Molecular dynamics simulations of large macromolecular complexes

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins' function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software's uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

Molecular Dynamics of Apolipoprotein Genotypes APOE4 and SNARE Family Proteins and Their Impact on Alzheimer's Disease

Alzheimer's disease is a chronic neurodegenerative disorder characterized by progressive memory loss and a significant impact on quality of life. The APOE ε4 allele is a major genetic contributor to AD pathogenesis, with synaptic dysfunction being a central hallmark in its pathophysiology. While the role of APOE4 in reducing SNARE protein levels has been established, the underlying molecular mechanisms of this interaction remain obscure. Our research employs molecular dynamics simulations to analyze interactions between APOE4 and APOE3 isoforms and the synaptic proteins VAMP2, SNAP25, and SYNTAXIN1, which play crucial roles in the presynaptic membrane. Our findings reveal that APOE4 significantly destabilizes the SNARE complex, suppresses its structural dynamics, and reduces hydrogen bonding, consequently partially hindering neurotransmitter release-a very likely discovery for elucidating synaptic dysfunction in Alzheimer's disease. We identified that APOE4 exhibits a diminished affinity for the SNARE complex in comparison to APOE3. This observation suggests that APOE4 may play a role in modulating the stability of the SNARE complex, potentially impacting the progression and occurrence of Alzheimer's disease through free energy analysis. This work highlights the perturbations in synaptic function mediated by APOE4, which may offer novel insights into the molecular underpinnings of AD. By elucidating the molecular interplay between APOE4 and the SNARE complex, our study not only enhances our comprehension of AD's synaptic pathology but also paves the way for devising innovative therapeutic interventions, such as targeting the APOE4-SNARE complex interaction or to restore neurotransmitter release.

On the specificity of the recognition of m6A-RNA by YTH reader domains

Most processes of life are the result of polyvalent interactions between macromolecules, often of heterogeneous types and sizes. Frequently, the times associated with these interactions are prohibitively long for interrogation using atomistic simulations. Here, we study the recognition of N6-methylated adenine (m6A) in RNA by the reader domain YTHDC1, a prototypical, cognate pair that challenges simulations through its composition and required timescales. Simulations of RNA pentanucleotides in water reveal that the unbound state can impact (un)binding kinetics in a manner that is both model- and sequence-dependent. This is important because there are two contributions to the specificity of the recognition of the Gm6AC motif: from the sequence adjacent to the central adenine and from its methylation. Next, we establish a reductionist model consisting of an RNA trinucleotide binding to the isolated reader domain in high salt. An adaptive sampling protocol allows us to quantitatively study the dissociation of this complex. Through joint analysis of a data set including both the cognate and control sequences (GAC, Am6AA, and AAA), we derive that both contributions to specificity, sequence, and methylation, are significant and in good agreement with experimental numbers. Analysis of the kinetics suggests that flexibility in both the RNA and the YTHDC1 recognition loop leads to many low-populated unbinding pathways. This multiple-pathway mechanism might be dominant for the binding of unstructured polymers, including RNA and peptides, to proteins when their association is driven by polyvalent, electrostatic interactions.

BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical datasets. Concurrently, our ability to perform long-timescale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins' function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software's uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

Exploration of lipid bilayer mechanical properties using molecular dynamics simulation

Important biological structures known for their exceptional mechanical qualities, lipid bilayers are essential to many cellular functions. Fluidity, elasticity, permeability, stiffness, tensile strength, compressibility, shear viscosity, line tension, and curvature elasticity are some of the fundamental characteristics affecting their behavior. The purpose of this review is to examine these characteristics in more detail by molecular dynamics simulation, elucidating their importance and the elements that lead to their appearance in lipid bilayers. Comprehending the mechanical characteristics of lipid bilayers is critical for creating medications, drug delivery systems, and biomaterials that interact with biological membranes because it allows one to understand how these materials respond to different stresses and deformations. The influence of mechanical characteristics on important lipid bilayer properties is examined in this review. The mechanical properties of lipid bilayers were clarified through the use of molecular dynamics simulation analysis techniques, including bilayer thickness, stress-strain analysis, lipid bilayer area compressibility, membrane bending rigidity, and time- or ensemble-averaged the area per lipid evaluation. We explain the significance of molecular dynamics simulation analysis methods, providing important new information about the stability and dynamic behavior of the bilayer. In the end, we hope to use molecular dynamics simulation to provide a comprehensive understanding of the mechanical properties and behavior of lipid bilayers, laying the groundwork for further studies and applications. Taken together, careful investigation of these mechanical aspects deepens our understanding of the adaptive capacities and functional roles of lipid bilayers in biological environments.

