肽
主要组织相容性复合体
计算生物学
进化生物学
生物
化学
神经科学
计算机科学
免疫系统
生物化学
免疫学
作者
Eric Wilson,John Kevin Cava,Diego Chowell,Remya Raja,Kiran K. Mangalaparthi,Akhilesh Pandey,Marion Curtis,Karen S. Anderson,Abhishek Singharoy
出处
期刊:Cell systems
[Elsevier BV]
日期:2024-03-29
卷期号:15 (4): 362-373.e7
被引量:1
标识
DOI:10.1016/j.cels.2024.03.001
摘要
Summary
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
科研通智能强力驱动
Strongly Powered by AbleSci AI