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      DDGun: an untrained predictor of protein stability changes upon amino acid variants

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          Abstract

          Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔ G). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔ G prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔ G values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔ G of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔ G, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔ G prediction.

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          DDGun: an untrained predictor of protein stability changes upon amino acid variants.

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          Most cited references26

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          Amino acid substitution matrices from protein blocks.

          Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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            The FoldX web server: an online force field

            FoldX is an empirical force field that was developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX, namely the calculation of the free energy of a macromolecule based on its high-resolution 3D structure, is now publicly available through a web server at . The current release allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation. Alanine scanning, the systematic truncation of side chains to alanine, is also included. In addition, some reporting functions have been added, and it is now possible to print both the atomic interaction networks that constitute the protein, print the structural and energetic details of the interactions per atom or per residue, as well as generate a general quality report of the pdb structure. This core functionality will be further extended as more FoldX applications are developed.
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              HH-suite3 for fast remote homology detection and deep protein annotation

              Background HH-suite is a widely used open source software suite for sensitive sequence similarity searches and protein fold recognition. It is based on pairwise alignment of profile Hidden Markov models (HMMs), which represent multiple sequence alignments of homologous proteins. Results We developed a single-instruction multiple-data (SIMD) vectorized implementation of the Viterbi algorithm for profile HMM alignment and introduced various other speed-ups. These accelerated the search methods HHsearch by a factor 4 and HHblits by a factor 2 over the previous version 2.0.16. HHblits3 is ∼10× faster than PSI-BLAST and ∼20× faster than HMMER3. Jobs to perform HHsearch and HHblits searches with many query profile HMMs can be parallelized over cores and over cluster servers using OpenMP and message passing interface (MPI). The free, open-source, GPLv3-licensed software is available at https://github.com/soedinglab/hh-suite. Conclusion The added functionalities and increased speed of HHsearch and HHblits should facilitate their use in large-scale protein structure and function prediction, e.g. in metagenomics and genomics projects.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                05 July 2022
                07 May 2022
                07 May 2022
                : 50
                : W1
                : W222-W227
                Affiliations
                Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , 9500 Euclid Avenue, Cleveland, OH 44195, USA
                BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna , Via F. Selmi 3, 40126 Bologna, Italy
                Department of Medical Sciences, University of Torino , Via Santena 19, 10126, Torino, Italy
                Department of Medical Sciences, University of Torino , Via Santena 19, 10126, Torino, Italy
                Department of Medical Sciences, University of Torino , Via Santena 19, 10126, Torino, Italy
                Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic , 9500 Euclid Avenue, Cleveland, OH 44195, USA
                Department of Medical Sciences, University of Torino , Via Santena 19, 10126, Torino, Italy
                Author notes
                To whom correspondence should be addressed. Tel: +39 011 6705871; Fax: +39 011 6705610; Email: piero.fariselli@ 123456unito.it

                The authors wish it to be known that, in their opinion, these authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0002-2323-0963
                https://orcid.org/0000-0003-0160-9312
                https://orcid.org/0000-0002-5173-9636
                https://orcid.org/0000-0003-1811-4762
                Article
                gkac325
                10.1093/nar/gkac325
                9252764
                35524565
                51f0a4ed-48b3-478f-9e43-6ebe9d2e217d
                © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 04 May 2022
                : 15 April 2022
                : 24 March 2022
                Page count
                Pages: 6
                Funding
                Funded by: Ministero dell'Università e della Ricerca, DOI 10.13039/501100021856;
                Award ID: PRIN201744NR8S
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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