Adam R Griffith

Title: Innovations in Protein Structure Prediction by Adam R Griffith

Introduction

Adam R Griffith is an accomplished inventor based in Pasadena, CA. He has made significant contributions to the field of computational biology, particularly in the prediction of protein structures. His innovative methods have the potential to revolutionize drug design and development.

Latest Patents

Griffith holds a patent titled "Methods for predicting three-dimensional structures for alpha helical membrane proteins and their use in design of selective ligands." This patent describes a method for practically predicting the three-dimensional structure of α-helical membrane proteins (HMPs). The method allows for the prediction of binding sites and structures for strongly bound ligands. It combines a protocol of computational methods that enable a complete ensemble of packings to be sampled. The ensemble is systematically reduced to progressively more accurate structures, ultimately identifying a few that are functionally relevant. This method is particularly well-suited for automatic operation, making it practical to obtain important structures for all human G protein-coupled receptors (GPCRs). The method also presents an infimum approach to maximize efficacy toward selected targets while minimizing binding to other GPCRs, thereby reducing toxicity and improving drug success rates.

Career Highlights

Griffith is affiliated with the California Institute of Technology, where he continues to advance research in protein structure prediction. His work has garnered attention for its innovative approach and practical applications in drug design.

Collaborations

Griffith has collaborated with notable colleagues, including Ravinder Abrol and William A Goddard, III. Their combined expertise enhances the research and development of new methodologies in the field.

Conclusion

Adam R Griffith's contributions to the prediction of protein structures represent a significant advancement in computational biology. His innovative methods hold promise for improving drug design and efficacy, ultimately benefiting the medical field.