Deciphering gp120 sequence variation and structural dynamics in HIV neutralization phenotype by molecular dynamics simulations and graph machine learning

Human immunodeficiency virus (HIV) exploits the sequence variation and structural dynamics of the envelope glycoprotein gp120 to evade the immune attack of neutralization antibodies, contributing to various HIV neutralization phenotypes. Although the HIV neutralization phenotype has been experimentally characterized, the roles of rapid sequence variability and significant structural dynamics of gp120 are not well understood. Here, 45 prefusion gp120 from different HIV strains belong to three tiers of sensitive, moderate, and resistant neutralization phenotype are structurally modeled by homology modeling and then investigated by molecular dynamics (MD) simulations and graph machine learning (ML). Our results show that the structural deviations, population distribution, and conformational flexibility of gp120 are related to the HIV neutralization phenotype. Per-residue dynamics indicate the local regions especially in the second structural elements with high-flexibility, may be responsible for the HIV neutralization phenotype. Moreover, a graph ML model with the attention mechanism was trained to explore inherent representation related to the classification of the HIV neutralization phenotype, further distinguishing the strong related gp120 sequence variation together with structural dynamics in the HIV neutralization phenotype. Our study not only deciphers gp120 sequence variation and structural dynamics in the HIV neutralization phenotype but also explores complex relationships between the sequence, structure, and dynamics of protein by combining MD simulations and ML.