Publications

Abstract : Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural-dynamics match for a pair of segments from two dissimilar proteins, that may share even <10% sequence identity. To screen these matches, corresponding one microsecond coarse-grained molecular dynamics trajectories were used to compute Normalized-Root-Mean-Square-Fluctuation graphs and select mobile segments, which were thereafter matched for all pairs using unweighted 3D-dimensional auto-correlation vectors. Our in-house custom-built forcefield, extensively validated against dynamics information obtained from experimental NMR data, was specifically used to generate the coarse-grained dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true-positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional-motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made coarse-grained forcefield, useful to all. © Proteins 2014;. © 2014 Wiley Periodicals, Inc.