Biomolecular simulation: a computational microscope for molecular biology

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics

Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to identify CVs for biomolecular dynamics. To find slow CVs, AMUSEt transforms input features (e.g., pairwise atomic distances) into nonlinear basis functions (e.g., Gaussian functions) and encodes these nonlinear basis functions within a tensor-train structure via time-lagged correlation functions. Due to the need to fit these tensor-train data structures into computer memory, AMUSEt can handle only a limited number of input features. Consequently, AMUSEt relies on manually selecting and ranking features based on physical intuition to fully capture the slow dynamics. However, when applied to complex biological systems with numerous features, this selection and ranking process becomes increasingly challenging. To address this challenge, here we present AMUSET-TICA (AMUSEt-based Time-lagged Independent Component Analysis), a CV-identification method using time-structure-independent components (tICs) as the input features for AMUSEt. The key insight of AMUSET-TICA lies in its highly effective embedding of high-dimensional atomistic protein conformations, achieved by expanding orthogonal tICs into overlapping Gaussian basis functions through a tensor-product data structure. This eliminates the need for manually selecting and ranking input features for a wide range of biomolecular systems. We demonstrate that AMUSET-TICA consistently and significantly outperforms AMUSEt and tICA in identifying slow CVs for three different biomolecular systems: alanine dipeptide, the N-terminal domain of L9 (NTL9), and the FIP35 WW domain. For all these systems, the CVs generated by AMUSET-TICA accurately describe the slowest dynamical modes underlying these biological conformational changes. Furthermore, we show that AMUSET-TICA achieves performance comparable to deep-learning approaches like VAMPnets in identifying the slowest dynamical modes, while being significantly more computationally efficient in terms of CPU time. In addition, the CVs yielded by AMUSET-TICA provide insights into the folding mechanisms of NTL9 and the FIP35 WW domain, including CV3 and CV4 of the WW domain, which capture its two parallel folding pathways. We expect AMUSET-TICA can be widely applied to facilitate the investigation of biomolecular dynamics.

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.

Identifying a non-conserved site for achieving allosteric covalent inhibition of CECR2

The bromodomain (BRD) represents a highly conserved structural module that provides BRD proteins with fundamental functionality in modulating protein-protein interactions involved in diverse biological processes such as chromatin-mediated gene transcription, DNA recombination, replication and repair. Consequently, dysregulation of BRD proteins has been implicated in the pathogenesis of numerous human diseases. In recent years, considerable scientific endeavors have focused on unraveling the molecular mechanisms underlying BRDs and developing inhibitors that target these domains. While these inhibitors compete for binding with the acetylated lysine binding site of BRDs, achieving inhibition of BRD proteins via competitive pocket binding has proven challenging due to the conserved nature of these pockets. To address this limitation, the present study employed dynamic simulations for a comprehensive analysis, leading to the identification of a non-conserved pocket in CECR2 for achieving BRD family inhibition through allosteric modulation. Subsequently, the compound BAY 11-7085 was proven capable of covalently binding to C494 of this pocket after covalent docking and biological verification in vitro. The allosteric inhibition strategy of CECR2 was further verified by the structurally optimized compound LC-CE-7, which is an allosteric covalent CECR2 inhibitor with anti-cancer effects in MDA-MB-231 cells.

The density-based many-body expansion for poly-peptides and proteins

Fragmentation schemes enable the efficient quantum-chemical treatment of large biomolecular systems, and provide an ideal starting point for the development of accurate machine-learning potentials for proteins. Here, we present a fragment-based method that only uses calculations for single-amino acids and their dimers, and is able to reduce the fragmentation error in total energies to ca. 1 kJ mol-1 per amino acid for polypeptides and proteins across different structural motifs. This is achieved by combining a two-body extension of the molecular fractionation with conjugate caps (MFCC) scheme with the density-based many-body expansion (db-MBE), thus extending the applicability of the db-MBE from molecular clusters to polypeptides and proteins.

Identification of Potential Inhibitors of Plasmodium falciparum L-lactate Dehydrogenase From Selected African Compound Libraries: Virtual Screening, Molecular Mechanics-Generalized Born Surface Area, and Molecular Dynamics Studies

We evaluated 4,512 natural products from natural product library from Central African medicinal plants for drug discovery and South African natural compounds database libraries for the identification of potential Plasmodium falciparum L-lactate dehydrogenase inhibitors considering the virtual screening process. Extra precision virtual screening enabled the ranking of the top hundred hit molecules based on their docking properties. The selected hits were further shortened based on docking and molecular mechanics-generalized born surface area parameters in comparison with the reference. As a result, four hits were chosen: Mol1, Mol2, Mol3, and Mol4, all of them from the chalcone and quinone families. These molecules showed good predicted absorption, distribution, metabolism, and excretion, and toxicity properties. Finally, the molecular dynamics simulation results showed that the three chalcones, Mol1-4, formed an H-bond, hydrophobic interaction with key amino acids in the active site. This in silico study suggests that the chalcone compounds could serve as a potential source for developing new effective antimalarial drugs to combat malaria. Further in vitro or in vivo studies might be conducted to determine their actual effectiveness.

Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies

Membrane transporters are integral membrane proteins that act as gatekeepers of the cell, controlling fundamental processes such as recruitment of nutrients and expulsion of waste material. At a basic level, transporters operate using the "alternating access model," in which transported substances are accessible from only one side of the membrane at a time. This model usually involves large-scale structural changes in the transporter, which often cannot be captured using unbiased, conventional molecular simulation techniques. In this article, we provide an overview of some of the major simulation techniques that have been applied to characterize the structural dynamics and energetics involved in the transition of membrane transporters between their functional states. After briefly introducing each technique, we discuss some of their advantages and limitations and provide some recent examples of their application to membrane transporters.

