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Adiyaman R, McGuffin LJ. Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy. Methods Mol Biol 2023; 2627:119-140. [PMID: 36959445 DOI: 10.1007/978-1-0716-2974-1_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.
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Affiliation(s)
- Recep Adiyaman
- School of Biological Sciences, University of Reading, Reading, UK
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, UK.
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2
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Chen R, Liu Y, Chen S, Wang M, Zhu Y, Hu T, Wei Q, Yin X, Xie T. Protein Engineering of a Germacrene A Synthase From Lactuca sativa and Its Application in High Productivity of Germacrene A in Escherichia coli. FRONTIERS IN PLANT SCIENCE 2022; 13:932966. [PMID: 36035671 PMCID: PMC9403833 DOI: 10.3389/fpls.2022.932966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
Germacrene A (GA) is a key intermediate for the synthesis of medicinal active compounds, especially for β-elemene, which is a broad-spectrum anticancer drug. The production of sufficient GA in the microbial platform is vital for the precursors supply of active compounds. In this study, Escherichia coli BL21 Star (DE3) was used as the host and cultivated in SBMSN medium, obtaining a highest yield of FPP. The GA synthase from Lactuca sativa (LTC2) exhibited the highest level of GA production. Secondly, two residues involved in product release (T410 and T392) were substituted with Ser and Ala, respectively, responsible for relatively higher activities. Next, substitution of selected residues S243 with Asn caused an increase in activity. Furthermore, I364K-T410S and T392A-T410S were created by combination with the beneficial mutation, and they demonstrated dramatically enhanced titers with 1.90-fold and per-cell productivity with 5.44-fold, respectively. Finally, the production titer of GA reached 126.4 mg/L, and the highest productivity was 7.02 mg/L.h by the I364K-T410S mutant in a shake-flask batch culture after fermentation for 18 h. To our knowledge, the productivity of the I364K-T410S mutant is the highest level ever reported. These results highlight a promising method for the industrial production of GA in E. coli, and lay a foundation for pathway reconstruction and the production of valuable natural sesquiterpenes.
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Affiliation(s)
- Rong Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
- School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Yuheng Liu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Shu Chen
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Ming Wang
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Yao Zhu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Tianyuan Hu
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Qiuhui Wei
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Xiaopu Yin
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Tian Xie
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, Engineering Laboratory of Development and Application of Traditional Chinese Medicine from Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
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3
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Schauperl M, Denny RA. AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges. J Chem Inf Model 2022; 62:3142-3156. [PMID: 35727311 DOI: 10.1021/acs.jcim.2c00026] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.
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Affiliation(s)
- Michael Schauperl
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
| | - Rajiah Aldrin Denny
- Department of Computational Sciences HotSpot Therapeutics 50 Milk Street, Boston, Massachusetts 02110, United States
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4
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Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein. Gene 2022; 819:146206. [PMID: 35092861 DOI: 10.1016/j.gene.2022.146206] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/04/2021] [Accepted: 01/13/2022] [Indexed: 01/28/2023]
Abstract
The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.
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5
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Abass OA, Timofeev VI, Sarkar B, Onobun DO, Ogunsola SO, Aiyenuro AE, Aborode AT, Aigboje AE, Omobolanle BN, Imolele AG, Abiodun AA. Immunoinformatics analysis to design novel epitope based vaccine candidate targeting the glycoprotein and nucleoprotein of Lassa mammarenavirus (LASMV) using strains from Nigeria. J Biomol Struct Dyn 2021; 40:7283-7302. [PMID: 33719908 DOI: 10.1080/07391102.2021.1896387] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Lassa mammarenavirus (LASMV) is responsible for a specific type of acute viral hemorrhagic fever known as Lassa fever. Lack of effective treatments and counter-measures against the virus has resulted in a high mortality rate in its endemic regions. Therefore, in this study, a novel epitope-based vaccine has been designed using the methods of immunoinformatics targeting the glycoprotein and nucleoprotein of the virus. After numerous robust analyses, two CTL epitopes, eight HTL epitopes and seven B-cell epitopes were finally selected for constructing the vaccine. All these most promising epitopes were found to be antigenic, non-allergenic, nontoxic and non-human homolog, which made them suitable for designing the subunit vaccine. Furthermore, the selected T-cell epitopes which were found to be fully conserved across different isolates of the virus, were also considered for final vaccine construction. After that, numerous validation experiments, i.e. molecular docking, molecular dynamics simulation and immune simulation were conducted, which predicted that our designed vaccine should be stable within the biological environment and effective in combating the LASMV infection. In the end, codon adaptation and in silico cloning studies were performed to design a recombinant plasmid for producing the vaccine industrially. However, further in vitro and in vivo assessments should be done on the constructed vaccine to finally confirm its safety and efficacy.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ohilebo Abdulateef Abass
