De novoinference of protein function from coarse-grained dynamics

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, which may share even <10% sequence identity. To screen these matches, corresponding 1 µs coarse-grained (CG) 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 three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG 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 CG FF, useful to all.

Exploring the use of molecular dynamics in assessing protein variants for phenotypic alterations

“…We ran the coarse‐grained MD simulation on the pseudo‐atoms located at the C α atom position of the protein structure for 1 μs with CGMM forcefield (Bhadra & Pal, ; Bhadra & Pal, ) at 300 K temperature using Gromacs software Version 4.6.5 (van der Spoel et al, ). The pseudo‐atom types supported by the CGMM forcefield is available at cgmm.itp (http://pallab.cds.iisc.ac.in/CGMM/cgmm_download.php).…”

“…For this, we first calculate a three‐dimensional (3D) unweighted autocorrelation vector (ACV) for individual flexible regions based on residues in each frame. The formula for calculating 3D ACV is (Bhadra & Pal, ): where …”

“…In this paper, we introduce the use of coarse‐grained MD as a tool for generic interpretation of loss‐of‐function upon mutation and extend the inference to predict alteration of a phenotype. This method is based on our previous work (Bhadra & Pal, ) which shows a proof of principle that fluctuations in the protein segments obtained from coarse‐grained MD can be directly used to screen for molecular function. The full pipeline of the methodology based on the use of our handcrafted Coarse‐Grained Molecular Mechanics (CGMM) forcefield has been extensively tested (Bhadra & Pal, ) and also shown to improve protein function annotation (Das, Bhadra, Ramakumar, & Pal, ).…”

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer

“…The most stable structure was used to further create mutant models by replacing the specific amino acids. Each was subjected to C α atom‐based MD simulation for 1 microsecond with Coarse‐Grained Molecular Mechanics force field (Bhadra & Pal, ) at 300 K in vacuum. Identical parameters were used for each simulation namely, steepest descent energy minimization (max.…”

“…A single short sequence of 273–279 K simulated annealing in six steps was used within the 70 ps equilibration step before reference temperature coupling. Structures during unconstrained dynamics simulation were recorded every 100 ps time from which 11 frames at every 100 ns were used for finding flexible regions based on RMSF norm (Bhadra & Pal, ). The filtered wild type protein and the variant pair were sent for a similarity score calculation using the formula: Similarity score = a/b, where a is the number of flexible regions in mutated protein and wild type (b).…”