Subtle Changes at the RBD/hACE2 Interface During SARS-CoV-2 Variant Evolution: A Molecular Dynamics Study

The SARS-CoV-2 Omicron variants show different behavior compared to the previous variants, especially with respect to the Delta variant, which promotes a lower morbidity despite being much more contagious. In this perspective, we performed molecular dynamics (MD) simulations of the different spike RBD/hACE2 complexes corresponding to the WT, Delta and four Omicron variants. Carrying out a comprehensive analysis of residue interactions within and between the two partners allowed us to draw the profile of each variant by using complementary methods (PairInt, hydrophobic potential, contact PCA). PairInt calculations highlighted the residues most involved in electrostatic interactions, which make a strong contribution to the binding with highly stable interactions between spike RBD and hACE2. Apolar contacts made a substantial and complementary contribution in Omicron with the detection of two hydrophobic patches. Contact networks and cross-correlation matrices were able to detect subtle changes at point mutations as the S375F mutation occurring in all Omicron variants, which is likely to confer an advantage in binding stability. This study brings new highlights on the dynamic binding of spike RBD to hACE2, which may explain the final persistence of Omicron over Delta.

Disentangling Folding from Energetic Traps in Simulations of Disordered Proteins

Protein conformational heterogeneity plays an essential role in a myriad of different biological processes. Extensive conformational heterogeneity is especially characteristic of intrinsically disordered proteins and protein regions (collectively IDRs), which lack a well-defined three-dimensional structure and instead rapidly exchange between a diverse ensemble of configurations. An emerging paradigm recognizes that the conformational biases encoded in IDR ensembles can play a central role in their biological function, necessitating understanding these sequence-ensemble relations. All-atom simulations have provided critical insight into our modern understanding of the solution behavior of IDRs. However, effectively exploring the accessible conformational space associated with large, heterogeneous ensembles is challenging. In particular, identifying poorly sampled or energetically trapped regions of disordered proteins in simulations often relies on qualitative assessment based on visual inspection of simulations and/or analysis data. These approaches, while convenient, run the risk of masking poorly sampled simulations. In this work, we present an algorithm for quantifying per-residue local conformational heterogeneity in protein simulations. Our work builds on prior work and compares the similarity between backbone dihedral angle distributions generated from molecular simulations in a limiting polymer model and across independent all-atom simulations. In this regime, the polymer model serves as a statistical reference model for extensive conformational heterogeneity in a real chain. Quantitative comparisons of probability vectors generated from these simulations reveal the extent of conformational sampling in a simulation, enabling us to distinguish between situations in which protein regions are well-sampled, poorly sampled, or folded. To demonstrate the effectiveness of this approach, we apply our algorithm to several toy, synthetic, and biological systems. Accurately assessing local conformational sampling in simulations of IDRs will help better quantify new enhanced sampling methods, ensure force field comparisons are equivalent, and provide confidence that conclusions drawn from simulations are robust.

Lattice Models: Non-Conventional simulation methods for mechanobiology

Computational methods represent a powerful tool to explore biophysical phenomena occurring at small scales and hence difficult to observe through experimental setups. In detail, they can provide a support to mechanobiology, with the aim of understanding the behavior of living cells interacting with the surrounding environment. To this end, lattice models can provide a simulation framework that is highly reliable and easy to implement, even for simulations involving large deformations and topological changes during time evolution. In this review article, elastic network models for studying biological molecules are described, several lattice spring models for investigating cell behaviors are discussed, and the adoption of lattice beam models for biomimetic structures design is presented. The lattice modelling approaches could be regarded as a valuable option to conduct in-silico experiments and consolidate the emergent mechanobiology research field.

Optimal scaling of protein-water interactions coupled with targeted torsional refinements yields balanced force fields suitable for simulations of single-chain folded proteins, disordered polypeptides, and protein-protein complexes

All-atom molecular dynamics (MD) simulations based on physics-based force fields, serve as an essential complement to experiments for investigating protein structure, dynamics, and interactions. Despite significant advances in force field development, achieving a consistent balance of molecular interactions that stabilize folded proteins and protein-protein complexes while simultaneously capturing the conformational dynamics of intrinsically disordered polypeptides (IDPs), remains challenging. In this work, we systematically evaluated two current state-of-the-art force fields (i) AMBER ff03ws, and (ii) AMBER ff99SBws, by comprehensively assessing their performance on both folded domains and IDPs. By selectively scaling side chain-water interactions for uncharged residues, the refined AMBER ff03w-sc force field demonstrated improved conformational stability of folded proteins while maintaining accurate representations of IDPs. However, AMBER ff03w-sc failed to correct the discrepancies in NMR-derived ps-ns timescale backbone dynamics associated with flexible loops. Interestingly, AMBER ff99SBws retained its structural stability despite the application of upscaled interactions with water for both sidechain and backbone atoms and displayed robust agreement with NMR-derived backbone dynamics. Further, a targeted refinement of glutamine backbone torsion parameters, yielded AMBER ff99SBws-STQ', which effectively resolved discrepancies associated with glutamine α-helicity predictions. Extensive validation against small angle X-ray scattering (SAXS) and NMR chemical shifts, revealed that both refined force fields accurately reproduced chain dimensions and secondary structure propensities of disordered peptides and prion-like domains. Importantly, both force fields reliably maintained the stability of protein-protein complexes over microsecond timescales. Our systematic refinement strategies provide improved accuracy and transferability for simulating diverse protein systems, from folded domains to IDPs and protein complexes.

Enhancing Bacillus thuringiensis Performance: Fertilizer-Driven Improvements in Biofilm Formation, UV Protection, and Pest Control Efficacy

This study investigated the effects of fertilizers on the biofilm formation, ultraviolet (UV) resistance, and insecticidal activity of Bacillus thuringiensis (Bt). Bacillus thuringiensis, a widely used microbial pesticide, has a minimal environmental impact and is highly effective against specific pests but is susceptible to environmental factors in field applications. Bacterial biofilms provide protection for Bt, enhancing its survival and functionality in the environment. However, the mechanisms by which fertilizers regulate the characteristics of microbial pesticides and enhance biofilm formation are not well understood. This study evaluated the effects of six fertilizers on the bacterial biofilm formation, the UV resistance, and the insecticidal activities of Bt wettable powders. The results demonstrated that fertilizers significantly enhanced the performance of three Bt preparations (Lv'an, Kang'xin, and Lu'kang). A compound fertilizer with 8.346 g/L of KCl, 2.751 g/L of ZnSO4·7H2O, and 25.681 μL/mL of humic acid was identified by response surface optimization, achieving the maximum BBF formation with OD595 value of 2.738. Furthermore, KH2PO4, HA, and ZnSO4·7H2O notably improved the survivability of Bt preparations under prolonged UV exposure, with the compound fertilizer providing the greatest protection. What's more, fertilizers reduced the LC50 values of all Bt preparations, with the compound fertilizer decreasing the LC50 of the Lv'an Bt wettable powder to 0.139 g/L, a 3.12-fold increase in efficacy. This study demonstrated that fertilizers significantly enhance the UV resistance and insecticidal activity of Bt wettable powders by promoting bacterial biofilm formation. Herein, this study provides new strategies and theoretical support for Bt applications in modern sustainable agriculture.

