Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 54 tok/s
Gemini 2.5 Pro 54 tok/s Pro
GPT-5 Medium 22 tok/s Pro
GPT-5 High 25 tok/s Pro
GPT-4o 99 tok/s Pro
Kimi K2 196 tok/s Pro
GPT OSS 120B 333 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

maze: Heterogeneous Ligand Unbinding along Transient Protein Tunnels (1904.03929v2)

Published 8 Apr 2019 in physics.bio-ph, cond-mat.stat-mech, physics.chem-ph, and physics.comp-ph

Abstract: Recent developments in enhanced sampling methods showed that it is possible to reconstruct ligand unbinding pathways with spatial and temporal resolution inaccessible to experiments. Ideally, such techniques should provide an atomistic definition of possibly many reaction pathways, because crude estimates may lead either to overestimating energy barriers, or inability to sample hidden energy barriers that are not captured by reaction pathway estimates. Here we provide an implementation of a new method [J. Rydzewski & O. Valsson, J. Chem. Phys. {\bf 150}, 221101 (2019)] dedicated entirely to sampling the reaction pathways of the ligand-protein dissociation process. The program, called \texttt{maze}, is implemented as an official module for PLUMED 2, an open source library for enhanced sampling in molecular systems, and comprises algorithms to find multiple heterogeneous reaction pathways of ligand unbinding from proteins during atomistic simulations. The \texttt{maze} module requires only a crystallographic structure to start a simulation, and does not depend on many \textit{ad hoc} parameters. The program is based on enhanced sampling and non-convex optimization methods. To present its applicability and flexibility, we provide several examples of ligand unbinding pathways along transient protein tunnels reconstructed by \texttt{maze} in a model ligand-protein system, and discuss the details of the implementation.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube