Leveraging Large Language Models for Predicting Microbial Virulence from Protein Structure and Sequence
Leveraging Large Language Models for Predicting Microbial Virulence from Protein Structure and Sequence
复制标题
利用大型语言模型根据蛋白质结构和序列预测微生物毒力
DOI：
10.1145/3584371.3612953
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Kavraki, Lydia
中科院分区：
文献类型：
--
作者：
Quintana, Felix;Treangen, Todd;Kavraki, Lydia
新冠肺炎 (COVID-19) 疫情过后，病原体筛查成为了一个前所未有的重要问题。然而，由于多种因素，病原体的计算筛选具有挑战性，包括（i）宿主的复杂性和作用，（ii）毒力因子的分歧和动态，以及（iii）种群和群落水平的动态。考虑潜在病原体的分子相互作用，特别是单个蛋白质和蛋白质相互作用，可以帮助查明给定微生物的潜在致病蛋白质。然而，现有的病原体筛选工具依赖于现有的注释（KEGG、GO 等），使得评估新型和未注释的蛋白质更具挑战性。在这里，我们提出了一种受法学硕士启发的方法，该方法考虑蛋白质序列和结构来预测蛋白质毒力。我们提出了一个两阶段模型，结合了从 DistilProtBert 语言模型捕获的进化特征和图卷积网络中的蛋白质结构。当存在高质量结构时，我们的模型在毒力功能方面比单独的序列表现更好，从而代表了新型和未注释蛋白质的毒力预测的前进道路。
In the aftermath of COVID-19, screening for pathogens has never been a more relevant problem. However, computational screening for pathogens is challenging due to a variety of factors, including (i) the complexity and role of the host, (ii) virulence factor divergence and dynamics, and (iii) population and community-level dynamics. Considering a potential pathogen's molecular interactions, specifically individual proteins and protein interactions can help pinpoint a potential protein of a given microbe to cause disease. However, existing tools for pathogen screening rely on existing annotations (KEGG, GO, etc), making the assessment of novel and unannotated proteins more challenging. Here, we present an LLM-inspired approach that considers protein sequence and structure to predict protein virulence. We present a two-stage model incorporating evolutionary features captured from the DistilProtBert language model and protein structure in a graph convolutional network. Our model performs better than sequence alone for virulence function when high-quality structures are present, thus representing a path forward for virulence prediction of novel and unannotated proteins.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
14.9
作者：
UniProt Consortium
通讯作者：
UniProt Consortium
影响因子：
48
作者：
Mirdita, Milot;Schutze, Konstantin;Moriwaki, Yoshitaka;Heo, Lim;Ovchinnikov, Sergey;Steinegger, Martin
通讯作者：
Steinegger, Martin
影响因子：
46.9
作者：
Madani, Ali;Ben Krause, Ben;Greene, Eric R.;Subramanian, Subu;Mohr, Benjamin P.;Holton, James M.;Olmos, Jose Luis;Xiong, Caiming;Sun, Zachary Z. Z.;Socher, Richard;Fraser, James S.;Naik, Nikhil
通讯作者：
Naik, Nikhil