Synthesis, biological evaluation, molecular docking, and MD simulation of novel 2,4-disubstituted quinazoline derivatives as selective butyrylcholinesterase inhibitors and antioxidant agents.

Alzheimer's disease is the most prevalent neurodegenerative disorder characterized by significant memory loss and cognitive impairments. Studies have shown that the expression level and activity of the butyrylcholinesterase enzyme increases significantly in the late stages of Alzheimer's disease, so butyrylcholinesterase can be considered as a promising therapeutic target for potential Alzheimer's treatments. In the present study, a novel series of 2,4-disubstituted quinazoline derivatives (6a-j) were synthesized and evaluated for their inhibitory activities against acetylcholinesterase (AChE) and butyrylcholinestrase (BuChE) enzymes, as well as for their antioxidant activities. The biological evaluation revealed that compounds 6f, 6h, and 6j showed potent inhibitory activities against eqBuChE, with IC50 values of 0.52, 6.74, and 3.65 µM, respectively. These potent compounds showed high selectivity for eqBuChE over eelAChE. The kinetic study demonstrated a mixed-type inhibition pattern for both enzymes, which revealed that the potent compounds might be able to bind to both the catalytic active site and peripheral anionic site of eelAChE and eqBuChE. In addition, molecular docking studies and molecular dynamic simulations indicated that potent compounds have favorable interactions with the active sites of BuChE. The antioxidant screening showed that compounds 6b, 6c, and 6j displayed superior scavenging capabilities compared to the other compounds. The obtained results suggest that compounds 6f, 6h, and 6j are promising lead compounds for the further development of new potent and selective BuChE inhibitors.

QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy.

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure-activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of - 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction.

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

Design and synthesis of a nucleobase functionalized peptide hydrogel: in vitro assessment of anti-inflammatory and wound healing effects.

Over the past several years, a significant increase in the expanding field of biomaterial sciences has been observed due to the development of biocompatible materials based on peptide derivatives that have intrinsic therapeutic potential. In this report, we synthesized nucleobase functionalized peptide derivatives (NPs). Hydrogelation in the synthesized NPs was induced by increasing their hydrophobicity with an aromatic moiety. The aggregation behavior of the NPs was analyzed by performing molecular dynamics simulations and DOSY NMR experiments. We performed circular dichroism (CD), thioflavin-T binding and PXRD to characterize the supramolecular aggregation in the NP1 hydrogel. The mechanical strength of the NP1 hydrogel was tested by performing rheological experiments. TEM and SEM experiments were performed to investigate the morphology of the NP1 hydrogel. The biocompatibility of the newly synthesized NP1 hydrogel was investigated using McCoy and A549 cell lines. The hemolytic activity of the NP1 hydrogel was examined in human blood cells. The stability of the newly formed NP1 hydrogel was examined using proteinase K and α-chymotrypsin. The NP1 hydrogel was used for in vitro wound healing. Western blotting, qRT-PCR and DCFDA assay were performed to determine the anti-inflammatory activity of the NP1 hydrogel. The synthesized NP1 hydrogel also exhibits antibacterial efficacy.

Residual Membrane Fluidity in Mycobacterial Cell Envelope Layers under Extreme Conditions Underlines Membrane-Centric Adaptation.

One of the routes for adaptation to extreme environments is via remodeling of cell membrane structure, composition, and biophysical properties rendering a functional membrane. Collective studies suggest some form of membrane feedback in mycobacterial species that harbor complex lipids within the outer and inner cell wall layers. Here, we study the homeostatic membrane landscape of mycobacteria in response to high hydrostatic pressure and temperature triggers using high pressure fluorescence, mass and infrared spectroscopies, NMR, SAXS, and molecular dynamics simulations. Our findings reveal that mycobacterial membrane possesses unique and lipid-specific pressure-induced signatures that attenuate progression to highly ordered phases. Both inner and outer membrane layers exhibit phase coexistence of nearly identical lipid phases keeping residual fluidity over a wide range of temperature and pressure, but with different sensitivities. Lipidomic analysis of bacteria grown under pressure revealed lipidome remodeling in terms of chain length, unsaturation, and specific long-chained characteristic mycobacterial lipids, rendering a fluid bacterial membrane. These findings could help understand how bacteria may adapt to a broad spectrum of harsh environments by modulating their lipidome to select lipids that enable the maintenance of a fluid functional cell envelope.

A Joint Experimental and Theoretical Study on the Structural and Spectroscopic Properties of the Piv-Pro-d-Ser-NHMe Peptide.

