NLM DIR Seminar Schedule
UPCOMING SEMINARS
-
March 25, 2025 Yifan Yang
TBD -
April 1, 2025 Roman Kogay
TBD -
April 8, 2025 Jaya Srivastava
TBD -
April 15, 2025 Pascal Mutz
TBD -
April 18, 2025 Valentina Boeva, Department of Computer Science, ETH Zurich
Decoding tumor heterogeneity: computational methods for scRNA-seq and spatial omics
RECENT SEMINARS
-
March 11, 2025 Sofya Garushyants
Tmn – bacterial anti-phage defense system -
March 4, 2025 Sanasar Babajanyan
Evolution of antivirus defense in prokaryotes depending on the environmental virus load -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases -
Feb. 18, 2025 Samuel Lee
Efficient predictions of alternative protein conformations by AlphaFold2-based sequence association -
Feb. 11, 2025 Po-Ting Lai
Enhancing Biomedical Relation Extraction with Directionality
Scheduled Seminars on May 28, 2024
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
We developed a new approach that simulates protein fold evolution with atomistic details, providing insights into mechanisms of large-scale conformational changes during evolution. For many years, the origin and evolution of protein folds remained among the most challenging problems in biology. Although many hypotheses offer plausible scenarios of protein evolution, realistic simulation of this process was not feasible because of the lack of fast and reliable approaches for protein structure prediction, a situation that changed with the advent of AlphaFold. Our method introduces random mutations in a population of proteins, evaluates the effect of mutations on protein structure, and selects a new set of proteins for further mutagenesis. Repeating this process iteratively allows tracking the evolutionary trajectory of a changing protein fold that evolves under selective pressure, which can be protein fold stability, interaction with another protein, or other arbitrary features shaping the fitness landscape. We used protein fold evolution simulation (PFES) to demonstrate how protein folds could evolve from random amino acid sequences in a monomeric or homooligomeric state or in a complex with an interacting partner. We demonstrated the stability of the proteins that evolved in our simulations with physics-based methods even if these proteins do not exist in nature and their structure cannot be predicted with AlphaFold. PFES provides a complete evolutionary history from simulations that describes all intermediate states at the sequence and structure levels that can be used to test different hypotheses on protein fold evolution.