Biology subjects

Zerze, G.

Publications and source records attributed to Zerze, G..

5 recordsLinked to original sources

bioRxiv2024

Optimizing On-the-Fly Probability Enhanced Sampling for Complex RNA Systems: Sampling Free Energy Surfaces of an H-Type Pseudoknot

All-atom molecular dynamics (MD) simulations offer crucial insights into biomolecular dynamics, but inherent time scale constraints often limit their effectiveness. Advanced sampling techniques help overcome these limitations, enabling predictions of deeply rugged folding free energy surfaces (FES) of RNAs at atomistic resolution. The Multithermal-Multiumbrella On-the-Fly Probability Enhanced Sampling (MM-OPES) method, which combines temperature and collective variables (CVs) to accelerate sampling, has shown promise and cost-effectiveness. However, the applications have so far been limited to simpler RNA systems, such as stem-loops. In this study, we optimized the MM-OPES method to explore the FES of an H-type RNA pseudoknot, a more complex fundamental RNA folding unit. Through systematic exploration of CV combinations and temperature ranges, we identified an optimal strategy for both sampling and analysis. Our findings demonstrate that treating the native-like contacts in two stems as independent CVs and using a temperature range of 300-480 K provides the most effective sampling, while projections onto native Watson-Crick-type hydrogen bond CVs yield the best resolution FES prediction. Additionally, our sampling scheme also revealed various folding/unfolding pathways. This study provides practical insights and detailed decision-making strategies for adopting the MM-OPES method, facilitating its application to complex RNA structures at atomistic resolution. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=165 SRC="FIGDIR/small/620366v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@dd590org.highwire.dtl.DTLVardef@4007f2org.highwire.dtl.DTLVardef@1c9b168org.highwire.dtl.DTLVardef@9e49b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare event problems, the computational costs and/or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information of transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

biophysics↗

bioRxiv2022

Optimizing the Martini 3 force field reveals the effects of the intricate balance between protein-water interaction strength and salt concentration on biomolecular condensate formation

Condensation/dissolution has become a widely acknowledged biological macromolecular assembly phenomenon in subcellular compartmentalization. MARTINI force field offers a coarse-grained protein model with a resolution that preserves molecular details with an explicit (CG) solvent. Despite its relatively higher resolution, it can still achieve condensate formation in reasonable computing time with explicit solvent and ionic species. Therefore, it is highly desirable to tune this force field to be able to reproduce the experimentally observed properties of the condensate formation. In this work, we studied the condensate formation of the low-sequence complexity (LC) domain of FUsed in Sarcoma (FUS) protein using a MARTINI 3 force field by systematically modifying (increasing) the protein-water interaction strength and varying salt concentration. We found that the condensate formation is sensitive both to the protein-water interaction strength and the presence of salt. While the unmodified MARTINI force field yields a complete collapse of proteins into one dense phase (i.e., no dilute phase), we reported a range of modified protein-water interaction strength that is capable of capturing the experimentally found transfer free energy between dense and dilute phases. We also found that the condensates lose their spherical shape upon the addition of salt, especially when the protein-water interactions are weak. Inter-chain amino acid contact map analysis showed one explanation for this observation: the protein-protein contact fraction reduces as salt is added to systems (when the protein-water interactions are weak), consistent with electrostatic screening effects. This reduction might be responsible for the condensates becoming nonspherical upon the addition of salt by reducing the need for minimizing interfacial area. However, as the protein-water interactions become stronger to the extent that makes the transfer free energy agree well with experimentally observed transfer free energy, we found an increase in protein-protein contact fraction upon the addition of salt, consistent with the salting-out effects. Therefore, we concluded that there is an intricate balance between screening effects and salting-out effects upon the addition of salt and this balance is highly sensitive to the strength of protein-water interactions.

biophysics↗

bioRxiv2020

Thermodynamics of DNA Hybridization from Atomistic Simulations

Studying the DNA hybridization equilibrium via brute force molecular dynamics (MD) or commonly used advanced sampling approches is notoriously difficult at atomistic lengthscale. However, besides providing a more realistic modeling of this microscopic phenomenon, atomistic resolution is a necessity for some fundamental research questions, such as the ones related to DNAs chirality. Here, we describe an order parameter-based advanced sampling technique to calculate the free energy surface of hybridization and estimate melting temperature of DNA oligomers at atomistic resolution, using a native topology-based order parameter. We show that the melting temperatures estimated from our atomistic simulations follow an order consistent with the predictions from melting experiments and those from the nearest neighbor model, for a range of DNA sequences of different GC content. Moreover, free energy surfaces and melting temperatures are calculated to be identical for D- and L-enantiomers of Drew-Dickerson dodecamer. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=171 SRC="FIGDIR/small/238485v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1194603org.highwire.dtl.DTLVardef@438a31org.highwire.dtl.DTLVardef@259e47org.highwire.dtl.DTLVardef@1ba82e3_HPS_FORMAT_FIGEXP M_FIG Graphical TOC Entry C_FIG

biophysics↗

bioRxiv2018

Computational modeling highlights disordered Formin Homology 1 domain’s role in profilin-actin transfer

Formins accelerate actin polymerization, assumed to occur through flexible FH1 domain mediated transfer of profilin-actin to the barbed end. To study FH1 properties and address sequence effects including varying length/distribution of profilin-binding proline-rich motifs, we performed all-atom simulations of mouse mDia1, mDia2; budding yeast Bni1, Bnr1; fission yeast Cdc12, For3, and Fus1 FH1s. We find FH1 has flexible regions between high propensity polyproline helix regions. A coarse-grained model retaining sequence-specificity, assuming rigid polyproline segments, describes their size. Multiple profilins and profilin-actin complexes can simultaneously bind, expanding mDia1-FH1, which may be important in cells. Simulations of the barbed end bound to Bni1-FH1-FH2 dimer show the leading FH1 can better transfer profilin or profilin-actin, having decreasing probability with increasing distance from FH2.

biophysics↗