Tackling Multifaceted Drug Design Problems with Lambda Dynamics Based Technologies

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Project Summary Modern day drug discovery is a long and expensive process requiring teams of scientists, multiple years of research, and millions of dollars to identify preclinical drug candidates suitable for clinical tests. The incorporation of computational tools into drug discovery has proved an effective means to reduce these costs. All-atom molecular dynamics simulations coupled with alchemical free energy calculations have been extremely beneficial tools for studying structural and thermodynamic properties of protein-ligand complexes and optimizing drug candidates for improved binding affinity to a target of interest. Lambda dynamics (LD), a newer alchemical free energy method, facilitates the sampling of multiple perturbations to a chemical system, simultaneously, within a single molecular dynamics simulation, overcoming inherent scalability limitations associated with conventional free energy methods. To date, a variety of chemical perturbations, including diverse ligand functional group transformations and protein side chain mutations, have been performed with (LD) on a single chemical entity, e.g., a small molecule or protein, with much success. Tens to hundreds of chemical states have been efficiently sampled using an order of magnitude less computational resources compared to conventional methods. This proposal seeks support to build upon these findings and apply LD-based techniques to explore multifaceted design problems in drug discovery featuring chemical modifications on multiple binding partners. Specifically, three challenging areas of drug discovery will be investigated: (1) understanding and overcoming drug resistance originating from missense mutations in a drug target, (2) characterizing protein-protein interactions and binding specificities, and (3) automating the generation of novel, target-specific lead compound analogs by integrating LD calculations with machine- or deep-learning algorithms. Success in these efforts will require searching through large combinatorial chemical spaces that can only be accomplished with LD-based techniques. Model protein-target systems of high therapeutic importance from Multiple Myeloma or Alzheimer’s Disease will be investigated in accomplishing our goals. Thus, this work will assist in accelerating preclinical structure-based drug design by enabling complex molecular design scenarios to be addressed in these devastating diseases.