Binding characteristics and structural dynamics of two general odorant-binding proteins with plant volatiles in the olfactory recognition of Glyphodes pyloalis

Glyphodes pyloalis Walker (Lepidoptera: Pyralidae) is the most destructive pest, causing severe damage to mulberry production in China's sericulture industry. The insecticide application in mulberry orchards poses a significant risk of poisoning to Bombyx mori. Shifting from insecticides to odor attractants is a beneficial alternative, but not much data is available on the olfactory system of G. pyloalis. We identified 114 chemosensory genes from the antennal transcriptome database of G. pyloalis, with 18 odorant-binding protein (OBP) and 17 chemosensory protein (CSP) genes significantly expressed in the antennae. Ligand-binding assays for two antennae-biased expressed general odorant-binding proteins (GOBPs) showed high binding affinities of GOBP1 to hexadecanal, β-ionone, and 2-ethylhexyl acrylate, while GOBP2 exhibited binding to 4-tert-octylphenol, benzyl benzoate, β-ionone, and farnesol. Computational simulations indicated that van der Waal forces predominantly contributed to the binding free energy in the binding processes of complexes. Among them, Phe12 of GOBP1 and Phe19 of GOBP2 were demonstrated to play crucial roles in their bindings to plant volatiles using site-directed mutagenesis experiments. Moreover, hexadecanal and β-ionone attracted G. pyloalis male moths in the behavioral assays, while none of the candidate plant volatiles significantly affected female moths. Our findings provide a comprehensive understanding of the molecular mechanisms underlying olfactory recognition in G. pyloalis, setting the groundwork for novel mulberry pests control strategies based on insect olfaction.

Recent advances in computational prediction of molecular properties in food chemistry

The combination of food chemistry and computational simulation has brought many impacts to food research, moving from experimental chemistry to computer chemistry. This paper will systematically review in detail the important role played by computational simulations in the development of the molecular structure of food, mainly from the atomic, molecular, and multicomponent dimension. It will also discuss how different computational chemistry models can be constructed and analyzed to obtain reliable conclusions. From the calculation principle to case analysis, this paper focuses on the selection and application of quantum mechanics, molecular mechanics and coarse-grained molecular dynamics in food chemistry research. Finally, experiments and computations of food chemistry are compared and summarized to obtain the best balance between them. The above review and outlook will provide an important reference for the intersection of food chemistry and computational chemistry, and is expected to provide innovative thinking for structural research in food chemistry.

Asymmetric crowders and membrane morphology at the nexus of intracellular trafficking and oncology

A definitive understanding of the interplay between protein binding/migration and membrane curvature evolution is emerging but needs further study. The mechanisms defining such phenomena are critical to intracellular transport and trafficking of proteins. Among trafficking modalities, exosomes have drawn attention in cancer research as these nano-sized naturally occurring vehicles are implicated in intercellular communication in the tumor microenvironment, suppressing anti-tumor immunity and preparing the metastatic niche for progression. A significant question in the field is how the release and composition of tumor exosomes are regulated. In this perspective article, we explore how physical factors such as geometry and tissue mechanics regulate cell cortical tension to influence exosome production by co-opting the biophysics as well as the signaling dynamics of intracellular trafficking pathways and how these exosomes contribute to the suppression of anti-tumor immunity and promote metastasis. We describe a multiscale modeling approach whose impact goes beyond the fundamental investigation of specific cellular processes toward actual clinical translation. Exosomal mechanisms are critical to developing and approving liquid biopsy technologies, poised to transform future non-invasive, longitudinal profiling of evolving tumors and resistance to cancer therapies to bring us one step closer to the promise of personalized medicine.

A review of biophysical strategies to investigate protein-ligand binding: What have we employed?

The protein-ligand binding frequently occurs in living organisms and plays a crucial role in the execution of the functions of proteins and drugs. It is also an indispensable part of drug discovery and screening. While the methods for investigating protein-ligand binding are diverse, each has its own objectives, strengths, and limitations, which all influence the choice of method. Many studies concentrate on one or a few specific methods, suggesting that comprehensive summaries are lacking. Therefore in this review, these methods are comprehensively summarized and are discussed in detail: prediction and simulation methods, thermal and thermodynamic methods, spectroscopic methods, methods of determining three-dimensional structures of the complex, mass spectrometry-based methods and others. It is also important to integrate these methods based on the specific objectives of the research. With the aim of advancing pharmaceutical research, this review seeks to deepen the understanding of the protein-ligand binding process.

