Insight into the molecular recognition of human and polar bear pregnane X receptor by three organic pollutants using molecular docking and molecular dynamics simulations.

Pregnane X receptor (PXR) is a heterologous biosensor that is involved in the metabolic pathway of environmental pollutants, regulating the transcription of genes involved in biotransformation. There are significant differences in the selectivity and specificity of organic pollutants (OPs) toward polar bear PXR (pbPXR) and human PXR (hPXR), but the detailed dynamical characteristics of their interactions are unclear. Homology Modeling, molecular docking, molecular dynamics simulation, and free energy calculation were used to analyze the recognition of pbPXR and hPXR by three OPs: BPA, chlordane and toxaphene. Comparing interaction patterns along with binding free energy of pbPXR and hPXR with these three OPs revealed that although pbPXR and hPXR interact similar with these three OPs, these OPs have different effects on the internal dynamics of pbPXR and hPXR. This results in significant alterations in the interaction of key residues near Leu209, Met243, Phe288, Met323, and His407 with OPs, thereby influencing their binding energy. Non-polar interactions, especially van der Waals interactions, were found to be the dominating factors in interacting of these OPs with PXRs. The region surrounding these key residues facilitates hydrophobic contacts with PXR, which are crucial for the selective activation of PXRs in different species by these three OPs. These findings are of significant guidance in understanding the impacts of environmental endocrine disruptors on different organisms.

Oncogenic and telomeric G-quadruplexes: Targets for porphyrin-triphenylphosphonium conjugates.

DNA chains with sequential guanine (G) repeats can lead to the formation of G-quadruplexes (G4), which are found in functional DNA and RNA regions like telomeres and oncogene promoters. The development of molecules with adequate structural features to selectively stabilize G4 structures can counteract cell immortality, highly described for cancer cells, and also downregulate transcription events underlying cell apoptosis and/or senescence processes. We describe here, the efficiency of four highly charged porphyrins-phosphonium conjugates to act as G4 stabilizing agents. The spectrophotometric results allowed to select the conjugates P2-PPh3 and P3-PPh3 as the most promising ones to stabilize selectively G4 structures. Molecular dynamics simulation experiments were performed and support the preferential binding of P2-PPh3 namely to MYC and of P3-PPh3 to KRAS. The ability of both ligands to block the activity of Taq polymerase was confirmed and also their higher cytotoxicity against the two melanoma cell lines A375 and SK-MEL-28 than to immortalized skin keratinocytes. Both ligands present efficient cellular uptake, nuclear co-localization and high ability to generate 1O2 namely when interacting with G4 structure. The obtained data points the synthesized porphyrins as promising ligands to be used in a dual approach that can combine G4 stabilization and Photodynamic therapy (PDT).

Structure based computational RNA design towards MafA transcriptional repressor implicated in multiple myeloma.

Multiple myeloma is recognized as the second most common hematological cancer. MafA transcriptional repressor is an established mediator of myelomagenesis. While there are multitude of drugs available for targeting various effectors in multiple myeloma, current literature lacks a candidate RNA based MafA modulator. Thus, using the structure of MafA homodimer-consensus target DNA, a computational effort was implemented to design a novel RNA based chemical modulator against MafA. First, available MafA-consensus DNA structure was employed to generate an RNA library. This library was further subjected to global docking to select the most plausible RNA candidates, preferring to bind DNA binding region of MafA. Following global docking, MD-ready complexes that were prepared via local docking program, were subjected to 500 ns of MD simulations. First, each of these MD simulations were analyzed for relative binding free energy through MM-PBSA method, which pointed towards a strong RNA based MafA binder, RNA1. Second, through a detailed MD analysis, RNA1 was shown to prefer binding to a single monomer of the dimeric DNA binding domain of MafA using higher number of hydrophobic interactions compared with positive control MafA-DNA complex. At the final phase, a principal component analyses was conducted, which led us to identify the actual interaction region of RNA1 and MafA monomer. Overall, to our knowledge, this is the first computational study that presents an RNA molecule capable of potentially targeting MafA protein. Furthermore, limitations of our study together with possible future implications of RNA1 in multiple myeloma were also discussed.

