Protein structural variation in computational models and crystallographic data

Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. Simple models defined by contact topology, known as elastic network models, have been used to model a variety of systems, but the validation is typically limited to individual modes for a single protein. We use anisotropic displacement parameters from crystallography to test the quality of prediction of both the magnitude and directionality of conformational variance. Normal modes from four simple elastic network model potentials and from the CHARMM forcefield are calculated for a data set of 83 diverse, ultrahigh resolution crystal structures. While all five potentials provide good predictions of the magnitude of flexibility, the methods that consider all atoms have a clear edge at prediction of directionality, and the CHARMM potential produces the best agreement. The low-frequency modes from different potentials are similar, but those computed from the CHARMM potential show the greatest difference from the elastic network models. This was illustrated by computing the dynamic correlation matrices from different potentials for a PDZ domain structure. Comparison of normal mode results with anisotropic temperature factors opens the possibility of using ultrahigh resolution crystallographic data as a quantitative measure of molecular flexibility. The comprehensive evaluation demonstrates the costs and benefits of using normal mode potentials of varying complexity. Comparison of the dynamic correlation matrices suggests that a combination of topological and chemical potentials may help identify residues in which chemical forces make large contributions to intramolecular coupling.Comment: 17 pages, 4 figure

Microarray analyses reveal strain-specific antibody responses to Plasmodium falciparum apical membrane antigen 1 variants following natural infection and vaccination

Vaccines based on Plasmodium falciparum apical membrane antigen 1 (AMA1) have failed due to extensive polymorphism in AMA1. To assess the strain-specificity of antibody responses to malaria infection and AMA1 vaccination, we designed protein and peptide microarrays representing hundreds of unique AMA1 variants. Following clinical malaria episodes, children had short-lived, sequence-independent increases in average whole-protein seroreactivity, as well as strain-specific responses to peptides representing diverse epitopes. Vaccination resulted in dramatically increased seroreactivity to all 263 AMA1 whole-protein variants. High-density peptide analysis revealed that vaccinated children had increases in seroreactivity to four distinct epitopes that exceeded responses to natural infection. A single amino acid change was critical to seroreactivity to peptides in a region of AMA1 associated with strain-specific vaccine efficacy. Antibody measurements using whole antigens may be biased towards conserved, immunodominant epitopes. Peptide microarrays may help to identify immunogenic epitopes, define correlates of vaccine protection, and measure strain-specific vaccine-induced antibodies