Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular Complexes

Bottom-up coarse-graining refers to the development of low-resolution simulation models that are thermodynamically consistent with certain distributions from fully atomistic simulations. Force-matching and relative entropy minimization represent two major, frequently applied methods that allow to develop such bottom-up models. Nevertheless, atomistic simulations can often provide only limited sampling of the phase space. For bottom-up coarse-graining, these limitations may result in overfitting of the atomistic reference data, especially for large molecular complexes, where the learning may be agnostic of the actual affinities between binding partners. As a solution to this problem, we devise a data-driven machine learning hybrid coarse-graining concept that represents a regularized version of the relative entropy minimization approach. We demonstrate that this new approach allows one to develop coarse-grained models for molecular complexes that reproduce the targeted binding affinity but also describe the underlying complex structure accurately. The trained models therefore show diverse behavior as they can undergo frequent unbinding and binding events and are also transferable for simulating entire protein lattices, e.g., for a virus capsid.

MDTAP: a tool to analyze permeation events across membrane proteins

Motivation

Molecular dynamics (MD) simulations provide critical insights into the transport of solutes, solvents, and drug molecules across protein channels embedded in a membrane bilayer. However, identifying and analyzing the permeation events from complex simulation data remains as a challenging and laborious task. Thus, an automated tool that facilitates the capture of permeation events of any molecular type across any channel is essential to streamline MD trajectory analysis and enhance the understanding of biological processes in a timely manner.

Results

Molecular Dynamics Trajectory Analysis of Permeation (MDTAP) is a Linux/Mac-based software that automatically detects permeation events across membrane-embedded protein and nucleic acid channels. The tool accepts trajectories in DCD (CHARMM/NAMD) and PDB format (obtained from any MD simulation package) and employs bash scripts to analyze the input trajectories to characterize the molecular permeation. The efficiency of MDTAP is demonstrated using MD trajectories of Escherichia coli outer membrane protein Wzi and E. coli Aquaporin Z. MDTAP can also analyze permeation across heterogeneous lipid membranes and artificial nucleic acid channels, addressing their growing importance. Thus, MDTAP simplifies trajectory analysis and also reduces the need for manual inspection.

Availability and implementation

MDTAP is open-source and is freely available on GitHub (https://github.com/MBL-lab/MDTAP), including source code, installation instructions, and usage documentation.

Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs

Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. Capturing these ensembles is critical to understanding their biological roles, yet Molecular Dynamics (MD) simulations, though accurate and widely used, are computationally expensive and struggle to sample rare, transient states. Artificial intelligence (AI) offers a transformative alternative, with deep learning (DL) enabling efficient and scalable conformational sampling. They leverage large-scale datasets to learn complex, non-linear, sequence-to-structure relationships, allowing for the modeling of conformational ensembles in IDPs without the constraints of traditional physics-based approaches. Such DL approaches have been shown to outperform MD in generating diverse ensembles with comparable accuracy. Most models rely primarily on simulated data for training and experimental data serves a critical role in validation, aligning the generated conformational ensembles with observable physical and biochemical properties. However, challenges remain, including dependence on data quality, limited interpretability, and scalability for larger proteins. Hybrid approaches combining AI and MD can bridge the gaps by integrating statistical learning with thermodynamic feasibility. Future directions include incorporating physics-based constraints and learning experimental observables into DL frameworks to refine predictions and enhance applicability. AI-driven methods hold significant promise in IDP research, offering novel insights into protein dynamics and therapeutic targeting while overcoming the limitations of traditional MD simulations.

