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Molecular dynamics (MD) simulation involves solving Newton's equations of motion for a system of atoms, by calculating forces and updating atomic positions and velocities over a timestep Δt. Despite the large amount of computing power currently available, the timescale of MD simulations is limited by both the small timestep required for propagation, and the expensive algorithm for computing pairwise forces. These issues are currently addressed through the development of efficient simulation methods, some of which make acceptable approximations and as a result can afford larger timesteps. We present MDLab, a development environment for MD simulations built with Python which facilitates prototyping, testing, and debugging of these methods. MDLab provides constructs which allow the development of propagators, force calculators, and high level sampling protocols that run several instances of molecular dynamics. For computationally demanding sampling protocols which require testing on large biomolecules, MDL includes an interface to the OpenMM libraries of Friedrichs et al. which execute on graphical processing units (GPUs) and achieve considerable speedup over execution on the CPU. As an example of an interesting high level method developed in MDLab, we present a parallel implementation of the On-The-Fly string method of Maragliano and Vanden-Eijnden. MDLab is available at http://mdlab.sourceforge.net. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [ABSTRACT FROM AUTHOR]

This article presents information on PROTOMOL, an object-oriented framework for prototyping novel algorithms for molecular dynamics. PROTOMOL is a framework for rapid development of efficient algorithms and applications in molecular modeling, particularly of biomolecules such as proteins and DNA. It supports state-of-the-art molecular dynamics (MD) and N-body algorithms that improve the speed and efficiency of these simulations. PROTOMOL automatically detects appropriate parameters for many of the algorithms implemented. Thus, algorithms successfully incorporated into this framework can be more easily incorporated into the most popular MD programs. PROTOMOL provides components to handle input/output, graphical user interface and visualization, as well as optimized algorithmic frameworks for easy incorporation and efficient implementation of new algorithms. An important optimization encapsulated in PROTOMOL is parallelism at a modest scale (e.g., clusters or medium size symmetric multiprocessors with typical size of 32 to 64 nodes), which addresses the needs of many MD users.

Molecular mimicry between viral antigens and host proteins can produce cross-reacting antibodies leading to autoimmunity. The coronavirus SARS-CoV-2 causes COVID-19, a disease curiously resulting in varied symptoms and outcomes, ranging from asymptomatic to fatal. Autoimmunity due to cross-reacting antibodies resulting from molecular mimicry between viral antigens and host proteins may provide an explanation. Thus, we computationally investigated molecular mimicry between SARS-CoV-2 Spike and known epitopes. We discovered molecular mimicry hotspots in Spike and highlight two examples with tentative high autoimmune potential and implications for understanding COVID-19 complications. We show that a TQLPP motif in Spike and thrombopoietin shares similar antibody binding properties. Antibodies cross-reacting with thrombopoietin may induce thrombocytopenia, a condition observed in COVID-19 patients. Another motif, ELDKY, is shared in multiple human proteins, such as PRKG1 involved in platelet activation and calcium regulation, and tropomyosin, which is linked to cardiac disease. Antibodies cross-reacting with PRKG1 and tropomyosin may cause known COVID-19 complications such as blood-clotting disorders and cardiac disease, respectively. Our findings illuminate COVID-19 pathogenesis and highlight the importance of considering autoimmune potential when developing therapeutic interventions to reduce adverse reactions. [ABSTRACT FROM AUTHOR]