Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.

Reaction coordinates are of central importance for correct understanding of reaction dynamics in complex systems, but their counter-intuitive nature made it a daunting challenge to identify them. Starting from an energetic view of a reaction process as stochastic energy flows biased towards preferred channels, which we deemed the reaction coordinates, we developed a rigorous scheme for decomposing energy changes of a system, both potential and kinetic, into pairwise components. The pairwise energy flows between different coordinates provide a concrete statistical mechanical language for depicting reaction mechanisms. Application of this scheme to the C_7eq → C_7ax transition of the alanine dipeptide in vacuum revealed novel and intriguing mechanisms that eluded previous investigations of this well studied prototype system for biomolecular conformational dynamics. Using a cost function developed from the energy decomposition components by proper averaging over the transition path ensemble, we were able to identify signatures of the reaction coordinates of this system without requiring any input from human intuition.

Background: A large schizophrenia genome-wide association study (GWAS) and a subsequent extensive replication study of individuals of European ancestry identified eight new loci with genome-wide significance and suggested that theMIR137-mediated pathway plays a role in the predisposition for schizophrenia.Aims: To validate the above findings in a Han Chinese population.Method: We analysed the single nucleotide polymorphisms (SNPs) in the newly identified schizophrenia candidate loci and predictedMIR137target genes based on our published Han Chinese populations (BIOX) GWAS data. We then analysed 18 SNPs from the candidate regions in an independent cohort that consisted of 3585 patients with schizophrenia and 5496 controls of Han Chinese ancestry.Results: We replicated the associations of five markers (P<0.05), including three that were located in the predictedMIR137target genes. Two loci (ITIH3/4: rs2239547,P=1.17×10^–10andCALN1: rs2944829,P=9.97×10^–9) exhibited genome-wide significance in the Han Chinese population.Conclusions: TheITIH3/4locus has been reported to be of genome-wide significance in the European population. The successful replication of this finding in a different ethnic group provides stronger evidence for the association between schizophrenia andITIH3/4. We detected the first genome-wide significant association of schizophrenia withCALN1, which is a predicted target ofMIR137, and thus provide new evidence for the associations betweenMIR137targets and schizophrenia.

The molecular mechanism of a reaction is embedded in its transition path ensemble, the complete collection of reactive trajectories. Utilizing the information in the transition path ensemble alone, we developed a novel metric, which we termed the emergent potential energy, for distinguishing reaction coordinates from the bath modes. The emergent potential energy can be understood as the average energy cost for making a displacement of a coordinate in the transition path ensemble. Where displacing a bath mode invokes essentially no cost, it costs significantly to move the reaction coordinate. Based on some general assumptions of the behaviors of reaction and bath coordinates in the transition path ensemble, we proved theoretically with statistical mechanics that the emergent potential energy could serve as a benchmark of reaction coordinates and demonstrated its effectiveness by applying it to a prototypical system of biomolecular dynamics. Using the emergent potential energy as guidance, we developed a committor-free and intuition-independent method for identifying reaction coordinates in complex systems. We expect this method to be applicable to a wide range of reaction processes in complex biomolecular systems.