Improved data-driven collective variables for biased sampling through iteration on biased data

Our ability to efficiently sample conformational transitions between two known states of a biomolecule using collective variable (CV) based sampling depends strongly on the choice of the CV. We previously reported a data-driven approach to clustering biomolecular configurations with a probabilistic clustering model termed ShapeGMM. ShapeGMM is a Gaussian Mixture Model in cartesian coordinates, with means and covariances in each cluster representing the harmonic approximation to the conformational ensemble around a metastable state. We subsequently showed that Linear Discriminant Analysis on positions (posLDA) is a good reaction coordinate to characterize the transition between two of these states, and moreover can be biased to produce transitions between the states using Metadynamics-like approaches. However, the quality of these LDA coordinates depends on the amount of data used to characterize the states, and here we demonstrate the ability to systematically improve them using enhanced sampling data. Specifically, we demonstrate that improved CVs for sampling can be generated by iteratively performing biased sampling along a posLDA coordinate and then generating a new shapeGMM model from biased data in the previous iteration. The new coordinates derived from our iterative approach show a substantial improvement in being able to induce transitions between metastable states, and to converge a free energy surface.

Phase Space Invaders' podcast episode with Tamar Schlick: a trajectory from mathematics to biology

We present a transcript of the Phase Space Invaders podcast interview, with Tamar Schlick interviewed by Miłosz Wieczór. The conversation covers topics in computational biophysics and beyond: DNA and RNA research from genome organization to viral RNA frameshifting, transitioning from applied math to biology, developing algorithms and their utility in molecular dynamics and complex multiscale systems, the role of computers in biophysical research, writing reviews and books, collaborating in science, and using long-distance running as a template for building supportive communities.

Prospects for synthetic biology in 21st Century agriculture

Plant synthetic biology has emerged as a transformative field in agriculture, offering innovative solutions to enhance food security, provide resilience to climate change, and transition to sustainable farming practices. By integrating advanced genetic tools, computational modeling, and systems biology, researchers can precisely modify plant genomes to enhance traits such as yield, stress tolerance, and nutrient use efficiency. The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges. Here, we highlight recent advancements and applications of plant synthetic biology in agriculture, focusing on key areas such as photosynthetic efficiency, nitrogen fixation, drought tolerance, pathogen resistance, nutrient use efficiency, biofortification, climate resilience, microbiology engineering, synthetic plant genomes, and the integration of artificial intelligence (AI) with synthetic biology. These innovations aim to maximize resource use efficiency, reduce reliance on external inputs, and mitigate environmental impacts associated with conventional agricultural practices. Despite challenges related to regulatory approval and public acceptance, the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems, contributing to global food security and environmental sustainability. Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.

Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value. However, the initial belief might be unknown to the user, as it depends on both the technique and the system of study. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data while at the same time finding the best interpretation of the data and the structures compatible with it. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of 12 proteins with coarse physical insights and the binding of peptides with varying affinities to the extraterminal domain using chemical shift perturbation data. We find that subtle differences in data structure (e.g., locally clustered or globally distributed), starting belief, and force field preferences can have an impact on the predictions, limiting the possibility of a transferable protocol across all systems and data types. Nonetheless, we find a wide range of initial setup conditions that will lead to successful sampling and identification of native states, leading to a robust pipeline. Furthermore, disagreements about how much data is enforced and satisfied rapidly serve to identify incorrect setup conditions.

Integrating computational methods and i n vitro experimental validation reveals the pharmacological mechanism of Selaginella bryopteris (L.) Baker targeting major proteins in breast cancer

Breast cancer remains a significant global health challenge, necessitating the exploration of novel therapeutic options. The present study employs an integrated approach encompassing network pharmacology, molecular docking, molecular dynamics simulations, and in-vitro validation to investigate the potential of Selaginella bryopteris in breast cancer treatment. Initial network pharmacology analysis revealed different potential targets and pathways associated with breast cancer that could be modulated by S. bryopteris phytochemical constituents. Molecular docking and dynamics simulations further elucidated the stability and dynamics of protein-ligand complexes (lanaroflavone-EGFR and sequoiaflavone-CTNNB1). The in-vitro assays demonstrated the ability of S. bryopteris crude extract to inhibit cancer cell growth (IC50 - 78.34 μg/mL) migration and invasion, supporting the computational predictions. The integrated approach employed in the present study offers a robust framework for the systematic exploration of S. bryopteris in drug discovery as a promising candidate for breast cancer treatment.

