Journal of Computational Chemistry

doi: 10.1002/jcc.26914pmid: N/A

Zhao, Mingtian; Kognole, Abhishek A.; Jo, Sunhwan; Tao, Aoxiang; Hazel, Anthony; MacKerell, Alexander D.

doi: 10.1002/jcc.27121pmid: 37093676

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μex, volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity‐bias and configurational‐bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational‐bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53‐fold through the use of system partitioning. The improved algorithm is then combined with an enhanced μex oscillation protocol and shown to be of utility in the context of the site‐identification by ligand competitive saturation (SILCS) co‐solvent sampling approach as illustrated through application to the protein CDK2.

Yashmin, Farnaz; Mazumder, Lakhya J.; Sharma, Rohan; Sharma, Pankaz K.

doi: 10.1002/jcc.27122pmid: 37119009

Quantum chemical calculations were carried out to investigate the noble gas binding ability of Be3B+ cluster. Calculations reveal that heavier noble gas atoms (ArXe) form stable complexes with this cluster. Detailed bonding analyses reveal that the noble gas atoms act as donor fragment in the formation of Ng → Be donor–acceptor bonds. Three noble gas atoms can consecutively form bonds with the Be atom of the Be3B+ cluster.

Jung, Jaewoon; Kobayashi, Chigusa; Sugita, Yuji

doi: 10.1002/jcc.27124pmid: 37141320

Generalized replica exchange with solute tempering (gREST) is one of the enhanced sampling algorithms for proteins or other systems with rugged energy landscapes. Unlike the replica‐exchange molecular dynamics (REMD) method, solvent temperatures are the same in all replicas, while solute temperatures are different and are exchanged frequently between replicas for exploring various solute structures. Here, we apply the gREST scheme to large biological systems containing over one million atoms using a large number of processors in a supercomputer. First, communication time on a multi‐dimensional torus network is reduced by matching each replica to MPI processors optimally. This is applicable not only to gREST but also to other multi‐copy algorithms. Second, energy evaluations, which are necessary for the multistate bennet acceptance ratio (MBAR) method for free energy estimations, are performed on‐the‐fly during the gREST simulations. Using these two advanced schemes, we observed 57.72 ns/day performance in 128‐replica gREST calculations with 1.5 million atoms system using 16,384 nodes in Fugaku. These schemes implemented in the latest version of GENESIS software could open new possibilities to answer unresolved questions on large biomolecular complex systems with slow conformational dynamics.

Lefebvre, C.; Klein, J.; Khartabil, H.; Boisson, J.‐C.; Hénon, E.

doi: 10.1002/jcc.27123pmid: 37177853

We describe the development and features of a program called IGMPlot, which is based on the independent gradient model (IGM) and its local descriptor δg. The IGM approach analyzes the gradient of the electron density (ED) in a molecular system to identify regions of space where chemical interactions take place. IGMPlot is intended for use by both experimental scientists and theoretical chemists. It is standalone software written in C++, with versions available for multiple platforms. Some key features are: probing and quantifying interactions between two given molecular fragments, determining bond strength (IBSI), estimating the atomic contributions to an intermolecular interaction and preparing data to build 2D and 3D representations of interaction regions. The software has been updated to include new features: critical point analysis of the ED, assessing ED asymmetry of a given bond (PDA) and a new descriptor called qg designed to enhance the IGM‐δg analysis. The program can be found at: http://igmplot.univ-reims.fr.