Regulating the electronic structure of Pt SAs-Ni2P for enhanced hydrogen evolution reaction.

The single atom catalysts (SACs) show immense promise as catalytic materials. By doping the single atoms (SAs) of precious metals onto substrates, the atomic utilization of these metals can be maximized, thereby reducing catalyst costs. The electronic structure of precious metal SAs is significantly influenced by compositions of doped substrates. Therefore, optimizing the electronic structure through appropriate doping of substrates can further enhance catalytic activity. Here, Pt single atoms (Pt SAs) are doped onto transition metal sulfide substrate NiS2 (Pt SAs-NiS2) and phosphide substrate Ni2P (Pt SAs-Ni2P) to design and prepare catalysts. Compared to the Pt SAs-NiS2 catalyst, the Pt SAs-Ni2P catalyst exhibits better hydrogen evolution catalytic performance and stability. Under 1 M KOH conditions, the hydrogen evolution mass activity current density of the Pt SAs-Ni2P catalyst reaches 0.225 A mgPt-1 at 50 mV, which is 33 times higher than that of commercial Pt/C catalysts. It requires only 44.9 mV to achieve a current density of 10 mA cm-2. In contrast, for the Pt SAs-NiS2 catalyst, the hydrogen evolution mass activity current density is 0.178 A mgPt-1, requiring 77.8 mV to achieve a current density of 10 mA cm-2. Theoretical calculations indicate that in Pt SAs-Ni2P, the interaction between Pt SAs and the Ni2P substrate causes the Pt d-band center to shift downward, enhancing the H2O desorption and providing optimal H binding sites. Additionally, the hollow octahedral morphology of Ni2P provides a larger surface area, exposing more reactive sites and improving reaction kinetics. This study presents an effective pathway for preparing high-performance hydrogen evolution electrocatalysts by selecting appropriate doped substrates to control the electronic structure of Pt SAs.

Quality assessment of the wide-angle detection option planned at the high-intensity/extended Q-range SANS diffractometer KWS-2 combining experiments and McStas simulations.

For a reliable characterization of materials and systems featuring multiple structural levels, a broad length scale from a few ångström to hundreds of nanometres must be analyzed and an extended Q range must be covered in X-ray and neutron scattering experiments. For certain samples or effects, it is advantageous to perform such characterization with a single instrument. Neutrons offer the unique advantage of contrast variation and matching by D-labeling, which is of great value in the characterization of natural or synthetic polymers. Some time-of-flight small-angle neutron scattering (TOF-SANS) instruments at neutron spallation sources can cover an extended Q range by using a broad wavelength band and a multitude of detectors. The detectors are arranged to cover a wide range of scattering angles with a resolution that allows both large-scale morphology and crystalline structure to be resolved simultaneously. However, for such analyses, the SANS instruments at steady-state sources operating in conventional monochromatic pinhole mode rely on additional wide-angle neutron scattering (WANS) detectors. The resolution must be tuned via a system of choppers and a TOF data acquisition option to reliably measure the atomic to mesoscale structures. The KWS-2 SANS diffractometer at Jülich Centre for Neutron Science allows the exploration of a wide Q range using conventional pinhole and lens focusing modes and an adjustable resolution Δλ/λ between 2 and 20%. This is achieved through the use of a versatile mechanical velocity selector combined with a variable slit opening and rotation frequency chopper. The installation of WANS detectors planned on the instrument required a detailed analysis of the quality of the data measured over a wide angular range with variable resolution. This article presents an assessment of the WANS performance by comparison with a McStas [Willendrup, Farhi & Lefmann (2004). Physica B, 350, E735-E737] simulation of ideal experimental conditions at the instrument.

In Silico Approach for Assessment of the Anti-tumor Potential of Cannabinoid Compounds by Targeting Glucose-6-phosphate Dehydrogenase Enzyme.