Plant protein-lipid interfaces studied by molecular dynamics simulations

The delineation of protein-lipid interfaces is essential for understanding the mechanisms of various membrane-associated processes crucial to plant development and growth, including signalling, trafficking, and membrane transport. Due to their highly dynamic nature, the precise characterization of lipid-protein interactions by experimental techniques is challenging. Molecular dynamics simulations provide a powerful computational alternative with a spatial-temporal resolution allowing the atomistic-level description. In this review, we aim to introduce plant scientists to molecular dynamics simulations. We describe different steps of performing molecular dynamics simulations and provide a broad survey of molecular dynamics studies investigating plant protein-lipid interfaces. Our aim is also to illustrate that combining molecular dynamics simulations with artificial intelligence-based protein structure determination opens up unprecedented possibilities for future investigations of dynamic plant protein-lipid interfaces.

Adjusting the Energy Profile for CH-O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

Molecular simulation approaches to probing the effects of mechanical forces in the actin cytoskeleton

In this article we give our perspective on the successes and promise of various molecular and coarse-grained simulation approaches to probing the effect of mechanical forces in the actin cytoskeleton.

Advancing biomolecular simulation through exascale HPC, AI and quantum computing

Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Concentration Fluctuation/Microheterogeneity Duality Illustrated with Aqueous 1,4-Dioxane Mixtures

The structural properties of aqueous 1-4 dioxane mixtures are studied by computer simulations of different water and dioxane force field models, from the perspective of illustrating the link between structural properties at the molecular level and measurable properties such as radiation scattering intensities and Kirkwood-Buff integrals (KBIs). A strategy to consistently correct the KBI obtained from simulations is proposed, which allows us to obtain the genuine KBI corresponding to a given pair of molecular species, in the entire concentration range, and without necessitating excessively large system sizes. The application of this method to the aqueous dioxane mixtures, with an all-atom CHARMM dioxane model and 2 water models, namely, SPC/E and TIP3P, allows one to understand the differences in the structure of the corresponding mixtures at the molecular level, particularly concerning the role of the water aggregates and its model dependence. This study allows us to characterize the dual role played by the concentration fluctuations and the domain segregation, particularly in what concerns the calculated X-ray spectra.

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology

Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

Improved Prediction of Molecular Response to Pulling by Combining Force Tempering with Replica Exchange Methods

Small mechanical forces play important functional roles in many crucial cellular processes, including in the dynamic behavior of the cytoskeleton and in the regulation of osmotic pressure through membrane-bound proteins. Molecular simulations offer the promise of being able to design the behavior of proteins that sense and respond to these forces. However, it is difficult to predict and identify the effect of the relevant piconewton (pN) scale forces due to their small magnitude. Previously, we introduced the Infinite Switch Simulated Tempering in Force (FISST) method, which allows one to estimate the effect of a range of applied forces from a single molecular dynamics simulation, and also demonstrated that FISST additionally accelerates sampling of a molecule's conformational landscape. For some problems, we find that this acceleration is not sufficient to capture all relevant conformational fluctuations, and hence, here we demonstrate that FISST can be combined with either temperature replica exchange or solute tempering approaches to produce a hybrid method that enables more robust prediction of the effect of small forces on molecular systems.

