Folding simulations are often time-consuming or highly sensitive to the initial conformation of the si-mulation even for mini protein like the Trp-cage. Here, we present a multiscale molecular dynamics method which appears to be both efficient and insensitive to the starting conformation based on the testing results from the Trp-cage protein. In this method the simulated system is simultaneously mod-eled on atoms and coarse-grained particles with incremental coarsening levels. The dynamics of coarse-grained particles are adapted to the recent trajectories of finer-grained particles instead of fixed and parameterized energy functions as used in previous coarse-grained models. In addition, the com-positions of coarse-grained particles are allowed to be updated automatically based on the coherence during its history. Starting from the fully extended conformation and other several different conforma-tions of the Trp-cage protein, our method successfully finds out the native-like conformations of the Trp-cage protein in the largest cluster of the trajectories in all of the eight performed simulations within at most 10 ns simulation time. The results show that approaches based on multiscale modeling are promising for ab initio protein structure prediction.
XIA XueFeng ZHANG Song HUANG Bo ZHOU Yun SUN ZhiRong
The interaction strength between 2 proteins is not constant but variable under different conditions. For a given biological process, identification of protein-protein interactions (PPIs) undergoing dynamic change in interaction strength is highly valuable but never achieved before. In this work, we presented a computational approach to identify changed PPIs (cPPIs) on a global scale by analyzing the coexpression level of genes encoding the interacting protein pairs. This approach stemmed from the biological con-ception that the change of protein-protein interaction bore imprint at the gene coexpression level. We applied this method to identify cPPIs in cells treated with a cytokine TGFβ, as well as cPPIs in rheumatoid arthritis (RA) patients. The accuracy of identification was evaluated by comparing our results with data from the high-throughput experiment and literature mining. Our analysis demonstrated that this is a simple and effective method to infer cPPIs from a given set of PPIs or even from the whole interactome. Further analysis uncovered the biological functions of the cPPIs in RA patients, which included muscle contraction and antigen presentation. Our method could help to elucidate molecular mechanisms of dynamic biological processes.