MDverse, shedding light on the dark matter of molecular dynamics simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11β1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein-ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein-ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein-ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure-activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Interactions of a Novel Anthracycline with Oligonucleotide DNA and Cyclodextrins in an Aqueous Environment

Berubicin, a chemotherapy medication belonging to the class of anthracyclines, is simulated in double-stranded DNA sequences and cyclodextrins in an aqueous environment via full-atom molecular dynamics simulations on the time scale of microseconds. The drug is studied in both the neutral and protonated states so as to better comprehend the role of its charge in the formed complexes. The noncovalent berubicin-DNA and berubicin-cyclodextrin complexes are investigated in detail, paying special attention to their thermodynamic description by employing the double decoupling method, the solvent balance method, the weighted solvent accessible surface model, and the linear interaction energy method. A novel approach for extracting the desolvation thermodynamics of the binding process is also presented. Both the binding and desolvation Gibbs energies are decomposed into entropic and enthalpic contributions so as to elucidate the nature of complexation and its driving forces. Selected structural and geometrical properties of all the complexes, which are all stable, are analyzed. Both cyclodextrins under consideration are widely utilized for drug delivery purposes, and a comparative investigation between their bound states with berubicin is carried out.

Modeling reveals the strength of weak interactions in stacked-ring assembly

Cells employ many large macromolecular machines for the execution and regulation of processes that are vital for cell and organismal viability. Interestingly, cells cannot synthesize these machines as functioning units. Instead, cells synthesize the molecular parts that must then assemble into the functional complex. Many important machines, including chaperones such as GroEL and proteases such as the proteasome, comprise protein rings that are stacked on top of one another. While there is some experimental data regarding how stacked-ring complexes such as the proteasome self-assemble, a comprehensive understanding of the dynamics of stacked-ring assembly is currently lacking. Here, we developed a mathematical model of stacked-trimer assembly and performed an analysis of the assembly of the stacked homomeric trimer, which is the simplest stacked-ring architecture. We found that stacked rings are particularly susceptible to a form of kinetic trapping that we term "deadlock," in which the system gets stuck in a state where there are many large intermediates that are not the fully assembled structure but that cannot productively react. When interaction affinities are uniformly strong, deadlock severely limits assembly yield. We thus predicted that stacked rings would avoid situations where all interfaces in the structure have high affinity. Analysis of available crystal structures indicated that indeed the majority-if not all-of stacked trimers do not contain uniformly strong interactions. Finally, to better understand the origins of deadlock, we developed a formal pathway analysis and showed that, when all the binding affinities are strong, many of the possible pathways are utilized. In contrast, optimal assembly strategies utilize only a small number of pathways. Our work suggests that deadlock is a critical factor influencing the evolution of macromolecular machines and provides general principles for understanding the self-assembly efficiency of existing machines.

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD files in generalist data repositories, constituting the dark matter of MD - data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 files and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on files produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identified systems with specific molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Polysaccharides screening for pulmonary mucus penetration by molecular dynamics simulation and in vitro verification

Mucus penetration is one of the physiologic barriers of inhalation and nanocarriers can effectively facilitate the permeation of drugs. The interactions between the nanocarriers and mucin are crucial for penetration across the mucus layer on the respiratory tract. In this study, we proposed a molecular dynamics (MD) simulation method for the screening of polysaccharides that acted as the surface modification materials for inhalable nano-preparations to facilitate mucus penetration. MD revealed all-atom interactions between the monomers of polysaccharides, including dextran (DEX)/hyaluronic acid (HA)/carboxymethyl chitosan (CMCS) and the human mucin protein MUC5AC (hMUC5AC). The obtained data showed that DEX formed stronger non-covalent bonds with hMUC5AC compared to HA and CMCS, which suggested that HA and CMCS had better mucus permeability than DEX. For the in vitro verification, HA/CMCS-coated liposomes and DEX/PEG-inserted liposomes were prepared. The results of mucin interactions and mucus penetration studies confirmed that HA and CMCS possessed the weakest interactions with mucin and facilitated the mucus penetration, which was in consistent with the data from MD simulation. This work may shed light on the MD simulation-based screening of surface modification materials for inhalable nano-preparations to facilitate mucus penetration.