Improving the Accuracy of Physics-Based Hydration-Free Energy Predictions by Machine Learning the Remaining Error Relative to the Experiment

The accuracy of computational models of water is key to atomistic simulations of biomolecules. We propose a computationally efficient way to improve the accuracy of the prediction of hydration-free energies (HFEs) of small molecules: the remaining errors of the physics-based models relative to the experiment are predicted and mitigated by machine learning (ML) as a postprocessing step. Specifically, the trained graph convolutional neural network attempts to identify the "blind spots" in the physics-based model predictions, where the complex physics of aqueous solvation is poorly accounted for, and partially corrects for them. The strategy is explored for five classical solvent models representing various accuracy/speed trade-offs, from the fast analytical generalized Born (GB) to the popular TIP3P explicit solvent model; experimental HFEs of small neutral molecules from the FreeSolv set are used for the training and testing. For all of the models, the ML correction reduces the resulting root-mean-square error relative to the experiment for HFEs of small molecules, without significant overfitting and with negligible computational overhead. For example, on the test set, the relative accuracy improvement is 47% for the fast analytical GB, making it, after the ML correction, almost as accurate as uncorrected TIP3P. For the TIP3P model, the accuracy improvement is about 39%, bringing the ML-corrected model's accuracy below the 1 kcal/mol threshold. In general, the relative benefit of the ML corrections is smaller for more accurate physics-based models, reaching the lower limit of about 20% relative accuracy gain compared with that of the physics-based treatment alone. The proposed strategy of using ML to learn the remaining error of physics-based models offers a distinct advantage over training ML alone directly on reference HFEs: it preserves the correct overall trend, even well outside of the training set.

Hybrid computational methods combining experimental information with molecular dynamics

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

Brownian dynamics simulations of mesoscale chromatin fibers

The relationship between chromatin architecture and function defines a central problem in biology and medicine. Many computational chromatin models with atomic, coarse-grained, mesoscale, and polymer resolution have been used to shed light onto the mechanisms that dictate genome folding and regulation of gene expression. The associated simulation techniques range from Monte Carlo to molecular, Brownian, and Langevin dynamics. Here, we present an efficient Compute Unified Device Architecture (CUDA) implementation of Brownian dynamics (BD) to simulate chromatin fibers at the nucleosome resolution with our chromatin mesoscale model. With the CUDA implementation for computer architectures with graphic processing units (GPUs), we significantly accelerate compute-intensive hydrodynamic tensor calculations in the BD simulations by massive parallelization, boosting the performance a hundred-fold compared with central processing unit calculations. We validate our BD simulation approach by reproducing experimental trends on fiber diffusion and structure as a function of salt, linker histone binding, and histone-tail composition, as well as Monte Carlo equilibrium sampling results. Our approach proves to be physically accurate with performance that makes feasible the study of chromatin fibers in the range of kb or hundreds of nucleosomes (small gene). Such simulations are essential to advance the study of biological processes such as gene regulation and aberrant genome-structure related diseases.

CHARMM-GUI PDB Manipulator: Various PDB Structural Modifications for Biomolecular Modeling and Simulation

Molecular modeling and simulation play important roles in biomedical research as they provide molecular-level insight into the underlying mechanisms of biological functions that are difficult to elucidate only with experiments. CHARMM-GUI (https://charmm-gui.org) is a web-based cyberinfrastructure that is widely used to generate various molecular simulation system and input files and thus facilitates and standardizes the usage of common and advanced simulation techniques. In particular, PDB Manipulator provides various chemical modification options as the starting point for most input generation modules in CHARMM-GUI. Here, we discuss recent additions to PDB Manipulator, such as non-standard amino acids/RNA substitutions, ubiquitylation and SUMOylation, Lys/Arg post-translational modifications, lipidation, peptide stapling, and improved parameterization options of small molecules. These additional features are expected to make complex PDB modifications easy for biomolecular modeling and simulation.

Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding

DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.

Structural intrinsic disorder in a functionalized potyviral coat protein as a main viability determinant of its assembled nanoparticles

The viability of viral-derived nanoparticles (virions and VLPs) aimed to nanobiotechnological functionalizations of the coat protein (CP) of turnip mosaic virus has been studied by means of advanced computational methodologies that include molecular dynamics. The study has allowed to model the structure of the complete CP and its functionalization with three different peptides and obtain essential structural features such as order/disorder, interactions, and electrostatic potentials of their constituent domains. The results provide for the first time a dynamic view of a complete potyvirus CP, since experimental available structures so far obtained lack N- and C-terminal segments. The relevance of disorder in the most distal N-terminal subdomain, and the interaction of the less distal N-terminal subdomain with the highly ordered CP core, stand out as crucial characteristic for a viable CP. Preserving them proved of outmost importance to obtain viable potyviral CPs presenting peptides at their N-terminus.