Small-Molecule Inhibitors of Amyloid Beta: Insights from Molecular Dynamics-Part A: Endogenous Compounds and Repurposed Drugs

The amyloid hypothesis is the predominant model of Alzheimer's disease (AD) pathogenesis, suggesting that amyloid beta (Aβ) peptide is the primary driver of neurotoxicity and a cascade of pathological events in the central nervous system. Aβ aggregation into oligomers and deposits triggers various processes, such as vascular damage, inflammation-induced astrocyte and microglia activation, disrupted neuronal ionic homeostasis, oxidative stress, abnormal kinase and phosphatase activity, tau phosphorylation, neurofibrillary tangle formation, cognitive dysfunction, synaptic loss, cell death, and, ultimately, dementia. Molecular dynamics (MD) is a powerful structure-based drug design (SBDD) approach that aids in understanding the properties, functions, and mechanisms of action or inhibition of biomolecules. As the only method capable of simulating atomic-level internal motions, MD provides unique insights that cannot be obtained through other techniques. Integrating experimental data with MD simulations allows for a more comprehensive understanding of biological processes and molecular interactions. This review summarizes and evaluates MD studies from the past decade on small molecules, including endogenous compounds and repurposed drugs, that inhibit amyloid beta. Furthermore, it outlines key considerations for future MD simulations of amyloid inhibitors, offering a potential framework for studies aimed at elucidating the mechanisms of amyloid beta inhibition by small molecules.

Innovative computational approaches in drug discovery and design

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

Activation and Reactivity of the Deubiquitinylase OTU Cezanne-2 from MD Simulations and QM/MM Calculations

Cezanne-2 (Cez2) is a deubiquitinylating (DUB) enzyme involved in the regulation of ubiquitin-driven cellular signaling and selectively targets Lys11-linked polyubiquitin chains. As a representative member of the ovarian tumor (OTU) subfamily DUBs, it performs cysteine proteolytic isopeptide bond cleavage; however, its exact catalytic mechanism is not yet resolved. In this work, we used different computational approaches to get molecular insights into the Cezanne-2 catalytic mechanism. Extensive molecular dynamics (MD) simulations were performed for 12 μs to model free Cez2 and the diubiquitin (diUb) substrate-bound protein-protein complex in two different charge states of Cez2, each corresponding to a distinct reactive state in its catalytic cycle. The simulations were analyzed in terms of the relevant structural parameters for productive enzymatic catalysis. Reactive diUb-Cez2 complex configurations were identified, which lead to isopeptide bond cleavage and stabilization of the tetrahedral oxyanion intermediate. The reliability of these complexes was further assessed by quantum mechanics/molecular mechanics (QM/MM) optimizations. The results show that Cez2 follows a modified cysteine protease mechanism involving a catalytic Cys210/His367 dyad, with the oxyanion hole to be a part of the "C-loop," and polarization of His367 by the formation of a strictly conserved water bridge with Glu173. The third residue has a dual role in catalysis as it mediates substrate binding and polarization of the catalytic dyad. A similar mechanism was identified for Cezanne-1, the paralogue of Cez2. In general, our simulations provide valuable molecular information that may help in the rational design of selective inhibitors of Cez2 and closely related enzymes.

Molecular insights into the elevator-type mechanism of the cyanobacterial bicarbonate transporter BicA

Cyanobacteria are responsible for up to 80% of aquatic carbon dioxide fixation and have evolved a specialized carbon concentrating mechanism to increase photosynthetic yield. As such, cyanobacteria are attractive targets for synthetic biology and engineering approaches to address the demands of global energy security, food production, and climate change for an increasing world's population. The bicarbonate transporter BicA is a sodium-dependent, low-affinity, high-flux bicarbonate symporter expressed in the plasma membrane of cyanobacteria. Despite extensive biochemical characterization of BicA, including the resolution of the BicA crystal structure, the dynamic understanding of the bicarbonate transport mechanism remains elusive. To this end, we have collected over 1 ms of all-atom molecular dynamics simulation data of the BicA dimer to elucidate the structural rearrangements involved in the substrate transport process. We further characterized the energetics of the transition of BicA protomers and investigated potential mutations that are shown to decrease the free energy barrier of conformational transitions. In all, our study illuminates a detailed mechanistic understanding of the conformational dynamics of bicarbonate transporters and provides atomistic insights to engineering these transporters for enhanced photosynthetic production.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Estimation of Ligand Binding Free Energy Using Multi-eGO

The computational study of ligand binding to a target protein provides mechanistic insight into the molecular determinants of this process and can improve the success rate of in silico drug design. All-atom molecular dynamics (MD) simulations can be used to evaluate the binding free energy, typically by thermodynamic integration, and to probe binding mechanisms, including the description of protein conformational dynamics. The advantages of MD come at a high computational cost, which limits its use. Such cost could be reduced by using coarse-grained models, but their use is generally associated with an undesirable loss of resolution and accuracy. To address the trade-off between speed and accuracy of MD simulations, we describe the use of the recently introduced multi-eGO atomic model for the estimation of binding free energies. We illustrate this approach in the case of the binding of benzene to lysozyme by both thermodynamic integration and metadynamics, showing multiple binding/unbinding pathways of benzene. We then provide equally accurate results for the binding free energy of dasatinib and PP1 to Src kinase by thermodynamic integration. Finally, we show how we can describe the binding of the small molecule 10074-G5 to Aβ42 by single molecule simulations and by explicit titration of the ligand as a function of concentration. These results demonstrate that multi-eGO has the potential to significantly reduce the cost of accurate binding free energy calculations and can be used to develop and benchmark in silico ligand binding techniques.

MDRepo-an open data warehouse for community-contributed molecular dynamics simulations of proteins

Molecular Dynamics (MD) simulation of biomolecules provides important insights into conformational changes and dynamic behavior, revealing critical information about folding and interactions with other molecules. The collection of simulations stored in computers across the world holds immense potential to serve as training data for future Machine Learning models that will transform the prediction of structure, dynamics, drug interactions, and more. Ideally, there should exist an open access repository that enables scientists to submit and store their MD simulations of proteins and protein-drug interactions, and to find, retrieve, analyze, and visualize simulations produced by others. However, despite the ubiquity of MD simulation in structural biology, no such repository exists; as a result, simulations are instead stored in scattered locations without uniform metadata or access protocols. Here, we introduce MDRepo, a robust infrastructure that provides a relatively simple process for standardized community contribution of simulations, activates common downstream analyses on stored data, and enables search, retrieval, and visualization of contributed data. MDRepo is built on top of the open-source CyVerse research cyber-infrastructure, and is capable of storing petabytes of simulations, while providing high bandwidth upload and download capabilities and laying a foundation for cloud-based access to its stored data.

Mechanisms of myosin II force generation: insights from novel experimental techniques and approaches

Myosin II is a molecular motor that converts chemical energy derived from ATP hydrolysis into mechanical work. Myosin II isoforms are responsible for muscle contraction and a range of cell functions relying on the development of force and motion. When the motor attaches to actin, ATP is hydrolyzed and inorganic phosphate (Pi) and ADP are released from its active site. These reactions are coordinated with changes in the structure of myosin, promoting the so-called "power stroke" that causes the sliding of actin filaments. The general features of the myosin-actin interactions are well accepted, but there are critical issues that remain poorly understood, mostly due to technological limitations. In recent years, there has been a significant advance in structural, biochemical, and mechanical methods that have advanced the field considerably. New modeling approaches have also allowed researchers to understand actomyosin interactions at different levels of analysis. This paper reviews recent studies looking into the interaction between myosin II and actin filaments, which leads to power stroke and force generation. It reviews studies conducted with single myosin molecules, myosins working in filaments, muscle sarcomeres, myofibrils, and fibers. It also reviews the mathematical models that have been used to understand the mechanics of myosin II in approaches focusing on single molecules to ensembles. Finally, it includes brief sections on translational aspects, how changes in the myosin motor by mutations and/or posttranslational modifications may cause detrimental effects in diseases and aging, among other conditions, and how myosin II has become an emerging drug target.

Use of Steered Molecular Dynamics to Explore the Conformational Changes of SNARE Proteins

Steered Molecular Dynamics (SMD) simulation is a powerful computational simulation technique that enables the controlled manipulation of molecular systems by applying external forces. This method is frequently utilized to investigate the slow processes of biomolecular systems that occur within sub-second to second time scales, achieved through SMD simulations that only span nanoseconds. SMD simulation can be utilized to study the detailed mechanism of protein conformational changes, protein unfolding, and ligand dissociation, etc. Here, we introduce the application of SMD in the study of intra-molecular interactions and regulatory mechanisms in SNAREs by analyzing their conformational change pathways.

Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck

The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional collective variable (CV) space and then partitioning it into bins. The efficacy of WE simulations heavily depends on the selection of CVs and binning schemes. The recently proposed state predictive information bottleneck (SPIB) method has emerged as a promising tool for automatically constructing CVs from data and guiding enhanced sampling through an iterative manner. In this work, we advance this data-driven pipeline by incorporating prior expert knowledge. Our hybrid approach combines SPIB-learned CVs to enhance sampling in explored regions with expert-based CVs to guide exploration in regions of interest, synergizing the strengths of both methods. Through benchmarking on alanine dipeptide and chignolin systems, we demonstrate that our hybrid approach effectively guides WE simulations to sample states of interest and reduces run-to-run variances. Moreover, our integration of the SPIB model also enhances the analysis and interpretation of WE simulation data by effectively identifying metastable states and pathways and offering direct visualization of dynamics.

3D variability analysis reveals a hidden conformational change controlling ammonia transport in human asparagine synthetase

Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we show that 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) identifies a functional role for the Arg-142 side chain and test this hypothesis experimentally by characterizing the R142I variant in which Arg-142 is replaced by isoleucine. Support for Arg-142 playing a role in the intramolecular translocation of ammonia between the active site of the enzyme is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS, and MD simulations provide a possible molecular mechanism for these findings. Combining 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.

Molecular dynamics-based computational investigations on the influence of tumor suppressor p53 binding protein against other proteins/peptides

The tumor-suppressing p-53 binding protein is a crucial protein that is involved in the prevention of cancer via its regulatory effect on a number of cellular processes. Recent evidence indicates that it interacts with a number of other proteins involved in cancer in ways that are not fully understood. An understanding of such interactions could provide insights into novel ways p53 further exerts its tumour prevention role via its interactions with diverse proteins. Thus, this study aimed to examine the interactions of the p53 protein with other proteins (peptides and histones) using molecular simulation dynamics. We opted for a total of seven proteins, namely 2LVM, 2MWO, 2MWP, 4CRI, 4 × 34, 5Z78, and 6MYO (control), and had their PBD files retrieved from the protein database. These proteins were then docked against the p-53 protein and the resulting interactions were examined using molecular docking simulations run at 500 ns. The result of the interactions revealed the utilisation of various amino acids in the process. The peptide that interacted with the highest number of amino acids was 5Z78 and these were Lys10, Gly21, Trp24, Pro105, His106, and Arg107, indicating a stronger interaction. The RMSD and RMSF values indicate that the complexes formed were stable, with 4CRI, 6MYO, and 2G3R giving the most stable values (less than 2.5 Å). Other parameters, including the SASA, Rg, and number of hydrogen bonds, all indicated the formation of fairly stable complexes. Our study indicates that overall, the interactions of 53BP1 with p53K370me2, p53K382me2, methylated K810 Rb, p53K381acK382me2, and tudor-interacting repair regulator protein indicated interactions that were not as strong as those with the histone protein. Thus, it could be that P53 may mediate its tumour suppressing effect via interactions with amino acids and histone.

