Advancements in massively parallel sampling of the conformational space of biomolecules enables, for example, protein structure prediction, in-silico drug development and cell signaling. Despite the existence of highly distributed protein simulation architectures like POEM@HOME, there was no abundant computational resource both strong and serial strength and in parallel sampling. In this study we investigate the optimization of our N-body Lennard-Jones force field for the efficient Monte-Carlo sampling of small to medium-size biomolecules on massively parallel architectures, like modern GPUs. We benchmark both NVIDIA and AMD GPU chipsets in the OpenCL framework on comparison to CPU architectures. The N-body interactions are broken down into small local grids, which fit into the local GPU caches to permit simultaneous evaluation. Using the N-body term we accelerate the Lennard-Jones and Clash-Potential of the complete free-energy PFF02 [1] shown to fold a multitude of different protein-folds and implement a modified structure-based Lennard Jones force field. We proof the applicability of our novel force field by reversible folding-simulations of a three-helix protein using this Go-potential from completely unifolded structures.<\/p>\n","protected":false},"excerpt":{"rendered":"
Advancements in massively parallel sampling of the conformational space of biomolecules enables, for example, protein structure prediction, in-silico drug development and cell signaling. Despite the existence of highly distributed protein simulation architectures like POEM@HOME, there was no abundant computational resource both strong and serial strength and in parallel sampling. In this study we investigate the […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,90,3],"tags":[7,455,451,1781,658,72,258,20,251,1793,298],"class_list":["post-6104","post","type-post","status-publish","format-standard","hentry","category-biology","category-opencl","category-paper","tag-ati","tag-ati-radeon-hd-5870","tag-benchmarking","tag-biology","tag-biomolecules","tag-monte-carlo-simulation","tag-n-body-simulation","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-opencl","tag-optimization"],"views":2833,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6104","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6104"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6104\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6104"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6104"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6104"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}