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| About Lance Westerhoff (Quantum Bio) |
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Lance Westerhoff earned his Ph.D. in Chemistry, in Dr. Kenneth M. Merz Jr.'s research group at Pennsylvania State University where he focused on the application of macromolecular, linear-scaling quantum mechanics and database design to problems in protein-ligand complexes. In 2001, while still a graduate student, Lance worked with Dr. Merz to form QuantumBio Inc. in order to commercialize the linear-scaling, quantum mechanics software under active development in the academic laboratory. Soon after graduation Lance took over as General Manager of QuantumBio and he has remained there ever since and has successfully funded various research projects aimed at validating and commercializing quantum-based methods to study proteins and their ligands. Today he leads a team of computational chemists and software developers employed under three different NIH SBIR/STTR projects bent on fleshing out these applications including quantum-based protein-ligand scoring and interaction profiling, X-ray refinement, and NMR-based pose scoring.
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Theory to Application: How Quantum Mechanics can be Applied to Structure-Based Drug Discovery
Lance Westerhoff (QuantumBio)
Traditionally, linear scaling, quantum mechanics-based (QM-based) methods for characterization of target/ligand complexes have been better suited to academic environments as they are sometimes difficult applications to access in the industrial domain. Recently, QuantumBio has bridged that gap through the development of several QM-based interaction profiling tools specifically tailored to the structure-based drug discovery process. When plugged into MOE, these tools – including scoring, pair-wise interaction energy decomposition, and QSAR – become better integrated with the workflows commonly used in the field. To date, this work has lead to the development of three major MOE svl plugins: MOE/QMScore, MOE/NMRScore, and MOE/QM-PWD. We are now able to prepare any number of QM simulations using the MOE graphical user interface (GUI), execute the simulations in parallel using MOE's message passing infrastructure, and finally import the results back into the MOE GUI for further analysis.
As a use case, these QM simulations have been carried out for a series of protein kinase B inhibitors derived from fragment (FBDD) and structure-based drug design (SBDD). These protein-ligand complexes were selected because they represent a consistent set of experimental data that includes both crystal structures and affinities. Seven scoring functions were constructed based on a mixture of several quantum- and molecular- mechanical methods. The optimal models obtained by statistical analysis of the aligned poses are predictive as measured by a number of standard statistical criteria and validation with an external data set. Together, this model provides residue-based contributions to the overall binding affinity, and these results are treated using both native MOE analytical methodologies and customized widgets including the QM-PWD Interaction Energy (IE) Map, Structure/Activity Relationship (SAR) Map, and results tables. The IE map highlights the most important residues for ligand binding, while the SAR Map highlights residues that are most critical to discriminating between more and less potent ligands. Taken together the Interaction Energy and SAR Maps provide useful insights into drug design that would be difficult to garner in any other way.
Class members working in small groups will be able to study together the setup of the QM methods during the workshop applicable to their case studies. As calculations can be computationally-intensive, we will offer the possibilities of running computations both before and after the workshop, made available through a collaboration environment.
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