Enhancing the Assembly Properties of Bottom-Up Coarse-Grained Phospholipids

A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing amphipathic self-assembling behavior in coarse-grained models. We show that bottom-up coarse-grained lipid models can possess metastable morphological behavior and that this potential metastability has ramifications for accurate development and training. We in turn develop a training algorithm which evades metastability issues by linking model training to self-assembling behavior, and demonstrate its robustness via construction of solvent-free coarse-grained models of various phospholipid membranes, including lipid species such as phosphatidylcholines, phosphatidylserines, sphingolipids, and cholesterol. The resulting coarse-grained lipid models are orders of magnitude faster than their atomistic counterparts while retaining structural fidelity and constitute a promising direction for the development of coarse-grained models of realistic cell membranes.

Identifying potential inhibitors against galectin-3-binding protein (LGALS3BP) for therapeutic targeting of glioma

Glioblastoma is one of the most lethal types of Gliomas, and its treatment greatly depends on the stage of its detection. Galactin-3-binding protein (LGALS3BP) serves as a novel circulatory biomarker for detecting glioma at an early stage. This protein is also responsible for metastasis, proliferative signaling, angiogenesis, and immune system evasion in the case of brain tumors. Inhibition of LGALS3BP can help in the reduction of metastasis and progression of the disease. Currently, no effective drug is available that can completely treat glioma. In this study, we have virtually screened the National Cancer Institute (NCI) drug databank to discover potential inhibitors of LGALS3BP. Based on the binding free energy calculations using MMPBSA, three compounds, 627861 (-16.69 kcal/mol), 329090 (-13.66 kcal/mol), and 627855 (-10.01 kcal/mol), were selected as potent inhibitors. 200 ns MD simulation studies further complemented this study. Finally, we recommend three molecules, 627861, 329090, and 627855, can be potential inhibitors of LGAL3SBP. The structural scaffolds of these molecules can also lead to the optimization of better inhibitors of LGALS3BP and be implicated in the therapeutic management of glioma after desired experimental validations.

Structural Insights into Dopamine Receptor-Ligand Interactions: From Agonists to Antagonists

This study explores the intricacies of dopamine receptor-ligand interactions, focusing on the D1R and D5R subtypes. Using molecular modeling techniques, we investigated the binding of the pan-agonist rotigotine, revealing a universal binding mode at the orthosteric binding pocket. Additionally, we analyze the stability of antagonist-receptor complexes with SKF83566 and SCH23390. By examining the impact of specific mutations on ligand-receptor interactions through computational simulations and thermostability assays, we gain insights into binding stability. Our research also delves into the structural and energetic aspects of antagonist binding to D1R and D5R in their inactive states. These findings enhance our understanding of dopamine receptor pharmacology and hold promise for drug development in central nervous system disorders, opening doors to future research and innovation in this field.

Deciphering the toxicological and computational assessment of Verbascum yemenese.: Integrating phytochemistry and bioinformatics tools

This study explores the phytochemical composition and biological activities of Verbascum yemenense, a plant known for its medicinal properties. The plant extract revealed a rich presence of bioactive compounds that exhibited significant antioxidant properties against free radicals. The enzyme inhibition potential was particularly notable against cholinesterases (AChE: 2.56 mg GALAE/g; BChE: 1.98 mg GALAE/g), and tyrosinase (87.94 mg KAE/g), α-glucosidase suggesting potential therapeutic applications in neurodegenerative diseases, skin disorders and diabetes. Molecular docking studies and Molecular Dynamics simulations, providing insights into the interaction mechanisms of the identified compounds with the target proteins. Molecular docking studies revealed high binding affinities of the phytoconstituents, with compounds like VY4 and phyllanthusol-A (VY15) showing substantial docking scores against AChE (-9.840 kcal/mol) and BChE (-9.853 kcal/mol), respectively. For instance, the RMSD values during the MD simulations for compound VY17 in the AML complex showed a stable conformation, fluctuating within a range of 0.75 Å to 1.75 Å, indicating a strong and consistent interaction with the enzyme. MESP studies highlighted VY17's distinctive electrostatic features, notably a pronounced electronegative region, which might contribute to its binding efficiency. These findings suggest that V. yemenense is a promising candidate for developing novel therapeutic agents.

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

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

Exploring the mechanism of action of Modified Simiao Powder in the treatment of osteoarthritis: an in-silico study

Introduction

Osteoarthritis (OA) is the most common form of arthritis and the leading musculoskeletal disorders in adults. Modified Simiao Powder (MSMP) has been widely used in the treatment of OA with remarkable clinical ecaciousness.

Objective

This study aimed to elucidate underlying mechanisms of MSMP in OA by employing network pharmacology, molecular docking, and molecular dynamics simulations, due to the unclear mode of action.

Methods

Bioinformatic analysis was used to evaluate the major chemical constituents of MSMP, determine prospective target genes, and screen genes associated with OA. Network pharmacology methods were then applied to identify the crucial target genes of MSMP in OA treatment. Further analyses included gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. These key targets within the pertinent pathways was further confirmed by molecular docking, binding energy evaluation, and molecular dynamics simulations.

Results

Network pharmacology analysis identified an MSMP component-target-pathway network comprising 11 central active compounds, 25 gene targets, and 12 biological pathways.

Discussion

These findings imply that the therapeutic effects of MSMP was potentially mediated by targeting several pivotal genes, such as androgen receptor (AR), NFKB1, AKT1, MAPK1, and CASP3, and regulating some pathways, including lipid metabolism and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, the PI3K-Akt signaling pathway, fluid shear stress, atherosclerosis, and Kaposi's sarcoma-associated herpesvirus infection. Molecular docking assessments demonstrated that these compounds of MSMP, such as berberine, kaempferol, quercetin, and luteolin, exhibit high binding anities to AR and AKT1. Molecular dynamics simulations validated the interactions between these compounds and targets.

Conclusion

The therapeutic effect of MSMP likely attributed to the modulation of multiple pathways, including lipid metabolism, atherosclerosis, the AGE-RAGE signaling pathway, and the PI3K-Akt signaling pathway, by the active components such as berberine, kaempferol, luteolin, and quercetin. Especially, their actions on target genes like AR and AKT1 contribute to the therapeutic benefits of MSMP observed in the treatment of OA.

BEMM-GEN: A Toolkit for Generating a Biomolecular Environment-Mimicking Model for Molecular Dynamics Simulation

Understanding the influence of the cellular environment on protein conformations is crucial for elucidating protein functions within living cells. In studies using molecular dynamics (MD) simulation, carbon nanotubes and hydrophobic cages have been widely used to emulate the cellular environment inside specific large biomolecules such as ribosome tunnels and chaperones. However, recent studies suggest that these uniform hydrophobic models may not adequately capture the environmental effects inside each biomolecule. Based on these facts, it is necessary to generate spherical and cylindrical models with varied chemical properties corresponding to the components within target biomolecules. We developed a biomolecular environment-mimicking model generator (BEMM-GEN) that generates spherical and cylindrical models with user-specified chemical properties and allows the integration of arbitrary protein conformations into the generated models. BEMM-GEN provides model and protein complex structures, along with the corresponding parameter files for MD simulation (AMBER and GROMACS), and users immediately run their MD simulation based on the generated input files. BEMM-GEN can be freely downloaded and installed via a Python package manager (pip install BEMM-gen). The source code files and a user manual for operation are provided on GitHub (https://github.com/y4suda/BEMM-GEN).

Structure features of Streptococcus pneumoniae FabG and virtual screening of allosteric inhibitors

Streptococcus pneumoniae, a gram-positive bacterium, is responsible for diverse infections globally, and its antibiotic resistance presents significant challenges to medical advancements. It is imperative to employ various strategies to identify antibiotics. 3-oxoacyl-[acyl-carrier-protein] reductase (FabG) is a key component in the type II fatty acid synthase (FAS II) system, which is a developing target for new anti-streptococcal drugs. We first demonstrated the function of SpFabG in vivo and in vitro and the 2 Å SpFabG structure was elucidated using X-ray diffraction technique. It was observed that the NADPH binding promotes the transformation from tetramers to dimers in solution, suggesting dimers but not tetramer may be the active conformation. By comparing the structures of FabG homologues, we have identified the conserved tetramerization site and further confirmed the mechanism that the tetramerization site mutation leads to a loss of function and destabilization through mutagenesis experiments. Starting from 533,600 compounds, we proceeded with a sequential workflow involving pharmacophore-based virtual screening, molecular docking, and binding energy calculations. Combining all the structural analysis, we identified L1, L2 and L5 as a promising candidate for SpFabG inhibitor, based on the most stable binding mode in comparison to other evaluated inhibitors.

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning

Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here, we present a simple and generic coarse-grained model for DNA-protein and protein-protein interactions and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand canonical ensemble, and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal-component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and protein-induced DNA compaction and provides a systematic and semi-quantitative approach for analyzing their mesoscale complexes.

Molecular Integrative Study on Inhibitory Effects of Pentapeptides on Polymerization and Cell Toxicity of Amyloid-β Peptide (1-42)

Alzheimer's Disease (AD) is a multifaceted neurodegenerative disease predominantly defined by the extracellular accumulation of amyloid-β (Aβ) peptide. In light of this, in the past decade, several clinical approaches have been used aiming at developing peptides for therapeutic use in AD. The use of cationic arginine-rich peptides (CARPs) in targeting protein aggregations has been on the rise. Also, the process of peptide development employing computational approaches has attracted a lot of attention recently. Using a structure database containing pentapeptides made from 20 L-α amino acids, we employed molecular docking to sort pentapeptides that can bind to Aβ42, then performed molecular dynamics (MD) analyses, including analysis of the binding stability, interaction energy, and binding free energy to screen ligands. Transmission electron microscopy (TEM), circular dichroism (CD), thioflavin T (ThT) fluorescence detection of Aβ42 polymerization, MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay, and the flow cytometry of reactive oxygen species (ROS) were carried out to evaluate the influence of pentapeptides on the aggregation and cell toxicity of Aβ42. Two pentapeptides (TRRRR and ARRGR) were found to have strong effects on inhibiting the aggregation of Aβ42 and reducing the toxicity of Aβ42 secreted by SH-SY5Y cells, including cell death, reactive oxygen species (ROS) production, and apoptosis.

Molecular simulation reveals that pathogenic mutations in BTB/ANK domains of Arabidopsis thaliana NPR1 circumscribe the EDS1-mediated immune regulation

The NPR1 (nonexpressor of pathogenesis-related genes 1) is a key regulator of the salicylic-acid-mediated immune response caused by pathogens in Arabidopsis thaliana. Mutations C150Y and H334Y in the BTB/ANK domains of NPR1 inhibit the defense response, and transcriptional co-activity with enhanced disease susceptibility 1 (EDS1) has been revealed experimentally. This study examined the conformational changes and reduced NPR1-EDS1 interaction upon mutation using a molecular dynamics simulation. Initially, BTBC150YNPR1 and ANKH334YNPR1 were categorized as pathological mutations rather than others based on sequence conservation. A distant ortholog was used to map the common residues shared among the wild-type because the mutations were highly conserved. Overall, 179 of 373 residues were determining the secondary structures and fold versatility of conformations. In addition, the mutational hotspots Cys150, Asp152, Glu153, Cys155, His157, Cys160, His334, Arg339 and Lys370 were crucial for oligomer-to-monomer exchange. Subsequently, the atomistic simulations with free energy (MM/PB(GB)SA) calculations predicted structural displacements engaging in the N-termini α5133-178α7 linker connecting the central ANK regions (α13260-290α14 and α18320-390α22), where prominent long helices (α516) and short helices (α310) replaced with β-turns and loops disrupting hydrogen bonds and salt bridges in both mutants implicating functional regulation and activation. Furthermore, the mutation repositions the intact stability of multiple regions (L13C149-N356α20BTB/ANK-α17W301-E357α21N-ter/coiled-coil) compromising a dynamic interaction of NPR1-EDS1. By unveiling the transitions between the distinct functions of mutational perception, this study paves the way for future investigation to orchestrate additive host-adapted transcriptional reprogramming that controls defense-related regulatory mechanisms of NPR1s in plants.

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.

