NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
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.