Glucose-6-phosphate dehydrogenase (G6PD) is a pentose phosphate pathway (PPP) enzyme that generates NADPH, which is required for cellular redox equilibrium and reductive biosynthesis. It has been demonstrated that abnormal G6PD activation promotes cancer cell proliferation and metastasis. To date, no G6PD inhibitor has passed clinical testing successfully enough to be launched as a medicine. As a result, in this investigation, cannabinoids were chosen to evaluate their anticancer potential by targeting G6PD. Molecular docking indicated that three molecules, Tetrahydrocannabinolic acid (THCA), Cannabichromenic acid (CBCA), and tetrahydrocannabivarin (THCV), have the highest binding affinities for G6PD of -8.61, - 8.39, and 8.01 Kcal/mol. ADMET analysis found that all of them were safe prospective drug candidates. Molecular dynamics (MD) simulation and MM-PBSA analysis confirm the structural compactness and lower conformational variation of protein-ligand complexes, thereby maintaining structural stability and rigidity. Thus, our in silico investigation exhibited all three cannabinoids as potential competitive inhibitors of G6PD.

MLgrating: a program for simulating multilayer gratings for tender X-ray applications.

Multilayer gratings are increasingly popular optical elements at X-ray beamlines, as they can provide much higher photon flux in the tender X-ray range compared with traditional single-layer coated gratings. While there are several proprietary software tools that provide the functionality to simulate the efficiencies of such gratings, until now the X-ray community has lacked an open-source alternative. Here MLgrating is presented, a program for simulating the efficiencies of both multilayer gratings and single-layer coated gratings for X-ray applications. MLgrating is benchmarked by comparing its output with that of other software tools and plans are discussed for how the program could be extended in the future.

Dissecting molecular mechanisms underlying the inhibition of ß-glucuronidase by alkaloids from Hibiscus trionum: Integrating in vitro and in silico perspectives.

ß-Glucuronidase, a crucial enzyme in drug metabolism and detoxification, represents a promising target for therapeutic intervention due to its potential to modulate drug pharmacokinetics and enhance therapeutic efficacy. Herein, we assessed the inhibitory potential of phytochemicals from Hibiscus trionum against ß-glucuronidase. Grossamide and grossamide K emerged as the most potent ß-glucuronidase inhibitors with IC50 values of 0.73 ± 0.03 and 1.24 ± 0.03 µM, respectively. The investigated alkaloids effectively inhibited ß-glucuronidase-catalyzed PNPG hydrolysis through a noncompetitive inhibition mode, whereas steppogenin displayed a mixed inhibition mechanism. Molecular docking analyses highlighted grossamide and grossamide K as inhibitors with the lowest binding free energy, all compounds successfully docked into the same main binding site occupied by the reference drug Epigallocatechin gallate (EGCG). We explored the interaction dynamics of isolated compounds with ß-glucuronidase through a 200 ns molecular dynamics (MD) simulation. Analysis of various MD parameters revealed that grossamide and grossamide K maintained stable trajectories and demonstrated significant energy stabilization upon binding to ß-glucuronidase. Additionally, these compounds exhibited the lowest average interaction energies with the target enzyme. The MM/PBSA calculations further supported these findings, showing the lowest binding free energies for grossamide and grossamide K. These computational results are consistent with experimental data, suggesting that grossamide and grossamide K could be potent inhibitors of ß-glucuronidase.

Intrinsic dynamics of emulsions: Experiments in microgravity on the International Space Station.

HYPOTHESIS: In order to understand the basic mechanisms affecting emulsion stability, the intrinsic dynamics of the drop population must be investigated. We hypothesize that transient ballistic motion can serve as a marker of interactions between drops. In 1G conditions, buoyancy-induced drop motion obscures these interactions. The microgravity condition onboard the International Space Station enable this investigation. EXPERIMENTS: We performed Diffusing Wave Spectroscopy (DWS) experiments in the ESA Soft Matter Dynamics (SMD) facility. We used Monte Carlo simulations of photon trajectory to support data analysis. The analysis framework was validated by ground-based characterizations of the initial drop size distribution (DSD) and the properties of the oil/water interface in the presence of surfactant. FINDINGS: We characterized the drop size distribution and found to be bi-disperse. Drop dynamics shows transient ballistic features at early times, reaching a stationary regime of primarily diffusion-dominated motion. This suggests different ageing mechanisms: immediately after emulsification, the main mechanism is coalescence or aggregation between small drops. However at later times, ageing proceeds via coalescence or aggregation of small with large drops in some emulsions. Our results elucidate new processes relevant to emulsion stability with potential impact on industrial processes on Earth, as well as enabling technologies for space exploration.

Distribution of Single-Particle Resonances Determines the Plasmonic Response of Disordered Nanoparticle Ensembles.

Understanding how colloidal soft materials interact with light is crucial to the rational design of optical metamaterials. Electromagnetic simulations are computationally expensive and have primarily been limited to model systems described by a small number of particles-dimers, small clusters, and small periodic unit cells of superlattices. In this work we study the optical properties of bulk, disordered materials comprising a large number of plasmonic colloidal nanoparticles using Brownian dynamics simulations and the mutual polarization method. We investigate the far-field and near-field optical properties of both colloidal fluids and gels, which require thousands of nanoparticles to describe statistically. We show that these disordered materials exhibit a distribution of particle-level plasmonic resonance frequencies that determines their ensemble optical response. Nanoparticles with similar resonant frequencies form anisotropic and oriented clusters embedded within the otherwise isotropic and disordered microstructures. These collectively resonating morphologies can be tuned with the frequency and polarization of incident light. Knowledge of particle resonant distributions may help to interpret and compare the optical responses of different colloidal structures, correlate and predict optical properties, and rationally design soft materials for applications harnessing light.

Exploration of dynamic interaction between ß-lactoglobulin and casein micelles during UHT milk process.

The protein aggregation induced by UHT treatment shortens the shelf life of UHT milk. However, the mechanism of ß-Lg induced casein micelle aggregation remains unclear. Herein, the dynamic interaction between ß-Lg and casein micelles during UHT processing was investigated by experimental techniques and molecular dynamics simulations. Results showed that ß-Lg decreased the stability of casein micelles, increased their size and zeta potential. Raman and FTIR spectra analysis suggested that hydrogen and disulfide bonds facilitated their interaction. Cryo-TEM showed that the formation of the casein micelle/ß-Lg complex involved rigid binding, flexible linking, and severe cross-linking aggregation during UHT processing. SAXS and MST demonstrated ß-Lg bound to κ-casein on micelle surfaces with a dissociation constant (Kd) of 3.84 ± 1.14 µm. Molecular docking and dynamic simulations identified the interacting amino acid residues and clarified that electrostatic and van der Waals forces drove the interaction. UHT treatment increased hydrogen bonds and decreased total binding energy. The non-covalent binding promoted the formation of disulfide bonds between ß-Lg and casein micelles under heat treatment. Ultimately, it was concluded that non-covalent interaction and disulfide bonding resulted in casein micelle/ß-Lg aggregates. These findings provided scientific insights into protein aggregation in UHT milk.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.

BACKGROUND: Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. OBJECTIVES/AIMS: This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. METHODS: This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. RESULTS: The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. CONCLUSION: Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Spacing effects on flows around two square cylinders in staggered arrangement via LBM.

This study presents a computational analysis of fluid flow characteristics around two staggered arranged square cylinders using the Lattice Boltzmann Method (LBM). With Reynolds number (Re) fixed at 200, numerical simulations explore the influence of varying gap ratios (G) ranging from 0 to 10 times the cylinder size. Emphasis is placed on understanding the impact of cylinders spacing on flow structure mechanisms and induced forces. Investigation of fluid flow parameters includes vorticity behavior, pressure streamlines, and variations in drag and lift coefficients alongside the Strouhal number under different values of G. From the results, four distinct flow patterns emerge: single bluff body flow, flip flopping flow, modulated synchronized flow, and synchronized flow, each exhibiting unique characteristics. This study reveals the strong dependence of fluid forces on G, with low spacing values leading to complex vortex structures and fluctuating forces influenced by jet flow effects. At higher spacing values, proximity effects between cylinders diminish, resulting in a smoother periodic flow. The Strouhal number, average drag force and the rms values of drag and lift force coefficients vary abruptly at narrow gaps and become smooth at higher gap ratios. Unlike the tandem and side-by-side arrangements the staggered cylinders arrangement is found to have significant impact on the pressure variations around both cylinders. Overall, this research could contribute to a comprehensive understanding of staggered cylinder arrangements and their implications for engineering applications.

Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method.

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.

Selective detection of food contaminants using engineered gallium-organic frameworks with MD and metadynamics simulations.

The exclusion mechanism of food contaminants such as bisphenol A (BPA), Flavonoids (FLA), and Goitrin (GOI) onto the novel gallium-metal organic framework (MOF) and functionalized MOF with oxalamide group (MOF-OX) is evaluated by utilizing molecular dynamics (MD) and Metadynamics simulations. The atoms in molecules (AIM) analysis detected different types of atomic interactions between contaminant molecules and substrates. To assess this procedure, a range of descriptors including interaction energies, root mean square displacement, radial distribution function (RDF), density, hydrogen bond count (HB), and contact numbers are examined across the simulation trajectories. The most important elements in the stability of the systems under examination are found to be stacking π-π and HB interactions. It was confirmed by a significant value of total interaction energy for BPA/MOF-OX (- 338.21 kJ mol-1) and BPA/MOF (- 389.95 kJ mol-1) complexes. Evaluation of interaction energies reveals that L-J interaction plays an essential role in the adsorption of food contaminants on the substrates. The free energy values for the stability systems of BPA/MOF and BPA/MOF-OX complexes at their global minima reached about BPA/MOF = - 254.29 kJ mol-1 and BPA/MOF-OX = - 187.62 kJ mol-1, respectively. Nevertheless, this work provides a new strategy for the preparation of a new hierarchical tree-dimensional of the Ga-MOF hybrid material for the adsorption and exclusion of food contaminates and their effect on human health.

Phytochemical Targeting of Nerve Growth Factor by Thymoquinone and Cuscutin: A Molecular Dynamics Simulation Study.

Background Nerve growth factor (NGF) is a novel target of pain therapeutics for oral cancer, and it plays a main role in the nociception of chronic pain. Surgery, along with chemotherapy or radiotherapy, is the gold standard for treating patients, but the side effects are significant as well. Newer effective interventions with natural phytochemicals could improve patient compliance and enhance the quality of life among patients with oral cancer. A literature search revealed a positive correlation between NGF and oral cancer pain. Nigella sativa (N. sativa) and Cuscuta reflexa (C. reflexa) have proven anticancer effects, but their activity with NGF is unexplored. Aims and objectives We aimed to identify the potential phytochemicals in N. sativa and C. reflexa. We also checked the NGF-blocking activity of the phytochemicals. Molecular docking and molecular dynamic (MD) simulations evaluated the binding energy and stability between the NGF protein and selected phytochemical ligands. Materials and methods We obtained protein NGF structure from UniProt (ID: 4EDX, P01138, Beta-nerve growth factor), ligand (thymoquinone) structure using PubChem ID: 10281, and ligand (cuscutin) structure using PubChem ID: 66065. Maestro protein (Schrödinger Inc., Mannheim, Germany) was used for molecular docking. Desmond Simulation Package (Schrödinger Inc., Mannheim, Germany) was used to model MD for 100 nanoseconds (ns). We have assessed the interaction between the protein and ligands by root mean square deviation (RMSD) values.  Results The interaction of thymoquinone and cuscutin with NGF was assessed. While interacting with thymoquinone, there was mild fluctuation from 0.6 Å to 2.5 Å up to 80 ns and ended up at 4.8 Å up to 100 ns. While interacting with cuscutin, mild fluctuation was seen from 0.8 Å to 4.8 Å till 90 ns and ended at 6.4 Å up to 100 ns. We found a stable interaction between our drug combination and the NGF receptor. Conclusion We have identified a stable interaction between thymoquinone, cuscutin, and NGF by our MD simulations. Hence, it could be used as an NGF inhibitor for pain relief and to control tumor progression. Further in vitro and in vivo evaluations of this novel drug combination with phytochemicals will help us understand their biological activities and potential clinical applications in oral cancer therapeutics.