Effective Inclusion of Electronic Polarization Improves the Description of Electrostatic Interactions: The prosECCo75 Biomolecular Force Field

prosECCo75 is an optimized force field effectively incorporating electronic polarization via charge scaling. It aims to enhance the accuracy of nominally nonpolarizable molecular dynamics simulations for interactions in biologically relevant systems involving water, ions, proteins, lipids, and saccharides. Recognizing the inherent limitations of nonpolarizable force fields in precisely modeling electrostatic interactions essential for various biological processes, we mitigate these shortcomings by accounting for electronic polarizability in a physically rigorous mean-field way that does not add to computational costs. With this scaling of (both integer and partial) charges within the CHARMM36 framework, prosECCo75 addresses overbinding artifacts. This improves agreement with experimental ion binding data across a broad spectrum of systems─lipid membranes, proteins (including peptides and amino acids), and saccharides─without compromising their biomolecular structures. prosECCo75 thus emerges as a computationally efficient tool providing enhanced accuracy and broader applicability in simulating the complex interplay of interactions between ions and biomolecules, pivotal for improving our understanding of many biological processes.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

Towards realizing nano-enabled precision delivery in plants

Nanocarriers (NCs) that can precisely deliver active agents, nutrients and genetic materials into plants will make crop agriculture more resilient to climate change and sustainable. As a research field, nano-agriculture is still developing, with significant scientific and societal barriers to overcome. In this Review, we argue that lessons can be learned from mammalian nanomedicine. In particular, it may be possible to enhance efficiency and efficacy by improving our understanding of how NC properties affect their interactions with plant surfaces and biomolecules, and their ability to carry and deliver cargo to specific locations. New tools are required to rapidly assess NC-plant interactions and to explore and verify the range of viable targeting approaches in plants. Elucidating these interactions can lead to the creation of computer-generated in silico models (digital twins) to predict the impact of different NC and plant properties, biological responses, and environmental conditions on the efficiency and efficacy of nanotechnology approaches. Finally, we highlight the need for nano-agriculture researchers and social scientists to converge in order to develop sustainable, safe and socially acceptable NCs.

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints

Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction. Simultaneously, it is essential to provide a meaningful explanation of the biological mechanism behind dimensionality. To tackle this challenge, we propose a new unsupervised model named RevGraphVAMP to intelligently analyze the simulation trajectory. This model is based on the variational approach for Markov processes (VAMP) and integrates graph convolutional neural networks and physical constraint optimization to enhance the learning performance. Additionally, we introduce attention mechanism to assess the importance of key interaction region, facilitating the interpretation of molecular mechanism. In comparison to other VAMPNets models, our model showcases competitive performance, improved accuracy in state transition prediction, as demonstrated through its application to two public datasets and the Shank3-Rap1 complex, which is associated with autism spectrum disorder. Moreover, it enhanced dimensionality reduction discrimination across different substates and provides interpretable results for protein structural characterization.

Comparative Pore Structure and Dynamics for Bacterial Microcompartment Shell Protein Assemblies in Sheets or Shells

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria that encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins, analogous to many membrane-bound organelles. The shell proteins assemble into multimeric tiles; hexamers, trimers, and pentamers, and these tiles self-assemble into larger assemblies with icosahedral symmetry. While icosahedral shells are the predominant form in vivo, the tiles can also form nanoscale cylinders or sheets. The individual multimeric tiles feature central pores that are key to regulating transport across the protein shell. Our primary interest is to quantify pore shape changes in response to alternative component morphologies at the nanoscale. We used molecular modeling tools to develop atomically detailed models for both planar sheets of tiles and curved structures representative of the complete shells found in vivo. Subsequently, these models were animated using classical molecular dynamics simulations. From the resulting trajectories, we analyzed the overall structural stability, water accessibility to individual residues, water residence time, and pore geometry for the hexameric and trimeric protein tiles from the Haliangium ochraceum model BMC shell. These exhaustive analyses suggest no substantial variation in pore structure or solvent accessibility between the flat and curved shell geometries. We additionally compare our analysis to hydroxyl radical footprinting data to serve as a check against our simulation results, highlighting specific residues where water molecules are bound for a long time. Although with little variation in morphology or water interaction, we propose that the planar and capsular morphology can be used interchangeably when studying permeability through BMC pores.

Molecular simulations guide immobilization of lipase on nest-like ZIFs with regulatable hydrophilic/hydrophobic surface

The catalytic performance of immobilized lipase is greatly influenced by functional support, which attracts growing interest for designing supports to achieve their promotive catalytic activity. Many lipases bind strongly to hydrophobic surfaces where they undergo interfacial activation. Herein, the behavioral differences of lipases with distinct lid structures on interfaces of varying hydrophobicity levels were firstly investigated by molecular simulations. It was found that a reasonable hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation. Building on these findings, a novel "nest"-like superhydrophobic ZIFs (ZIFN) composed of hydrophobic ligands was prepared for the first time and used to immobilize lipase from Aspergillus oryzae (AOL@ZIFN). The AOL@ZIFN exhibited 2.0-folds higher activity than free lipase in the hydrolysis of p-Nitrophenyl palmitate (p-NPP). Especially, the modification of superhydrophobic ZIFN with an appropriate amount of hydrophilic tannic acid can significantly improve the activity of the immobilized lipase (AOL@ZIFN-TA). The AOL@ZIFN-TA exhibited 30-folds higher activity than free lipase, and still maintained 82% of its initial activity after 5 consecutive cycles, indicating good reusability. These results demonstrated that nanomaterials with rational arrangement of the hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation and improve its activity, displaying the potential of the extensive application.

Computational modelling of extrusion process temperatures on the interactions between black soldier fly larvae protein and corn flour starch

Insects such as the black soldier fly (BSF) are recently being studied as food sources to address concerns about how to meet the food demand of the growing world population, as conventional production lines for meat proteins are currently unsustainable sources. Studies have been conducted evaluating the use of insect proteins to produce extruded foods such as expanded snacks and meat analogues. However, this field of study is still quite new and not much has been studied beyond digestibility and growth performance. The purpose of this work was to evaluate the compatibility of protein extracted from BSF flour with corn flour starch within an extruded balanced shrimp feed model through molecular dynamics simulations, for which cohesive energy density and solubility parameter (δ) of both components were determined. The calculations' results for the protein molecule systems yielded an average δ of 14.961 MPa0.5, while the δ for starch was calculated to be 23.166 MPa0.5. The range of difference between both δ (10 > δ > 7) suggests that the interaction of the BSF protein with corn starch is of a semi-miscible nature. These results suggest that it is possible to obtain a stable starch-protein mixture through the extrusion process.