Binding characteristics of pheromone-binding protein 1 in Glyphodes pyloalis to organophosphorus insecticides: Insights from computational and experimental approaches

Glyphodes pyloalis (Lepidoptera: Pyralidae) is one of the major pests in mulberry production in China, which has developed resistance to various insecticides. Chemoreception is one of the most crucial physiological tactics in insects, playing a pivotal role in recognizing chemical stimuli in the environment, including noxious stimuli such as insecticides. Herein, we obtained recombinant pheromone-binding protein 1 (GpylPBP1) that exhibited antennae-biased expression in G. pyloalis. Ligand-binding assays indicated that GpylPBP1 had the binding affinities to two organophosphorus insecticides, with a higher binding affinity to chlorpyrifos than to phoxim. Computational simulations showed that a mass of nonpolar amino acid residues formed the binding pocket of GpylPBP1 and contributed to the hydrophobic interactions in the bindings of GpylPBP1 to both insecticides. Furthermore, the binding affinities of three GpylPBP1 mutants (F12A, I52A, and F118A) to both insecticides were all significantly reduced compared to those of the GpylPBP1-wild type, suggesting that Phe12, Ile52, and Phe118 residues were crucial binding sites and played crucial roles in the bindings of GpylPBP1 to both insecticides. Our findings can be instrumental in elucidating the effects of insecticides on olfactory recognition in moths and facilitating the development of novel pest management strategies using PBPs as targets based on insect olfaction.

Formalizing Coarse-Grained Representations of Anisotropic Interactions at Multimeric Protein Interfaces Using Virtual Sites

Molecular simulations of biomacromolecules that assemble into multimeric complexes remain a challenge due to computationally inaccessible length and time scales. Low-resolution and implicit-solvent coarse-grained modeling approaches using traditional nonbonded interactions (both pairwise and spherically isotropic) have been able to partially address this gap. However, these models may fail to capture the complex anisotropic interactions present at macromolecular interfaces unless higher-order interaction potentials are incorporated at the expense of the computational cost. In this work, we introduce an alternate and systematic approach to represent directional interactions at protein-protein interfaces by using virtual sites restricted to pairwise interactions. We show that virtual site interaction parameters can be optimized within a relative entropy minimization framework by using only information from known statistics between coarse-grained sites. We compare our virtual site models to traditional coarse-grained models using two case studies of multimeric protein assemblies and find that the virtual site models predict pairwise correlations with higher fidelity and, more importantly, assembly behavior that is morphologically consistent with experiments. Our study underscores the importance of anisotropic interaction representations and paves the way for more accurate yet computationally efficient coarse-grained simulations of macromolecular assembly in future research.

AMBERff at Scale: Multimillion-Atom Simulations with AMBER Force Fields in NAMD

All-atom molecular dynamics (MD) simulations are an essential structural biology technique with increasing application to multimillion-atom systems, including viruses and cellular machinery. Classical MD simulations rely on parameter sets, such as the AMBER family of force fields (AMBERff), to accurately describe molecular motion. Here, we present an implementation of AMBERff for use in NAMD that overcomes previous limitations to enable high-performance, massively parallel simulations encompassing up to two billion atoms. Single-point potential energy comparisons and case studies on model systems demonstrate that the implementation produces results that are as accurate as running AMBERff in its native engine.

Comprehending the intermolecular interaction of JAK inhibitor fedratinib with bovine serum albumin (BSA)/human alpha-1-acid glycoprotein (HAG): Multispectral methodologies and molecular simulation

The primary coverage of this paper is to investigate the molecular interaction of JAK2 inhibitor, fedratinib (FED) with BSA/HAG proteins through multispectral approaches and molecular docking as well as MD calculation. Arrival at a conclusion, the endogenous fluorescence of BSA/HAG protein was quenched separately in the form of static and mixed quenching. The FED-BSA and FED-HAG complexes with the stoichiometric ratio of 1:1 were formed in the interaction process. And, The Kb values of these complexes were of 104-105 M-1 and 105-106 M-1, respectively, representing that the FED-HAG complex exhibited a comparatively high affinity compared to the FED-BSA complex. It is confimed that FED inserted into the interface area between subdomain IIA and IIB of BSA (marked as site II') and the bucket-shaped hydrophobic cavity of HAG, respectively, resulting in the slight alteration in the secondary structure of BSA/HAG and the micro-environment round Tyr and Trp residues. The expetimental results also confirmed that van der Waals forces (VDW), hydrogen bonds and hydrophobic interaction played a dominant role in the formation of two stable complexes. The above experimental results were supplemented and verified through molecular docking and MD simulation. Meanwhile, the effects of common ions on affinity were explored. This study could shine a light on evaluating the pharmacological properties of the JAK inhibitor FED, understanding the distribution and operation of the drug in the body, and leading to the development of the creation of novel medication devise.

Molecular Dynamics Simulations in Protein-Protein Docking

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Dynamics and Function of sRNA/mRNAs Under the Scrutiny of Computational Simulation Methods

Molecular dynamics simulations have proved extremely useful in investigating the functioning of proteins with atomic-scale resolution. Many applications to the study of RNA also exist, and their number increases by the day. However, implementing MD simulations for RNA molecules in solution faces challenges that the MD practitioner must be aware of for the appropriate use of this tool. In this chapter, we present the fundamentals of MD simulations, in general, and the peculiarities of RNA simulations, in particular. We discuss the strengths and limitations of the technique and provide examples of its application to elucidate small RNA's performance.

