Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications

Comparing the binding interactions in the receptor binding domains of SARS-CoV-2 and SARS-CoV

COVID-19, since emerged in Wuhan, China, has been a major concern due to its high infection rate, leaving more than one million infected people around the world. Huge number of studies tried to reveal the structure of the SARS-CoV-2 compared to the SARS-CoV-1, in order to suppress this high infection rate. Some of these studies showed that the mutations in the SARS-CoV-1 Spike protein might be responsible for its higher affinity to the ACE2 human cell receptor. In this work, we used molecular dynamics simulations and Monte Carlo sampling to compare the binding affinities of the spike proteins of SARS-CoV and SARS-CoV-2 to the ACE2. We found that the SARS-CoV-2 binds to ACE2 stronger than SARS-CoV by 7 kcal/mol, due to enhanced electrostatic interactions. The major contributions to the electrostatic binding energies are resulting from the salt-bridges formed between R426 and ACE2-E329 in case of SARS-CoV and K417 and ACE2-D30 for SARS-CoV2. In addition, there is no significant contribution from a single mutant to the binding energies. However, these mutations induce sophisticated structural changes that enhance the binding energies. Our results also indicate that the SARS-CoV-2 is unlikely a lab engineered virus

Extending fragment-based free energy calculations with library Monte Carlo simulation: Annealing in interaction space

Pre-calculated libraries of molecular fragment configurations have previously been used as a basis for both equilibrium sampling (via "library-based Monte Carlo") and for obtaining absolute free energies using a polymer-growth formalism. Here, we combine the two approaches to extend the size of systems for which free energies can be calculated. We study a series of all-atom poly-alanine systems in a simple dielectric "solvent" and find that precise free energies can be obtained rapidly. For instance, for 12 residues, less than an hour of single-processor is required. The combined approach is formally equivalent to the "annealed importance sampling" algorithm; instead of annealing by decreasing temperature, however, interactions among fragments are gradually added as the molecule is "grown." We discuss implications for future binding affinity calculations in which a ligand is grown into a binding site

GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition

<p>Abstract</p> <p>Background</p> <p>Calculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques. We describe a quaternion based method, GPU-Q-J, that is stable with single precision calculations and suitable for graphics processor units (GPUs). The application was implemented on an ATI 4770 graphics card in C/C++ and Brook+ in Linux where it was 260 to 760 times faster than existing unoptimized CPU methods. Source code is available from the Compbio website <url>http://software.compbio.washington.edu/misc/downloads/st_gpu_fit/</url> or from the author LHH.</p> <p>Findings</p> <p>The Nutritious Rice for the World Project (NRW) on World Community Grid predicted <it>de novo</it>, the structures of over 62,000 small proteins and protein domains returning a total of 10 billion candidate structures. Clustering ensembles of structures on this scale requires calculation of large similarity matrices consisting of RMSDs between each pair of structures in the set. As a real-world test, we calculated the matrices for 6 different ensembles from NRW. The GPU method was 260 times faster that the fastest existing CPU based method and over 500 times faster than the method that had been previously used.</p> <p>Conclusions</p> <p>GPU-Q-J is a significant advance over previous CPU methods. It relieves a major bottleneck in the clustering of large numbers of structures for NRW. It also has applications in structure comparison methods that involve multiple superposition and RMSD determination steps, particularly when such methods are applied on a proteome and genome wide scale.</p

Nonequilibrium candidate Monte Carlo: A new tool for efficient equilibrium simulation

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Here, we introduce a new class of moves based on nonequilibrium dynamics: candidate configurations are generated through a finite-time process in which a system is actively driven out of equilibrium, and accepted with criteria that preserve the equilibrium distribution. The acceptance rule is similar to the Metropolis acceptance probability, but related to the nonequilibrium work rather than the instantaneous energy difference. Our method is applicable to sampling from both a single thermodynamic state or a mixture of thermodynamic states, and allows both coordinates and thermodynamic parameters to be driven in nonequilibrium proposals. While generating finite-time switching trajectories incurs an additional cost, driving some degrees of freedom while allowing others to evolve naturally can lead to large enhancements in acceptance probabilities, greatly reducing structural correlation times. Using nonequilibrium driven processes vastly expands the repertoire of useful Monte Carlo proposals in simulations of dense solvated systems