Genome modeling: From chromatin fibers to genes

The intricacies of the 3D hierarchical organization of the genome have been approached by many creative modeling studies. The specific model/simulation technique combination defines and restricts the system and phenomena that can be investigated. We present the latest modeling developments and studies of the genome, involving models ranging from nucleosome systems and small polynucleosome arrays to chromatin fibers in the kb-range, chromosomes, and whole genomes, while emphasizing gene folding from first principles. Clever combinations allow the exploration of many interesting phenomena involved in gene regulation, such as nucleosome structure and dynamics, nucleosome-nucleosome stacking, polynucleosome array folding, protein regulation of chromatin architecture, mechanisms of gene folding, loop formation, compartmentalization, and structural transitions at the chromosome and genome levels. Gene-level modeling with full details on nucleosome positions, epigenetic factors, and protein binding, in particular, can in principle be scaled up to model chromosomes and cells to study fundamental biological regulation.

Effects of External Perturbations on Protein Systems: A Microscopic View

Protein folding can be viewed as the origami engineering of biology resulting from the long process of evolution. Even decades after its recognition, research efforts worldwide focus on demystifying molecular factors that underlie protein structure-function relationships; this is particularly relevant in the era of proteopathic disease. A complex co-occurrence of different physicochemical factors such as temperature, pressure, solvent, cosolvent, macromolecular crowding, confinement, and mutations that represent realistic biological environments are known to modulate the folding process and protein stability in unique ways. In the current review, we have contextually summarized the substantial efforts in unveiling individual effects of these perturbative factors, with major attention toward bottom-up approaches. Moreover, we briefly present some of the biotechnological applications of the insights derived from these studies over various applications including pharmaceuticals, biofuels, cryopreservation, and novel materials. Finally, we conclude by summarizing the challenges in studying the combined effects of multifactorial perturbations in protein folding and refer to complementary advances in experiment and computational techniques that lend insights to the emergent challenges.

Fast Polarizable Water Model for Atomistic Simulations

Simulating water accurately has been a major challenge in atomistic simulations for decades. Inclusion of electronic polarizability effects holds considerable promise, yet existing approaches suffer from significant computational overheads compared to the widely used nonpolarizable water models. We have developed a globally optimal polarizable water model, OPC3-pol, that explicitly accounts for electronic polarizability with minimal impact on the computational efficiency. OPC3-pol reproduces five key bulk water properties at room temperature with an average relative error of 0.6%. In atomistic simulations, OPC3-pol's computational efficiency is in between that of 3- and 4-point nonpolarizable models; the model supports increased (4 fs) integration time step. OPC3-pol is tested in simulations of globular protein ubiquitin and a B-DNA dodecamer with several AMBER force fields, ff99SB, ff14SB, ff19SB, and OL15, demonstrating structure stability close to reference on multi-microsecond time scale. Simulation of an intrinsically disordered amyloid β-peptide yields an ensemble with the radius of gyration of a random coil. The proposed water model can be trivially adopted by any package that supports standard nonpolarizable force fields and water models; its intended use is in long classical atomistic simulations where water polarization effects are expected to be important.

Computer-aided molecular modeling and structural analysis of the human centromere protein–HIKM complex

Background

Protein–peptide and protein–protein interactions play an essential role in different functional and structural cellular organizational aspects. While Cryo-EM and X-ray crystallography generate the most complete structural characterization, most biological interactions exist in biomolecular complexes that are neither compliant nor responsive to direct experimental analysis. The development of computational docking approaches is therefore necessary. This starts from component protein structures to the prediction of their complexes, preferentially with precision close to complex structures generated by X-ray crystallography.

