https:\/\/lnkd.in\/dW79CFN2<\/a><\/p>\nIn this work:<\/p>\n
![\"\u2705\"](\"https:\/\/fonts.gstatic.com\/s\/e\/notoemoji\/16.0\/2705\/32.png\")
The ability of diferent approaches\u00a0 \u2014from force fields to inverse folding tools like \u2014 is evaluated for predicting the effect of multiple simultaneous mutations.<\/p>\n
TriCombine, a new tool, is presented. It analyzed residue triplets in proteins to guide the selection of promising mutants.<\/p>\n
Predictors are validated using a collection of 36 mutants, including 11 cristal structures, and a laarge-scale analysis of over 300.000 variants in both natural and the novo domains.<\/p>\n
The article concludes that combining AI-based modeling with physics-based scoring functions (such as FoldX) delivers the most realible results, specially when redesigning proteins with multiple mutations.<\/p>\n
The work highlights the importance of hybrid strategies in designing robust proteins, particularly when dealing with de novo proteins or proteins without resolved crystal structures.<\/p>\n
![\"\ud83d\udd17\"](\"https:\/\/fonts.gstatic.com\/s\/e\/notoemoji\/16.0\/1f517\/32.png\")
A big thank you to all the authors for this outstanding multidisciplinary effort!<\/p>\n","protected":false},"excerpt":{"rendered":"
\u00a1New scientific publication! Today we are celebrating great news: our researcher\u00a0Ana Mar\u00eda Fern\u00e1ndez Escamilla has just published a new study analyzing the performance of various deep learning tools and first-principles-based methods for protein redesign, with a focus on their therapeutic aplicability. Title of the article: Artificial intelligence and first-principle methods in protein redesign: A marriage […]<\/p>\n","protected":false},"author":30306,"featured_media":22651,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/posts\/22657"}],"collection":[{"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/users\/30306"}],"replies":[{"embeddable":true,"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/comments?post=22657"}],"version-history":[{"count":0,"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/posts\/22657\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/media\/22651"}],"wp:attachment":[{"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/media?parent=22657"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/categories?post=22657"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/idibe.umh.es\/en\/wp-json\/wp\/v2\/tags?post=22657"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\ud83d\udd2c\"](\"https:\/\/fonts.gstatic.com\/s\/e\/notoemoji\/16.0\/1f52c\/32.png\")
![\"\u2728\"](\"https:\/\/fonts.gstatic.com\/s\/e\/notoemoji\/16.0\/2728\/32.png\")
\u00a1New scientific publication!<\/p>\n![\"\ud83d\udcc4\"](\"https:\/\/fonts.gstatic.com\/s\/e\/notoemoji\/16.0\/1f4c4\/32.png\")
Title of the article: Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?<\/p>\n![\"\ud83d\udcda\"](\"https:\/\/fonts.gstatic.com\/s\/e\/notoemoji\/16.0\/1f4da\/32.png\")
\u00a0DOI: