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<font face="Calibri, Helvetica, sans-serif" class=""><span style="font-size: 14.666666984558105px;" class="">This is an announcement of Matthew McPartlon’s Dissertation Defense</span></font>
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Candidate: Matthew McPartlon</div>
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Date: Monday, May 22, 2023</div>
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Time: 9:00 am CST</div>
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Location: JCL 223</div>
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Dissertation Paper Title: Accelerating Protein Design with Deep Learning</div>
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<div class="ContentPasted0"><font face="Calibri, Helvetica, sans-serif" style="font-size: 14.666666984558105px;" class="">Abstract: </font><span style="white-space: pre-wrap; font-size: 15px;" class="">The human proteome comprises tens of thousands of proteins,
each tailored for a specific</span></div>
<div class=""><font color="#000000" style="font-size: 15px;" class=""><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">function by the selective pressures of evolution. The field of protein design seeks to
develop</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">proteins with new or enhanced functions at will, ultimately bypassing the evolutionary clock.</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">In this thesis, we introduce general machine-learning methods for accelerating protein design,</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">focusing on modeling protein structure.</span></font></div>
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<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">First, we propose an approach for fixed-backbone design (Chapter 2), the problem of</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">designing primary sequence and side-chain rotamers for a given backbone conformation.</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">Whereas classic approaches formulate sequence and rotamer design tasks separately, we offer</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">a single approach to predict both simultaneously. To realize this, we develop a deep neural</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">network that effectively leverages backbone coordinates. By exploiting backbone geometry,</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">we can efficiently represent atomic microenvironments at the coordinate level and ultimately</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">avoid discrete rotamer sampling. This results in more robust designs and accurate quality</span><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
<span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">estimates for downstream tasks.</span></font></div>
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</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">Next, we introduce a framework for flexible protein-protein docking (Chapter 3), the</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">task of determining the structure of a protein complex given the unbound structures of its</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">components. Traditional docking methods are limited by their reliance on empirical physics-</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">based scoring functions, inability to accommodate conformational flexibility, and failure to</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">incorporate information on binding sites. To address these challenges, we propose an end-to-</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">end approach that can model conformational changes and target specific interactions while</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">significantly reducing computational time. As one of the pioneering deep learning methods</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">for this task, we uncover key determinants underlying our success and provide important</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">insights for future research. Finally, we highlight our method’s generality by extending it to</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">simultaneously dock and co-design the sequence and structure of antibody complementarity-</span><span style="box-sizing: border-box; white-space: pre-wrap;" class=""><br role="presentation" style="box-sizing: border-box; white-space: pre-wrap;" class="">
</span><span role="presentation" dir="ltr" style="box-sizing: border-box; white-space: pre-wrap;" class="">determining regions targeting a specified epitope</span></font></div>
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Advisors: Jinbo Xu & Andy Drucker</div>
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Committee Members: Jinbo Xu, Andy Drucker, Janos Simon</div>
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