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      Ten quick tips for homology modeling of high-resolution protein 3D structures

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Author summary

          The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

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          Most cited references 56

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          Hidden Markov models in computational biology. Applications to protein modeling.

          Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage over PROFILESEARCH in terms of lower rates of false negatives and false positives, even though the HMM is trained using only unaligned sequences, whereas PROFILESEARCH requires aligned training sequences. Our results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the alpha-1 subunit of L-type calcium channels which play an important role in excitation-contraction coupling. This region has been suggested to contain the functional domains that are typical or essential for all L-type calcium channels regardless of whether they couple to ryanodine receptors, conduct ions or both.
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            Pymol: An open-source molecular graphics tool

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              QMEAN: A comprehensive scoring function for model quality assessment.

              In protein structure prediction, a considerable number of alternative models are usually produced from which subsequently the final model has to be selected. Thus, a scoring function for the identification of the best model within an ensemble of alternative models is a key component of most protein structure prediction pipelines. QMEAN, which stands for Qualitative Model Energy ANalysis, is a composite scoring function describing the major geometrical aspects of protein structures. Five different structural descriptors are used. The local geometry is analyzed by a new kind of torsion angle potential over three consecutive amino acids. A secondary structure-specific distance-dependent pairwise residue-level potential is used to assess long-range interactions. A solvation potential describes the burial status of the residues. Two simple terms describing the agreement of predicted and calculated secondary structure and solvent accessibility, respectively, are also included. A variety of different implementations are investigated and several approaches to combine and optimize them are discussed. QMEAN was tested on several standard decoy sets including a molecular dynamics simulation decoy set as well as on a comprehensive data set of totally 22,420 models from server predictions for the 95 targets of CASP7. In a comparison to five well-established model quality assessment programs, QMEAN shows a statistically significant improvement over nearly all quality measures describing the ability of the scoring function to identify the native structure and to discriminate good from bad models. The three-residue torsion angle potential turned out to be very effective in recognizing the native fold. (c) 2007 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                2 April 2020
                April 2020
                : 16
                : 4
                Affiliations
                [1 ] Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic
                [2 ] Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
                University of Toronto, CANADA
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-19-00299
                10.1371/journal.pcbi.1007449
                7117658
                32240155
                © 2020 Haddad et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 2, Tables: 2, Pages: 19
                Product
                Funding
                We gratefully acknowledge the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 759585), the Czech Science Agency (project no. 18-10251S) and CEITEC 2020 (LQ1601) for financial support of this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Education
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Alignment
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Physical Sciences
                Physics
                Condensed Matter Physics
                Solid State Physics
                Crystallography
                Crystal Structure
                Biology and Life Sciences
                Biochemistry
                Proteins
                Structural Proteins
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Comparison
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Comparison
                Research and Analysis Methods
                Computational Techniques
                Split-Decomposition Method
                Multiple Alignment Calculation
                Physical Sciences
                Chemistry
                Chemical Elements
                Hydrogen

                Quantitative & Systems biology

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