1
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Escobedo N, Monzon AM, Fornasari MS, Palopoli N, Parisi G. Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q. Curr Protoc 2023; 3:e764. [PMID: 37184204 DOI: 10.1002/cpz1.764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q Basic Protocol 2: Exploring conformational diversity in a protein family Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family Basic Protocol 3: Representing conformational diversity in a phylogenetic context.
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Affiliation(s)
- Nahuel Escobedo
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | | | - María Silvina Fornasari
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Nicolas Palopoli
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Gustavo Parisi
- Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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2
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Tran NH, Xu J, Li M. A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction. Brief Bioinform 2022; 23:bbab493. [PMID: 34891158 PMCID: PMC8769896 DOI: 10.1093/bib/bbab493] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/11/2021] [Accepted: 10/26/2021] [Indexed: 12/30/2022] Open
Abstract
In this article, we review two challenging computational questions in protein science: neoantigen prediction and protein structure prediction. Both topics have seen significant leaps forward by deep learning within the past five years, which immediately unlocked new developments of drugs and immunotherapies. We show that deep learning models offer unique advantages, such as representation learning and multi-layer architecture, which make them an ideal choice to leverage a huge amount of protein sequence and structure data to address those two problems. We also discuss the impact and future possibilities enabled by those two applications, especially how the data-driven approach by deep learning shall accelerate the progress towards personalized biomedicine.
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Affiliation(s)
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, USA
| | - Ming Li
- University of Waterloo, Canada
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3
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Yadav NS, Kumar P, Singh I. Structural and functional analysis of protein. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00026-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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4
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Kryshtafovych A, Moult J, Billings WM, Della Corte D, Fidelis K, Kwon S, Olechnovič K, Seok C, Venclovas Č, Won J. Modeling SARS-CoV-2 proteins in the CASP-commons experiment. Proteins 2021; 89:1987-1996. [PMID: 34462960 PMCID: PMC8616790 DOI: 10.1002/prot.26231] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/23/2021] [Accepted: 08/26/2021] [Indexed: 01/21/2023]
Abstract
Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).
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Affiliation(s)
| | - John Moult
- Department of Cell Biology and Molecular genetics, Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland, USA
| | - Wendy M Billings
- Department of Physics & Astronomy, Brigham Young University, Provo, Utah, USA
| | - Dennis Della Corte
- Department of Physics & Astronomy, Brigham Young University, Provo, Utah, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, Davis, California, USA
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, South Korea
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, South Korea
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, South Korea
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5
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Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy. Int J Mol Sci 2021; 22:ijms22094408. [PMID: 33922489 PMCID: PMC8122964 DOI: 10.3390/ijms22094408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 12/02/2022] Open
Abstract
Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.
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6
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Jia K, Jernigan RL. New amino acid substitution matrix brings sequence alignments into agreement with structure matches. Proteins 2021; 89:671-682. [PMID: 33469973 PMCID: PMC8641535 DOI: 10.1002/prot.26050] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/08/2021] [Accepted: 01/12/2021] [Indexed: 12/27/2022]
Abstract
Protein sequence matching presently fails to identify many structures that are highly similar, even when they are known to have the same function. The high packing densities in globular proteins lead to interdependent substitutions, which have not previously been considered for amino acid similarities. At present, sequence matching compares sequences based only upon the similarities of single amino acids, ignoring the fact that in densely packed protein, there are additional conservative substitutions representing exchanges between two interacting amino acids, such as a small-large pair changing to a large-small pair substitutions that are not individually so conservative. Here we show that including information for such pairs of substitutions yields improved sequence matches, and that these yield significant gains in the agreements between sequence alignments and structure matches of the same protein pair. The result shows sequence segments matched where structure segments are aligned. There are gains for all 2002 collected cases where the sequence alignments that were not previously congruent with the structure matches. Our results also demonstrate a significant gain in detecting homology for “twilight zone” protein sequences. The amino acid substitution metrics derived have many other potential applications, for annotations, protein design, mutagenesis design, and empirical potential derivation.
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Affiliation(s)
- Kejue Jia
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
| | - Robert L Jernigan
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
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7
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Saurabh S, Sivakumar PM, Perumal V, Khosravi A, Sugumaran A, Prabhawathi V. Molecular Dynamics Simulations in Drug Discovery and Drug Delivery. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/978-3-030-36260-7_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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8
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Sala D, Huang YJ, Cole CA, Snyder DA, Liu G, Ishida Y, Swapna GVT, Brock KP, Sander C, Fidelis K, Kryshtafovych A, Inouye M, Tejero R, Valafar H, Rosato A, Montelione GT. Protein structure prediction assisted with sparse NMR data in CASP13. Proteins 2019; 87:1315-1332. [PMID: 31603581 DOI: 10.1002/prot.25837] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 01/05/2023]
Abstract
CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.
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Affiliation(s)
- Davide Sala
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York
| | - Casey A Cole
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University, Wayne, New Jersey
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Nexomics Biosciences, Bordentown, New Jersey
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts.,cBio Center, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Masayori Inouye
- Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Roberto Tejero
- Departamento de Quimica Fisica, Universidad de Valencia, Valencia, Spain
| | - Homayoun Valafar
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - Antonio Rosato
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
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9
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Guzenko D, Lafita A, Monastyrskyy B, Kryshtafovych A, Duarte JM. Assessment of protein assembly prediction in CASP13. Proteins 2019; 87:1190-1199. [PMID: 31374138 DOI: 10.1002/prot.25795] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/11/2019] [Accepted: 07/27/2019] [Indexed: 01/08/2023]
Abstract
We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in participation, more oligomeric targets released, and consistent, albeit modest, improvement of the predictions quality. Looking at the tertiary structure predictions, we observe that ignoring the oligomeric state of the targets hinders modeling success. We also note that some contact prediction groups successfully predicted homomeric interfacial contacts, though it appears that these predictions were not used for assembly modeling. Homology modeling with sizeable human intervention appears to form the basis of the assembly prediction techniques in this round of CASP. Future developments should see more integrated approaches where subunits are modeled in the context of the assemblies they form.
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Affiliation(s)
- Dmytro Guzenko
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, California
| | - Aleix Lafita
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Bohdan Monastyrskyy
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, California
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10
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Wang D, Geng L, Zhao YJ, Yang Y, Huang Y, Zhang Y, Shen HB. Artificial intelligence-based multi-objective optimization protocol for protein structure refinement. Bioinformatics 2019; 36:437-448. [PMID: 31274151 PMCID: PMC7999140 DOI: 10.1093/bioinformatics/btz544] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/06/2019] [Accepted: 07/04/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising. AVAILABILITY AND IMPLEMENTATION The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Yu-Jun Zhao
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Yang Yang
- Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Huang
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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11
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Pfeiffenberger E, Bates PA. Predicting improved protein conformations with a temporal deep recurrent neural network. PLoS One 2018; 13:e0202652. [PMID: 30180164 PMCID: PMC6122789 DOI: 10.1371/journal.pone.0202652] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/07/2018] [Indexed: 02/03/2023] Open
Abstract
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from MD trajectory data in order to classify whether each recorded simulation snapshot is an improved quality conformational state, decreased quality conformational state or whether there is no perceivable change in state with respect to the starting conformation. The model was trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.