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Exploring Conformational Landscapes Along Anharmonic Low-Frequency Vibrations

We aim to automatize the identification of collective variables to simplify and speed up enhanced sampling simulations of conformational dynamics in biomolecules. We focus on anharmonic low-frequency vibrations that exhibit fluctuations on time scales faster than conformational transitions but describe a path of least resistance toward structural change. A key challenge is that harmonic approximations are ill-suited to characterize these vibrations, which are observed at far-infrared frequencies and are easily excited by thermal collisions at room temperature. Here, we approached this problem with a frequency-selective anharmonic (FRESEAN) mode analysis that does not rely on harmonic approximations and successfully isolates anharmonic low-frequency vibrations from short molecular dynamics simulation trajectories. We applied FRESEAN mode analysis to simulations of alanine dipeptide, a common test system for enhanced sampling simulation protocols, and compared the performance of isolated low-frequency vibrations to conventional user-defined collective variables (here backbone dihedral angles) in enhanced sampling simulations. The comparison shows that enhanced sampling along anharmonic low-frequency vibrations not only reproduces known conformational dynamics but can even further improve the sampling of slow transitions compared to user-defined collective variables. Notably, free energy surfaces spanned by low-frequency anharmonic vibrational modes exhibit lower barriers associated with conformational transitions relative to representations in backbone dihedral space. We thus conclude that anharmonic low-frequency vibrations provide a promising path for highly effective and fully automated enhanced sampling simulations of conformational dynamics in biomolecules.

Mapping structural and dynamic divergence across the MBOAT family

Membrane-bound O-acyltransferases (MBOATs) are membrane-embedded enzymes that catalyze acyl chain transfer to a diverse group of substrates, including lipids, small molecules, and proteins. MBOATs share a conserved structural core, despite wide-ranging functional specificity across both prokaryotes and eukaryotes. The structural basis of catalytic specificity, regulation and interactions with the surrounding environment remain uncertain. Here, we combine comparative molecular dynamics (MD) simulations with bioinformatics to assess molecular and interactional divergence across the family. In simulations, MBOATs differentially distort the bilayer depending on their substrate type. Additionally, we identify lipid binding sites surrounding reactant gates in the surrounding membrane. Complementary bioinformatic analyses reveal a conserved role for re-entrant loop-2 in MBOAT fold stabilization and a key hydrogen bond bridging DGAT1 dimerization. Finally, we predict differences in MBOAT solvation and water gating properties. These data are pertinent to the design of MBOAT-specific inhibitors that encompass dynamic information within cellular mimetic environments.

Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment

Introduction: This study bridges traditional remedies and modern pharmacology by exploring the synergy between natural compounds and Ceritinib in treating Non-Small Cell Lung Cancer (NSCLC), aiming to enhance efficacy and reduce toxicities. Methods: Using a combined approach of computational analysis, machine learning, and experimental procedures, we identified and analyzed PD173074, Isoquercitrin, and Rhapontin as potential inhibitors of fibroblast growth factor receptor 3 (FGFR3). Machine learning algorithms guided the initial selection, followed by Quantitative Structure-Activity Relationship (QSAR) modeling and molecular dynamics simulations to evaluate the interaction dynamics and stability of Rhapontin. Physicochemical assessments further verified its drug-like properties and specificity. Results: Our experiments demonstrate that Rhapontin, when combined with Ceritinib, significantly suppresses tumor activity in NSCLC while sparing healthy cells. The molecular simulations and physicochemical evaluations confirm Rhapontin's stability and favorable interaction with FGFR3, highlighting its potential as an effective adjunct in NSCLC therapy. Discussion: The integration of natural compounds with established cancer therapies offers a promising avenue for enhancing treatment outcomes in NSCLC. By combining the ancient wisdom of natural remedies with the precision of modern science, this study contributes to evolving cancer treatment paradigms, potentially mitigating the side effects associated with current therapies.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Molecular choreography: Unveiling the dynamic landscape of type IIA DNA topoisomerases before T-segment passage through all-atom simulations

Type IIA DNA topoisomerases are molecular nanomachines responsible for controlling topological states of DNA molecules. Here, we explore the dynamic landscape of yeast topoisomerase IIA during key stages of its catalytic cycle, focusing in particular on the events preceding the passage of the T-segment. To this end, we generated six configurations of fully catalytic yeast topo IIA, strategically inserted a T-segment into the N-gate in relevant configurations, and performed all-atom simulations. The essential motion of topo IIA protein dimer was characterized by rotational gyrating-like movement together with sliding motion within the DNA-gate. Both appear to be inherent properties of the enzyme and an inbuilt feature that allows passage of the T-segment through the cleaved G-segment. Coupled dynamics of the N-gate and DNA-gate residues may be particularly important for controlled and smooth passage of the T-segment and consequently the prevention of DNA double-strand breaks. QTK loop residue Lys367, which interacts with ATP and ADP molecules, is involved in regulating the size and stability of the N-gate. The unveiled features of the simulated configurations provide insights into the catalytic cycle of type IIA topoisomerases and elucidate the molecular choreography governing their ability to modulate the topological states of DNA topology.

Evaluation of Essential Dynamics and Fixed-Length Coarse Graining for Multidomain Proteins

For multiscale modeling of biomolecules, reliable coarse-grained (CG) models can offer great potential to simulate larger temporal and spatial scales than traditional all-atom (AA) models. In this study, we explore the essential dynamics coarse graining (EDCG) and fixed-length coarse graining (FLCG) approaches for constructing highly coarse-grained models for multidomain proteins (MDPs), with 1 to 10 amino acid residues per CG site. In the studies of 13 MDPs, our data indicate that both EDCG and FLCG can preserve the protein dynamics of MDPs. FLCG, which restricts an equal number of residues in each CG site, represents an excellent approximation to EDCG and a straightforward approach for coarse-graining MDPs. Furthermore, FLCG is tested with a class B G-protein-coupled receptor protein, and the agreement with prior experiments suggests its general application to various MDPs in different environments or conditions. Finally, we demonstrate another application of FLCG through progressive backmapping, showcasing the ability to recover from lower-resolution CG models (6 residues/CG site) to higher-resolution ones (1 residue/CG site). These promising outcomes underscore the broad applicability of FLCG to construct highly or ultra-coarse-grained models of complex biomolecules for multiscale simulations.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.