作者：Kirilenko, Bogdan M.; Munegowda, Chetan; Osipova, Ekaterina; Jebb, David; Sharma, Virag; Blumer, Moritz; Morales, Ariadna E.; Ahmed, Alexis-Walid; Kontopoulos, Dimitrios-Georgios; Hilgers, Leon; Lindblad-Toh, Kerstin; Karlsson, Elinor K.; Hiller, Michael
作者单位：Max Planck Society; Max Planck Society; Leibniz Association; Senckenberg Gesellschaft fur Naturforschung (SGN); Goethe University Frankfurt; Uppsala University; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; University of Massachusetts System; University of Massachusetts Worcester; UMass Chan Medical School; University of Massachusetts System; University of Massachusetts Worcester; UMass Chan Medical School; University of Limerick
摘要：Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, a...
作者：Lutz, Isaac D.; Wang, Shunzhi; Norn, Christoffer; Courbet, Alexis; Borst, Andrew J.; Zhao, Yan Ting; Dosey, Annie; Cao, Longxing; Xu, Jinwei; Leaf, Elizabeth M.; Treichel, Catherine; Litvicov, Patrisia; Li, Zhe; Goodson, Alexander D.; Rivera-Sanchez, Paula; Bratovianu, Ana -Maria; Baek, Minkyung; King, Neil P.; Ruohola-Baker, Hannele; Baker, David
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Howard Hughes Medical Institute; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Westlake University; Seoul National University (SNU)
摘要：As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a top-down reinforcement learning- based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-elec...
作者：Machado, Ricardo B.; Aguiar, Ludmilla M. S.
作者单位：Universidade de Brasilia
作者：McCutcheon, Jeffrey R.; Mauter, Meagan S.
作者单位：University of Connecticut; Stanford University; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory
