1
|
BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction. J Mol Biol 2022; 434:167556. [DOI: 10.1016/j.jmb.2022.167556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/12/2022] [Accepted: 03/16/2022] [Indexed: 11/20/2022]
|
2
|
Pan T, Gao Y, Xu G, Li Y. Bioinformatics Methods for Modeling microRNA Regulatory Networks in Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:161-186. [DOI: 10.1007/978-3-031-08356-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
3
|
Arici MK, Tuncbag N. Performance Assessment of the Network Reconstruction Approaches on Various Interactomes. Front Mol Biosci 2021; 8:666705. [PMID: 34676243 PMCID: PMC8523993 DOI: 10.3389/fmolb.2021.666705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.
Collapse
Affiliation(s)
- M Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.,School of Medicine, Koc University, Istanbul, Turkey
| |
Collapse
|
4
|
Alborzi SZ, Ahmed Nacer A, Najjar H, Ritchie DW, Devignes MD. PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions. PLoS Comput Biol 2021; 17:e1008844. [PMID: 34370723 PMCID: PMC8376228 DOI: 10.1371/journal.pcbi.1008844] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 08/19/2021] [Accepted: 07/12/2021] [Indexed: 12/26/2022] Open
Abstract
Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/. We revisit at a large scale the question of inferring DDIs from PPIs. Compared to previous studies, we take a unified approach accross multiple sources of PPIs. This approach is a method for inferring new edges in a tripartite graph setting and can be compared to link prediction approaches in knowledge graphs. Aggregation of several sources is performed using an optimized weighted average of the individual scores calculated in each source. A huge dataset of over 84K DDIs is produced which far exceeds the previous datasets. We show that a significant portion of the PPIDM dataset covers a large number of PPIs from curated (IMEx) or non curated (STRING) databases. Such a reservoir of DDIs deserves further exploration and can be combined with high-throughput methods such as cross-linking mass spectrometry to identify plausible protein partners of proteins of interest.
Collapse
|
5
|
Zhao Z, Xu W, Chen A, Han Y, Xia S, Xiang C, Wang C, Jiao J, Wang H, Yuan X, Gu L. Protein functional module identification method combining topological features and gene expression data. BMC Genomics 2021; 22:423. [PMID: 34103008 PMCID: PMC8185953 DOI: 10.1186/s12864-021-07620-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. RESULTS This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. CONCLUSIONS The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.
Collapse
Affiliation(s)
- Zihao Zhao
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Wenjun Xu
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Aiwen Chen
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Yueyue Han
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Shengrong Xia
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - ChuLei Xiang
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Chao Wang
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Jun Jiao
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Hui Wang
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Xiaohui Yuan
- Department of Computer Science and Engineering, University of North Texas, Denton, TX, 76203, United States
| | - Lichuan Gu
- School of Computer and Information, Anhui Agricultural University, Hefei, Anhui, 230036, China.
| |
Collapse
|
6
|
Narykov O, Bogatov D, Korkin D. DISPOT: a simple knowledge-based protein domain interaction statistical potential. Bioinformatics 2020; 35:5374-5378. [PMID: 31350874 PMCID: PMC6954640 DOI: 10.1093/bioinformatics/btz587] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 06/17/2019] [Accepted: 07/22/2019] [Indexed: 01/01/2023] Open
Abstract
MOTIVATION The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task. RESULTS Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. AVAILABILITY AND IMPLEMENTATION DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Oleksandr Narykov
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Dmytro Bogatov
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Dmitry Korkin
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA.,Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA
| |
Collapse
|
7
|
Singh V, Singh G, Singh V. TulsiPIN: An Interologous Protein Interactome of Ocimum tenuiflorum. J Proteome Res 2020; 19:884-899. [PMID: 31789043 DOI: 10.1021/acs.jproteome.9b00683] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Ocimum tenuiflorum, commonly known as holy basil or tulsi, is globally recognized for its multitude of medicinal properties. However, a comprehensive study revealing the complex interplay among its constituent proteins at subcellular level is still lacking. To bridge this gap, in this work, a genome-scale interologous protein-protein interaction (PPI) network, TulsiPIN, is developed using 36 template plants, which consists of 13 660 nodes and 327 409 binary interactions. A high confidence network, hc-TulsiPIN, consisting of 7719 nodes having 95 532 interactions is inferred using domain-domain interaction information along with interolog-based statistics, and its reliability is assessed using pathway enrichment, functional homogeneity, and protein colocalization of PPIs. Examination of topological features revealed that hc-TulsiPIN possesses conventional properties, like small-world, scale-free, and modular architecture. A total of 1625 vital proteins are predicted by statistically evaluating hc-TulsiPIN with two ensembles of corresponding random networks, each consisting of 10 000 realizations of Erdoős-Rényi and Barabási-Albert models. Also, numerous regulatory proteins like transcription factors, transcription regulators, and protein kinases are profiled. Using 36 guide genes participating in 9 secondary metabolite biosynthetic pathways, a subnetwork consisting of 171 proteins and 612 interactions was constructed, and 127 of these proteins could be successfully characterized. Detailed information of TulsiPIN is available at https://cuhpcbbtulsipin.shinyapps.io/tulsipin_v0/ .