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Affiliation(s)
- Erik Pfeiffenberger
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, United Kingdom
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, United Kingdom
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12
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A Critical Note on Symmetry Contact Artifacts and the Evaluation of the Quality of Homology Models. Symmetry (Basel) 2018. [DOI: 10.3390/sym10010025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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13
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Kryshtafovych A, Monastyrskyy B, Fidelis K, Moult J, Schwede T, Tramontano A. Evaluation of the template-based modeling in CASP12. Proteins 2017; 86 Suppl 1:321-334. [PMID: 29159950 DOI: 10.1002/prot.25425] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/22/2017] [Accepted: 11/16/2017] [Indexed: 01/29/2023]
Abstract
The article describes results of numerical evaluation of CASP12 models submitted on targets for which structural templates could be identified and for which servers produced models of relatively high accuracy. The emphasis is on analysis of details of models, and how well the models compete with experimental structures. Performance of contributing research groups is measured in terms of backbone accuracy, all-atom local geometry, and the ability to estimate local errors in models. Separate analyses for all participating groups and automatic servers were carried out. Compared with the last CASP, two years ago, there have been significant improvements in a number of areas, particularly the accuracy of protein backbone atoms, accuracy of sequence alignment between models and available structures, increased accuracy over that which can be obtained from simple copying of a closest template, and accuracy of modeling of sub-structures not present in the closest template. These advancements are likely associated with more effective strategies to build non-template regions of the targets ab initio, better algorithms to combine information from multiple templates, enhanced refinement methods, and better methods for estimating model accuracy.
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Affiliation(s)
- Andriy Kryshtafovych
- Protein Structure Prediction Center, Genome Center, University of California, Davis, California
| | - Bohdan Monastyrskyy
- Protein Structure Prediction Center, Genome Center, University of California, Davis, California
| | - Krzysztof Fidelis
- Protein Structure Prediction Center, Genome Center, University of California, Davis, California
| | - John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland
| | - Torsten Schwede
- Biozentrum, University of Basel, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Tramontano
- Department of Biochemical Sciences, Sapienza - University of Rome, P. le A. Moro, 5, Rome, 00185
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14
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Directional Force Originating from ATP Hydrolysis Drives the GroEL Conformational Change. Biophys J 2017; 112:1561-1570. [PMID: 28445748 DOI: 10.1016/j.bpj.2017.03.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 12/16/2016] [Accepted: 03/01/2017] [Indexed: 11/23/2022] Open
Abstract
Protein functional mechanisms usually require conformational changes, and often there are known structures for the different conformational states. However, usually neither the origin of the driving force nor the underlying pathways for these conformational transitions is known. Exothermic chemical reactions may be an important source of forces that drive conformational changes. Here we investigate this type of force originating from ATP hydrolysis in the chaperonin GroEL, by applying forces originating from the chemical reaction. Specifically, we apply directed forces to drive the GroEL conformational changes and learn that there is a highly specific direction for applied forces to drive the closed form to the open form. For this purpose, we utilize coarse-grained elastic network models. Principal component analysis on 38 GroEL experimental structures yields the most important motions, and these are used in structural interpolation for the construction of a coarse-grained free energy landscape. In addition, we investigate a more random application of forces with a Monte Carlo method and demonstrate pathways for the closed-open conformational transition in both directions by computing trajectories that are shown upon the free energy landscape. Initial root mean square deviation (RMSD) between the open and closed forms of the subunit is 14.7 Å and final forms from our simulations reach an average RMSD of 3.6 Å from the target forms, closely matching the level of resolution of the coarse-grained model.
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15
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Chapman SD, Adami C, Wilke CO, B Kc D. The evolution of logic circuits for the purpose of protein contact map prediction. PeerJ 2017; 5:e3139. [PMID: 28439455 PMCID: PMC5398280 DOI: 10.7717/peerj.3139] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 03/02/2017] [Indexed: 11/20/2022] Open
Abstract
Predicting protein structure from sequence remains a major open problem in protein biochemistry. One component of predicting complete structures is the prediction of inter-residue contact patterns (contact maps). Here, we discuss protein contact map prediction by machine learning. We describe a novel method for contact map prediction that uses the evolution of logic circuits. These logic circuits operate on feature data and output whether or not two amino acids in a protein are in contact or not. We show that such a method is feasible, and in addition that evolution allows the logic circuits to be trained on the dataset in an unbiased manner so that it can be used in both contact map prediction and the selection of relevant features in a dataset.
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Affiliation(s)
- Samuel D Chapman
- Department of Comptuational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Christoph Adami
- Department of Microbiology and Molecular Genetics and Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA
| | - Claus O Wilke
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Dukka B Kc
- Department of Comptuational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
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16
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Grisci B, Dorn M. NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies. J Bioinform Comput Biol 2017; 15:1750009. [DOI: 10.1142/s0219720017500093] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The development of computational methods to accurately model three-dimensional protein structures from sequences of amino acid residues is becoming increasingly important to the structural biology field. This paper addresses the challenge of predicting the tertiary structure of a given amino acid sequence, which has been reported to belong to the NP-Complete class of problems. We present a new method, namely NEAT–FLEX, based on NeuroEvolution of Augmenting Topologies (NEAT) to extract structural features from (ABS) proteins that are determined experimentally. The proposed method manipulates structural information from the Protein Data Bank (PDB) and predicts the conformational flexibility (FLEX) of residues of a target amino acid sequence. This information may be used in three-dimensional structure prediction approaches as a way to reduce the conformational search space. The proposed method was tested with 24 different amino acid sequences. Evolving neural networks were compared against a traditional error back-propagation algorithm; results show that the proposed method is a powerful way to extract and represent structural information from protein molecules that are determined experimentally.
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Affiliation(s)
- Bruno Grisci
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, RS, Brazil
| | - Márcio Dorn
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, RS, Brazil
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17
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Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks. Methods Mol Biol 2017; 1558:415-436. [PMID: 28150250 DOI: 10.1007/978-1-4939-6783-4_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies.
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18
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Brender JR, Shultis D, Khattak NA, Zhang Y. An Evolution-Based Approach to De Novo Protein Design. Methods Mol Biol 2017; 1529:243-264. [PMID: 27914055 PMCID: PMC5667548 DOI: 10.1007/978-1-4939-6637-0_12] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
EvoDesign is a computational algorithm that allows the rapid creation of new protein sequences that are compatible with specific protein structures. As such, it can be used to optimize protein stability, to resculpt the protein surface to eliminate undesired protein-protein interactions, and to optimize protein-protein binding. A major distinguishing feature of EvoDesign in comparison to other protein design programs is the use of evolutionary information in the design process to guide the sequence search toward native-like sequences known to adopt structurally similar folds as the target. The observed frequencies of amino acids in specific positions in the structure in the form of structural profiles collected from proteins with similar folds and complexes with similar interfaces can implicitly capture many subtle effects that are essential for correct folding and protein-binding interactions. As a result of the inclusion of evolutionary information, the sequences designed by EvoDesign have native-like folding and binding properties not seen by other physics-based design methods. In this chapter, we describe how EvoDesign can be used to redesign proteins with a focus on the computational and experimental procedures that can be used to validate the designs.
