This paper introduces DynamicFlow, a full-atom stochastic flow matching model that simultaneously generates ligand molecules and transforms protein pockets from apo to holo states. It also contributes a new dataset of MD-simulated apo-holo pairs derived from MISATO.
InvMSAFold replaces autoregressive decoding with a Potts model parameter generator, enabling diverse protein sequence sampling orders of magnitude faster than ESM-IF1.
This seminal work resolves Levinthal’s paradox by replacing the single-pathway view with a statistical energy landscape approach. It introduces the concepts of the folding funnel, the glass transition in proteins, and the ‘stability gap’ as a design principle for foldable sequences.
Levinthal’s 1969 perspective paper defined the protein folding paradox by demonstrating the impossibility of random search, establishing the need for kinetic pathways that guide folding faster than thermodynamic equilibration allows.
A practical guide to simulating mini-proteins using GROMACS; from alanine dipeptide to tryptophan systems for ML training data generation.
An automated GROMACS pipeline for generating high-fidelity molecular dynamics datasets suitable for machine learning, simulating capped dipeptides across nine residue types with 0.1 ps resolution and atomic force extraction optimized for training Neural Network Potentials.