BAGIM is an active community of Boston area scientists bringing together people from diverse fields of modeling and informatics to impact life and health sciences. BAGIM strives to create a forum for great scientific discussions covering a wide range of topics including data management, visualization, computational chemistry, drug discovery, protein structure, molecular modeling, structure-based drug design, data mining, software tools, and the sharing of goals and experiences. Our community is made up of participants from academia, government, and industry whose goal is to engage in the discussion of science involving a synthesis of theory and technology. Discussions sponsored by BAGIM are targeted to the needs and interests of informatics scientists, computational chemists, medicinal chemists, and statisticians. BAGIM also provides opportunities for networking within these disciplines as well as an arena for the dissemination of information of specific interest to the membership.

Saturday, March 16, 2024

Chris Williams - Autoph4: Pharmacophore Analysis of Multiple Protein Structures



We are hosting an April BAGIM in-person event with CCG. We welcome Chris Williams with the presentation Database autoph4: Pharmacophore Analysis of Multiple Protein Structures.

Date: April 24, 2024 @ 6:00 PM

LocationLe Meridien Boston Cambridge, 20 Sidney Street, Cambridge, MA, 02139 (MAP) - We will be in meeting room: Hunsaker Ballroom

6:00 PM - 6:45 PM - Presentation & Q&A
6:45 PM - onward - Reception

Please arrive early to get checked in. CCG is hosting free workshop on the same day (April 24). Please visit their website (HERE) for more information and to sign-up.

More information to be posted.

Host: Chemical Computing Group and BAGIM

Speaker: Chris Williams, PhD, Principal Scientist, Chemical Computing Group


An automated approach to summarize pocket shapes and binding hot-spots from a collection of protein structures is presented. Pocket shapes are described using pocket volumes derived from Alpha Sites and molecular surfaces. Binding hot-spots are located using pharmacophore features generated by AutoPH4. Collections of pocket volumes and pharmacophores are analyzed using feature densities which map onto a universal grid the fraction of structures that possess a given feature at each point in space. Regions with high pharmacophore feature densities identify the most persistent interaction binding hot-spots over the collection of structures. Pocket volume densities detect and classify binding site regions into core pockets and sub-pocket regions. Fingerprints that represent pocket shape, sub-pocket presence and pharmacophore feature presence are derived and used to cluster and classify multiple protein structures using standard fingerprint clustering tools. Application of the method to fragment-based drug design, minor pocket detection, selectivity mapping, binding-mode classification and custom docking scoring function creation is presented.

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