This $1,330,376 Project Grant from the National Science Foundation Division of Molecular and Cellular Biosciences Biological Sciences program (CFDA 47.074) will fund the development of new computational methods for remote homology detection with evolutionary profile hidden Markov models. The grantee, President and Fellows of Harvard College, will implement divergence-parameterized statistical models of sequence evolution within the HMMER software package. This aims to improve the sensitivity of homology detection for very remote protein homologs by stretching probabilistic methods beyond currently observed evolutionary distances, guided by an evolutionary model. The grantee will also develop evolutionary models dealing with insertion and deletion events to apply to standard profile methods for homology detection and multiple sequence alignment. Completion is expected by May 31, 2025.
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