Collapse
Affiliation(s)
- Vikram Singh
- Centre for Computational Biology and Bioinformatics , Central University of Himahcal Pradesh , Dharamshala 176206 , India
| | - Gagandeep Singh
- Centre for Computational Biology and Bioinformatics , Central University of Himahcal Pradesh , Dharamshala 176206 , India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics , Central University of Himahcal Pradesh , Dharamshala 176206 , India
| |
Collapse
|
8
|
Sanchez-Garcia R, Sorzano COS, Carazo JM, Segura J. BIPSPI: a method for the prediction of partner-specific protein-protein interfaces. Bioinformatics 2019; 35:470-477. [PMID: 30020406 PMCID: PMC6361243 DOI: 10.1093/bioinformatics/bty647] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 07/17/2018] [Indexed: 11/15/2022] Open
Abstract
Motivation Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction. Computational approaches are orders of magnitude cheaper and faster than experimental ones, leading to proliferation of multiple methods aimed to predict which residues belong to the interface of an interaction. Results We present BIPSPI, a new machine learning-based method for the prediction of partner-specific PPI sites. Contrary to most binding site prediction methods, the proposed approach takes into account a pair of interacting proteins rather than a single one in order to predict partner-specific binding sites. BIPSPI has been trained employing sequence-based and structural features from both protein partners of each complex compiled in the Protein-Protein Docking Benchmark version 5.0 and in an additional set independently compiled. Also, a version trained only on sequences has been developed. The performance of our approach has been assessed by a leave-one-out cross-validation over different benchmarks, outperforming state-of-the-art methods. Availability and implementation BIPSPI web server is freely available at http://bipspi.cnb.csic.es. BIPSPI code is available at https://github.com/bioinsilico/BIPSPI. Docker image is available at https://hub.docker.com/r/bioinsilico/bipspi/. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ruben Sanchez-Garcia
- GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain
| | - C O S Sorzano
- GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain
| | - J M Carazo
- GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain
| | - Joan Segura
- GN7 of the Spanish National Institute for Bioinformatics (INB), Biocomputing Unit, National Center of Biotechnology (CSIC), Instruct Image Processing Center, Madrid, Spain
| |
Collapse
|
9
|
Alkan F, Erten C. RedNemo: topology-based PPI network reconstruction via repeated diffusion with neighborhood modifications. Bioinformatics 2017; 33:537-544. [PMID: 27797764 DOI: 10.1093/bioinformatics/btw655] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 10/12/2016] [Indexed: 01/28/2023] Open
Abstract
Motivation Analysis of protein-protein interaction (PPI) networks provides invaluable insight into several systems biology problems. High-throughput experimental techniques together with computational methods provide large-scale PPI networks. However, a major issue with these networks is their erroneous nature; they contain false-positive interactions and usually many more false-negatives. Recently, several computational methods have been proposed for network reconstruction based on topology, where given an input PPI network the goal is to reconstruct the network by identifying false-positives/-negatives as correctly as possible. Results We observe that the existing topology-based network reconstruction algorithms suffer several shortcomings. An important issue is regarding the scalability of their computational requirements, especially in terms of execution times, with the network sizes. They have only been tested on small-scale networks thus far and when applied on large-scale networks of popular PPI databases, the executions require unreasonable amounts of time, or may even crash without producing any output for some instances even after several months of execution. We provide an algorithm, RedNemo, for the topology-based network reconstruction problem. It provides more accurate