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<p class="MsoNormal"><span style="font-size:10.0pt">Cool!<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt">“For synthetic biology, iteratively querying a model of the mutational fitness landscape could help efficiently guide the introduction of mutations to enhance protein function (Romero & Arnold, 2009), inform
protein design using a combination of activating mutants (Hu et al., 2018), and make rational substitutions to optimize protein properties such as substrate specificity (Packer et al., 2017), stability (Tan et al., 2014), and binding (Ricatti et al., 2019).”<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt">Get a few billion people to get full genome sequencing, and let the TPUs discover how we work! Everyone gets a custom cocktail to improve stamina, fight off cancer, etc. etc.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt">Marcus <o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:12.0pt;color:black">From: </span></b><span style="font-size:12.0pt;color:black">Friam <friam-bounces@redfish.com> on behalf of Roger Critchlow <rec@elf.org><br>
<b>Reply-To: </b>The Friday Morning Applied Complexity Coffee Group <Friam@redfish.com><br>
<b>Date: </b>Tuesday, April 30, 2019 at 8:49 PM<br>
<b>To: </b>The Friday Morning Applied Complexity Coffee Group <Friam@redfish.com><br>
<b>Subject: </b>[FRIAM] More on levels of sequence organization<o:p></o:p></span></p>
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<p class="MsoNormal">This just turned up on hacker news: <o:p></o:p></p>
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<p class="MsoNormal"> <a href="https://www.biorxiv.org/content/10.1101/622803v1">https://www.biorxiv.org/content/10.1101/622803v1</a><o:p></o:p></p>
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<p class="MsoNormal">[...] <span style="font-family:Helvetica;color:#191919">To this end we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million sequences spanning evolutionary diversity. The resulting
model maps raw sequences to representations of biological properties without labels or prior domain knowledge. The learned representation space organizes sequences at multiple levels of biological granularity from the biochemical to proteomic levels. [...]</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-family:Helvetica;color:#191919">Don't know if I have the energy to plow through the text.</span><o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><span style="font-family:Helvetica;color:#191919">-- rec --</span><o:p></o:p></p>
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