Results

To guarantee faithful chromosomal segregation, there must be a proper assembling of the kinetochore (a protein complex with multiple subunits) at the centromere during the process of cell division. As an important member of the inner kinetochore, defects in any of the subunits making up the CENP-HIKM complex lead to kinetochore dysfunction and an eventual chromosomal mis-segregation and cell death. Previous studies in an attempt to understand the assembly and mechanism devised by the CENP-HIKM in promoting the functionality of the kinetochore have reconstituted the protein complex from different organisms including fungi and yeast. Here, we present a detailed computational model of the physical interactions that exist between each component of the human CENP-HIKM, while validating each modeled structure using orthologs with existing crystal structures from the protein data bank.

Conclusions

Results from this study substantiate the existing hypothesis that the human CENP-HIK complex shares a similar architecture with its fungal and yeast orthologs, and likewise validate the binding mode of CENP-M to the C-terminus of the human CENP-I based on existing experimental reports.

Graphical abstract

Progress in Simulation Studies of Insulin Structure and Function

Insulin is a peptide hormone known for chiefly regulating glucose level in blood among several other metabolic processes. Insulin remains the most effective drug for treating diabetes mellitus. Insulin is synthesized in the pancreatic β-cells where it exists in a compact hexameric architecture although its biologically active form is monomeric. Insulin exhibits a sequence of conformational variations during the transition from the hexamer state to its biologically-active monomer state. The structural transitions and the mechanism of action of insulin have been investigated using several experimental and computational methods. This review primarily highlights the contributions of molecular dynamics (MD) simulations in elucidating the atomic-level details of conformational dynamics in insulin, where the structure of the hormone has been probed as a monomer, dimer, and hexamer. The effect of solvent, pH, temperature, and pressure have been probed at the microscopic scale. Given the focus of this review on the structure of the hormone, simulation studies involving interactions between the hormone and its receptor are only briefly highlighted, and studies on other related peptides (e.g., insulin-like growth factors) are not discussed. However, the review highlights conformational dynamics underlying the activities of reported insulin analogs and mimetics. The future prospects for computational methods in developing promising synthetic insulin analogs are also briefly highlighted.

Finite element modeling of alpha-helices and tropocollagen molecules with reference to the spike of SARS-CoV-2

The newly developed finite element modeling at the atomic scale was used to predict the static and dynamic response of the alpha-helix (AH) and tropocollagen (TC) protein fragments, the main building blocks of the spike of the SARS-CoV-2. The geometry and morphology of the spike's stalk and its connection to the viral envelope were determined from the combination of most recent Molecular Dynamics simulation and images of Cryo-Electron microscope. The stiffness parameters of the covalent bonds in the main chain of the helix were taken from the literature. The AH and TC were modeled using both beam elements (wire model) and shell elements (ribbon model) in finite element analysis to predict their mechanical properties under tension. The asymptotic stiffening features of AH and TC under tensile loading were revealed and compared with a new analytical solution. The mechanical stiffnesses under other loading conditions, including compression, torsion and bending were also predicted numerically and correlated with the results of the existing MD simulations and tests. The mode shapes and natural frequencies of the spike were predicted using the built FE model. The frequencies were shown to be within the safe range of 1-20 MHz routinely used for medical imaging and diagnosis by means of ultrasound. These results provide a solid theoretical basis for using ultrasound to study damaging coronavirus through transient and resonant vibration at large deformations.

Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers

Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics. Contacts are derived from trRosetta-predicted distance histograms (distograms) and are integrated into MELD's atomistic MD as spatial restraints through parametrized potential functions. In the CASP14 blind prediction event, ML x MELD x MD predicted 13 native structures to better than 4.5 Å error, including for 10 proteins in the range of 115-250 amino acids long. Also, the scaling of simulation time vs protein length is much better than unguided MD: tsim ∼ e0.023N for ML x MELD x MD vs tsim ∼ e0.168N for MD alone. This shows how machine learning information can be leveraged to advance physics-based modeling of proteins.