networks than the alternatives as far as biological qualities measured in terms of most metrics based on gene ontology annotations. The recovery of a high-confidence network modified via random edge removals and rewirings is also better with RedNemo than with the alternatives under most of the experimented removal/rewiring ratios. Furthermore, through extensive tests on databases of varying sizes, we show that RedNemo achieves these results with much better running time performances. Availability and Implementation Supplementary material including source code, useful scripts, experimental data and the results are available at http://webprs.khas.edu.tr/~cesim/RedNemo.tar.gz. Contact cesim@khas.edu.tr. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ferhat Alkan
- Center for Non-coding RNA in Technology and Health.,Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Grønnegardsvej 3, Frederiksberg, DK1870, Denmark
| | - Cesim Erten
- Department of Computer Engineering, Kadir Has University, Cibali, 34083 Istanbul, Turkey
| |
Collapse
|
10
|
Conesa Mingo P, Gutierrez J, Quintana A, de la Rosa Trevín JM, Zaldívar-Peraza A, Cuenca Alba J, Kazemi M, Vargas J, Del Cano L, Segura J, Sorzano COS, Carazo JM. Scipion web tools: Easy to use cryo-EM image processing over the web. Protein Sci 2017; 27:269-275. [PMID: 28971542 DOI: 10.1002/pro.3315] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 09/28/2017] [Accepted: 09/28/2017] [Indexed: 11/08/2022]
Abstract
Macromolecular structural determination by Electron Microscopy under cryogenic conditions is revolutionizing the field of structural biology, interesting a large community of potential users. Still, the path from raw images to density maps is complex, and sophisticated image processing suites are required in this process, often demanding the installation and understanding of different software packages. Here, we present Scipion Web Tools, a web-based set of tools/workflows derived from the Scipion image processing framework, specially tailored to nonexpert users in need of very precise answers at several key stages of the structural elucidation process.
Collapse
Affiliation(s)
- Pablo Conesa Mingo
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | - José Gutierrez
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | - Adrián Quintana
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | | | | | - Jesús Cuenca Alba
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | - Mohsen Kazemi
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | - Javier Vargas
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | - Laura Del Cano
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | - Joan Segura
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| | | | - Jose María Carazo
- Centro Nacional de Biotecnología (CNB-CSIC), Cantoblanco, Madrid, 28049, Spain
| |
Collapse
|
11
|
Garcia-Garcia J, Valls-Comamala V, Guney E, Andreu D, Muñoz FJ, Fernandez-Fuentes N, Oliva B. iFrag: A Protein–Protein Interface Prediction Server Based on Sequence Fragments. J Mol Biol 2017; 429:382-389. [DOI: 10.1016/j.jmb.2016.11.034] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Revised: 11/27/2016] [Accepted: 11/30/2016] [Indexed: 01/08/2023]
|
12
|
Segura J, Sanchez-Garcia R, Tabas-Madrid D, Cuenca-Alba J, Sorzano COS, Carazo JM. 3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling. Biophys J 2016; 110:766-75. [PMID: 26772592 PMCID: PMC4775853 DOI: 10.1016/j.bpj.2015.11.3519] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Revised: 11/27/2015] [Accepted: 11/30/2015] [Indexed: 11/19/2022] Open
Abstract
Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es.
Collapse
Affiliation(s)
- Joan Segura
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain.
| | - Ruben Sanchez-Garcia
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Daniel Tabas-Madrid
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Jesus Cuenca-Alba
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Carlos Oscar S Sorzano
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| | - Jose Maria Carazo
- GN7, Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, Madrid, Spain
| |
Collapse
|