{"id":14258,"date":"2014-04-09T09:17:00","date_gmt":"2014-04-09T13:17:00","guid":{"rendered":"http:\/\/www.bu.edu\/systems\/?p=14258"},"modified":"2022-01-24T19:19:43","modified_gmt":"2022-01-25T00:19:43","slug":"interdisciplinary-team-sheds-light-on-how-proteins-bind","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/cise\/interdisciplinary-team-sheds-light-on-how-proteins-bind\/","title":{"rendered":"Interdisciplinary Team Sheds Light on How Proteins Bind"},"content":{"rendered":"

Finding Could Open Up New Drug Discovery Opportunities<\/strong><\/h3>\n

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Transient encounter complexes” of an enzyme produced by a bacterium present in the human gut binding to a fragment of a protein in turkey eggs which inhibits the enzyme<\/figcaption><\/figure>\n

Over the past six years, an interdisciplinary team of College of Engineering faculty members\u2014Professor\u00a0Sandor Vajda<\/a>\u00a0(BME, SE), Research Assistant Professor\u00a0Dima Kozakov<\/a>\u00a0(BME), Professor\u00a0Yannis Paschalidis<\/a>\u00a0(ECE, SE) and Associate Professor\u00a0Pirooz Vakili<\/a> (ME, SE)\u2014have been developing a set of powerful optimization algorithms for predicting the structures of complexes that form when two proteins bind together\u2014structures that, in some cases, generate erroneous cell signaling pathways that can trigger cancer and other inflammatory diseases.<\/p>\n

Incorporated into Vajda\u2019s and Kozakov\u2019s protein-protein docking server\u00a0ClusPro<\/a>\u2014a website to which any user can submit the three-dimensional coordinates of two proteins and receive a supercomputer-calculated prediction of the structure of the complex formed by those proteins\u2014these algorithms have enabled more than 3,000 research groups across the globe to better understand the inner-workings of the cell and explore potential drug targets without having to run expensive, time-consuming lab experiments.<\/p>\n

Now the research team behind these algorithms has, through lab experiments and computational analysis, obtained a sharper understanding of how two proteins come together to form a complex, and plans to apply that knowledge to boost the speed and accuracy of ClusPro\u2019s predictions. They\u00a0and collaborators from the Hebrew University of Jerusalem and the National Institutes of Health (NIH)\u00a0report on this new development in a\u00a0new article<\/a>\u00a0in\u00a0eLife<\/i>,<\/i> an open source journal for outstanding biomedical research.<\/p>\n

A joint effort of Boston University\u2019s\u00a0Center for Information and Systems Engineering\u00a0and\u00a0Biomolecular Engineering Research Center<\/a> supported by a five-year, $1.6 million grant from the NIH, the project combines Paschalidis\u2019 and Vakili\u2019s expertise in optimization and systems theory with Vajda and Kozakov\u2019s knowledge of biophysics and bioinformatics.<\/p>\n

\u201cThe research was a beautiful combination of physics with mathematics,\u201d said Paschalidis. \u201cWe leveraged techniques popular in control systems developed to describe movement of complex 3-D objects, such as a robot arm, as well as machine learning methods used to analyze large data sets.\u201d<\/p>\n

\u201cPreventing proteins from binding to the wrong partners is an increasingly prominent concept in drug design,\u201d said Janna Wehrle, PhD, of the NIH National Institute of General Medical Sciences, which partially funded the research. \u201cThese new computational methods developed by the Boston University team will help researchers quickly discover both healthy protein pairs and disease-causing pairs that we might want to break up.\u201d<\/p>\n

Until now, scientists were unable to characterize how protein-protein complexes form from two individual proteins\u2014each analogous to a distinctly-shaped Lego block\u2014because their interactions from the moment they come in contact to the moment they \u201csnap into place\u201d were too fast to detect. But an emerging nuclear magnetic resonance (NMR) technique has made it possible to track their rapidly changing configurations from rendezvous to docking using radio waves.<\/p>\n

Applying this technique, the College of Engineering team determined that its protein-protein docking algorithms were already generating these exact transitional states, but labelling them as \u201cfalse positives\u201d alongside the correctly identified final protein-protein complex.<\/p>\n

\u201cWhat we have so far been calling false positives are \u2018transient encounter complexes,\u2019 temporary structures the proteins form as they \u2018search\u2019 for the one orientation that will enable them to bind successfully,\u201d said Paschalidis.<\/p>\n

All protein-protein encounter complexes are characterized by low energy, with the lowest energy expected to occur at the final, stable complex. By systematically analyzing the energy values corresponding to the transient complexes, the researchers found that with each successive interaction, the intersecting proteins have fewer and fewer ways to twist and turn, thereby accelerating their path to binding. This explains how two proteins can dock very quickly despite the many nooks and crannies that must line up to seal the deal.<\/p>\n

The College of Engineering team next aims to exploit its findings to make its docking algorithms faster and more accurate. The researchers also plan to examine the implications of their work for protein-DNA and protein-small molecule interactions that are important in genetic regulation and drug discovery, respectively.
\n<\/i><\/p>\n

See\u00a0<\/i>movie<\/i><\/a>\u00a0of transient protein-protein encounter complexes.<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"

Finding Could Open Up New Drug Discovery Opportunities   Over the past six years, an interdisciplinary team of College of Engineering faculty members\u2014Professor\u00a0Sandor Vajda\u00a0(BME, SE), Research Assistant Professor\u00a0Dima Kozakov\u00a0(BME), Professor\u00a0Yannis Paschalidis\u00a0(ECE, SE) and Associate Professor\u00a0Pirooz Vakili (ME, SE)\u2014have been developing a set of powerful optimization algorithms for predicting the structures of complexes that form when […]<\/p>\n","protected":false},"author":1500,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[76],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/14258"}],"collection":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/users\/1500"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/comments?post=14258"}],"version-history":[{"count":6,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/14258\/revisions"}],"predecessor-version":[{"id":35438,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/14258\/revisions\/35438"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media?parent=14258"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/categories?post=14258"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/tags?post=14258"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}