Molecular mechanism of cellulose dissolution in N-methyl morpholine-N-oxide: A molecular dynamics simulation study

N-methyl morpholine-N-oxide (NMMO) is the only commercialised solvent to dissolve cellulose and produce lyocell. However, the molecular mechanism of NMMO-induced cellulose solubilisation is unknown which limits further process development. In this work, and for the first time the complete dissolution process of a large cellulose bunch was simulated in NMMO monohydrate using long microsecond molecular dynamic simulations. The dissolution process was also simulated in 1-ethyl-3-methylimidazolium acetate (EmimAc) as an efficient ionic liquid in cellulose dissolution and the results were compared with the aqueous conditions. While the cellulose bunch showed a stable and insoluble structure in pure water, it was completely and efficiently dissolved in both NMMO monohydrate and EmimAc. It was shown that the dissolution time of cellulose in NMMO monohydrate is almost twice that in EmimAc, which is in agreement with the experimental observations. Although it is revealed that hydrogen bonding is the main driving force of cellulose dissolution in NMMO monohydrate, one cannot explain the complete molecular mechanism of NMMO-induced cellulose dissolution only by considering hydrogen bonds. A straightforward molecular mechanism was proposed, in which the interactions of NMMO molecules, not with cellulose, but with the other NMMO molecules play a critical role in the dissolution process.

NICE-FF: A non-empirical, intermolecular, consistent, and extensible force field for nucleic acids and beyond

A new non-empirical ab initio intermolecular force field (NICE-FF in buffered 14-7 potential form) has been developed for nucleic acids and beyond based on the dimer interaction energies (IEs) calculated at the spin component scaled-MI-second order Møller-Plesset perturbation theory. A fully automatic framework has been implemented for this purpose, capable of generating well-polished computational grids, performing the necessary ab initio calculations, conducting machine learning (ML) assisted force field (FF) parametrization, and extending existing FF parameters by incorporating new atom types. For the ML-assisted parametrization of NICE-FF, interaction energies of ∼18 000 dimer geometries (with IE < 0) were used, and the best fit gave a mean square deviation of about 0.46 kcal/mol. During this parametrization, atom types apparent in four deoxyribonucleic acid (DNA) bases have been first trained using the generated DNA base datasets. Both uracil and hypoxanthine, which contain the same atom types found in DNA bases, have been considered as test molecules. Three new atom types have been added to the DNA atom types by using IE datasets of both pyrazinamide and 9-methylhypoxanthine. Finally, the last test molecule, theophylline, has been selected, which contains already-fitted atom-type parameters. The performance of NICE-FF has been investigated on the S22 dataset, and it has been found that NICE-FF outperforms the well-known FFs by generating the most consistent IEs with the high-level ab initio ones. Moreover, NICE-FF has been integrated into our in-house developed crystal structure prediction (CSP) tool [called FFCASP (Fast and Flexible CrystAl Structure Predictor)], aiming to find the experimental crystal structures of all considered molecules. CSPs, which were performed up to 4 formula units (Z), resulted in NICE-FF being able to locate almost all the known experimental crystal structures with sufficiently low RMSD20 values to provide good starting points for density functional theory optimizations.

Cryo-EM of a heterogeneous biochemical fraction elucidates multiple protein complexes from a multicellular thermophilic eukaryote

Biomolecular complexes and their interactions govern cellular structure and function. Understanding their architecture is a prerequisite for dissecting the cell's inner workings, but their higher-order assembly is often transient and challenging for structural analysis. Here, we performed cryo-EM on a single, highly heterogeneous biochemical fraction derived from Chaetomium thermophilum cell extracts to visualize the biomolecular content of the multicellular eukaryote. After cryo-EM single-particle image processing, results showed that a simultaneous three-dimensional structural characterization of multiple chemically diverse biomacromolecules is feasible. Namely, the thermophilic, eukaryotic complexes of (a) ATP citrate-lyase, (b) Hsp90, (c) 20S proteasome, (d) Hsp60 and (e) UDP-glucose pyrophosphorylase were characterized. In total, all five complexes have been structurally dissected in a thermophilic eukaryote in a total imaged sample area of 190.64 μm2, and two, in particular, 20S proteasome and Hsp60, exhibit side-chain resolution features. The C. thermophilum Hsp60 near-atomic model was resolved at 3.46 Å (FSC = 0.143) and shows a hinge-like conformational change of its equatorial domain, highly similar to the one previously shown for its bacterial orthologue, GroEL. This work demonstrates that cryo-EM of cell extracts will greatly accelerate the structural analysis of cellular complexes and provide unprecedented opportunities to annotate architectures of biomolecules in a holistic approach.

