{"664290":{"#nid":"664290","#data":{"type":"news","title":"AF2Complex \u2018Computational Microscope\u2019 Predicts Protein Interactions, Potential Paths to New Antibiotics  ","body":[{"value":"\u003Cp\u003EThough it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn a new paper published in \u003Cem\u003EeLife\u003C\/em\u003E, researchers in the \u003Ca href=\u0022https:\/\/biosciences.gatech.edu\/\u0022 target=\u0022_blank\u0022\u003ESchool of Biological Sciences\u003C\/a\u003E and the \u003Ca href=\u0022https:\/\/www.cc.gatech.edu\/\u0022 target=\u0022_blank\u0022\u003ESchool of Computer Science\u003C\/a\u003E have shown that AF2Complex may be able to lend a hand.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBuilding on the models of \u003Ca href=\u0022https:\/\/www.deepmind.com\/\u0022 target=\u0022_blank\u0022\u003EDeepMind\u003C\/a\u003E\u2019s \u003Ca href=\u0022https:\/\/www.deepmind.com\/research\/highlighted-research\/alphafold\u0022 target=\u0022_blank\u0022\u003EAlphaFold 2\u003C\/a\u003E, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex \u2014 short for AlphaFold 2 Complex \u2014 is a deep learning tool designed to \u003Ca href=\u0022https:\/\/cos.gatech.edu\/news\/af2complex-researchers-leverage-deep-learning-predict-physical-interactions-protein-complexes\u0022 target=\u0022_blank\u0022\u003Epredict the physical interactions of multiple proteins\u003C\/a\u003E. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cWe essentially conduct computational experiments that try to figure out the atomic details of supercomplexes (large interacting groups of proteins) important to biological functions,\u201d explained \u003Ca href=\u0022https:\/\/biosciences.gatech.edu\/people\/jeffrey-skolnick\u0022 target=\u0022_blank\u0022\u003EJeffrey Skolnick\u003C\/a\u003E, Regents\u2019 Professor and Mary and Maisie Gibson Chair in the School of Biological Sciences, and one of the corresponding authors of the study. With AF2Complex, which was developed last year by the same research team, it\u2019s \u201clike using a computational microscope powered by deep learning and supercomputing.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn their latest study, the researchers used this \u2018computational microscope\u2019 to examine a complicated protein synthesis and transport pathway, hoping to clarify how proteins in the pathway interact to ultimately transport a newly synthesized protein from the interior to the outer membrane of the bacteria \u2014 and identify players that experiments might have missed. Insights into this pathway may identify new targets for antibiotic and therapeutic design while providing a foundation for using AF2Complex to computationally expedite this type of biology research as a whole.\u003C\/p\u003E\r\n\r\n\u003Ch3\u003EComputing complexes\u003C\/h3\u003E\r\n\r\n\u003Cp\u003ECreated by London-based artificial intelligence lab DeepMind, AlphaFold 2 is a deep learning tool able to generate accurate predictions about the three-dimensional structure of single proteins using just their building blocks, amino acids. Taking things a step further, AF2Complex uses these structures to predict the likelihood that proteins are able to interact to form a functional complex, what aspects of each structure are the likely interaction sites, and even what protein complexes are likely to pair up to create even larger functional groups called supercomplexes.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cThe successful development of AF2Complex earlier this year makes us believe that this approach has tremendous potential in identifying and characterizing the set of protein-protein interactions important to life,\u201d shared \u003Ca href=\u0022https:\/\/biosciences.gatech.edu\/people\/mu_gao\u0022 target=\u0022_blank\u0022\u003EMu Gao\u003C\/a\u003E, a senior research scientist at Georgia Tech. \u201cTo further convince the broad molecular biology community, we [had to] demonstrate it with a more convincing, high impact application.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe researchers chose to apply AF2Complex to a pathway in \u003Cem\u003EEscherichia coli\u003C\/em\u003E (\u003Cem\u003EE. coli\u003C\/em\u003E), a model organism in life sciences research commonly used for experimental DNA manipulation and protein production due to its relative simplicity and fast growth.