Paper List
-
Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
-
CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
-
On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
-
EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
-
Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
-
Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
-
Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
-
scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
Department of Computer Science and Genome Center, University of California, Davis | Architecture et Dynamique des Macromolécules Biologiques, UMR 3528 du CNRS, Institut Pasteur | Department of Physics, School of Sciences, Great Bay University | Université Paris-Saclay, CNRS, CEA, Institut de Physique Théorique
The 30-Second View
IN SHORT: This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein conformations, a problem where existing methods often produce non-physical trajectories due to oversimplified energy surfaces and steric clashes.
Innovation (TL;DR)
- Methodology Introduces SIDE (Stochastic Integro-Differential Equation), a novel Langevin bridge-based framework that efficiently approximates exact bridge equations at low temperatures to generate constrained transition trajectories.
- Methodology Develops a new coarse-grained potential that combines a Gō-like term (to preserve native backbone geometry) with a Rouse-type elastic energy term (from polymer physics), avoiding the problematic mixing of start/target conformation information used in prior methods like MinActionPath.
- Theory Provides a rigorous stochastic integro-differential formulation derived from the Langevin bridge formalism, which explicitly constrains trajectories to reach a target state within finite time, moving beyond Minimum Action Path (MAP) principles.
Key conclusions
- The SIDE framework generates smooth, low-energy transition trajectories that maintain realistic molecular geometry, as demonstrated on several proteins undergoing large-scale conformational changes.
- SIDE frequently recovers experimentally supported intermediate states along transition paths, suggesting its paths have biological relevance beyond mere endpoint interpolation.
- Compared to established methods like MinActionPath and EBDIMS, SIDE offers improved physical realism and computational efficiency for modeling biomolecular conformational transitions, though challenges remain for highly complex motions.
Abstract: We introduce a computational framework for generating realistic transition paths between distinct conformations of large biomolecular systems. The method is built on a stochastic integro-differential formulation derived from the Langevin bridge formalism, which constrains molecular trajectories to reach a prescribed final state within a finite time and yields an efficient low-temperature approximation of the exact bridge equation. To obtain physically meaningful protein transitions, we couple this formulation to a new coarse-grained potential combining a Gō-like term that preserves native backbone geometry with a Rouse-type elastic energy term from polymer physics; we refer to the resulting approach as SIDE. We evaluate SIDE on several proteins undergoing large-scale conformational changes and compare its performance with established methods such as MinActionPath and EBDIMS. SIDE generates smooth, low-energy trajectories that maintain molecular geometry and frequently recover experimentally supported intermediate states. Although challenges remain for highly complex motions—largely due to the simplified coarse-grained potential—our results demonstrate that SIDE offers a powerful and computationally efficient strategy for modeling biomolecular conformational transitions.