The lab's research focuses on leveraging evolutionary and computational methods to understand protein function and the impact of genetic variants. Explore our list of publications below for an overview of our work.
[1] Lichtarge, O., Bourne, H. R. & Cohen, F. E. An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol 257, 342-358 (1996). [PMID: 8609628]
[2] Mihalek, I., Res, I. & Lichtarge, O. A family of evolution-entropy hybrid methods for ranking protein residues by importance. J Mol Biol 336, 1265-1282 (2004). [PMID: 15037084]
[3] Katsonis, P. & Lichtarge, O. A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness. Genome Res 24, 2050-2058 (2014). [PMID: 25217195]
[4] Katsonis, P., Wilhelm, K., Williams, A., & Lichtarge, O. Genome interpretation using in silico predictors of variant impact. Human genetics, 141(10), 1549–1577 (2022). [PMID: 35488922]
[5] Hsu, T. K., Asmussen, J., Koire, A., Choi, B. K., Gadhikar, M. A., Huh, E., Lin, C. H., Konecki, D. M., Kim, Y. W., Pickering, C. R., Kimmel, M., Donehower, L. A., Frederick, M. J., Myers, J. N., Katsonis, P., & Lichtarge, O. A general calculus of fitness landscapes finds genes under selection in cancers. Genome research, 32(5), 916–929 (2022). [PMID: 35301263]
[6] Koire, A., Katsonis, P., Kim, Y. W., Buchovecky, C., Wilson, S. J., & Lichtarge, O. A method to delineate de novo missense variants across pathways prioritizes genes linked to autism. Science translational medicine, 13(594), eabc1739 (2021). [PMID: 34011629]
[7] Bourquard, T., Lee, K., Al-Ramahi, I., Pham, M., Shapiro, D., Lagisetty, Y., Soleimani, S., Mota, S., Wilhelm, K., Samieinasab, M., Kim, Y. W., Huh, E., Asmussen, J., Katsonis, P., Botas, J., & Lichtarge, O. Functional variants identify sex-specific genes and pathways in Alzheimer's Disease. Nature communications, 14(1), 2765 (2023). [PMID: 37179358]
[8] Parvandeh, S., Donehower, L. A., Katsonis, P., Hsu, T. K., Asmussen, J. K., Lee, K., & Lichtarge, O. EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants. Nucleic acids research, 50(12), e70 (2022). [PMID: 35412634]
[9] Lagisetty, Y., Bourquard, T., Al-Ramahi, I., Mangleburg, C. G., Mota, S., Soleimani, S., Shulman, J. M., Botas, J., Lee, K., & Lichtarge, O.Identification of risk genes for Alzheimer's disease by gene embedding. Cell genomics, 2(9), 100162 (2022). [PMID: 36268052]
[10] Shapiro, D., Lee, K., Asmussen, J., Bourquard, T., & Lichtarge, O. Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease. Journal of the American Heart Association, 12(17), e029103 (2023). [PMID: 37642027]
[11] Marciano, D. C., Wang, C., Hsu, T. K., Bourquard, T., Atri, B., Nehring, R. B., Abel, N. S., Bowling, E. A., Chen, T. J., Lurie, P. D., Katsonis, P., Rosenberg, S. M., Herman, C., & Lichtarge, O. Evolutionary action of mutations reveals antimicrobial resistance genes in Escherichia coli. Nature communications, 13(1), 3189 (2022). [PMID: 35680894]
[12] Lichtarge, O., & Sowa, M. E. Evolutionary predictions of binding surfaces and interactions. Current opinion in structural biology, 12(1), 21–27 (2002). [PMID: 11839485]
[13] Wilkins, A. D., Bachman, B. J., Erdin, S., & Lichtarge, O. The use of evolutionary patterns in protein annotation. Current opinion in structural biology, 22(3), 316–325 (2012). [PMID: 22633559]