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19
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Sankar K, Liu J, Wang Y, Jernigan RL. Distributions of experimental protein structures on coarse-grained free energy landscapes. J Chem Phys 2016; 143:243153. [PMID: 26723638 DOI: 10.1063/1.4937940] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Predicting conformational changes of proteins is needed in order to fully comprehend functional mechanisms. With the large number of available structures in sets of related proteins, it is now possible to directly visualize the clusters of conformations and their conformational transitions through the use of principal component analysis. The most striking observation about the distributions of the structures along the principal components is their highly non-uniform distributions. In this work, we use principal component analysis of experimental structures of 50 diverse proteins to extract the most important directions of their motions, sample structures along these directions, and estimate their free energy landscapes by combining knowledge-based potentials and entropy computed from elastic network models. When these resulting motions are visualized upon their coarse-grained free energy landscapes, the basis for conformational pathways becomes readily apparent. Using three well-studied proteins, T4 lysozyme, serum albumin, and sarco-endoplasmic reticular Ca(2+) adenosine triphosphatase (SERCA), as examples, we show that such free energy landscapes of conformational changes provide meaningful insights into the functional dynamics and suggest transition pathways between different conformational states. As a further example, we also show that Monte Carlo simulations on the coarse-grained landscape of HIV-1 protease can directly yield pathways for force-driven conformational changes.
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Affiliation(s)
- Kannan Sankar
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA
| | - Jie Liu
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA
| | - Yuan Wang
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA
| | - Robert L Jernigan
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA
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20
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Manibog K, Sankar K, Kim SA, Zhang Y, Jernigan RL, Sivasankar S. Molecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape. Proc Natl Acad Sci U S A 2016; 113:E5711-20. [PMID: 27621473 PMCID: PMC5047164 DOI: 10.1073/pnas.1604012113] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Classical cadherin cell-cell adhesion proteins are essential for the formation and maintenance of tissue structures; their primary function is to physically couple neighboring cells and withstand mechanical force. Cadherins from opposing cells bind in two distinct trans conformations: strand-swap dimers and X-dimers. As cadherins convert between these conformations, they form ideal bonds (i.e., adhesive interactions that are insensitive to force). However, the biophysical mechanism for ideal bond formation is unknown. Here, we integrate single-molecule force measurements with coarse-grained and atomistic simulations to resolve the mechanistic basis for cadherin ideal bond formation. Using simulations, we predict the energy landscape for cadherin adhesion, the transition pathways for interconversion between X-dimers and strand-swap dimers, and the cadherin structures that form ideal bonds. Based on these predictions, we engineer cadherin mutants that promote or inhibit ideal bond formation and measure their force-dependent kinetics using single-molecule force-clamp measurements with an atomic force microscope. Our data establish that cadherins adopt an intermediate conformation as they shuttle between X-dimers and strand-swap dimers; pulling on this conformation induces a torsional motion perpendicular to the pulling direction that unbinds the proteins and forms force-independent ideal bonds. Torsional motion is blocked when cadherins associate laterally in a cis orientation, suggesting that ideal bonds may play a role in mechanically regulating cadherin clustering on cell surfaces.
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Affiliation(s)
- Kristine Manibog
- Department of Physics and Astronomy, Iowa State University, Ames, IA 50011; Ames Laboratory, US Department of Energy, Ames, IA 50011
| | - Kannan Sankar
- Bioinformatics and Computational Biology Interdepartmental Program, Iowa State University, Ames, IA 50011; Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011
| | - Sun-Ae Kim
- Department of Physics and Astronomy, Iowa State University, Ames, IA 50011; Ames Laboratory, US Department of Energy, Ames, IA 50011
| | - Yunxiang Zhang
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA 94305
| | - Robert L Jernigan
- Department of Physics and Astronomy, Iowa State University, Ames, IA 50011; Bioinformatics and Computational Biology Interdepartmental Program, Iowa State University, Ames, IA 50011; Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011; L. H. Baker Center for Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011
| | - Sanjeevi Sivasankar
- Department of Physics and Astronomy, Iowa State University, Ames, IA 50011; Ames Laboratory, US Department of Energy, Ames, IA 50011;
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21
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Lensink MF, Velankar S, Kryshtafovych A, Huang SY, Schneidman-Duhovny D, Sali A, Segura J, Fernandez-Fuentes N, Viswanath S, Elber R, Grudinin S, Popov P, Neveu E, Lee H, Baek M, Park S, Heo L, Rie Lee G, Seok C, Qin S, Zhou HX, Ritchie DW, Maigret B, Devignes MD, Ghoorah A, Torchala M, Chaleil RAG, Bates PA, Ben-Zeev E, Eisenstein M, Negi SS, Weng Z, Vreven T, Pierce BG, Borrman TM, Yu J, Ochsenbein F, Guerois R, Vangone A, Rodrigues JPGLM, van Zundert G, Nellen M, Xue L, Karaca E, Melquiond ASJ, Visscher K, Kastritis PL, Bonvin AMJJ, Xu X, Qiu L, Yan C, Li J, Ma Z, Cheng J, Zou X, Shen Y, Peterson LX, Kim HR, Roy A, Han X, Esquivel-Rodriguez J, Kihara D, Yu X, Bruce NJ, Fuller JC, Wade RC, Anishchenko I, Kundrotas PJ, Vakser IA, Imai K, Yamada K, Oda T, Nakamura T, Tomii K, Pallara C, Romero-Durana M, Jiménez-García B, Moal IH, Férnandez-Recio J, Joung JY, Kim JY, Joo K, Lee J, Kozakov D, Vajda S, Mottarella S, Hall DR, Beglov D, Mamonov A, Xia B, Bohnuud T, Del Carpio CA, Ichiishi E, Marze N, Kuroda D, Roy Burman SS, Gray JJ, Chermak E, Cavallo L, Oliva R, Tovchigrechko A, Wodak SJ. Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment. Proteins 2016; 84 Suppl 1:323-48. [PMID: 27122118 PMCID: PMC5030136 DOI: 10.1002/prot.25007] [Citation(s) in RCA: 119] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 12/30/2015] [Accepted: 02/02/2016] [Indexed: 12/26/2022]
Abstract
We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marc F Lensink
- University Lille, CNRS UMR8576 UGSF, Lille, F-59000, France.
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | | | - Shen-You Huang
- Research Support Computing, University of Missouri Bioinformatics Consortium, and Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Dina Schneidman-Duhovny
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, 94158
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94158
- California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, California, 94158
| | - Joan Segura
- GN7 of the National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC), Madrid, 28049, Spain
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, SY233FG, United Kingdom
| | - Shruthi Viswanath
- Department of Computer Science, University of Texas at Austin, Austin, Texas, 78712
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
| | - Ron Elber
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712
- Department of Chemistry, University of Texas at Austin, Austin, Texas, 78712
| | - Sergei Grudinin
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Petr Popov
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Emilie Neveu
- LJK, University Grenoble Alpes, CNRS, Grenoble, 38000, France
- INRIA, Grenoble, 38000, France
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sangwoo Park
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida, 32306, USA
| | | | - Bernard Maigret
- CNRS, LORIA, Campus Scientifique, BP 239, Vandœuvre-lès-Nancy, 54506, France
| | | | - Anisah Ghoorah
- Department of Computer Science and Engineering, University of Mauritius, Reduit, Mauritius
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Raphaël A G Chaleil
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Paul A Bates
- Biomolecular Modelling Laboratory, the Francis Crick Institute, Lincoln's Inn Fields Laboratory, London, WC2A 3LY, United Kingdom
| | - Efrat Ben-Zeev
- G-INCPM, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Miriam Eisenstein
- Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Surendra S Negi
- Sealy Center for Structural Biology and Molecular Biophysics, University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas, 77555-0857
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Brian G Pierce
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Tyler M Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
| | - Jinchao Yu
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Françoise Ochsenbein
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Raphaël Guerois
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, University Paris-Saclay, CEA-Saclay, Gif-sur-Yvette, 91191, France
| | - Anna Vangone
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - João P G L M Rodrigues
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Gydo van Zundert
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Mehdi Nellen
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Li Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Ezgi Karaca
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Adrien S J Melquiond
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Koen Visscher
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
| | - Chengfei Yan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, 65211
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, 65211
- Informatics Institute, University of Missouri, Columbia, Missouri, 65211
- Department of Biochemistry, University of Missouri, Columbia, Missouri, 65211
| | - Yang Shen
- Toyota Technological Institute at Chicago, 6045 S Kenwood Avenue, Chicago, Illinois, 60637
| | - Lenna X Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Hyung-Rae Kim
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | - Amit Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, National Institutes of Health, Hamilton, Montano 59840
| | - Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
| | | | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907
- Department of Computer Science, Purdue University, West Lafayette, IN, USA, 47907
| | - Xiaofeng Yu
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Neil J Bruce
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Jonathan C Fuller
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Rebecca C Wade
- Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
- Center for Molecular Biology (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
| | - Ivan Anishchenko
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Petras J Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
| | - Ilya A Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, 66047
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, 66047
| | - Kenichiro Imai
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Kazunori Yamada
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Toshiyuki Oda
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
| | - Tsukasa Nakamura
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Kentaro Tomii
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Koto-Ku, Japan
- Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Chiara Pallara
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Miguel Romero-Durana
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Brian Jiménez-García
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Iain H Moal
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Juan Férnandez-Recio
- Joint BSC-CRG-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, C/Jordi Girona 29, Barcelona, 08034, Spain
| | - Jong Young Joung
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jong Yun Kim
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Keehyoung Joo
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Jooyoung Lee
- Center for in-Silico Protein Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
- School of Computational Science, Korea Institute for Advanced Study, Seoul, 130-722, Korea
| | - Dima Kozakov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Scott Mottarella
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - David R Hall
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Artem Mamonov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Bing Xia
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Tanggis Bohnuud
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Carlos A Del Carpio
- Institute of Biological Diversity, International Pacific Institute of Indiana, Bloomington, Indiana, 47401
- Drosophila Genetic Resource Center, Kyoto Institute of Technology, Ukyo-Ku, 616-8354, Japan
| | - Eichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Asushiobara-City, Tochigi Prefecture, 329-2763, Japan
| | - Nicholas Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Edrisse Chermak
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Andrey Tovchigrechko
- J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, Maryland, 20850
| | - Shoshana J Wodak
- Departments of Biochemistry and Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- VIB Structural Biology Research Center, VUB Pleinlaan 2, Brussels, 1050, Belgium.
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22
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Butts CT, Bierma JC, Martin RW. Novel proteases from the genome of the carnivorous plant Drosera capensis: Structural prediction and comparative analysis. Proteins 2016; 84:1517-33. [PMID: 27353064 DOI: 10.1002/prot.25095] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Revised: 05/16/2016] [Accepted: 06/13/2016] [Indexed: 12/21/2022]
Abstract
In his 1875 monograph on insectivorous plants, Darwin described the feeding reactions of Drosera flypaper traps and predicted that their secretions contained a "ferment" similar to mammalian pepsin, an aspartic protease. Here we report a high-quality draft genome sequence for the cape sundew, Drosera capensis, the first genome of a carnivorous plant from order Caryophyllales, which also includes the Venus flytrap (Dionaea) and the tropical pitcher plants (Nepenthes). This species was selected in part for its hardiness and ease of cultivation, making it an excellent model organism for further investigations of plant carnivory. Analysis of predicted protein sequences yields genes encoding proteases homologous to those found in other plants, some of which display sequence and structural features that suggest novel functionalities. Because the sequence similarity to proteins of known structure is in most cases too low for traditional homology modeling, 3D structures of representative proteases are predicted using comparative modeling with all-atom refinement. Although the overall folds and active residues for these proteins are conserved, we find structural and sequence differences consistent with a diversity of substrate recognition patterns. Finally, we predict differences in substrate specificities using in silico experiments, providing targets for structure/function studies of novel enzymes with biological and technological significance. Proteins 2016; 84:1517-1533. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Carter T Butts
- Department of Electrical Engineering and Computer Science, UC Irvine, Irvine, California, 92697. .,Department of Statistics, UC Irvine, Irvine, California, 92697. .,Department of Sociology, UC Irvine, Irvine, California, 92697.
| | - Jan C Bierma
- Department of Molecular Biology and Biochemistry, UC Irvine, Irvine, California, 92697
| | - Rachel W Martin
- Department of Molecular Biology and Biochemistry, UC Irvine, Irvine, California, 92697. .,Department of Chemistry, UC Irvine, Irvine, California, 92697.
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23
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Kaushik R, Jayaram B. Structural difficulty index: a reliable measure for modelability of protein tertiary structures. Protein Eng Des Sel 2016; 29:391-7. [PMID: 27334454 DOI: 10.1093/protein/gzw025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Accepted: 05/27/2016] [Indexed: 11/13/2022] Open
Abstract
The success in protein tertiary-structure prediction is considered to be a function of coverage and similarity/identity of their sequences with suitable templates in the structural databases. However, this measure of modelability of a protein sequence into its structure may be misleading. Addressing this limitation, we propose here a 'structural difficulty (SD)' index, which is derived from secondary structures, homology and physicochemical features of protein sequences. The SD index reflects the capability of predicting accurate structures and helps to assess the potential for developing proteome level structural databases for various organisms with some of the best methodologies available currently. For instance, the plausibility of populating the structural database of human proteome with reliable quality structures under 3 Å root mean square deviation from the corresponding natives is found to be ∼37% of a total of 11 084 manually curated soluble proteins and ∼64% for all annotated and reviewed unique soluble protein (344 661 sequences) of UniProtKB. Also for 77 human pathogenic viruses comprising 2365 globular viral proteins out of which only 162 structures are solved experimentally, SD index scores 1336 proteins in the modelable zone. Availability of reliable protein structures may prove a crucial aid in developing species-wise structural proteomic databases for accelerating function annotation and for drug development endeavors.
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Affiliation(s)
- Rahul Kaushik
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
| | - B Jayaram
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
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24
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Modi V, Xu Q, Adhikari S, Dunbrack RL. Assessment of template-based modeling of protein structure in CASP11. Proteins 2016; 84 Suppl 1:200-20. [PMID: 27081927 DOI: 10.1002/prot.25049] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2016] [Revised: 04/04/2016] [Accepted: 04/11/2016] [Indexed: 12/27/2022]
Abstract
We present the assessment of predictions submitted in the template-based modeling (TBM) category of CASP11 (Critical Assessment of Protein Structure Prediction). Model quality was judged on the basis of global and local measures of accuracy on all atoms including side chains. The top groups on 39 human-server targets based on model 1 predictions were LEER, Zhang, LEE, MULTICOM, and Zhang-Server. The top groups on 81 targets by server groups based on model 1 predictions were Zhang-Server, nns, BAKER-ROSETTASERVER, QUARK, and myprotein-me. In CASP11, the best models for most targets were equal to or better than the best template available in the Protein Data Bank, even for targets with poor templates. The overall performance in CASP11 is similar to the performance of predictors in CASP10 with slightly better performance on the hardest targets. For most targets, assessment measures exhibited bimodal probability density distributions. Multi-dimensional scaling of an RMSD matrix for each target typically revealed a single cluster with models similar to the target structure, with a mode in the GDT-TS density between 40 and 90, and a wide distribution of models highly divergent from each other and from the experimental structure, with density mode at a GDT-TS value of ∼20. The models in this peak in the density were either compact models with entirely the wrong fold, or highly non-compact models. The results argue for a density-driven approach in future CASP TBM assessments that accounts for the bimodal nature of these distributions instead of Z scores, which assume a unimodal, Gaussian distribution. Proteins 2016; 84(Suppl 1):200-220. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Vivek Modi
- Fox Chase Cancer Center, Institute for Cancer Research, Philadelphia, Pennsylvania, 19111
| | - Qifang Xu
- Fox Chase Cancer Center, Institute for Cancer Research, Philadelphia, Pennsylvania, 19111
| | - Sam Adhikari
- Fox Chase Cancer Center, Institute for Cancer Research, Philadelphia, Pennsylvania, 19111
| | - Roland L Dunbrack
- Fox Chase Cancer Center, Institute for Cancer Research, Philadelphia, Pennsylvania, 19111.
