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

Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions Not only does this combination of a

/pdf/gromacs-4-algorithms-for-highly-efficient-load-balanced-and-16xnd83poh.pdf

GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation

http://kth.diva-portal.org/smash/get/diva2:1303357/FULLTEXT01

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

/pdf/charmm-the-biomolecular-simulation-program-4kexem0xkx.pdf

CHARMM: the biomolecular simulation program.

Motivation: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources.

Results: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations.

Availability: GROMACS is an open source and free software available from http://www.gromacs.org.

Contact: erik.lindahl@scilifelab.se

Supplementary information:Supplementary data are available at Bioinformatics online.

Gromacs 4.5

GROMACS is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world. Here, we describe some of the ways we have been able to realize this through the use of parallelization on all levels, combined with a constant focus on absolute performance. Release 4.6 of GROMACS uses SIMD acceleration on a wide range of architectures, GPU offloading acceleration, and both OpenMP and MPI parallelism within and between nodes, respectively. The recent work on acceleration made it necessary to revisit the fundamental algorithms of molecular simulation, including the concept of neighborsearching, and we discuss the present and future challenges we see for exascale simulation - in particular a very fine-grained task parallelism. We also discuss the software management, code peer review and continuous integration testing required for a project of this complexity.

https://www.mpibpc.mpg.de/14875675/Kutzner_2015_Springer.pdf

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

Most plasmalemmal proteins organize in submicrometer-sized clusters whose architecture and dynamics are still enigmatic. With syntaxin 1 as an example, we applied a combination of far-field optical nanoscopy, biochemistry, fluorescence recovery after photobleaching (FRAP) analysis, and simulations to show that clustering can be explained by self-organization based on simple physical principles. On average, the syntaxin clusters exhibit a diameter of 50 to 60 nanometers and contain 75 densely crowded syntaxins that dynamically exchange with freely diffusing molecules. Self-association depends on weak homophilic protein-protein interactions. Simulations suggest that clustering immobilizes and conformationally constrains the molecules. Moreover, a balance between self-association and crowding-induced steric repulsions is sufficient to explain both the size and dynamics of syntaxin clusters and likely of many oligomerizing membrane proteins that form supramolecular structures.

/pdf/anatomy-and-dynamics-of-a-supramolecular-membrane-protein-1zkzywqyhe.pdf

Anatomy and dynamics of a supramolecular membrane protein cluster

) was used. As secondary antibody on immunoblots we applied stabilized goat anti-mouse HRP-conjugated antibodies (product No. 1858413, Pierce). For immunofluorescence sheep-anti-mouse immunoglobulins G (catalogue No. 515-005-003, Dianova) were labeled with the fluorescent dye Atto532 or Atto647N (provided by K. H. Drexhage, Dept. of Chemistry, University of Siegen, Germany).

Carsten Kutzner

Papers

GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

Anatomy and dynamics of a supramolecular membrane protein cluster

Supporting Online Material for Anatomy and Dynamics of a Supramolecular Membrane Protein Cluster