- Department of Bioinformatics & Computational Biology, Centre for BioCode, Benin, Nigeria.,Department of Biochemistry, Faculty of Life Sciences, Ambrose Alli University, Ekpoma, Nigeria
| | - Vladimir I Timofeev
- Shubnikov Institute of Crystallography of Federal Scientific Research Centre "Crystallography and Photonics" of Russian Academy of Sciences, Moscow, Russian Federation
| | - Bishajit Sarkar
- Department of Biotechnology & Genetic Engineering, Faculty of Biological Sciences, Jahangirnagar University, Dhaka, Bangladesh
| | - Desmond Odiamehi Onobun
- Department of Bioinformatics & Computational Biology, Centre for BioCode, Benin, Nigeria.,Department of Biochemistry, Faculty of Life Sciences, Ambrose Alli University, Ekpoma, Nigeria
| | | | | | - Abdullahi Tunde Aborode
- Research & Development, Shaping Women in STEM (SWIS) Africa, Lagos, Nigeria.,Research & Development, Healthy Africans Platform, Ibadan, Nigeria
| | | | | | | | - Alade Adebowale Abiodun
- Bio-Computing Research Unit, Molecular Biology & Simulations (Mols & Sims) Centre, Ado-Ekiti, Nigeria
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Abstract
Biologists are increasingly aware of the importance of protein structure in revealing function. The computational tools now exist which allow researchers to model unknown proteins simply on the basis of their primary sequence. However, for the non-specialist bioinformatician, there is a dazzling array of terminology, acronyms, and competing computer software available for this process. This review is intended to highlight the key stages of computational protein structure prediction, as well as explain the reasons behind some of the procedures and list some established workarounds for common pitfalls. Thereafter follows a review of five one-stop servers for start-to-finish structure prediction.
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Abstract
For two decades, Rosetta has consistently been at the forefront of protein structure
prediction. While it has become a very large package comprising programs, scripts, and tools, for
different types of macromolecular modelling such as ligand docking, protein-protein docking,
protein design, and loop modelling, it started as the implementation of an algorithm for ab initio
protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the
literature to describe that algorithm and its contribution to the third edition of the community wide
Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta
stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers
have been contributing to deciphering ’the second half of the genetic code’. Although the focus of
Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is
associated with its fragment-assembly protein structure prediction approach. Following a
presentation of the main concepts underpinning its foundation, especially sequence-structure
correlation and usage of fragments, we review the main stages of its developments and highlight
the milestones it has achieved in terms of protein structure prediction, particularly in CASP.
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Affiliation(s)
- Jad Abbass
- Department of Computer Science, Lebanese International University, Bekaa, Lebanon
| | - Jean-Christophe Nebel
- Faculty of Science, Engineering and Computing, Kingston University, London, KT1 2EE, United Kingdom
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8
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Insights into the interactions of bisphenol and phthalate compounds with unamended and carnitine-amended montmorillonite clays. Comput Chem Eng 2020; 143. [PMID: 33122868 PMCID: PMC7591107 DOI: 10.1016/j.compchemeng.2020.107063] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Montmorillonite clays could be promising sorbents to mitigate toxic compound exposures. Bisphenols A (BPA) and S (BPS) as well as phthalates, dibutyl phthalate (DBP) and di-2-ethylhexyl phthalate (DEHP), are ubiquitous environmental contaminants linked to adverse health effects. Here, we combined computational and experimental methods to investigate the ability of montmorillonite clays to sorb these compounds. Molecular dynamics simulations predicted that parent, unamended, clay has higher binding propensity for BPA and BPS than for DBP and DEHP; carnitine-amended clay improved BPA and BPS binding, through carnitine simultaneously anchoring to the clay through its quaternary ammonium cation and forming hydrogen bonds with BPA and BPS. Experimental isothermal analysis confirmed that carnitine-amended clay has enhanced BPA binding capacity, affinity and enthalpy. Our studies demonstrate how computational and experimental methods, combined, can characterize clay binding and sorption of toxic compounds, paving the way for future investigation of clays to reduce BPA and BPS exposure.
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9
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Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
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10
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Onel M, Kieslich CA, Pistikopoulos EN. A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process. AIChE J 2019; 65:992-1005. [PMID: 32377021 DOI: 10.1002/aic.16497] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C-SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results.