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETo demonstrate the tool\u2019s power, the team examined the synthesis and transport of proteins that are essential for exchanging nutrients and responding to environmental stressors: outer membrane proteins, or OMPs for short. These proteins reside on the outermost membrane of gram-negative bacteria, a large family of bacteria characterized by the presence of inner and outer membranes, like \u003Cem\u003EE. coli\u003C\/em\u003E. However, the proteins are created inside the cell and must be transported to their final destinations.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cAfter more than two decades of experimental studies, researchers have identified some of the protein complexes of key players, but certainly not all of them,\u201d Gao explained. AF2Complex \u201ccould enable us to discover some novel and interesting features of the OMP biogenesis pathway that were missed in previous experimental studies.\u201d\u003C\/p\u003E\r\n\r\n\u003Ch3\u003ENew insights\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EUsing the \u003Ca href=\u0022https:\/\/www.olcf.ornl.gov\/summit\/\u0022 target=\u0022_blank\u0022\u003ESummit\u003C\/a\u003E supercomputer at the \u003Ca href=\u0022https:\/\/www.ornl.gov\/\u0022 target=\u0022_blank\u0022\u003EOak Ridge National Laboratory\u003C\/a\u003E, the team, which included computer science undergraduate \u003Ca href=\u0022https:\/\/davinan.github.io\/dna\/\u0022 target=\u0022_blank\u0022\u003EDavi Nakajima An\u003C\/a\u003E, put AF2Complex to the test. They compared a few proteins known to be important in the synthesis and transport of OMPs to roughly 1,500 other proteins \u2014 all of the known proteins in \u003Cem\u003EE. coli\u003C\/em\u003E\u2019s cell envelope \u2014 to see which pairs the tool computed as most likely to interact, and which of those pairs were likely to form supercomplexes.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETo determine if AF2Complex\u2019s predictions were correct, the researchers compared the tool\u2019s predictions to known experimental data. \u201cEncouragingly,\u201d said Skolnick, \u201camong the top hits from computational screening, we found previously known interacting partners.\u201d Even within those protein pairs known to interact, AF2Complex was able to highlight structural details of those interactions that explain data from previous experiments, lending additional confidence to the tool\u2019s accuracy.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn addition to known interactions, AF2Complex predicted several unknown pairs. Digging further into these unexpected partners revealed details on what aspects of the pairs might interact to form larger groups of functional proteins, likely active configurations of complexes that have previously eluded experimentalists, and new potential mechanisms for how OMPs are synthesized and transported.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cSince the outer membrane pathway is both vital and unique to gram-negative bacteria, the key proteins involved in this pathway could be novel targets for new antibiotics,\u201d said Skolnick. \u201cAs such, our work that provides molecular insights about these new drug targets might be valuable to new therapeutic design.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBeyond this pathway, the researchers are hopeful that AF2Complex could mean big things for biology research.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cUnlike predicting structures of a single protein sequence, predicting the structural model of a supercomplex can be very complicated, especially when the components or stoichiometry of the complex is unknown,\u201d Gao noted. \u201cIn this regard, AF2Complex could be a new computational tool for biologists to conduct trial experiments of different combinations of proteins,\u201d potentially expediting and increasing the efficiency of this type of biology research as a whole.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAF2Complex is an open-source tool available to the public and can be downloaded \u003Ca href=\u0022https:\/\/github.com\/FreshAirTonight\/af2complex\u0022 target=\u0022_blank\u0022\u003Ehere\u003C\/a\u003E.\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cem\u003EThis work was supported in part by the DOE Office of Science, Office of Biological and Environmental Research (DOE DE-SC0021303) and the Division of General Medical Sciences of the National Institute Health (NIH R35GM118039).