Most of my work in Computational Biology is directed towards applications in structural biology, but more recently, we have started to develop methods for automating scientific research and engineering.
Protein Design
I am currently developing Bayesian optimization methods for molecular design.
Primary publications in this area
Bayesian Optimization with Evolutionary and Structure-Based Regularization for Directed Protein Evolution, Algorithms for Molecular Biology, TS Frisby, CJ Langmead, Algorithms for Molecular Biology 16:13, 2021 , DOI
Fold Family-Regularized Bayesian Optimization for Directed Protein Evolution, TS Frisby, CJ Langmead, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020)
Bayesian Optimization with Evolutionary and Structure-Based Regularization for Directed Protein Evolution, TS Frisby, CJ Langmead, Algorithms for Molecular Biology, 2021 (in press)
Automating Scientific Research and Engineering
We are presently developing methods that select experiments automatically, using Active Learning and Sequential Model Based Optimization.
Primary publication in this area
Asynchronous Parallel Bayesian Optimization for AI-driven Cloud Laboratories, T. Frisby, Z. Gong, C.J. Langmead ISMB/ECCB; Bioinformatics 2021
Generative Models of Protein Sequence and Structure
My group has developed a variety of methods for learning generative models of sequence and structure. These models can be used in a variety of applications, ranging from free energy calculations to protein design. Our most popular method is an algorithm we call GREMLIN. The Baker Lab has adopted and adapted our method.
Primary publications in this area
Free energy estimates of all-atom protein structures using generalized belief propagation, H Kamisetty, EP Xing, CJ Langmead, Journal of Computational Biology 15 (7), 755-766, 2008
GREMLIN PAPER Learning generative models for protein fold families, S Balakrishnan, H Kamisetty, JG Carbonell, SI Lee, CJ Langmead, Proteins: Structure, Function, and Bioinformatics 79 (4), 1061-1078, 2011
Accounting for conformational entropy in predicting binding free energies of protein‐protein interactions, H Kamisetty, A Ramanathan, C Bailey‐Kellogg, CJ Langmead, Proteins: Structure, Function, and Bioinformatics 79 (2), 444-462
A minimal ligand binding pocket within a network of correlated mutations identified by multiple sequence and structural analysis of G protein coupled receptors, S Moitra, K Tirupula, J Klein-Seetharaman, C Langmead, BMC biophysics 5 (1), 13, 2012
Molecular Dynamics
I have developed a variety of methods for analyzing molecular dynamics simulation data, to extract information relevant to understanding the relationship between structure, dynamics and function
Primary publications in this area
Dynamic allostery governs cyclophilin A–HIV capsid interplay, M Lu, G Hou, H Zhang, CL Suiter, J Ahn, IJL Byeon, JR Perilla, et al Proceedings of the National Academy of Sciences 112 (47), 14617-14622, 2015
HIV-1 capsid function is regulated by dynamics: Quantitative atomic-resolution insights by integrating magic-angle-spinning NMR, QM/MM, and MD, H Zhang, G Hou, M Lu, J Ahn, IJL Byeon, CJ Langmead, JR Perilla, et al, Journal of the American Chemical Society 138 (42), 14066-14075, 2016