficient even for simple queries. This article discusses the optimization of the GSP4PDB database by implementing two techniques: denormalization and indexing. The empirical evaluation described in the article shows that combining these techniques enhances the efficiency of the database when querying both real and artificial graph-based structural patterns.
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Affiliation(s)
- Renzo Angles
- Department of Computer Science, Faculty of Engineering, Universidad de Talca, Camino a Los Niches Km. 1, Curicó, Región del Maule 3340000, Chile
- Millennium Institute for Foundational Research on Data (IMFD), Vicuña Mackenna 4860, Macul, Santiago, Región Metropolitana 7810000, Chile
| | - Mauricio Arenas-Salinas
- Centro de Bioinformática y Simulación Molecular (CBSM), Faculty of Engineering, Universidad de Talca, Av. Lircay s/n, Talca Región del Maule 34600000, Chile
| | - Roberto García
- Millennium Institute for Foundational Research on Data (IMFD), Vicuña Mackenna 4860, Macul, Santiago, Región Metropolitana 7810000, Chile
- Engineering Systems Doctoral Program, Faculty of Engineering, Universidad de Talca, Camino a Los Niches Km 1, Curicó, Región del Maule 3340000, Chile
| | - Ben Ingram
- School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire MK43 0AL, England
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Harini K, Kihara D, Michael Gromiha M. PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods 2023; 213:10-17. [PMID: 36924867 PMCID: PMC10563387 DOI: 10.1016/j.ymeth.2023.03.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/17/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein-DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (ΔG) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to understand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experimental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein-DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/.
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Affiliation(s)
- K Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States; Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan.
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5
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Chou HH, Hsu CT, Hsu CW, Yao KH, Wang HC, Hsieh SY. Novel Algorithm for Improved Protein Classification Using Graph Similarity. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3135-3143. [PMID: 34748498 DOI: 10.1109/tcbb.2021.3125836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Considerable sequence data are produced in genome annotation projects that relate to molecular levels, structural similarities, and molecular and biological functions. In structural genomics, the most essential task involves resolving protein structures efficiently with hardware or software, understanding these structures, and assigning their biological functions. Understanding the characteristics and functions of proteins enables the exploration of the molecular mechanisms of life. In this paper, we examine the problems of protein classification. Because they perform similar biological functions, proteins in the same family usually share similar structural characteristics. We employed this premise in designing a classification algorithm. In this algorithm, auxiliary graphs are used to represent proteins, with every amino acid in a protein to a vertex in a graph. Moreover, the links between amino acids correspond to the edges between the vertices. The proposed algorithm classifies proteins according to the similarities in their graphical structures. The proposed algorithm is efficient and accurate in distinguishing proteins from different families and outperformed related algorithms experimentally.
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Valdés-Jiménez A, Jiménez-González D, Kiper AK, Rinné S, Decher N, González W, Reyes-Parada M, Núñez-Vivanco G. A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels. Front Pharmacol 2022; 13:855792. [PMID: 35370665 PMCID: PMC8971525 DOI: 10.3389/fphar.2022.855792] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 02/21/2022] [Indexed: 01/01/2023] Open
Abstract
The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.
