Gevorg Grigoryan

Gevorg Grigoryan serves as a Research Associate Professor in the Department of Computer Science at Dartmouth College, with joint appointments in the Departments of Biological Sciences and Chemistry. He is affiliated with the Institute for Biomolecular Targeting and maintains an active research program at the intersection of computational science and protein biology.

Education:

• B.S. from University of Maryland Baltimore County
• Ph.D. from Massachusetts Institute of Technology

Dr. Grigoryan's research program focuses on deciphering how protein function emerges from molecular structure and amino acid sequences. He employs an interdisciplinary approach combining computational modeling with experimental validation to investigate protein-protein recognition mechanisms, structural basis of allostery, and principles of protein design. His work spans computational biology, biophysics, and structure-based molecular engineering with applications in therapeutic development and biomaterials design.

His recent publications demonstrate consistent contributions to understanding transmembrane protein interactions, structural motif analysis, and de novo protein design. The research integrates statistical mechanics, computational algorithms, and molecular modeling to address fundamental questions in protein science.

Dr. Grigoryan teaches across multiple levels of the curriculum including COSC 75/175 (Introduction to Bioinformatics), COSC 86/186 (Computational Structural Biology), COSC 70/170 (Numerical and Computational Tools for Applied Science), and COSC 10 (Problem Solving via Object Oriented Programming). He mentors graduate students through Dartmouth's Molecular and Cellular Biology, Computer Science, and Quantitative Biomedical Sciences programs.

His laboratory pursues quantitative approaches to protein design principles, with particular interest in transmembrane protein interactions, molecular recognition, and energy landscapes of protein folding. The lab maintains active collaborations across disciplines to bridge computational predictions with experimental validation in protein engineering.