ArticleJournal of chemical information and modeling2023
LAST: Latent Space-Assisted Adaptive Sampling for Protein Trajectories.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- DreamFold: A World Model to efficiently generate protein folding pathways in the latent space.Protein science : a publication of the Protein Society · 2026Article
- mdBIRCH for Fast, Scalable, Online Clustering of Molecular Dynamics Trajectories.Journal of chemical theory and computation · 2026Article
- Structural Plasticity and Functional Dynamics of Pigeon Cryptochrome 4 as Avian Magnetoreceptor.Journal of molecular biology · 2025Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
- Nucleotide asymmetry and flexible linker dynamics modulate drug efflux cycle of P-glycoprotein, A computational study.Computational and structural biotechnology journal · 2025Article
- Rare-Event Sampling using a Reinforcement Learning-Based Weighted Ensemble Method.bioRxiv : the preprint server for biology · 2024Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Research square · 2024Article
- Assessments of Variational Autoencoder in Protein Conformation Exploration.Journal of computational biophysics and chemistry · 2023Article
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
- PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning.Frontiers in molecular biosciences · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Molecular dynamics (MD) simulation is widely used to study protein conformations and dynamics. However, conventional simulation suffers from being trapped in some local energy minima that are hard to escape. Thus, most of the computational time is spent sampling in the already visited regions. This leads to an inefficient sampling process and further hinders the exploration of protein movements in affordable simulation time. The advancement of deep learning provides new opportunities for protein sampling. Variational autoencoders are a class of deep learning models to learn a low-dimensional representation (referred to as the latent space) that can capture the key features of the input data. Based on this characteristic, we proposed a new adaptive sampling method, latent space-assisted adaptive sampling for protein trajectories (LAST), to accelerate the exploration of protein conformational space. This method comprises cycles of (i) variational autoencoder training, (ii) seed structure selection on the latent space, and (iii) conformational sampling through additional MD simulations. The proposed approach is validated through the sampling of four structures of two protein systems: two metastable states of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.