Searching for Low Probability Opening Events in a DNA Sliding Clamp

The β subunit of E. coli DNA polymererase III is a DNA sliding clamp associated with increasing the processivity of DNA synthesis. In its free form, it is a circular homodimer structure that can accomodate double-stranded DNA in a nonspecific manner. An open state of the clamp must be accessible before loading the DNA. The opening mechanism is still a matter of debate, as is the effect of bound DNA on opening/closing kinetics. We use a combination of atomistic, coarse-grained, and enhanced sampling strategies in both explicit and implicit solvents to identify opening events in the sliding clamp. Such simulations of large nucleic acid and their complexes are becoming available and are being driven by improvements in force fields and the creation of faster computers. Different models support alternative opening mechanisms, either through an in-plane or out-of-plane opening event. We further note some of the current limitations, despite advances, in modeling these highly charged systems with implicit solvent.

Recent Advances in Structure, Function, and Pharmacology of Class A Lipid GPCRs: Opportunities and Challenges for Drug Discovery

Great progress has been made over the past decade in understanding the structural, functional, and pharmacological diversity of lipid GPCRs. From the first determination of the crystal structure of bovine rhodopsin in 2000, much progress has been made in the field of GPCR structural biology. The extraordinary progress in structural biology and pharmacology of GPCRs, coupled with rapid advances in computational approaches to study receptor dynamics and receptor-ligand interactions, has broadened our comprehension of the structural and functional facets of the receptor family members and has helped usher in a modern age of structure-based drug design and development. First, we provide a primer on lipid mediators and lipid GPCRs and their role in physiology and diseases as well as their value as drug targets. Second, we summarize the current advancements in the understanding of structural features of lipid GPCRs, such as the structural variation of their extracellular domains, diversity of their orthosteric and allosteric ligand binding sites, and molecular mechanisms of ligand binding. Third, we close by collating the emerging paradigms and opportunities in targeting lipid GPCRs, including a brief discussion on current strategies, challenges, and the future outlook.

Biomolecular modeling thrives in the age of technology

The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.

A MULTISCALE VISION-ILLUSTRATIVE APPLICATIONS FROM BIOLOGY TO ENGINEERING

Modeling and simulation have quickly become equivalent pillars of research along with traditional theory and experimentation. The growing realization that most complex phenomena of interest span many orders of spatial and temporal scales has led to an exponential rise in the development and application of multiscale modeling and simulation over the past two decades. In this perspective, the associate editors of the International Journal for Multiscale Computational Engineering and their co-workers illustrate current applications in their respective fields spanning biomolecular structure and dynamics, civil engineering and materials science, computational mechanics, aerospace and mechanical engineering, and more. Such applications are highly tailored, exploit the latest and ever-evolving advances in both computer hardware and software, and contribute significantly to science, technology, and medical challenges in the 21st century.

Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems

ABSTRACT

A dynamical approach to nonequilibrium molecular dynamics (D-NEMD), proposed in the 1970s by Ciccotti et al., is undergoing a renaissance and is having increasing impact in the study of biological macromolecules. This D-NEMD approach, combining MD simulations in stationary (in particular, equilibrium) and nonequilibrium conditions, allows for the determination of the time-dependent structural response of a system using the Kubo-Onsager relation. Besides providing a detailed picture of the system's dynamic structural response to an external perturbation, this approach also has the advantage that the statistical significance of the response can be assessed. The D-NEMD approach has been used recently to identify a general mechanism of inter-domain signal propagation in nicotinic acetylcholine receptors, and allosteric effects in β -lactamase enzymes, for example. It complements equilibrium MD and is a very promising approach to identifying and analysing allosteric effects. Here, we review the D-NEMD approach and its application to biomolecular systems, including transporters, receptors, and enzymes.