Vibronics of multi-material nanopillared membranes and impact on the thermal conductivity.

Atomic motion in nanopillars standing on the surface of a silicon membrane generates vibrons, which are wavenumber-independent phonons that act as local resonances. These vibrons couple with the vast majority of the phonon population, including heat-carrying phonons, traveling along the base membrane causing a reduction in the in-plane lattice thermal conductivity. In this work, we examine isolated silicon and gallium nitride nanopillars and for each compare the vibrons density of states (DOS) to those of phonons in an isolated version of the silicon membrane. We show that while the conformity of the phonon-vibron DOS distribution between the two components across the full spectrum is a key factor in reducing the thermal conductivity of the assembled nanostructure, the presence of an intense vibron population at more dominant low frequencies plays a competing role. We report predictions from molecular dynamics simulations showing lower thermal conductivities for a silicon membrane with gallium-nitride nanopillars compared to a silicon membrane with silicon nanopillars.

Response of Foldable Protein Conformations to Non-physiological Perturbations: Interplay of Thermal Factors and Confinement.

Technological advances frequently interface biomolecules with nanomaterials at non-physiological conditions, necessitating response characterization of key processes. Similar encounters are expected in cellular contexts. We report in silico investigations of the response of diverse protein conformational states to lowering of temperature and imposition of spatial constraints. Conformational states are represented by folded form of the Albumin binding domain (ABD) protein, its compact denatured form, and structurally disordered nascent folding elements. Data from extensive simulations are evaluated to elicit structural, thermodynamic and dynamic responses of the states and their associated environment. Analyses reveal alterations to folding propensity with reduced thermal energy and confinement, with signatures of trend reversal in highly disordered states. Across temperatures, confinement has restrictive effects on volume and energetic fluctuations, leading to narrowing of differences in isothermal compressibility (κ) and heat capacities (Cp). While excess (over ideal gas) entropy of the hydration layer marks dependence on the conformational state at bulk, confinement triggers erasure of differences. These observations are largely consistent with timescales of protein-water hydrogen bonding dynamics. The results implicate multi-factorial associations within a simple bio-nano complex. We expect the current study to motivate investigations of more biologically relevant interfaces towards mechanistic understanding and potential applications.

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins.

INTRODUCTION: Molecular Dynamics (MD) simulations can support mechanism-based drug design. Indeed, MD simulations by capturing biomolecule motions at finite temperatures can reveal hidden binding sites, accurately predict drug-binding poses, and estimate the thermodynamics and kinetics, crucial information for drug discovery campaigns. Small-Guanosine Triphosphate Phosphohydrolases (GTPases) regulate a cascade of signaling events, that affect most cellular processes. Their deregulation is linked to several diseases, making them appealing drug targets. The broad roles of small-GTPases in cellular processes and the recent approval of a covalent KRas inhibitor as an anticancer agent renewed the interest in targeting small-GTPase with small molecules. AREA COVERED: This review emphasizes the role of MD simulations in elucidating small-GTPase mechanisms, assessing the impact of cancer-related variants, and discovering novel inhibitors. EXPERT OPINION: The application of MD simulations to small-GTPases exemplifies the role of MD simulations in the structure-based drug design process for challenging biomolecular targets. Furthermore, AI and machine learning-enhanced MD simulations, coupled with the upcoming power of quantum computing, are promising instruments to target elusive small-GTPases mutations and splice variants. This powerful synergy will aid in developing innovative therapeutic strategies associated to small-GTPases deregulation, which could potentially be used for personalized therapies and in a tissue-agnostic manner to treat tumors with mutations in small-GTPases.