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Affiliation(s)
- Alejandro Valdés-Jiménez
- Center for Bioinformatics, Simulations and Modelling, Faculty of Engineering, University of Talca, Talca, Chile
- Computer Architecture Department, Universitat Politécnica de Catalunya, Barcelona, Spain
| | - Daniel Jiménez-González
- Computer Architecture Department, Universitat Politécnica de Catalunya, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Aytug K. Kiper
- Institute for Physiology and Pathophysiology, Philipps-University Marburg, Marburg, Germany
| | - Susanne Rinné
- Institute for Physiology and Pathophysiology, Philipps-University Marburg, Marburg, Germany
| | - Niels Decher
- Institute for Physiology and Pathophysiology, Philipps-University Marburg, Marburg, Germany
| | - Wendy González
- Center for Bioinformatics, Simulations and Modelling, Faculty of Engineering, University of Talca, Talca, Chile
- Millennium Nucleus of Ion Channels-Associated Diseases (MiNICAD), Universidad de Talca, Talca, Chile
- *Correspondence: Wendy González, ; Miguel Reyes-Parada, ; Gabriel Núñez-Vivanco,
| | - Miguel Reyes-Parada
- Centro de Investigación Biomédica y Aplicada (CIBAP), Escuela de Medicina, Facultad de Ciencias Médicas, Universidad de Santiago de Chile, Santiago, Chile
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile
- *Correspondence: Wendy González, ; Miguel Reyes-Parada, ; Gabriel Núñez-Vivanco,
| | - Gabriel Núñez-Vivanco
- Departamento de Ciencias Naturales y Tecnología, Universidad de Aysén, Coyhaique, Chile
- *Correspondence: Wendy González, ; Miguel Reyes-Parada, ; Gabriel Núñez-Vivanco,
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7
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Angles R, Arenas-Salinas M, García R, Reyes-Suarez JA, Pohl E. GSP4PDB: a web tool to visualize, search and explore protein-ligand structural patterns. BMC Bioinformatics 2020; 21:85. [PMID: 32164553 PMCID: PMC7068854 DOI: 10.1186/s12859-020-3352-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank. RESULTS We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand - amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can "draw" a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern. The user can further explore and analyse the results using a wide range of filters, or download their related information for external post-processing and analysis. CONCLUSIONS GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction.
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Affiliation(s)
- Renzo Angles
- Department of Computer Science, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
- Millennium Institute for Foundational Research on Data, Santiago, Chile
| | - Mauricio Arenas-Salinas
- Centro de Bioinformática y Simulación Molecular, Universidad de Talca, Talca, Chile
- Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
| | - Roberto García
- Millennium Institute for Foundational Research on Data, Santiago, Chile
- Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
| | - Jose Antonio Reyes-Suarez
- Centro de Bioinformática y Simulación Molecular, Universidad de Talca, Talca, Chile
- Faculty of Engineering, Universidad de Talca, Camino Los Niches Km 1, Curicó, Chile
| | - Ehmke Pohl
- Department of Chemistry, Durham University, Durham, DH1 3LE United Kingdom
- Department of Biosciences, Durham University, Durham, DH1 3LE United Kingdom
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Mayol E, Campillo M, Cordomí A, Olivella M. Inter-residue interactions in alpha-helical transmembrane proteins. Bioinformatics 2019; 35:2578-2584. [PMID: 30566615 DOI: 10.1093/bioinformatics/bty978] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 10/19/2018] [Accepted: 12/17/2018] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in α-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date. RESULTS Our analysis of residue-residue interactions in TM segments of membrane proteins shows that almost all interactions involve aliphatic residues and Phe. There is lack of polar-polar, polar-charged and charged-charged interactions except for those between Thr or Ser sidechains and the backbone carbonyl of aliphatic and Phe residues. The results are discussed in the context of the preferences of amino acids to be in the protein core or exposed to the lipid bilayer and to occupy specific positions along the TM segment. Comparison to datasets of β-barrel membrane proteins and of α-helical globular proteins unveils the specific patterns of interactions and residue composition characteristic of α-helical membrane proteins that are the clue to understanding their structure. AVAILABILITY AND IMPLEMENTATION Results data and datasets used are available at http://lmc.uab.cat/TMalphaDB/interactions.php. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eduardo Mayol
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Mercedes Campillo
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Arnau Cordomí
- Laboratori de Medicina Computacional, Unitat de Bioestadística, Facultat de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Mireia Olivella
- Bioinformatics Area, School of International Studies, ESCI-UPF, Barcelona, Spain.,Bioinformatics and Medical Statistics Group, U Science Tech, Central University of Catalonia, Vic, Barcelona, Spain
