44 citations · 210 across the 12 of their papers we have counts for
13 papers
Computational Insights into Mechanostability and Dissociation Dynamics of the Dengue Virus Envelope Protein Ectodomain Dimer Across pH and Temperature Gradients
Georcki Ropón-Palacios, Luís G. F. Crespi, Jakub Rydzewski +2
Dengue virus (DENV) is an enveloped flavivirus of major public health importance. Its envelope (E) protein mediates viral entry through homodimer dissociation and subsequent membra…
Constructing Generalized Sample Transition Probabilities with Biased Simulations
Yanbin Wang, Jakub Rydzewski, Ming Chen
In molecular dynamics (MD) simulations, accessing transition probabilities between states is crucial for understanding kinetic information, such as reaction paths and rates. Howeve…
NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks
Patryk Tajs, Mateusz Skarupski, Jakub Rydzewski
Unsupervised machine learning has recently gained much attention in the field of molecular dynamics (MD). Particularly, dimensionality reduction techniques have been regularly empl…
Illuminating Protein Dynamics: A Review of Computational Methods for Studying Photoactive Proteins
Sylwia Czach, Jakub Rydzewski, Wiesław Nowak
Photoactive proteins absorb light and undergo structural changes that enable them to perform essential biological functions. These proteins are critical for understanding light-ind…
Selecting High-Dimensional Representations of Physical Systems by Reweighted Diffusion Maps
Jakub Rydzewski
Constructing reduced representations of high-dimensional systems is a fundamental problem in physical chemistry. Many unsupervised machine learning methods can automatically find s…
Spectral Map: Embedding Slow Kinetics in Collective Variables
Jakub Rydzewski
The dynamics of physical systems that require high-dimensional representation can often be captured in a few meaningful degrees of freedom called collective variables (CVs). Howeve…
Manifold Learning in Atomistic Simulations: A Conceptual Review
Jakub Rydzewski, Ming Chen, Omar Valsson
Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Suc…
Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations
Jakub Rydzewski, Ming Chen, Tushar K. Ghosh +1
Enhanced sampling methods are indispensable in computational physics and chemistry, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space…
Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling
Jakub Rydzewski, Omar Valsson
Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial…
maze: Heterogeneous Ligand Unbinding along Transient Protein Tunnels
Jakub Rydzewski
Recent developments in enhanced sampling methods showed that it is possible to reconstruct ligand unbinding pathways with spatial and temporal resolution inaccessible to experiment…
Rethinking Monocular Depth Estimation with Adversarial Training
Richard Chen, Faisal Mahmood, Alan Yuille +1
Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both sup…
Entropic measure to prevent energy over-minimization in molecular dynamics simulations
Jakub Rydzewski, Rafal Jakubowski, Wieslaw Nowak
This work examines the impact of energy over-minimization on an ensemble of biological molecules subjected to the potential energy minimization procedure in vacuum. In the studied…
Memetic Algorithms for Ligand Expulsion from Protein Cavities
Jakub Rydzewski, Wieslaw Nowak
Ligand diffusion through proteins is a fundamental process governing biological signaling and enzymatic catalysis. The complex topology of protein tunnels results in difficulties w…