Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS

Conflict of interest statement. None declared.

The ribosome is one of the main antibiotic targets in the bacterial cell. Crystal structures of naturally produced antibiotics and their semi-synthetic derivatives bound to ribosomal particles have provided unparalleled insight into their mechanisms of action, and they are also facilitating the design of more effective antibiotics for targeting multidrug-resistant bacteria. In this Review, I discuss the recent structural insights into the mechanism of action of ribosome-targeting antibiotics and the molecular mechanisms of bacterial resistance, in addition to the approaches that are being pursued for the production of improved drugs that inhibit bacterial protein synthesis.

Ribosome-targeting antibiotics and mechanisms of bacterial resistance

By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.

Perspective: Coarse-grained models for biomolecular systems.

Over the last 40 years, the area of computational molecular biophysics has grown and developed to the point where simulations can now provide detailed mechanistic insights, suggest theoretical principles that underpin function, and provide frameworks for understanding and interpreting experimental measurements. The success of molecular simulations has been the result of the unrelenting development of novel theoretical models, exponential growth in computational resources, and advances in structural biology techniques. Through the continued refinement and application of diverse classes of models, general themes in biomolecular dynamics are beginning to surface. In particular, molecular simulations are highlighting the pervasive role that orderdisorder events play in molecular biology. These dynamic modes of function are transforming our perspective on the role that entropy has in many large-scale biological processes.

How Simulations Reveal Dynamics, Disorder, and the Energy Landscapes of Biomolecular Function

Conformational changes are an essential feature for the function of some dynamic proteins. Understanding the mechanism of such motions may allow us to identify important properties, which may be directly related to the regulatory function of a protein. Also, this knowledge may be employed for a rational design of drugs that can shift the balance between active and inactive conformations, as well as affect the kinetics of the activation process. Here, the conformational changes in carboxyl-terminal Src kinase, the major catalytic repressor to the Src family of kinases, was investigated, and it was proposed as a functionally related hypothesis. A Cα Structure-Based Model (Cα-SBM) was applied to provide a description of the overall conformational landscape and further analysis complemented by detailed molecular dynamics simulations. As a first approach to Cα-SBM simulations, reversible transitions between active (closed) and inactive (open) forms were modeled as fluctuations between these two energetic basins. It was found that, in addition to the interdomain Carboxyl-terminal SRC Kinase (Csk) correlated motions, a conformational change in the αC helix is required for a complete conformational transition. The result reveals this as an important region of transition control and domain coordination. Restrictions in the αC helix region of the Csk protein were performed, and the analyses showed a direct correlation with the global conformational changes, with this location being propitious for future studies of ligands. Also, the Src Homology 3 (SH3) and SH3 plus Src Homology 2 (SH2) domains were excluded for a direct comparison with experimental results previously published. Simulations where the SH3 was deleted presented a reduction of the transitions during the simulations, while the SH3-SH2 deletion vanishes the Csk transitions, corroborating the experimental results mentioned and linking the conformational changes with the catalytic functionality of Csk. The study was complemented by the introduction of a known kinase inhibitor close to the Csk αC helix region where its consequences for the kinetic behavior and domain displacement of Csk were verified through detailed molecular dynamics. The findings describe the mechanisms involving the Csk αC helix for the transitions and also support the dynamic correlation between SH3 and SH2 domains against the Csk lobes and how local energetic restrictions or interactions in the Csk αC helix can play an important role for long-range motions. The results also allow speculation if the Csk activity is restricted to one specific conformation or a consequence of a state transition, this point being a target for future studies. However, the αC helix is revealed as a potential region for rational drug design.

Csk αC Helix: A Computational Analysis of an Essential Region for Conformational Transitions.

The invention provides methods of dynamically simulating molecular interactions between a target molecule and a plurality of ligand molecules. The ligand molecules may be presented in the model as a homogeneous set of identical ligands or as a heterogeneous set of different ligands, such as, for example, a set of structural variants of a ligand molecule. Typically, the ligand molecule will be a small organic compound, such as a drug or other small molecule, and the ligand will be a protein or a protein domain, a nucleic acid (i.e., DNA, RNA), or a biomolecular complex of proteins and/or nucleic acid molecules. Unlike all known molecular dynamics simulation methods, the invention provides ligand molecules to the simulation's interaction environment(s) in excess relative to the target molecule.

Stochastic molecular binding simulation

Experimental and computational techniques for studying structural dynamics and function of RNA

https://link.springer.com/content/pdf/10.1007/s12551-021-00884-9.pdf

Overview of the Biomolecular Association and Dynamics session at the 20th IUPAB congress, 45th Brazilian congress of SBBF, and the 50th annual meeting of SBBq

Paul C. Whitford

Papers

How Simulations Reveal Dynamics, Disorder, and the Energy Landscapes of Biomolecular Function

Csk αC Helix: A Computational Analysis of an Essential Region for Conformational Transitions.

Stochastic molecular binding simulation

Experimental and computational techniques for studying structural dynamics and function of RNA

Overview of the Biomolecular Association and Dynamics session at the 20th IUPAB congress, 45th Brazilian congress of SBBF, and the 50th annual meeting of SBBq