Abstract
HIV envelope protein gp120 is considered a primary molecular determinant of viral neutralization phenotype due to its critical role in viral entry and immune evasion. The intrinsically disordered regions (IDRs) in gp120 are responsible for their extensive sequence variations and significant structural rearrangements. Despite HIV neutralization phenotype and sequence/structural information of gp120 have been experimentally characterized, there remains a gap in our understanding of the correlation between the viral phenotype and IDRs in gp120. Here, we combined machine learning (ML) techniques and molecular dynamics (MD) simulations to gain data-driven and molecule-mechanism insights into relationships between viral sequence, structure, and phenotypes from the perspective of IDRs in gp120. ML models, trained only on the length and disorder score of IDRs, achieved equivalent performance to the best baseline model using amino acid sequences to discriminate HIV neutralization phenotype, indicating that the lengths or disorder of specific IDRs are strongly related to HIV neutralization phenotypes. Comparative MD analysis reveals that gp120 with extreme neutralization phenotypes in multiple conformational states, especially some IDRs, exhibit significantly distinct structural dynamics, conformational flexibility, and thermodynamic distributions. Taken together, our study provided insights into the role of IDRs in gp120 responding to HIV neutralization phenotypes, which will advance the understanding of molecular mechanisms underlying viral function associated with HIV neutralization phenotype and help develop antiviral vaccines or drugs.
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More From: Biochemical and Biophysical Research Communications
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