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25
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Li J, Cheng J. A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling. Sci Rep 2016; 6:25687. [PMID: 27161489 PMCID: PMC4861977 DOI: 10.1038/srep25687] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 04/21/2016] [Indexed: 12/04/2022] Open
Abstract
Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96–6.37% and 2.42–5.19% on the three datasets over using single templates. MTMG’s performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html.
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Affiliation(s)
- Jilong Li
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.,Informatics Institute, University of Missouri, Columbia, MO 65211, USA
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26
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Addressing the Role of Conformational Diversity in Protein Structure Prediction. PLoS One 2016; 11:e0154923. [PMID: 27159429 PMCID: PMC4861349 DOI: 10.1371/journal.pone.0154923] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 04/21/2016] [Indexed: 11/19/2022] Open
Abstract
Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.
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27
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Brunk E, Mih N, Monk J, Zhang Z, O’Brien EJ, Bliven SE, Chen K, Chang RL, Bourne PE, Palsson BO. Systems biology of the structural proteome. BMC SYSTEMS BIOLOGY 2016; 10:26. [PMID: 26969117 PMCID: PMC4787049 DOI: 10.1186/s12918-016-0271-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Accepted: 02/16/2016] [Indexed: 12/19/2022]
Abstract
BACKGROUND The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. RESULTS Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). CONCLUSIONS Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.
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Affiliation(s)
- Elizabeth Brunk
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
- />Joint BioEnergy Institute, Emeryville, CA 94608 USA
| | - Nathan Mih
- />Bioinformatics and Systems Biology Program, University of California, La Jolla, San Diego, CA 92093 USA
| | - Jonathan Monk
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Zhen Zhang
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Edward J. O’Brien
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Spencer E. Bliven
- />Bioinformatics and Systems Biology Program, University of California, La Jolla, San Diego, CA 92093 USA
- />National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Ke Chen
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Roger L. Chang
- />Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Philip E. Bourne
- />Office of the Director, National Institutes of Health, Bethesda, MD 20894 USA
| | - Bernhard O. Palsson
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
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28
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Kinch LN, Li W, Monastyrskyy B, Kryshtafovych A, Grishin NV. Assessment of CASP11 contact-assisted predictions. Proteins 2016; 84 Suppl 1:164-80. [PMID: 26889875 DOI: 10.1002/prot.25020] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 02/05/2016] [Accepted: 02/14/2016] [Indexed: 12/26/2022]
Abstract
We present an overview of contact-assisted predictions in the eleventh round of critical assessment of protein structure prediction (CASP11), which included four categories: predicted contacts (Tp), correct contacts (Tc), simulated sparse NMR contacts (Ts), and cross-linking contacts (Tx). Comparison of assisted to unassisted model quality highlighted a relatively poor overall performance in CASP11 using predicted Tp and crosslinked Tx contact information. However, average model quality significantly improved in the correct Tc and simulated NMR Ts categories for most targets, where maximum improvement of unassisted models reached an impressive 70 GDT_TS. Comparison of the performance in the correct Tc category to CASP10 suggested the improvement in CASP11 model quality originated from an increased number of provided contacts per target. Group rankings based on a combination of scores used in the CASP11 free modeling (FM) assessment for each category highlight four top-performing groups, with three from the Lee lab and one from the Baker lab. We used the overall performance of these groups in each category to develop hypotheses for their relative outperformance in the correct Tc and simulated NMR Ts categories, which stemmed from the fraction of correct contacts provided (correct Tc category) and a reduced fraction of correct contacts offset by an increased coverage of the correct contacts (simulated NMR Ts category). Proteins 2016; 84(Suppl 1):164-180. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Lisa N Kinch
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390-9050.
| | - Wenlin Li
- Department of Biophysics, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390-9050.,Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390-9050
| | | | | | - Nick V Grishin
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390-9050.,Department of Biophysics, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390-9050.,Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas, 75390-9050
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29
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Kinch LN, Li W, Monastyrskyy B, Kryshtafovych A, Grishin NV. Evaluation of free modeling targets in CASP11 and ROLL. Proteins 2016; 84 Suppl 1:51-66. [PMID: 26677002 DOI: 10.1002/prot.24973] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Accepted: 12/12/2015] [Indexed: 12/25/2022]
Abstract
We present an assessment of 'template-free modeling' (FM) in CASP11and ROLL. Community-wide server performance suggested the use of automated scores similar to previous CASPs would provide a good system of evaluating performance, even in the absence of comprehensive manual assessment. The CASP11 FM category included several outstanding examples, including successful prediction by the Baker group of a 256-residue target (T0806-D1) that lacked sequence similarity to any existing template. The top server model prediction by Zhang's Quark, which was apparently selected and refined by several manual groups, encompassed the entire fold of target T0837-D1. Methods from the same two groups tended to dominate overall CASP11 FM and ROLL rankings. Comparison of top FM predictions with those from the previous CASP experiment revealed progress in the category, particularly reflected in high prediction accuracy for larger protein domains. FM prediction models for two cases were sufficient to provide functional insights that were otherwise not obtainable by traditional sequence analysis methods. Importantly, CASP11 abstracts revealed that alignment-based contact prediction methods brought about much of the CASP11 progress, producing both of the functionally relevant models as well as several of the other outstanding structure predictions. These methodological advances enabled de novo modeling of much larger domain structures than was previously possible and allowed prediction of functional sites. Proteins 2016; 84(Suppl 1):51-66. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Lisa N Kinch
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, Texas 75390-9050.
| | - Wenlin Li
- Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, Texas 75390-9050
| | - Bohdan Monastyrskyy
- Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616
| | - Andriy Kryshtafovych
- Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616
| | - Nick V Grishin
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, Texas 75390-9050.,Department of Biophysics and Department of Biochemistry, University of Texas Southwestern Medical Center at Dallas, 6001 Forest Park Road, Dallas, Texas 75390-9050
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30
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Cao R, Bhattacharya D, Adhikari B, Li J, Cheng J. Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11. Proteins 2015; 84 Suppl 1:247-59. [PMID: 26369671 DOI: 10.1002/prot.24924] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 08/21/2015] [Accepted: 09/10/2015] [Indexed: 12/28/2022]
Abstract
Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Renzhi Cao
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | | | - Badri Adhikari
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Jilong Li
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri, 65211. .,Informatics Institute, University of Missouri, Columbia, Missouri, 65211.