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Affiliation(s)
- Melis Onel
- Artie McFerrin Dept. of Chemical Engineering Texas A&M University College Station, Texas 77843
- Texas A&M Energy Institute Texas A&M University College Station, Texas 77843
| | - Chris A. Kieslich
- Artie McFerrin Dept. of Chemical Engineering Texas A&M University College Station, Texas 77843
- Texas A&M Energy Institute Texas A&M University College Station, Texas 77843
- Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Atlanta Georgia
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Dept. of Chemical Engineering Texas A&M University College Station, Texas 77843
- Texas A&M Energy Institute Texas A&M University College Station, Texas 77843
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11
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Orr AA, Gonzalez-Rivera JC, Wilson M, Bhikha PR, Wang D, Contreras LM, Tamamis P. A high-throughput and rapid computational method for screening of RNA post-transcriptional modifications that can be recognized by target proteins. Methods 2018; 143:34-47. [DOI: 10.1016/j.ymeth.2018.01.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 01/14/2018] [Accepted: 01/26/2018] [Indexed: 12/25/2022] Open
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12
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Haas J, Barbato A, Behringer D, Studer G, Roth S, Bertoni M, Mostaguir K, Gumienny R, Schwede T. Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12. Proteins 2017; 86 Suppl 1:387-398. [PMID: 29178137 DOI: 10.1002/prot.25431] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 11/10/2017] [Accepted: 11/22/2017] [Indexed: 12/22/2022]
Abstract
Every second year, the community experiment "Critical Assessment of Techniques for Structure Prediction" (CASP) is conducting an independent blind assessment of structure prediction methods, providing a framework for comparing the performance of different approaches and discussing the latest developments in the field. Yet, developers of automated computational modeling methods clearly benefit from more frequent evaluations based on larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements the CASP experiment by conducting fully automated blind prediction assessments based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the PDB Protein Data Bank. CAMEO publishes weekly benchmarking results based on models collected during a 4-day prediction window, on average assessing ca. 100 targets during a time frame of 5 weeks. CAMEO benchmarking data is generated consistently for all participating methods at the same point in time, enabling developers to benchmark and cross-validate their method's performance, and directly refer to the benchmarking results in publications. In order to facilitate server development and promote shorter release cycles, CAMEO sends weekly email with submission statistics and low performance warnings. Many participants of CASP have successfully employed CAMEO when preparing their methods for upcoming community experiments. CAMEO offers a variety of scores to allow benchmarking diverse aspects of structure prediction methods. By introducing new scoring schemes, CAMEO facilitates new development in areas of active research, for example, modeling quaternary structure, complexes, or ligand binding sites.
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Affiliation(s)
- Jürgen Haas
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Alessandro Barbato
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Dario Behringer
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Gabriel Studer
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Steven Roth
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Martino Bertoni
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Khaled Mostaguir
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Rafal Gumienny
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
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13
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Terashi G, Kihara D. Protein structure model refinement in CASP12 using short and long molecular dynamics simulations in implicit solvent. Proteins 2017; 86 Suppl 1:189-201. [PMID: 28833585 DOI: 10.1002/prot.25373] [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] [Received: 06/21/2017] [Revised: 08/01/2017] [Accepted: 08/18/2017] [Indexed: 12/21/2022]
Abstract
Protein structure prediction has matured over years, particularly those which use structure templates for building a model. It can build a model with correct overall conformation in cases where appropriate templates are available. Models with the correct topology can be practically useful for limited purposes that need residue-level accuracy, but further improvement of the models can allow the models to be used in tasks that need detailed structures, such as molecular replacement in X-ray crystallography or structure-based drug screening. Thus, model refinement is an important final step in protein structure prediction to bridge predictions to real-life applications. Model refinement is one of the categories in recent rounds of critical assessment of techniques in protein structure prediction (CASP) and has recently been drawing more attention due to its realized importance. Here we report our group's performance in the refinement category in CASP12. Our method is based on inexpensive short molecular dynamics (MD) simulations in implicit solvent. Our performance in CASP12 was among the top, which was consistent with the previous round, CASP11. Our method with short MD runs achieved comparable performance with other methods that used longer simulations. Detailed analyses found that improvements typically occurred in entire regions of a structure rather than only in flexible loop regions. The remaining challenge in the structure refinement includes large conformational refinement which involves substantial motions of secondary structure elements or domains.
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Affiliation(s)
- Genki Terashi
- 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, Indiana, 47907
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