The development of efficient tools for allosteric ligand binding site identification in potential drug targets is an important step for computational drug design. Ligand binding specificity analysis (LIBSA) is one of the protocols that utilize filtering algorithms to assess the propensity of a site on a target structure or structures to bind a ligand. However, LIBSA requires expert skills to be properly executed. Thus, a Web interface, LBPI (Ligand Binding Pocket Identification) has been developed using Django, a Python-based web framework. A Python Wrapper has also been developed to streamline pre-existing algorithms of LIBSA. The Wrapper helps in the preparation of files, execution of individual programs and generation of appropriate results. LBPI provides an ideal platform for making complex binding site identification protocols readily available for non-expert users to submit jobs and monitor the results. The goal of LBPI is to integrate available algorithms in a systematic way and make it easily available for both experts and non-experts.
","abstract_html":"<p>The development of efficient tools for allosteric ligand binding site identification in potential drug targets is an important step for computational drug design. Ligand binding specificity analysis (LIBSA) is one of the protocols that utilize filtering algorithms to assess the propensity of a site on a target structure or structures to bind a ligand. However, LIBSA requires expert skills to be properly executed. Thus, a <em>Web interface</em>, LBPI (Ligand Binding Pocket Identification) has been developed using Django, a Python-based web framework. A Python <em>Wrapper</em> has also been developed to streamline pre-existing algorithms of LIBSA. The <em>Wrapper</em> helps in the preparation of files, execution of individual programs and generation of appropriate results. LBPI provides an ideal platform for making complex binding site identification protocols readily available for non-expert users to submit jobs and monitor the results. The goal of LBPI is to integrate available algorithms in a systematic way and make it easily available for both experts and non-experts.</p>","abstract_has_math":false,"creators":["Paudyal, Nabina","0000-0003-2967-831X
"],"institution":null,"degree_name":"Masters of Science (MS)","degree_level":"Thesis (MS)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Alemayehu Gorfe","Jeffrey Chang","Xiaodong Cheng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-01T07:00:00Z","date_published":"2017-08-01T07:00:00Z","updated_at":"2026-07-24T05:49:54Z","subjects":["LBPI","Web Interface","Computational Pipeline","Wrapper","LIBSA","Python","Django","Platform","Integrate","Availability","Biochemistry, Biophysics, and Structural Biology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/830","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alemayehu Gorfe","Jeffrey Chang","Xiaodong Cheng"]},{"key":"dc:creator","label":"Author","values":["Paudyal, Nabina","0000-0003-2967-831X
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