EPISOL: A Software Package with Expanded Functions to Perform 3D-RISM Calculations for the Solvation of Chemical and Biological Molecules

Integral equation theory (IET) provides an effective solvation model for chemical and biological systems that balances computational efficiency and accuracy. We present a new software package, the Expanded Package for IET-based Solvation (EPISOL), that performs 3D-reference interaction site model (3D-RISM) calculations to obtain the solvation structure and free energies of solute molecules in different solvents. In EPISOL, we have implemented 22 different closures, multiple free energy functionals, and new variations of 3D-RISM theory, including the recent hydrophobicity-induced density inhomogeneity (HI) theory for hydrophobic solutes and ion-dipole correction (IDC) theory for negatively charged solutes. To speed up the convergence and enhance the stability of the self-consistent iterations, we have introduced several numerical schemes in EPISOL, including a newly developed dynamic mixing approach. We show that these schemes have significantly reduced the failure rate of 3D-RISM calculations compared to AMBER-RISM software. EPISOL consists of both a user-friendly graphic interface and a kernel library that allows users to call its routines and adapt them to other programs. EPISOL is compatible with the force-field and coordinate files from both AMBER and GROMACS simulation packages. Moreover, EPISOL is equipped with an internal memory control to efficiently manage the use of physical memory, making it suitable for performing calculations on large biomolecules. We demonstrate that EPISOL can efficiently and accurately calculate solvation density distributions around various solute molecules (including a protein chaperone consisting of 120,715 atoms) and obtain solvent free energy for a wide range of organic compounds. We expect that EPISOL can be widely applied as a solvation model for chemical and biological systems. EPISOL is available at https://github.com/EPISOLrelease/EPISOL

Integrative Generalized Master Equation: A Theory to Study Long-timescale Biomolecular Dynamics via the Integrals of Memory Kernels

The generalized master equation (GME) provides a powerful approach to study biomolecular dynamics via non-Markovian dynamic models built from molecular dynamics (MD) simulations. Previously, we have implemented the GME for biomolecular dynamics, namely the quasi Markov State Model (qMSM), where we explicitly calculate the memory kernel and propagate protein dynamics using a discretized GME. qMSM can be constructed with much shorter MD simulation trajectories than the Markov State Model (MSM). However, since qMSM needs to explicitly compute the time-dependent memory kernels, it is heavily affected by the numerical fluctuations of simulation data when applied to study complicated conformational changes of biomolecules. This can lead to numerical instability of predicted long-time dynamics, greatly limiting the applicability of the qMSM in complicated molecules. In this paper, we propose a new theory, the Integrative GME (IGME), to overcome the challenges of the qMSM by using the time integrations of memory kernels. The IGME avoids the numerical instability induced by explicit computation of time-dependent memory kernel functions, giving more robust predictions of long-time dynamics. Using our analytical solution of the IGME, we propose a new approach to compute memory kernels and long-time dynamics in a numerically stable, accurate and efficient way. To demonstrate its effectiveness, we have applied the IGME in three biomolecules: the alanine dipeptide, the FIP35 WW domain, and Taq RNAP. In each system, the IGME achieves significantly smaller fluctuations for both memory kernels and long-time dynamics compared to the qMSM. We anticipate that the IGME can be widely applied to investigate complex conformational changes of biomolecules

Memory unlocks the future of biomolecular dynamics: Transformative tools to uncover physical insights accurately and efficiently

Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov State Model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this perspective, we discuss how memory can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov State Models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov State Models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities

Kinetic network models to elucidate aggregation dynamics of aggregation‐induced emission systems

Abstract Aggregation‐induced emission (AIE) is a phenomenon where a molecule that is weakly or non‐luminescent in a diluted solution becomes highly emissive when aggregated. AIE luminogens (AIEgens) hold promise in diverse applications like bioimaging, chemical sensing, and optoelectronics. Investigation in AIE luminescence is also critical for understanding aggregation kinetics as the aggregation process is an essential component of AIE emission. Experimental investigation of AIEgen aggregation is challenging due to the fast timescale of the aggregation and the amorphous aggregate structures. Computer simulations such as molecular dynamics (MD) simulation provide a valuable approach to complement experiments with atomic‐level knowledge to study the fast dynamics of aggregation processes. However, individual simulations still struggle to systematically elucidate heterogeneous kinetics of the formation of amorphous AIEgen aggregates. Kinetic network models (KNMs), constructed from an ensemble of MD simulations, hold great potential in addressing this challenge. In these models, dynamic processes are modeled as a series of Markovian transitions occurring among metastable conformational states at discrete time intervals. In this perspective article, we first review previous studies to characterize the AIEgen aggregation kinetics and their limitations. We then introduce KNMs as a promising approach to elucidate the complex kinetics of aggregations to address these limitations. More importantly, we discuss our perspective on linking the output of KNMs to experimental observations of time‐resolved AIE luminescence. We expect that this approach can validate the computational predictions and provide great insights into the aggregation kinetics for AIEgen aggregates. These insights will facilitate the rational design of improved AIEgens in their applications in biology and materials sciences

A Step-by-step Guide on How to Construct quasi-Markov State Models to Study Functional Conformational Changes of Biological Macromolecules

