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<h1 style="text-align: center">NLM DIR Seminar Schedule</h1>
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<h4>UPCOMING SEMINARS</h4>
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<li>
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<a href="/seminar/5213/detail/">March 11, 2025 <b>Sofya Garushyants</b></a><br>Tmn – bacterial anti-phage defense system
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</li>
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<li>
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<a href="/seminar/5214/detail/">March 18, 2025 <b>MG Hirsch</b></a><br>TBD
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<li>
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<a href="/seminar/5215/detail/">March 25, 2025 <b>Yifan Yang</b></a><br>TBD
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<li>
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<a href="/seminar/5216/detail/">April 1, 2025 <b>Roman Kogay</b></a><br>TBD
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</li>
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<li>
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<a href="/seminar/5217/detail/">April 8, 2025 <b>Jaya Srivastava</b></a><br>TBD
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<h4>RECENT SEMINARS</h4>
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<a href="/seminar/5212/detail/">March 4, 2025 <b>Sanasar Babajanyan</b></a><br>Evolution of antivirus defense in prokaryotes depending on the environmental virus load
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<!-- change object_list to recent_sem -->
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<a href="/seminar/5211/detail/">Feb. 25, 2025 <b>Zhizheng Wang</b></a><br>GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases
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<!-- change object_list to recent_sem -->
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<li>
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<a href="/seminar/5210/detail/">Feb. 18, 2025 <b>Samuel Lee</b></a><br>Efficient predictions of alternative protein conformations by AlphaFold2-based sequence association
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<!-- change object_list to recent_sem -->
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<li>
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<a href="/seminar/5209/detail/">Feb. 11, 2025 <b>Po-Ting Lai</b></a><br>Enhancing Biomedical Relation Extraction with Directionality
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<!-- change object_list to recent_sem -->
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<li>
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<a href="/seminar/5208/detail/">Feb. 4, 2025 <b>Victor Tobiasson</b></a><br>On the dominance of Asgard contributions to Eukaryogenesis
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<h2>Scheduled Seminars on Nov. 30, 2023</h2>
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<div class="tablet:grid-col-4"> Speaker</div>
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<div class="tablet:grid-col-8"><strong>Devlina Chakravarty</strong></div>
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<div class="tablet:grid-col-4"> PI/Lab</div>
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<div class="tablet:grid-col-8"><strong></strong></div>
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<div class="tablet:grid-col-4"> Time</div>
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<div class="tablet:grid-col-8"><strong>3 p.m.</strong></div>
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<div class="tablet:grid-col-4"> Presentation Title</div>
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<div class="tablet:grid-col-8"><strong> How much has AlphaFold2 learned about protein energy landscapes? </strong></div>
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<div class="tablet:grid-col-4"> Location</div>
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<div class="tablet:grid-col-8"><strong> Virtual, see link below </strong></div>
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<p class="padding-left-2">Contact <a href="mailto:NLMDIRSeminarScheduling@mail.nih.gov">NLMDIRSeminarScheduling@mail.nih.gov</a>
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with questions about this seminar.</p>
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<div class="grid-col padding-x-2 padding-bottom-2" id="textalign">
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<h4> Abstract: </h4>
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<p><p>Recent work suggests that AlphaFold2 (AF2)–a deep learning-based model that accurately infers protein structure from sequence–can also discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. Using several implementations of AF2, including two enhanced sampling approaches, we generated >280,000 models of 93 fold-switching proteins, the experimentally determined conformations of these were most-likely present in the AF2 training set. Combining all models, AF2 predicted fold switching with a modest success rate <25%, indicating that it does not readily sample both conformations of fold switchers overall. Furthermore, both of AF2’s confidence metrics selected against models consistent with experimentally determined fold-switching conformations in favor of inconsistent models. AF2’s performance on seven fold-switching proteins outside of its training set was then assessed by generating >159,000 structural models with the enhanced sampling method AF-cluster. Among all models of these seven targets, fold switching was accurately predicted in only one. Furthermore, AF2 demonstrated no ability to predict alternative conformations of two newly discovered targets without homologs in the set of 93 fold switchers. These results suggest that AF2 has more to learn about the underlying energetics of protein ensembles and indicate the need for further developments of methods that accurately predict multiple protein conformations.</p></p>
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<h4>ARCHIVES</h4>
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<ul class="font-sans-3xs sem-list">
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<li><a href="/archive_seminars/2024/"><b>2024</b> Seminars</a></li>
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<li><a href="/archive_seminars/2023/"><b>2023</b> Seminars</a></li>
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<li><a href="/archive_seminars/2022/"><b>2022</b> Seminars</a></li>
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<li><a href="/archive_seminars/2021/"><b>2021</b> Seminars</a></li>
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<li><a href="/archive_seminars/2020/"><b>2020</b> Seminars</a></li>
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<li><a href="/archive_seminars/2019/"><b>2019</b> Seminars</a></li>
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<li><a href="/archive_seminars/2018/"><b>2018</b> Seminars</a></li>
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<li><a href="/archive_seminars/2017/"><b>2017</b> Seminars</a></li>
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<li><a href="/archive_seminars/2016/"><b>2016</b> Seminars</a></li>
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<li><a href="/archive_seminars/2015/"><b>2015</b> Seminars</a></li>
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<li><a href="/archive_seminars/2014/"><b>2014</b> Seminars</a></li>
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<li><a href="/archive_seminars/2013/"><b>2013</b> Seminars</a></li>
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<li><a href="/archive_seminars/2012/"><b>2012</b> Seminars</a></li>
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<li><a href="/archive_seminars/2011/"><b>2011</b> Seminars</a></li>
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<li><a href="/archive_seminars/2010/"><b>2010</b> Seminars</a></li>
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<li><a href="/archive_seminars/2009/"><b>2009</b> Seminars</a></li>
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