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Under certain conditions, the dynamics of coarse-grained models of solvated proteins can be described using a Markov state model, which tracks the evolution of populations of configurations. The transition rates among states that appear in…

Soft Condensed Matter · Physics 2022-09-26 Margarita Colberg , Jeremy Schofield

We present a novel Monte Carlo simulation of protein folding, in which all heavy atoms are represented as interacting hard spheres. This model includes all degrees of freedom relevant to folding - all sidechain and backbone torsions - and…

Soft Condensed Matter · Physics 2007-05-23 J. Shimada , E. L. Kussell , E. I. Shakhnovich

Backtracking of RNA polymerase (RNAP) is an important pausing mechanism during DNA transcription that is part of the error correction process that enhances transcription fidelity. We model the backtracking mechanism of RNA polymerase, which…

Subcellular Processes · Quantitative Biology 2022-02-09 Xinzhe Zuo , Tom Chou

Metrics for indirectly predicting the folding rates of RNA sequences are of interest. In this letter, we introduce a simple metric of RNA structural complexity, which accounts for differences in the energetic contributions of RNA base…

Biomolecules · Quantitative Biology 2009-08-17 Asamoah Nkwanta , Wilfred Ndifon

We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges…

The branching of an RNA molecule is an important structural characteristic yet difficult to predict correctly, especially for longer sequences. Using plane trees as a combinatorial model for RNA folding, we consider the thermodynamic cost,…

Biomolecules · Quantitative Biology 2023-03-23 Christine Heitsch , Chi N. Y. Huynh , Greg Johnston

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction…

Machine Learning · Statistics 2015-06-18 Ali Shojaie , Alexandra Jauhiainen , Michael Kallitsis , George Michailidis

RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and…

Biomolecules · Quantitative Biology 2022-07-26 Nicola Calonaci , Alisha Jones , Francesca Cuturello , Michael Sattler , Giovanni Bussi

Proteins are the basic building blocks of life. They usually perform functions by folding to a particular structure. Understanding the folding process could help the researchers to understand the functions of proteins and could also help to…

Computational Engineering, Finance, and Science · Computer Science 2015-10-21 Jianzhu Ma

The computational study of conformational transitions in RNA and proteins with atomistic molecular dynamics often requires suitable enhanced sampling techniques. We here introduce a novel method where concurrent metadynamics are integrated…

Computational Physics · Physics 2015-09-01 Alejandro Gil-Ley , Giovanni Bussi

We extend recent methods for parametric sequence alignment to the parameter space for scoring RNA folds. This involves the construction of an RNA polytope. A vertex of this polytope corresponds to RNA secondary structures with common…

Biomolecules · Quantitative Biology 2009-04-24 Valerie Hower , Christine E. Heitsch

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and…

Biomolecules · Quantitative Biology 2016-04-22 Caleb Weinreb , Adam J. Riesselman , John B. Ingraham , Torsten Gross , Chris Sander , Debora S. Marks

Our world is ambiguous and this is reflected in the data we use to train our algorithms. This is particularly true when we try to model natural processes where collected data is affected by noisy measurements and differences in measurement…

Machine Learning · Computer Science 2023-07-19 Jörg K. H. Franke , Frederic Runge , Frank Hutter

Continuum robots (CR) offer excellent dexterity and compliance in contrast to rigid-link robots, making them suitable for navigating through, and interacting with, confined environments. However, the study of path planning for CRs while…

Robotics · Computer Science 2023-09-19 Yifan Wang , Yue Chen

Stochastic systems often exhibit multiple viable metastable states that are long-lived. Over very long timescales, fluctuations may push the system to transition between them, drastically changing its macroscopic configuration. In realistic…

Statistical Mechanics · Physics 2023-04-14 Tobias Grafke , Alessandro Laio

We analyze the thermodynamic properties of a simplified model for folded RNA molecules recently studied by G. Vernizzi, H. Orland, A. Zee (in {\it Phys. Rev. Lett.} {\bf 94} (2005) 168103). The model consists of a chain of one-flavor base…

Biomolecules · Quantitative Biology 2009-11-13 Matias dell'Erba , Guillermo R. Zemba

Molecular transitions -- such as protein folding, allostery, and membrane transport -- are central to biology yet remain notoriously difficult to simulate. Their intrinsic rarity pushes them beyond reach of standard molecular dynamics,…

In a previous paper [CSIAM Trans. Appl. Math. 2 (2021), 1-55], the authors proposed a theoretical framework for the analysis of RNA velocity, which is a promising concept in scRNA-seq data analysis to reveal the cell state-transition…

Numerical Analysis · Mathematics 2025-02-25 Tiejun Li , Yizhuo Wang , Guoguo Yang , Peijie Zhou

This paper presents an alternative approach for the computation of trajectory segments on slow manifolds of saddle type. This approach is based on iterative methods rather than collocation-type methods. Compared to collocation methods, that…

Dynamical Systems · Mathematics 2015-05-07 Kristian Uldall Kristiansen

Graph neural networks (GNNs) have achieved remarkable success in molecular property prediction. However, traditional graph representations struggle to effectively encode the inherent 3D spatial structures of molecules, as molecular…

Machine Learning · Computer Science 2025-07-03 Dian Jin
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