{"id":24238,"date":"2021-09-03T08:08:15","date_gmt":"2021-09-03T12:08:15","guid":{"rendered":"https:\/\/www.bu.edu\/hic\/?p=24238"},"modified":"2022-02-25T09:30:21","modified_gmt":"2022-02-25T14:30:21","slug":"focused-research-program-faculty-member-awarded-funding-from-the-camille-and-henry-dreyfus-foundation","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/hic\/2021\/09\/03\/focused-research-program-faculty-member-awarded-funding-from-the-camille-and-henry-dreyfus-foundation\/","title":{"rendered":"Focused Research Program Faculty Member Awarded Funding from The Camille and Henry Dreyfus Foundation"},"content":{"rendered":"

BY GINA MANTICA<\/p>\n

When you take medicine, the drug moves its way through your body until it attaches to parts of proteins that are capable of achieving the drug\u2019s desired effect. But figuring out which part of a protein a drug should target can be challenging: one small mutation in a protein can cause a huge change in the protein\u2019s structure and affect how the drug binds. Artificial intelligence experts are teaming up with chemists to better understand the relationship between the structure and function of proteins\u2019 binding sites.<\/p>\n

Qiang Cui, a Professor of Chemistry and Research Fellow at the Hariri Institute, received a 2021 Machine Learning in the Chemical Sciences & Engineering Award from The Camille and Henry Dreyfus Foundation<\/a> to develop a machine learning approach for analyzing protein allostery, or how the activity of a protein changes depending on how other molecules binding to it. The project, entitled, \u201cUnderstanding Protein Allostery using Machine Learning and Deep Mutation Data\u201d, stems from conversations Cui had with other experts in the Machine Learning for Chemistry & Materials Science Focused Research Program (FRP) about how to improve the robustness of a model developed with a collaborator at University of Wisconsin-Madison.<\/p>\n

\"\"
Qiang Cui, Professor of Chemistry, discussing preliminary results with Zhuang Liu, a Graduate Student in Physics.<\/figcaption><\/figure>\n

The model that Cui\u2019s team developed uses experimental data about the structure of proteins to predict whether a mutation would change the protein\u2019s allosteric functionality. But the model can only analyze single mutations. Cui wants to improve the model by incorporating sequence information of related proteins, so that researchers can better predict the function of a protein with multiple mutations.<\/p>\n

Pharmaceutical researchers take advantage of protein allostery to design drugs that bind to different parts of a protein, increasing a drug\u2019s specificity and effectiveness. Machine learning enables researchers to extract meaningful information from large experimental data sets on how complex mutations can affect a protein\u2019s structure and function, aiding in the discovery of new drugs.<\/p>\n

The Hariri Institute\u2019s FRPs catalyze transformative research projects that enable new methodologies, advancements, and technologies. \u201cThe tools we [Cui\u2019s team] use in this project are relatively established machine learning techniques, but we are not experts to improve the effectiveness and robustness of these tools,\u201d said Cui, \u201cThe FRP was very useful in terms of introducing us to local machine learning experts.\u201d<\/p>\n

\n

Interested in learning more about the research happening at the Hariri Institute?\u00a0<\/span>Sign up for our newsletter here.<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

BY GINA MANTICA When you take medicine, the drug moves its way through your body until it attaches to parts of proteins that are capable of achieving the drug\u2019s desired effect. But figuring out which part of a protein a drug should target can be challenging: one small mutation in a protein can cause a […]<\/p>\n","protected":false},"author":8550,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[11823,11716,1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts\/24238"}],"collection":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/users\/8550"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/comments?post=24238"}],"version-history":[{"count":2,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts\/24238\/revisions"}],"predecessor-version":[{"id":24252,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts\/24238\/revisions\/24252"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/media?parent=24238"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/categories?post=24238"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/tags?post=24238"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}