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31
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Lee J, Lee K, Joung I, Joo K, Brooks BR, Lee J. Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest. BMC Bioinformatics 2015; 16:94. [PMID: 25886990 PMCID: PMC4374281 DOI: 10.1186/s12859-015-0526-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Accepted: 03/04/2015] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND In template-based modeling when using a single template, inter-atomic distances of an unknown protein structure are assumed to be distributed by Gaussian probability density functions, whose center peaks are located at the distances between corresponding atoms in the template structure. The width of the Gaussian distribution, the variability of a spatial restraint, is closely related to the reliability of the restraint information extracted from a template, and it should be accurately estimated for successful template-based protein structure modeling. RESULTS To predict the variability of the spatial restraints in template-based modeling, we have devised a prediction model, Sigma-RF, by using the random forest (RF) algorithm. The benchmark results on 22 CASP9 targets show that the variability values from Sigma-RF are of higher correlations with the true distance deviation than those from Modeller. We assessed the effect of new sigma values by performing the single-domain homology modeling of 22 CASP9 targets and 24 CASP10 targets. For most of the targets tested, we could obtain more accurate 3D models from the identical alignments by using the Sigma-RF results than by using Modeller ones. CONCLUSIONS We find that the average alignment quality of residues located between and at two aligned residues, quasi-local information, is the most contributing factor, by investigating the importance of input features used in the RF machine learning. This average alignment quality is shown to be more important than the previously identified quantity of a local information: the product of alignment qualities at two aligned residues.
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Affiliation(s)
- Juyong Lee
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, 5635 Fishers Ln, Bethesda, 20852, USA.
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, Korea.
| | - Kiho Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, Korea.
| | - InSuk Joung
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, Korea.
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.
| | - Keehyoung Joo
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, Korea.
- Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, Korea.
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, 5635 Fishers Ln, Bethesda, 20852, USA.
| | - Jooyoung Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, Korea.
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.
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32
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Trolle T, Metushi IG, Greenbaum JA, Kim Y, Sidney J, Lund O, Sette A, Peters B, Nielsen M. Automated benchmarking of peptide-MHC class I binding predictions. ACTA ACUST UNITED AC 2015; 31:2174-81. [PMID: 25717196 DOI: 10.1093/bioinformatics/btv123] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Accepted: 02/21/2015] [Indexed: 01/31/2023]
Abstract
MOTIVATION Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. RESULTS The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. AVAILABILITY AND IMPLEMENTATION Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. CONTACT mniel@cbs.dtu.dk or bpeters@liai.org SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas Trolle
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Imir G Metushi
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Jason A Greenbaum
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Yohan Kim
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - John Sidney
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Ole Lund
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Alessandro Sette
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Bjoern Peters
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
| | - Morten Nielsen
- Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina Center for Biological Sequence Analysis, Department of Systems Biology, The Technical University of Denmark, Lyngby, Denmark, Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA and Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Buenos Aires, Argentina
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Pereira J, Johnson WE, O’Brien SJ, Jarvis ED, Zhang G, Gilbert MTP, Vasconcelos V, Antunes A. Evolutionary genomics and adaptive evolution of the Hedgehog gene family (Shh, Ihh and Dhh) in vertebrates. PLoS One 2014; 9:e74132. [PMID: 25549322 PMCID: PMC4280113 DOI: 10.1371/journal.pone.0074132] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Accepted: 07/29/2013] [Indexed: 12/21/2022] Open
Abstract
The Hedgehog (Hh) gene family codes for a class of secreted proteins composed of two active domains that act as signalling molecules during embryo development, namely for the development of the nervous and skeletal systems and the formation of the testis cord. While only one Hh gene is found typically in invertebrate genomes, most vertebrates species have three (Sonic hedgehog – Shh; Indian hedgehog – Ihh; and Desert hedgehog – Dhh), each with different expression patterns and functions, which likely helped promote the increasing complexity of vertebrates and their successful diversification. In this study, we used comparative genomic and adaptive evolutionary analyses to characterize the evolution of the Hh genes in vertebrates following the two major whole genome duplication (WGD) events. To overcome the lack of Hh-coding sequences on avian publicly available databases, we used an extensive dataset of 45 avian and three non-avian reptilian genomes to show that birds have all three Hh paralogs. We find suggestions that following the WGD events, vertebrate Hh paralogous genes evolved independently within similar linkage groups and under different evolutionary rates, especially within the catalytic domain. The structural regions around the ion-binding site were identified to be under positive selection in the signaling domain. These findings contrast with those observed in invertebrates, where different lineages that experienced gene duplication retained similar selective constraints in the Hh orthologs. Our results provide new insights on the evolutionary history of the Hh gene family, the functional roles of these paralogs in vertebrate species, and on the location of mutational hotspots.
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Affiliation(s)
- Joana Pereira
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal
| | - Warren E. Johnson
- Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, Virginia, United States of America
| | - Stephen J. O’Brien
- Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia
- Oceanographic Center, N. Ocean Drive, Nova Southeastern University, Ft. Lauderdale, Florida, United States of America
| | - Erich D. Jarvis
- Howard Hughes Medical Institute, Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Guojie Zhang
- BGI-Shenzhen, Beishan Industrial Zoon, Yantian District, Shenzhen, China
| | - M. Thomas P. Gilbert
- Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - Vitor Vasconcelos
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal
- * E-mail:
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34
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Three-dimensional protein structure prediction: Methods and computational strategies. Comput Biol Chem 2014; 53PB:251-276. [DOI: 10.1016/j.compbiolchem.2014.10.001] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 10/03/2014] [Accepted: 10/07/2014] [Indexed: 01/01/2023]
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35
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Vinkler M, Bainová H, Bryja J. Protein evolution of Toll-like receptors 4, 5 and 7 within Galloanserae birds. Genet Sel Evol 2014; 46:72. [PMID: 25387947 PMCID: PMC4228102 DOI: 10.1186/s12711-014-0072-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 10/10/2014] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Toll-like receptors (TLR) are essential activators of the innate part of the vertebrate immune system. In this study, we analysed the interspecific variability of three TLR (bacterial-sensing TLR4 and TLR5 and viral-sensing TLR7) within the Galloanserae bird clade, investigated their phylogeny, assessed their structural conservation and estimated site-specific selection pressures. RESULTS Physiochemical properties varied according to the TLR analysed, mainly with regards to the surface electrostatic potential distribution. The predicted ligand-binding features (mainly in TLR4 and TLR5) differed between the avian proteins and their fish and mammalian counterparts, but also varied within the Galloanserae birds. We identified 20 positively selected sites in the three TLR, among which several are topologically close to ligand-binding sites reported for mammalian and fish TLR. We described 26, 28 and 25 evolutionarily non-conservative sites in TLR4, TLR5 and TLR7, respectively. Thirteen of these sites in TLR4, and ten in TLR5 were located in functionally relevant regions. The variability appears to be functionally more conserved for viral-sensing TLR7 than for the bacterial-sensing TLR. Amino-acid positions 268, 270, 343, 383, 444 and 471 in TLR4 and 180, 183, 209, 216, 264, 342 and 379 in TLR5 are key candidates for further functional research. CONCLUSIONS Host-pathogen co-evolution has a major effect on the features of host immune receptors. Our results suggest that avian and mammalian TLR may be differentially adapted to pathogen-derived ligand recognition. We have detected signatures of positive selection even within the Galloanserae lineage. To our knowledge, this is the first study to depict evolutionary pressures on Galloanserae TLR and to estimate the validity of current knowledge on TLR function (based on mammalian and chicken models) for non-model species of this clade.
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Affiliation(s)
- Michal Vinkler
- Department of Zoology, Faculty of Science, Charles University in Prague, Praha, Czech Republic.
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36
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Huang YJ, Mao B, Aramini JM, Montelione GT. Assessment of template-based protein structure predictions in CASP10. Proteins 2014; 82 Suppl 2:43-56. [PMID: 24323734 DOI: 10.1002/prot.24488] [Citation(s) in RCA: 82] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 11/10/2013] [Accepted: 11/19/2013] [Indexed: 12/27/2022]
Abstract
Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.