Conformational changes play an important role for many biomolecules to perform their functions. In recent years, Markov State Model (MSM) has become a powerful tool to investigate these functional conformational changes by predicting long time-scale dynamics from many short molecular dynamics (MD) simulations. In MSM, dynamics are modelled by a first-order master equation, in which a biomolecule undergoes Markovian transitions among conformational states at discrete time intervals, called lag time. The lag time has to be sufficiently long to build a Markovian model, but this parameter is often bound by the length of MD simulations available for estimating the frequency of interstate transitions. To address this challenge, we recently employed the generalized master equation (GME) formalism (e.g., the quasi-Markov State Model or qMSM) to encode the non-Markovian dynamics in a time-dependent memory kernel. When applied to study protein dynamics, our qMSM can be built from MD simulations that are an order-of-magnitude shorter than MSM would have required. The construction of qMSM is more complicated than that of MSMs, as time-dependent memory kernels need to be properly extracted from the MD simulation trajectories. Here, we present a step-by-step guide on how to build qMSM from MD simulation datasets, and the materials accompanying this protocol are publicly available on Github: https://github.com/ykhdrew/qMSM_tutorial. We hope this protocol is useful for researchers who want to apply qMSM and study functional conformational changes in biomolecules

Effects of the <i>CDC10</i> (<i>Septin 7</i>) Gene on the Proliferation and Differentiation of Bovine Intramuscular Preadipocyte and 3T3-L1 Cells

Intramuscular fat content and marbling affecting meat quality are important economic traits in beef cattle. CDC10 (cell division cycle 10 or Septin 7), a member of the septin family involved in cellular proliferation, was considered as a functional and positional candidate gene for beef marbling. In a previous study, we revealed that the expression levels of CDC10 were also positively correlated with marbling scores in Japanese Black cattle. However, the regulatory mechanism of the CDC10 gene on IMF deposition in cattle remains unclear. In the present study, flow cytometry, EdU proliferation assays, and Oil Red O staining results showed that overexpression of CDC10 could promote the differentiation of bovine intramuscular preadipocyte (BIMP) and 3T3-L1 cells, whereas knockdown of CDC10 resulted in the opposite consequences. Furthermore, quantitative PCR and Western blotting results showed that overexpression of CDC10 could promote the expression levels of adipogenic marker genes PPARγ and C/EBPα at both mRNA and protein levels in BIMP and 3T3-L1 cells, whereas knockdown of CDC10 resulted in the opposite consequences. Our results provide new insights into the regulatory roles of CDC10 in adipocytes in animals

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein–RNA Condensate

Liquid–liquid phase separation mediated by proteins and/or nucleic acids is believed to underlie the formation of many distinct condensed phases, or membraneless organelles, within living cells. These condensates have been proposed to orchestrate a variety of important processes. Despite recent advances, the interactions that regulate the dynamics of molecules within a condensate remain poorly understood. We performed accumulated 564.7 μs all-atom molecular dynamics (MD) simulations (system size ∼200k atoms) of model condensates formed by a scaffold RNA oligomer and a scaffold peptide rich in arginine (Arg). These model condensates contained one of three possible guest peptides: the scaffold peptide itself or a variant in which six Arg residues were replaced by lysine (Lys) or asymmetric dimethyl arginine (ADMA). We found that the Arg-rich peptide can form the largest number of hydrogen bonds and bind the strongest to the scaffold RNA in the condensate, relative to the Lys- and ADMA-rich peptides. Our MD simulations also showed that the Arg-rich peptide diffused more slowly in the condensate relative to the other two guest peptides, which is consistent with a recent fluorescence microscopy study. There was no significant increase in the number of cation–π interactions between the Arg-rich peptide and the scaffold RNA compared to the Lys-rich and ADMA-rich peptides. Our results indicate that hydrogen bonds between the peptides and the RNA backbone, rather than cation–π interactions, play a major role in regulating peptide diffusion in the condensate

Submillisecond Atomistic Molecular Dynamics Simulations Reveal Hydrogen Bond-Driven Diffusion of a Guest Peptide in Protein–RNA Condensate

Liquid–liquid phase separation mediated by proteins and/or nucleic acids is believed to underlie the formation of many distinct condensed phases, or membraneless organelles, within living cells. These condensates have been proposed to orchestrate a variety of important processes. Despite recent advances, the interactions that regulate the dynamics of molecules within a condensate remain poorly understood. We performed accumulated 564.7 μs all-atom molecular dynamics (MD) simulations (system size ∼200k atoms) of model condensates formed by a scaffold RNA oligomer and a scaffold peptide rich in arginine (Arg). These model condensates contained one of three possible guest peptides: the scaffold peptide itself or a variant in which six Arg residues were replaced by lysine (Lys) or asymmetric dimethyl arginine (ADMA). We found that the Arg-rich peptide can form the largest number of hydrogen bonds and bind the strongest to the scaffold RNA in the condensate, relative to the Lys- and ADMA-rich peptides. Our MD simulations also showed that the Arg-rich peptide diffused more slowly in the condensate relative to the other two guest peptides, which is consistent with a recent fluorescence microscopy study. There was no significant increase in the number of cation–π interactions between the Arg-rich peptide and the scaffold RNA compared to the Lys-rich and ADMA-rich peptides. Our results indicate that hydrogen bonds between the peptides and the RNA backbone, rather than cation–π interactions, play a major role in regulating peptide diffusion in the condensate