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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.13806">arXiv:2310.13806</a> <span> [<a href="https://arxiv.org/pdf/2310.13806">pdf</a>, <a href="https://arxiv.org/format/2310.13806">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biomolecules">q-bio.BM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3584371.3612951">10.1145/3584371.3612951 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Coffland%2C+S">Sarah Coffland</a>, <a href="/search/q-bio?searchtype=author&query=Christensen%2C+K">Katie Christensen</a>, <a href="/search/q-bio?searchtype=author&query=Jagodzinski%2C+F">Filip Jagodzinski</a>, <a href="/search/q-bio?searchtype=author&query=Hutchinson%2C+B">Brian Hutchinson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2310.13806v1-abstract-short" style="display: inline;"> An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator protein for example give rise to cystic fibrosis. Unfortunately performing InDel mutations on physical proteins and studying their effects is a time prohibitive process. Consequently, modeling InDels computationally can s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13806v1-abstract-full').style.display = 'inline'; document.getElementById('2310.13806v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.13806v1-abstract-full" style="display: none;"> An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator protein for example give rise to cystic fibrosis. Unfortunately performing InDel mutations on physical proteins and studying their effects is a time prohibitive process. Consequently, modeling InDels computationally can supplement and inform wet lab experiments. In this work, we make use of our data sets of exhaustive double InDel mutations for three proteins which we computationally generated using a robotics inspired inverse kinematics approach available in Rosetta. We develop and train a neural network, RoseNet, on several structural and energetic metrics output by Rosetta during the mutant generation process. We explore and present how RoseNet is able to emulate the exhaustive data set using deep learning methods, and show to what extent it can predict Rosetta metrics for unseen mutant sequences with two InDels. RoseNet achieves a Pearson correlation coefficient median accuracy of 0.775 over all Rosetta scores for the largest protein. Furthermore, a sensitivity analysis is performed to determine the necessary quantity of data required to accurately emulate the structural scores for computationally generated mutants. We show that the model can be trained on minimal data (<50%) and still retain a high level of accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.13806v1-abstract-full').style.display = 'none'; document.getElementById('2310.13806v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Presented at Computational Structural Bioinformatics Workshop 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 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