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Affiliation(s)
- Yuanpeng J Huang
- Center for Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854; Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854
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37
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Ab Initio structure prediction for Escherichia coli: towards genome-wide protein structure modeling and fold assignment. Sci Rep 2014; 3:1895. [PMID: 23719418 PMCID: PMC3667494 DOI: 10.1038/srep01895] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Accepted: 05/08/2013] [Indexed: 11/09/2022] Open
Abstract
Genome-wide protein structure prediction and structure-based function annotation have been a long-term goal in molecular biology but not yet become possible due to difficulties in modeling distant-homology targets. We developed a hybrid pipeline combining ab initio folding and template-based modeling for genome-wide structure prediction applied to the Escherichia coli genome. The pipeline was tested on 43 known sequences, where QUARK-based ab initio folding simulation generated models with TM-score 17% higher than that by traditional comparative modeling methods. For 495 unknown hard sequences, 72 are predicted to have a correct fold (TM-score > 0.5) and 321 have a substantial portion of structure correctly modeled (TM-score > 0.35). 317 sequences can be reliably assigned to a SCOP fold family based on structural analogy to existing proteins in PDB. The presented results, as a case study of E. coli, represent promising progress towards genome-wide structure modeling and fold family assignment using state-of-the-art ab initio folding algorithms.
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38
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The complex and specific pMHC interactions with diverse HIV-1 TCR clonotypes reveal a structural basis for alterations in CTL function. Sci Rep 2014; 4:4087. [PMID: 24522437 PMCID: PMC3923210 DOI: 10.1038/srep04087] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 01/29/2014] [Indexed: 02/04/2023] Open
Abstract
Immune control of viral infections is modulated by diverse T cell receptor (TCR) clonotypes engaging peptide-MHC class I complexes on infected cells, but the relationship between TCR structure and antiviral function is unclear. Here we apply in silico molecular modeling with in vivo mutagenesis studies to investigate TCR-pMHC interactions from multiple CTL clonotypes specific for a well-defined HIV-1 epitope. Our molecular dynamics simulations of viral peptide-HLA-TCR complexes, based on two independent co-crystal structure templates, reveal that effective and ineffective clonotypes bind to the terminal portions of the peptide-MHC through similar salt bridges, but their hydrophobic side-chain packings can be very different, which accounts for the major part of the differences among these clonotypes. Non-specific hydrogen bonding to viral peptide also accommodates greater epitope variants. Furthermore, free energy perturbation calculations for point mutations on the viral peptide KK10 show excellent agreement with in vivo mutagenesis assays, with new predictions confirmed by additional experiments. These findings indicate a direct structural basis for heterogeneous CTL antiviral function.
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39
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)--round x. Proteins 2014. [PMID: 24344053 DOI: 10.1002/prot.24452.critical] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three-dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi-domain and multi-subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non-template regions.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, Rockville, Maryland, 20850
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40
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)--round x. Proteins 2014; 82 Suppl 2:1-6. [PMID: 24344053 PMCID: PMC4394854 DOI: 10.1002/prot.24452] [Citation(s) in RCA: 312] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Accepted: 10/21/2013] [Indexed: 12/28/2022]
Abstract
This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three-dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi-domain and multi-subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non-template regions.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research, and Department of Cell Biology and Molecular Genetics, University of Maryland, Rockville, Maryland 20850
| | | | | | - Torsten Schwede
- University of Basel, Biozentrum & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Tramontano
- Department of Physics and Istituto Pasteur-Fondazione Cenci Bolognetti, Sapienza University of Rome, 00185 Rome, Italy
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41
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Verma D, Guo JT, Jacobs DJ, Livesay DR. Towards comprehensive analysis of protein family quantitative stability-flexibility relationships using homology models. Methods Mol Biol 2014; 1084:239-254. [PMID: 24061925 PMCID: PMC4676804 DOI: 10.1007/978-1-62703-658-0_13] [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] [Indexed: 06/02/2023]
Abstract
The Distance Constraint Model (DCM) is a computational modeling scheme that uniquely integrates thermodynamic and mechanical descriptions of protein structure. As such, quantitative stability-flexibility relationships (QSFR) that describe the interrelationships of thermodynamics and mechanics can be quickly computed. Using comparative QSFR analyses, we have previously investigated these relationships across a small number of protein orthologs, ranging from two to a dozen [1, 2]. However, our ultimate goal is provide a comprehensive analysis of whole protein families, which requires consideration of many more structures. To that end, we have developed homology modeling and assessment protocols so that we can robustly calculate QSFR properties for proteins without experimentally derived structures. The approach, which is presented here, starts from a large ensemble of potential homology models and uses a clustering algorithm to identify the best models, thus paving the way for a comprehensive QSFR analysis across hundreds of proteins in a protein family.
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Affiliation(s)
- Deeptak Verma
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Jun-tao Guo
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Donald J. Jacobs
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Dennis R. Livesay
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
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42
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Abstract
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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Affiliation(s)
- Gregory Sliwoski
- Jr., Center for Structural Biology, 465 21st Ave South, BIOSCI/MRBIII, Room 5144A, Nashville, TN 37232-8725.
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43
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Abstract
This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three-dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi-domain and multi-subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non-template regions.
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44
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Basta HA, Sgro JY, Palmenberg AC. Modeling of the human rhinovirus C capsid suggests a novel topography with insights on receptor preference and immunogenicity. Virology 2013; 448:176-84. [PMID: 24314648 DOI: 10.1016/j.virol.2013.10.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Revised: 09/12/2013] [Accepted: 10/03/2013] [Indexed: 10/26/2022]
Abstract
Features of human rhinovirus (RV)-C virions that allow them to use novel cell receptors and evade immune responses are unknown. Unlike the RV-A+B, these isolates cannot be propagated in typical culture systems or grown for structure studies. Comparative sequencing, I-TASSER, MODELLER, ROBETTA, and refined alignment techniques led to a structural approximation for C15 virions, based on the extensive, resolved RV-A+B datasets. The model predicts that all RV-C VP1 proteins are shorter by 21 residues relative to the RV-A, and 35 residues relative to the RV-B, effectively shaving the RV 5-fold plateau from the particle. There are major alterations in VP1 neutralizing epitopes and the structural determinants for ICAM-1 and LDLR receptors. The VP2 and VP3 elements are similar among all RV, but the loss of sequence "words" contributing Nim1ab has increased the apparent selective pressure among the RV-C to fix mutations elsewhere in the VP1, creating a possible compensatory epitope.
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Affiliation(s)
- Holly A Basta
- Institute for Molecular Virology, University of Wisconsin, Madison, USA
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45
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Mitra P, Shultis D, Brender JR, Czajka J, Marsh D, Gray F, Cierpicki T, Zhang Y. An evolution-based approach to De Novo protein design and case study on Mycobacterium tuberculosis. PLoS Comput Biol 2013; 9:e1003298. [PMID: 24204234 PMCID: PMC3812052 DOI: 10.1371/journal.pcbi.1003298] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Accepted: 09/09/2013] [Indexed: 01/31/2023] Open
Abstract
Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality. The goal of computational protein design is to create new protein sequences of desirable structure and biological function. Most protein design methods are developed to search for sequences with the lowest free-energy based on physics-based force fields following Anfinsen's thermodynamic hypothesis. A major obstacle of such approaches is the inaccuracy of the force-field design, which cannot accurately describe atomic interactions or correctly recognize protein folds. We propose a novel method which uses evolutionary information, in the form of sequence profiles from structure families, to guide the sequence design. Since sequence profiles are generally more accurate than physics-based potentials in protein fold recognition, a unique advantage lies on that it targets the design procedure to a family of protein sequence profiles to enhance the robustness of designed sequences. The method was tested on 87 proteins and the designed sequences can be folded by I-TASSER to models with an average RMSD 2.1 Å. As a case study of large-scale application, the method is extended to redesign all structurally resolved proteins in the human pathogenic bacteria, Mycobacterium tuberculosis. Five sequences varying in fold and sizes were characterized by circular dichroism and NMR spectroscopy experiments and three were shown to have ordered tertiary structure.
