Monte Carlo Simulations of HIV Capsid Protein Homodimer

Capsid protein (CA) is the building block of virus coats. To help understand how the HIV CA proteins self-organize into large assemblies of various shapes, we aim to computationally evaluate the binding affinity and interfaces in a CA homodimer. We model the N- and C-terminal domains (NTD and CTD) of the CA as rigid bodies and treat the five-residue loop between the two domains as a flexible linker. We adopt a transferrable residue-level coarse-grained energy function to describe the interactions between the protein domains. In seven extensive Monte Carlo simulations with different volumes, a large number of binding/unbinding transitions between the two CA proteins are observed, thus allowing a reliable estimation of the equilibrium probabilities for the dimeric vs monomeric forms. The obtained dissociation constant for the CA homodimer from our simulations, 20–25 μM, is in reasonable agreement with experimental measurement. A wide range of binding interfaces, primarily between the NTDs, are identified in the simulations. Although some observed bound structures here closely resemble the major binding interfaces in the capsid assembly, they are statistically insignificant in our simulation trajectories. Our results suggest that although the general purpose energy functions adopted here could reasonably reproduce the overall binding affinity for the CA homodimer, further adjustment would be needed to accurately represent the relative strength of individual binding interfaces

Correlated Mutations: A Hallmark of Phenotypic Amino Acid Substitutions

Point mutations resulting in the substitution of a single amino acid can cause severe functional consequences, but can also be completely harmless. Understanding what determines the phenotypical impact is important both for planning targeted mutation experiments in the laboratory and for analyzing naturally occurring mutations found in patients. Common wisdom suggests using the extent of evolutionary conservation of a residue or a sequence motif as an indicator of its functional importance and thus vulnerability in case of mutation. In this work, we put forward the hypothesis that in addition to conservation, co-evolution of residues in a protein influences the likelihood of a residue to be functionally important and thus associated with disease. While the basic idea of a relation between co-evolution and functional sites has been explored before, we have conducted the first systematic and comprehensive analysis of point mutations causing disease in humans with respect to correlated mutations. We included 14,211 distinct positions with known disease-causing point mutations in 1,153 human proteins in our analysis. Our data show that (1) correlated positions are significantly more likely to be disease-associated than expected by chance, and that (2) this signal cannot be explained by conservation patterns of individual sequence positions. Although correlated residues have primarily been used to predict contact sites, our data are in agreement with previous observations that (3) many such correlations do not relate to physical contacts between amino acid residues. Access to our analysis results are provided at http://webclu.bio.wzw.tum.de/~pagel/supplements/correlated-positions/