Toward a structural identification of metastable molecular conformations

Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins.

The SIRAH force field: A suite for simulations of complex biological systems at the coarse-grained and multiscale levels

The different combinations of molecular dynamics simulations with coarse-grained representations have acquired considerable popularity among the scientific community. Especially in biocomputing, the significant speedup granted by simplified molecular models opened the possibility of increasing the diversity and complexity of macromolecular systems, providing realistic insights on large assemblies for more extended time windows. However, a holistic view of biological ensembles' structural and dynamic features requires a self-consistent force field, namely, a set of equations and parameters that describe the intra and intermolecular interactions among moieties of diverse chemical nature (i.e., nucleic and amino acids, lipids, solvent, ions, etc.). Nevertheless, examples of such force fields are scarce in the literature at the fully atomistic and coarse-grained levels. Moreover, the number of force fields capable of handling simultaneously different scales is restricted to a handful. Among those, the SIRAH force field, developed in our group, furnishes a set of topologies and tools that facilitate the setting up and running of molecular dynamics simulations at the coarse-grained and multiscale levels. SIRAH uses the same classical pairwise Hamiltonian function implemented in the most popular molecular dynamics software. In particular, it runs natively in AMBER and Gromacs engines, and porting it to other simulation packages is straightforward. This review describes the underlying philosophy behind the development of SIRAH over the years and across families of biological molecules, discussing current limitations and future implementations.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Simulations of cortical networks using spatially extended conductance-based neuronal models

The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike

Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.

Machines on Genes through the Computational Microscope

Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

Identification of Bacterial Strains and Development of anmRNA-Based Vaccine to Combat Antibiotic Resistance in Staphylococcus aureus via In Vitro and In Silico Approaches

The emergence of antibiotic-resistant microorganisms is a significant concern in global health. Antibiotic resistance is attributed to various virulent factors and genetic elements. This study investigated the virulence factors of Staphylococcus aureus to create an mRNA-based vaccine that could help prevent antibiotic resistance. Distinct strains of the bacteria were selected for molecular identification of virulence genes, such as spa, fmhA, lukD, and hla-D, which were performed utilizing PCR techniques. DNA extraction from samples of Staphylococcus aureus was conducted using the Cetyl Trimethyl Ammonium Bromide (CTAB) method, which was confirmed and visualized using a gel doc; 16S rRNA was utilized to identify the bacterial strains, and primers of spa, lukD, fmhA, and hla-D genes were employed to identify the specific genes. Sequencing was carried out at Applied Bioscience International (ABI) in Malaysia. Phylogenetic analysis and alignment of the strains were subsequently constructed. We also performed an in silico analysis of the spa, fmhA, lukD, and hla-D genes to generate an antigen-specific vaccine. The virulence genes were translated into proteins, and a chimera was created using various linkers. The mRNA vaccine candidate was produced utilizing 18 epitopes, linkers, and an adjuvant, known as RpfE, to target the immune system. Testing determined that this design covered 90% of the population conservancy. An in silico immunological vaccine simulation was conducted to verify the hypothesis, including validating and predicting secondary and tertiary structures and molecular dynamics simulations to evaluate the vaccine’s long-term viability. This vaccine design may be further evaluated through in vivo and in vitro testing to assess its efficacy.

Engineering excitonically coupled dimers in an artificial protein for light harvesting via computational modeling

In photosynthesis, pigment-protein complexes achieve outstanding photoinduced charge separation efficiencies through a set of strategies in which excited states delocalization over multiple pigments ("excitons") and charge-transfer states play key roles. These concepts, and their implementation in bioinspired artificial systems, are attracting increasing attention due to the vast potential that could be tapped by realizing efficient photochemical reactions. In particular, de novo designed proteins provide a diverse structural toolbox that can be used to manipulate the geometric and electronic properties of bound chromophore molecules. However, achieving excitonic and charge-transfer states requires closely spaced chromophores, a non-trivial aspect since a strong binding with the protein matrix needs to be maintained. Here, we show how a general-purpose artificial protein can be optimized via molecular dynamics simulations to improve its binding capacity of a chlorophyll derivative, achieving complexes in which chromophores form two closely spaced and strongly interacting dimers. Based on spectroscopy results and computational modeling, we demonstrate each dimer is excitonically coupled, and propose they display signatures of charge-transfer state mixing. This work could open new avenues for the rational design of chromophore-protein complexes with advanced functionalities.

