RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding
Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, geno...
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creator | Sora, Valentina Laspiur, Adrian Otamendi Degn, Kristine Arnaudi, Matteo Utichi, Mattia Beltrame, Ludovica De Menezes, Dayana Orlandi, Matteo Stoltze, Ulrik Kristoffer Rigina, Olga Sackett, Peter Wad Wadt, Karin Schmiegelow, Kjeld Tiberti, Matteo Papaleo, Elena |
description | Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction. |
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Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.</description><identifier>ISSN: 0961-8368</identifier><identifier>ISSN: 1469-896X</identifier><identifier>EISSN: 1469-896X</identifier><identifier>DOI: 10.1002/pro.4527</identifier><identifier>PMID: 36461907</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Amino acid sequence ; Amino acids ; Binding ; binding free energy ; Case studies ; Children ; Computer applications ; Entropy ; folding free energy ; Free energy ; free energy calculations ; Mutation ; Protein folding ; Protein Stability ; Proteins ; Proteins - chemistry ; Rosetta ; Software ; Stability ; Tools for Protein Science</subject><ispartof>Protein science, 2023-01, Vol.32 (1), p.e4527-n/a</ispartof><rights>2022 The Authors. published by Wiley Periodicals LLC on behalf of The Protein Society.</rights><rights>2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4387-d810cc5227951da801c72a77eaacd44cce07e7216d94e6748693fa6cd2e2609b3</citedby><cites>FETCH-LOGICAL-c4387-d810cc5227951da801c72a77eaacd44cce07e7216d94e6748693fa6cd2e2609b3</cites><orcidid>0000-0002-7376-5894</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795540/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795540/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,886,1418,1434,27929,27930,45579,45580,46414,46838,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36461907$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sora, Valentina</creatorcontrib><creatorcontrib>Laspiur, Adrian Otamendi</creatorcontrib><creatorcontrib>Degn, Kristine</creatorcontrib><creatorcontrib>Arnaudi, Matteo</creatorcontrib><creatorcontrib>Utichi, Mattia</creatorcontrib><creatorcontrib>Beltrame, Ludovica</creatorcontrib><creatorcontrib>De Menezes, Dayana</creatorcontrib><creatorcontrib>Orlandi, Matteo</creatorcontrib><creatorcontrib>Stoltze, Ulrik Kristoffer</creatorcontrib><creatorcontrib>Rigina, Olga</creatorcontrib><creatorcontrib>Sackett, Peter Wad</creatorcontrib><creatorcontrib>Wadt, Karin</creatorcontrib><creatorcontrib>Schmiegelow, Kjeld</creatorcontrib><creatorcontrib>Tiberti, Matteo</creatorcontrib><creatorcontrib>Papaleo, Elena</creatorcontrib><title>RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding</title><title>Protein science</title><addtitle>Protein Sci</addtitle><description>Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. 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Advances in experimental mutational scans allow high‐throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease‐related variants that can benefit from analyses with structure‐based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high‐throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high‐throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication‐ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>36461907</pmid><doi>10.1002/pro.4527</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0002-7376-5894</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Amino acid sequence Amino acids Binding binding free energy Case studies Children Computer applications Entropy folding free energy Free energy free energy calculations Mutation Protein folding Protein Stability Proteins Proteins - chemistry Rosetta Software Stability Tools for Protein Science |
title | RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding |
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