{"id":5815,"date":"2011-10-07T17:26:55","date_gmt":"2011-10-07T14:26:55","guid":{"rendered":"http:\/\/hgpu.org\/?p=5815"},"modified":"2011-10-07T17:26:55","modified_gmt":"2011-10-07T14:26:55","slug":"gpgpu-assisted-prediction-of-ion-binding-sites-in-proteins","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5815","title":{"rendered":"GPGPU-assisted prediction of ion binding sites in proteins"},"content":{"rendered":"<p>Prediction of binding sites for different types of ions in protein 3D structure context is a complex challenge for biophysical computational methods. One possible approach involves using empirical, also called as knowledge-based, potentials. In the current study, we present a new GPGPU program complex, PIONCA (Protein-ION CAlculator) for efficient generation of empirical potentials for protein-ion interaction, provide description of its characteristics, and also present a publically available online service based on it.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Prediction of binding sites for different types of ions in protein 3D structure context is a complex challenge for biophysical computational methods. One possible approach involves using empirical, also called as knowledge-based, potentials. In the current study, we present a new GPGPU program complex, PIONCA (Protein-ION CAlculator) for efficient generation of empirical potentials for protein-ion [&hellip;]<\/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":[10,89,3],"tags":[1781,29,14,264,20],"class_list":["post-5815","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-biology","tag-biophysics","tag-cuda","tag-molecular-modeling","tag-nvidia"],"views":2475,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5815","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=5815"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5815\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}