Million-atom molecular dynamics simulations reveal the interfacial interactions and assembly of plant PSII-LHCII supercomplex

Protein-protein interface interactions dictate efficient excitation energy transfer from light-harvesting antennas to the photosystem II (PSII) core. In this work, we construct a 1.2 million atom-scale model of plant C₂S₂-type PSII-LHCII supercomplex and perform microsecond-scale molecular dynamics (MD) simulations to explore the interactions and assembly mechanisms of the sizeable PSII-LHCII supercomplex. We optimize the nonbonding interactions of the PSII-LHCII cryo-EM structure using microsecond-scale MD simulations. Binding free energy calculations with component decompositions reveal that hydrophobic interactions predominantly drive antenna-core association and the antenna-antenna interactions are relatively weak. Despite the positive electrostatic interaction energies, hydrogen bonds and salt bridges mainly provide directional or anchoring forces for interface binding. Analysis of the roles of small intrinsic subunits of PSII suggests that LHCII and CP26 first interact with small intrinsic subunits and then bind to the core proteins, whereas CP29 adopts a one-step binding process to the PSII core without the assistance of other factors. Our study provides insights into the molecular underpinnings of the self-organization and regulation of plant PSII-LHCII. It lays the framework for deciphering the general assembly principles of photosynthetic supercomplexes and possibly other macromolecular structures. The finding also has implications for repurposing photosynthetic systems to enhance photosynthesis.

Long-time scale simulations of virus-like particles from three human-norovirus strains

The dynamics of the virus like particles (VLPs) corresponding to the GII.4 Houston, GII.2 SMV, and GI.1 Norwalk strains of human noroviruses (HuNoV) that cause gastroenteritis was investigated by means of long-time (about 30 μs in the laboratory timescale) molecular dynamics simulations with the coarse-grained UNRES force field. The main motion of VLP units turned out to be the bending at the junction between the P1 subdomain (that sits in the VLP shell) and the P2 subdomain (that protrudes outside) of the major VP1 protein, this resulting in a correlated wagging motion of the P2 subdomains with respect to the VLP surface. The fluctuations of the P2 subdomain were found to be more pronounced and the P2 domain made a greater angle with the normal to the VLP surface for the GII.2 strain, which could explain the inability of this strain to bind the histo-blood group antigens (HBGAs).

GPU-Enhanced DFTB Metadynamics for Efficiently Predicting Free Energies of Biochemical Systems

Metadynamics calculations of large chemical systems with ab initio methods are computationally prohibitive due to the extensive sampling required to simulate the large degrees of freedom in these systems. To address this computational bottleneck, we utilized a GPU-enhanced density functional tight binding (DFTB) approach on a massively parallelized cloud computing platform to efficiently calculate the thermodynamics and metadynamics of biochemical systems. To first validate our approach, we calculated the free-energy surfaces of alanine dipeptide and showed that our GPU-enhanced DFTB calculations qualitatively agree with computationally-intensive hybrid DFT benchmarks, whereas classical force fields give significant errors. Most importantly, we show that our GPU-accelerated DFTB calculations are significantly faster than previous approaches by up to two orders of magnitude. To further extend our GPU-enhanced DFTB approach, we also carried out a 10 ns metadynamics simulation of remdesivir, which is prohibitively out of reach for routine DFT-based metadynamics calculations. We find that the free-energy surfaces of remdesivir obtained from DFTB and classical force fields differ significantly, where the latter overestimates the internal energy contribution of high free-energy states. Taken together, our benchmark tests, analyses, and extensions to large biochemical systems highlight the use of GPU-enhanced DFTB simulations for efficiently predicting the free-energy surfaces/thermodynamics of large biochemical systems.

Molecular Dynamics Investigation of Wettability Alteration of Quartz Surface under Thermal Recovery Processes

One of the primary methods for bitumen and heavy oil recovery is a steam-assisted gravity drainage (SAGD) process. However, the mechanisms related to wettability alteration under the SAGD process still need to be fully understood. In this study, we used MD simulation to evaluate the wettability alteration under a steam injection process for bitumen and heavy oil recovery. Various oil droplets with different asphaltene contents were considered to determine the effect of an asphaltene content on the adsorption of the oil droplets onto quartz surfaces and wettability alteration. Based on the MD simulation outputs, the higher the asphaltene content, the higher the adsorption energy between the bitumen/heavy oil and quartz surfaces due to coulombic interactions. Additionally, the quartz surfaces became more oil-wet at temperatures well beyond the water boiling temperature; however, they were extremely water-wet at ambient conditions. The results of this work provide in-depth information regarding wettability alteration during in situ thermal processes for bitumen and heavy oil recovery. Furthermore, they provide helpful information for optimizing the in situ thermal processes for successful operations.

