NLM DIR Seminar Schedule
UPCOMING SEMINARS
RECENT SEMINARS
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Dec. 2, 2025 Qingqing Zhu
CT-Bench & CARE-CT: Building Reliable Multimodal AI for Lesion Analysis in Computed Tomography -
Nov. 25, 2025 Jing Wang
MIMIC-EXT-TE: Millions Clinical Temporal Event Time-Series Dataset -
Oct. 21, 2025 Yifan Yang
TBD -
Oct. 14, 2025 Devlina Chakravarty
TBD -
Oct. 9, 2025 Ziynet Nesibe Kesimoglu
TBD
Scheduled Seminars on May 29, 2025
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
The origin and evolution of protein folds are among the most challenging, long-standing problems in biology. We developed Protein Fold Evolution Simulator (PFES), a computational approach that simulates evolution of globular folds from random amino acid sequences with atomistic details. PFES introduces random mutations in a population of protein sequences, evaluates the effect of mutations on protein structure, and selects a new set of proteins for further evolution. Iteration of this process allows tracking the evolutionary trajectory of a changing protein fold that evolves under selective pressure for protein fold stability, interaction with other proteins, or other features shaping the fitness landscape. We employed PFES to show how stable, globular protein folds could evolve from random amino acid sequences as monomers or in complexes with other proteins. The simulations reproduce the evolution of many simple folds of natural proteins as well as emergence of distinct folds not known to exist in nature. We show that evolution of small globular protein folds from random sequences, on average, takes 1.15 to 3 amino acid replacements per site, depending on the population size, with some simulations yielding stable folds after as few as 0.2 replacements per site. These values are lower than the characteristic numbers of replacements in conserved proteins during the time since the Last Universal Common Ancestor (LUCA), suggesting that simple protein folds can evolve from random sequences relatively easily and quickly. PFES tracks the complete evolutionary history from simulations and can be used to test hypotheses on protein fold evolution.