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Affiliation(s)
- Pralay Mitra
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - David Shultis
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jeffrey R. Brender
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jeff Czajka
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - David Marsh
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Felicia Gray
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Tomasz Cierpicki
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail:
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46
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Kryshtafovych A, Monastyrskyy B, Fidelis K. CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL. Proteins 2013; 82 Suppl 2:7-13. [PMID: 24038551 DOI: 10.1002/prot.24399] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Revised: 08/08/2013] [Accepted: 08/14/2013] [Indexed: 12/27/2022]
Abstract
The Protein Structure Prediction Center at the University of California, Davis, supports the CASP experiments by identifying prediction targets, accepting predictions, performing standard evaluations, assisting independent CASP assessors, presenting and archiving results, and facilitating information exchange relating to CASP and structure prediction in general. We provide an overview of the CASP infrastructure implemented at the Center, and summarize standard measures used for evaluating predictions in the latest round of CASP. Several components were introduced or significantly redesigned for CASP10, in particular an improved assessors' common web-workspace; a Sphere Grinder visualization tool for analyzing local accuracy of predictions; brand new blocks for evaluation contact prediction and contact-assisted structure prediction; expanded evaluation and visualization tools for tertiary structure, refinement and quality assessment. Technical aspects of conducting the CASP10 and CASP ROLL experiments and relevant statistics are also provided.
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47
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Taylor TJ, Bai H, Tai CH, Lee B. Assessment of CASP10 contact-assisted predictions. Proteins 2013; 82 Suppl 2:84-97. [PMID: 23873510 DOI: 10.1002/prot.24367] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2013] [Accepted: 07/09/2013] [Indexed: 11/08/2022]
Abstract
In CASP10, for the first time, contact-assisted structure predictions have been assessed. Sets of pairs of contacting residues from target structures were provided to predictors for a second round of prediction after the initial round in which they were given only sequences. The objective of the experiment was to measure model quality improvement resulting from the added contact information and thereby assess and help develop so-called hybrid prediction methods--methods where some experimentally determined distance constraints are used to augment de novo computational prediction methods. The results of the experiment were, overall, quite promising.
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Affiliation(s)
- Todd J Taylor
- Laboratory of Molecular Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
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48
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MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates. Soft comput 2013. [DOI: 10.1007/s00500-013-1087-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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49
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Bhattacharya D, Cheng J. i3Drefine software for protein 3D structure refinement and its assessment in CASP10. PLoS One 2013; 8:e69648. [PMID: 23894517 PMCID: PMC3716612 DOI: 10.1371/journal.pone.0069648] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 06/13/2013] [Indexed: 12/25/2022] Open
Abstract
Protein structure refinement refers to the process of improving the qualities of protein structures during structure modeling processes to bring them closer to their native states. Structure refinement has been drawing increasing attention in the community-wide Critical Assessment of techniques for Protein Structure prediction (CASP) experiments since its addition in 8th CASP experiment. During the 9th and recently concluded 10th CASP experiments, a consistent growth in number of refinement targets and participating groups has been witnessed. Yet, protein structure refinement still remains a largely unsolved problem with majority of participating groups in CASP refinement category failed to consistently improve the quality of structures issued for refinement. In order to alleviate this need, we developed a completely automated and computationally efficient protein 3D structure refinement method, i3Drefine, based on an iterative and highly convergent energy minimization algorithm with a powerful all-atom composite physics and knowledge-based force fields and hydrogen bonding (HB) network optimization technique. In the recent community-wide blind experiment, CASP10, i3Drefine (as ‘MULTICOM-CONSTRUCT’) was ranked as the best method in the server section as per the official assessment of CASP10 experiment. Here we provide the community with free access to i3Drefine software and systematically analyse the performance of i3Drefine in strict blind mode on the refinement targets issued in CASP10 refinement category and compare with other state-of-the-art refinement methods participating in CASP10. Our analysis demonstrates that i3Drefine is only fully-automated server participating in CASP10 exhibiting consistent improvement over the initial structures in both global and local structural quality metrics. Executable version of i3Drefine is freely available at http://protein.rnet.missouri.edu/i3drefine/.
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Affiliation(s)
- Debswapna Bhattacharya
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Jianlin Cheng
- Department of Computer Science, Informatics Institute, Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
- * E-mail:
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50
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Palopoli N, Lanzarotti E, Parisi G. BeEP Server: Using evolutionary information for quality assessment of protein structure models. Nucleic Acids Res 2013; 41:W398-405. [PMID: 23729471 PMCID: PMC3692104 DOI: 10.1093/nar/gkt453] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The BeEP Server (http://www.embnet.qb.fcen.uba.ar/embnet/beep.php) is an online resource aimed to help in the endgame of protein structure prediction. It is able to rank submitted structural models of a protein through an explicit use of evolutionary information, a criterion differing from structural or energetic considerations commonly used in other assessment programs. The idea behind BeEP (Best Evolutionary Pattern) is to benefit from the substitution pattern derived from structural constraints present in a set of homologous proteins adopting a given protein conformation. The BeEP method uses a model of protein evolution that takes into account the structure of a protein to build site-specific substitution matrices. The suitability of these substitution matrices is assessed through maximum likelihood calculations from which position-specific and global scores can be derived. These scores estimate how well the structural constraints derived from each structural model are represented in a sequence alignment of homologous proteins. Our assessment on a subset of proteins from the Critical Assessment of techniques for protein Structure Prediction (CASP) experiment has shown that BeEP is capable of discriminating the models and selecting one or more native-like structures. Moreover, BeEP is not explicitly parameterized to find structural similarities between models and given targets, potentially helping to explore the conformational ensemble of the native state.
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Affiliation(s)
- Nicolas Palopoli
- Departamento de Ciencia y Tecnologia, Universidad Nacional de Quilmes, B1876BXD, Bernal, Buenos Aires, Argentina, Centre for Biological Sciences, University of Southampton, SO17 1BJ, Southampton, UK and Departamento de Quimica Biologica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EHA, Buenos Aires, Argentina
| | - Esteban Lanzarotti
- Departamento de Ciencia y Tecnologia, Universidad Nacional de Quilmes, B1876BXD, Bernal, Buenos Aires, Argentina, Centre for Biological Sciences, University of Southampton, SO17 1BJ, Southampton, UK and Departamento de Quimica Biologica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EHA, Buenos Aires, Argentina
| | - Gustavo Parisi
- Departamento de Ciencia y Tecnologia, Universidad Nacional de Quilmes, B1876BXD, Bernal, Buenos Aires, Argentina, Centre for Biological Sciences, University of Southampton, SO17 1BJ, Southampton, UK and Departamento de Quimica Biologica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EHA, Buenos Aires, Argentina
- *To whom correspondence should be addressed. Tel: +54 011 43657100 (ext. 4135); Fax: +54 011 437657101;
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