\u0026nbsp;DOI: \u003C\/em\u003E\u003Ca href=\u0022https:\/\/doi.org\/10.7554\/eLife.82885\u0022\u003E\u003Cem\u003Ehttps:\/\/doi.org\/10.7554\u003C\/em\u003E\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn a new paper published in \u003Cem\u003EeLife,\u003C\/em\u003E School of Biological Sciences and School of Computer Science researchers show how AF2Complex, a deep learning tool designed to predict the physical interactions of proteins, is lending new insights into protein synthesis and transport \u2014 and paving the way to computationally expedite biology research as a whole.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Researchers are using AF2Complex, a deep learning tool designed to predict the physical interactions of proteins, to shed light on an important biological pathway \u2014 and pave the way to computationally expedite biology research."}],"uid":"35575","created_gmt":"2023-01-03 17:14:14","changed_gmt":"2023-12-14 17:03:35","author":"adavidson38","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2023-01-04T00:00:00-05:00","iso_date":"2023-01-04T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"657354":{"id":"657354","type":"image","title":"Researchers Jeffrey Skolnick and Mu Gao at the Engineered Biosystems Building at Georgia Tech. (Photo: Jess Hunt-Ralston)","body":null,"created":"1650045007","gmt_created":"2022-04-15 17:50:07","changed":"1650045007","gmt_changed":"2022-04-15 17:50:07","alt":"","file":{"fid":"249155","name":"2022 04 Jeffrey Skolnick and Mu Gao - Biosci research copy.jpg","image_path":"\/sites\/default\/files\/images\/2022%2004%20Jeffrey%20Skolnick%20and%20Mu%20Gao%20-%20Biosci%20research%20copy.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/2022%2004%20Jeffrey%20Skolnick%20and%20Mu%20Gao%20-%20Biosci%20research%20copy.jpg","mime":"image\/jpeg","size":2689047,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/2022%2004%20Jeffrey%20Skolnick%20and%20Mu%20Gao%20-%20Biosci%20research%20copy.jpg?itok=8mMpA7I0"}},"664288":{"id":"664288","type":"image","title":"Examples of protein complexes modeled by AF2Complex residing between the inner and outer membranes of E. coli","body":null,"created":"1672765216","gmt_created":"2023-01-03 17:00:16","changed":"1672766090","gmt_changed":"2023-01-03 17:14:50","alt":"","file":{"fid":"251396","name":"cover image v7.png","image_path":"\/sites\/default\/files\/images\/cover%20image%20v7.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/cover%20image%20v7.png","mime":"image\/png","size":1849243,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/cover%20image%20v7.png?itok=i1aNOFpt"}}},"media_ids":["657354","664288"],"related_links":[{"url":"https:\/\/ascr-discovery.org\/2023\/01\/computing-function-from-form\/","title":"ASCR Discovery: Computing function from form"},{"url":"https:\/\/cos.gatech.edu\/news\/af2complex-researchers-leverage-deep-learning-predict-physical-interactions-protein-complexes","title":"AF2Complex: Researchers Leverage Deep Learning to Predict Physical Interactions of Protein Complexes"},{"url":"https:\/\/research.gatech.edu\/ai-tool-pairs-protein-pathways-clinical-side-effects-patient-comorbidities-suggest-targeted-covid","title":"AI Tool Pairs Protein Pathways with Clinical Side Effects, Patient Comorbidities to Suggest Targeted Covid-19 Treatments"},{"url":"https:\/\/github.com\/FreshAirTonight\/af2complex","title":"Download AF2Complex"}],"groups":[{"id":"1278","name":"College of Sciences"},{"id":"1188","name":"Research Horizons"},{"id":"1275","name":"School of Biological Sciences"}],"categories":[{"id":"138","name":"Biotechnology, Health, Bioengineering, Genetics"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"146","name":"Life Sciences and Biology"},{"id":"135","name":"Research"},{"id":"8862","name":"Student Research"}],"keywords":[{"id":"192258","name":"cos-data"},{"id":"192250","name":"cos-microbial"},{"id":"190336","name":"AF2Complex"},{"id":"12761","name":"E. Coli Bacteria"},{"id":"191799","name":"outer membrane proteins"},{"id":"166882","name":"School of Biological Sciences"},{"id":"187915","name":"go-researchnews"},{"id":"187582","name":"go-ibb"},{"id":"192863","name":"go-ai"}],"core_research_areas":[{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Cstrong\u003EWriter:\u0026nbsp;\u003C\/strong\u003E\u003Ca href=\u0022mailto:davidson.audra@gatech.edu\u0022\u003EAudra Davidson\u003C\/a\u003E\u003Cbr \/\u003E\r\nCommunications Officer\u003Cbr \/\u003E\r\nCollege of Sciences at Georgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EEditor:\u0026nbsp;\u003C\/strong\u003EJess Hunt-Ralston\u003Cbr \/\u003E\r\nDirector of Communications\u003Cbr \/\u003E\r\nCollege of Sciences at Georgia Tech\u003C\/p\u003E\r\n","format":"limited_html"}],"email":["jess@cos.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}