Biology subjects

de Pablo, J. J.

Publications and source records attributed to de Pablo, J. J..

5 recordsLinked to original sources

bioRxiv2026

CRANBERRY: An RNA Dynamics Model with Sugar Puckering and Noncanonical Base Pairing

We introduce a new coarse-grained model ''CRANBERRY'' that incorporates sugar puckering and non-canonical base pairing, two factors central to RNA structure and dynamics, yet rarely included in most coarse-grained models. Our model is parameterized through a contrastive divergence approach, combined with fine-tuning strategies to improve accuracy in generating disordered states, a feature that is critical for the accurate description of thermodynamics. This two-stage training procedure greatly enhances cooperative folding behavior. Due to these advances, the model's predictive performance is comparable to that of all-atom force fields for native-state structural fluctuations. Furthermore, CRANBERRY exhibits better agreement with experimental data on stacking free energies and disordered structures measured by Small Angle X-ray Scattering. In addition, CRANBERRY can reversibly fold tetraloops with a minimum RMSD of 1.4 Angstrom de novo, which continues to be challenging for all-atom models. It predicts melting temperatures in agreement with experimental values, and with a greater cooperativity than all-atom predictions.

biophysics↗

bioRxiv2024

Chromatin Structures from Integrated AI and Polymer Physics Model

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure from indirect measures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

biophysics↗

bioRxiv2024

The Impact of Charge Regulation and Ionic Intranuclear Environment on the Nucleosome Core Particle

We theoretically investigate how the intranuclear environment influences the charge of a nucleosome core particle (NCP) - the fundamental unit of chromatin consisting of DNA wrapped around a core of histone proteins. The molecular-based theory explicitly considers the size, shape, conformations, charges, and chemical states of all molecular species - thereby linking the structural state with the chemical/charged state of the system. We investigate how variations in monovalent and divalent salt concentrations, as well as pH, affect the charge distribution across different regions of an NCP and quantify the impact of charge regulation. The effective charge of an NCP emerges from a delicate and complex balance involving the chemical dissociation equilibrium of the amino acids and the DNA-phosphates, the electrostatic interaction between them, and the translational entropy of the mobile solution ions, i.e., counter ion release and ion condensation. From our results, we note the significant effect of divalent magnesium ions on the charge and electrostatic energy as well as the counterion cloud that surrounds an NCP, as a function of magnesium concentration, charge neutralization, and even charge inversion is predicted - in line with experimental observation of NCPs. The strong Mg-dependence of the nucleosome charge state arises from ion bridges between two DNA-phosphates and one Mg + ion. We demonstrate that to describe and predict the charged state of an NCP properly, it is essential to consider molecular details, such as DNA-phosphate ion condensation and the acid-base equilibrium of the amino acids that comprise the core histone proteins.

biophysics↗

bioRxiv2022

Molecular Insights into the Role of Water in Early-stage Human Amylin Aggregation

Human islet amyloid polypeptide (hIAPP or human amylin) is known to aggregate into amyloid fibrils and is implicated in the development of type II diabetes. Prefibrillar species in particular have been linked to cell loss, prompting detailed investigation of early-stage hIAPP aggregation. Insights into the mechanisms underlying early-stage aggregation and the key intermediate structures formed during aggregation are valuable in understanding disease onset at the molecular level and guiding design of effective therapeutic strategies. Here, we use atomistic molecular dynamics simulations with the finite temperature string method to identify and compare multiple pathways for hIAPP trimer formation in water. We focus on the comparison between trimerization from three disordered hIAPP chains (which we call "3-chain assembly") and trimerization from an hIAPP dimer approached by a single disordered chain (called "2+1 assembly"). We show that trimerization is a process uphill in free energy, regardless of the trimerization mechanism, and that a high free energy barrier of 40 kBT must be crossed in 2+1 assembly compared to a moderate barrier of 12 kBT for 3-chain assembly. We find this discrepancy to originate from differences in molecular-level water interactions involved in the two trimerization scenarios. Furthermore, we find that the more thermodynamically favorable 3-chain assembly begins from a previously identified dimer intermediate exhibiting transient {beta}-sheet character, which is then incorporated into a similar trimer intermediate, suggesting stepwise aggregation dynamics.

biophysics↗

bioRxiv2020

OpenAWSEM with Open3SPN2: a fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations

We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMMs Custom Forces software framework. By utilizing GPUs, we achieve more than a 100-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a CPU. We showcase the benefits of OpenMMs Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsens early Nobel prize winning experiments [1] by using OpenMMs Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology. Author summaryThe cells most important pieces of machinery are large complexes of proteins often along with nucleic acids. From the ribosome, to CRISPR-Cas9, to transcription factors and DNA-wrangling proteins like the SMC-Kleisins, these complexes allow organisms to replicate and enable cells to respond to environmental cues. Computer simulation is a key technology that can be used to connect physical theories with biological reality. Unfortunately, the time and length scales accessible to molecular simulation have not kept pace with our ambition to study the cells molecular factories. Many simulation codes also unfortunately remain effectively locked away from the user community who need to modify them as more of the underlying physics is learned. In this paper, we present OpenAWSEM and Open3SPN2, two new easy-to-use and easy to modify implementations of efficient and accurate coarse-grained protein and DNA simulation forcefields that can now be run hundreds of times faster than before, thereby making studies of large biomolecular machines more facile.

molecular biology↗