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Kaiser F, Labudde D. Unsupervised Discovery of Geometrically Common Structural Motifs and Long-Range Contacts in Protein 3D Structures. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:671-680. [PMID: 29990265 DOI: 10.1109/tcbb.2017.2786250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The essential role of small evolutionarily conserved structural units in proteins has been extensively researched and validated. A popular example are serine proteases, where the peptide cleavage reaction is realized by a configuration of only three residues. Brought to spatial proximity during the protein folding process, such structural motifs are often long-range contacts and usually hard to detect at sequence level. Due to the constantly increasing resource of protein 3D structure data, the computational identification of structural motifs can contribute significantly to the understanding of protein fold and function. Thus, we propose a method to discover structural motifs of high geometrical similarity and desired sequence separation in protein 3D structure data. By utilizing methods originated from data mining, no a priori knowledge is required. The applicability of the method is demonstrated by the identification of the catalytic unit of serine proteases and the ion-coordination center of cupredoxins. Furthermore, large-scale analysis of the entire Protein Data Bank points towards the presence of ubiquitous structural motifs, independent of any specific fold or function. We envision that our method is suitable to uncover functional mechanisms and to derive fingerprint libraries of structural motifs, which could be used to assess protein family association.
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Mrzic A, Meysman P, Bittremieux W, Moris P, Cule B, Goethals B, Laukens K. Grasping frequent subgraph mining for bioinformatics applications. BioData Min 2018; 11:20. [PMID: 30202444 PMCID: PMC6122726 DOI: 10.1186/s13040-018-0181-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 08/13/2018] [Indexed: 11/18/2022] Open
Abstract
Searching for interesting common subgraphs in graph data is a well-studied problem in data mining. Subgraph mining techniques focus on the discovery of patterns in graphs that exhibit a specific network structure that is deemed interesting within these data sets. The definition of which subgraphs are interesting and which are not is highly dependent on the application. These techniques have seen numerous applications and are able to tackle a range of biological research questions, spanning from the detection of common substructures in sets of biomolecular compounds, to the discovery of network motifs in large-scale molecular interaction networks. Thus far, information about the bioinformatics application of subgraph mining remains scattered over heterogeneous literature. In this review, we provide an introduction to subgraph mining for life scientists. We give an overview of various subgraph mining algorithms from a bioinformatics perspective and present several of their potential biomedical applications.
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Affiliation(s)
- Aida Mrzic
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.,2Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
| | - Pieter Meysman
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.,2Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
| | - Wout Bittremieux
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.,2Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
| | - Pieter Moris
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.,2Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
| | - Boris Cule
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - Bart Goethals
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- 1Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium.,2Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Antwerp, Belgium
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12
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Dhifli W, Diallo AB. ProtNN: fast and accurate protein 3D-structure classification in structural and topological space. BioData Min 2016; 9:30. [PMID: 27688811 PMCID: PMC5034655 DOI: 10.1186/s13040-016-0108-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 08/22/2016] [Indexed: 11/30/2022] Open
Abstract
Background Studying the functions and structures of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the classification of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the classification of protein structures. Results We propose ProtNN, a novel classification approach for protein 3D-structures. Given an unannotated query protein structure and a set of annotated proteins, ProtNN assigns to the query protein the class with the highest number of votes across the k nearest neighbor reference proteins, where k is a user-defined parameter. The search of the nearest neighbor annotated structures is based on a protein-graph representation model and pairwise similarities between vector embedding of the query and the reference protein structures in structural and topological spaces. Conclusions We demonstrate through an extensive experimental evaluation that ProtNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that ProtNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude in runtime compared to state-of-the-art approaches.
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Affiliation(s)
- Wajdi Dhifli
- Department of Computer Science, University of Quebec At Montreal, PO box 8888, Downtown stationMontreal, H3C 3P8 Canada
| | - Abdoulaye Baniré Diallo
- Department of Computer Science, University of Quebec At Montreal, PO box 8888, Downtown stationMontreal, H3C 3P8 Canada
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