One microsecond MD simulations of the SARS-CoV-2 main protease and hydroxychloroquine complex reveal the intricate nature of binding

Currently, several vaccines and antivirals across the globe are in clinical trials. Hydroxychloroquine (HCQ) was reported to inhibit the SARS-CoV-2 virus in antiviral assays. Here, it raises the curiosity about the molecular target of HCQ inside the cell. It may inhibit some of the viral targets, or some other complex mechanisms must be at disposal towards action mechanisms. In some of the viruses, proteases are experimentally reported to be a potential target of HCQ. However, no in-depth investigations are available in the literature yet. Henceforth, we have carried out extensive, one-microsecond long molecular dynamics simulations of the bound complex of hydroxychloroquine with main protease (Mpro) of SARS-CoV-2. Our analysis found that HCQ binds within the catalytic pocket of Mpro and remains stable upto one-third of simulation time but further causes increased fluctuations in simulation parameters. In the end, the HCQ does not possess any pre-formed hydrogen bond, other non-covalent interactions with Mpro, ultimately showing the unsteadiness in binding at catalytic binding pocket and may suggest that HCQ may not inhibit the Mpro. In the future, this study would require experimental validation on enzyme assays against Mpro, and that may be the final say. Communicated by Ramaswamy H. Sarma.

Mechanisms of E. coli chemotaxis signaling pathways visualized using cryoET and computational approaches

Chemotaxis signaling pathways enable bacteria to sense and respond to their chemical environment and, in some species, are critical for lifestyle processes such as biofilm formation and pathogenesis. The signal transduction underlying chemotaxis behavior is mediated by large, highly ordered protein complexes known as chemosensory arrays. For nearly two decades, cryo-electron tomography (cryoET) has been used to image chemosensory arrays, providing an increasingly detailed understanding of their structure and function. In this mini-review, we provide an overview of the use of cryoET to study chemosensory arrays, including imaging strategies, key results, and outstanding questions. We further discuss the application of molecular modeling and simulation techniques to complement structure determination efforts and provide insight into signaling mechanisms. We close the review with a brief outlook, highlighting promising future directions for the field.

Exploration of interaction property between nonylphenol and G protein-coupled receptor 30 based on molecular simulation and biological experiments

Nonylphenol (NP), a representative of environmental hormones, can cause extensive biological effects in the human body. In this study, we first analyzed the mutual binding modes of NP and G protein coupled estrogen receptor 30 (GPR30) by molecular simulation. The 3D structure of GPR30 was successfully constructed. We found that the binding sites of NP on GPR30 are similar to that of 17β-Estradiol (E2) on GPR30. The GPR30-E2 bond complex is more stable than GPR30-NP bond complex. Next CCK-8 assay was used to detect the regulatory effect of NP on SKBR-3 cell proliferation. When NP and E2 were used alone, low concentration could promote cell proliferation, while high concentration was the opposite. The presence of E2 can promote the cell proliferation effect of NP, and inhibit the inhibitory intensity. NP could promote both the cell proliferation effect and inhibition intensity of E2. Based on our results, we conclude that the binding modes of NP and GPR30 is similar to that of E2 and GPR30. In biology, NP can play estrogen role by activating GPR30 receptor, but it can also produce cytotoxicity at higher concentration.

Engineering flexible loops to enhance thermal stability of keratinase for efficient keratin degradation

Keratinase-catalyzed degradation of keratin waste has been shown to be a promising recycling method. Although the recombinant KerZ1 derived from Bacillus subtilis has shown the highest activity among the keratinases reported so far, the low thermal stability caused by the unstable flexible loops limited its keratin-degrading ability. To this end, the flexible loops of KerZ1 were engineered to be more hydrophobic and rigid through B-factor calculations, molecular dynamics simulations, and β-turn redesign. We developed several highly thermostable keratinase variants and showed enhanced keratin degradation activity. In particular, the loop regions of the variants KerZ1^A128D/L240N, KerZ1^T77E/L240N and KerZ1^T77C/A128D were designed to be more stable, with T_m values increased by 8 °C, 6 °C and 5 °C, and corresponding t_1/2 increased by 2.3, 3.3 and 5.0 times. The keratin degradation activity of the variant KerZ1^T77C/A128D at 60 °C was enhanced by 46 % compared with KerZ1^WT. The strategy of this research and the obtained keratinase variants will be a significant improvement in the complete degradation of keratin.