In this paper, we investigate the secondary structure of the Piv-Pro-d-Ser-NHMe peptide by means of nuclear magnetic resonance (NMR) and electronic circular dichroism (ECD) experiments, in conjunction with theoretical simulations based on molecular dynamics and time-dependent density functional theory calculations including polarizable embedding to account for solvent effects. The various experimental and theoretical protocols are assessed and validated, and are shown to provide a consistent description of the turn structure adopted by this peptide in solution. In addition, a simple fitting procedure is proposed to make the simulated and experimental ECD almost perfectly match. This full methodology is finally tested on another small peptide, enlightening its efficiency and robustness.

Molecular Dynamics Simulations of Soft and Reactive Landing of Proteins Desorbed by Argon Cluster Bombardment.

Reactive molecular dynamics (MD) simulations were conducted to investigate the soft and reactive landing of hyperthermal velocity proteins transferred to a vacuum using large argon clusters. Experimentally, the interaction of argon cluster ion beams (Ar1000-5000+) with a target biofilm was previously used in such a manner to transfer lysozymes onto a collector with the retention of their bioactivity, paving the way to a new solvent-free method for complex biosurface nanofabrication. However, the experiments did not give access to a microscopic view of the interactions needed for their full understanding, which can be provided by the MD model. Our reactive force field simulations clarify the landing mechanisms of the lysozymes and their fragments on collectors with different natures (gold- and hydrogen-terminated graphite). The results highlight the conditions of soft and reactive landing on rigid surfaces, the effects of the protein structure, energy, and incidence angle before landing, and the adhesion forces with the collector substrate. Many of the obtained results can be generalized to other soft and reactive landing approaches used for biomolecules such as electrospray ionization and matrix-assisted laser desorption ionization.

Coarse-Grained Modeling of Liquid-Liquid Phase Separation in Cells: Challenges and Opportunities.

Liquid-liquid phase separation (LLPS) within cells gives rise to membraneless organelles, which play pivotal roles in numerous cellular functions. A comprehensive understanding of the functional aspects of intrinsically disordered protein (IDP) condensates necessitates elucidating their inherent structures and establishing correlations with biological functions. Coarse-grained (CG) molecular dynamics (MD) simulations present a promising avenue for gaining insights into LLPS mechanisms of biomacromolecules. Essential to this endeavor is the development of tailored CG force fields for MD simulations, incorporating the full spectrum of biomolecules involved in the formation of condensates and accounting for real-time biochemical reactions coupled to the LLPS. Moreover, developing accurate theoretical frameworks and establishing links between condensate structure and its function are imperative for a thorough comprehension of LLPS of biological systems.

Exploring How System Dimensions and Periodic Boundary Conditions Influence the Molecular Dynamics Simulation of A6H Peptide Self-Assembly Nanostructures.

This work presents a study on the effects of periodic boundary conditions (PBC) on the energetic/structural properties and hydrogen bond dynamics (HB) using molecular dynamics (MD) simulations of peptide membranes composed of alanine and histidine. Our results highlight that simulations using small surface areas for the peptide membrane may result in nonconvergent values for membrane properties, which are only observed in regions simulated at a certain distance from the PBCs. Specifically, regarding hydrogen bonds, a property pervasive in peptide membranes, our findings indicate a significant increase in the lifetime of these interactions, reaching values ∼19% higher when observed in structures free from PBCs. For peptide mobility in these nanomembranes, our results compare regions simulated directly under the influence of PBCs with regions free from these conditions, emphasizing greater mobility of amino acid psi/phi angles in the latter model.

Sequence-Dependent Surface Coverage of ssDNA Coatings on Single-Wall Carbon Nanotubes.

A combination of experimental measurements and molecular dynamics (MD) simulations was used to investigate how the surfaces of single-wall carbon nanotubes (SWCNTs) are covered by adsorbed ssDNA oligos with different base compositions and lengths. By analyzing the UV absorption spectra of ssDNA-coated SWCNTs before and after coating displacement by a transparent surfactant, the mass ratios of adsorbed ssDNA to SWCNTs were determined for poly-T, poly-C, GT-containing, and AT-containing ssDNA oligos. Based on the measured mass ratios, it is estimated that an average of 20, 22, 26, or 32 carbon atoms are covered by one adsorbed thymine, cytosine, adenine, or guanine nucleotide, respectively. In addition, the UV spectra revealed electronic interactions of varying strengths between the nucleobase aromatic rings and the nanotube π-systems. Short poly-T DNA oligos show stronger π-π stacking interactions with SWCNT surfaces than do short poly-C DNA oligos, whereas both long poly-C and poly-T DNA oligos show strong interactions. These experiments were complemented by MD computations on simulated systems that were constrained to match the measured ssDNA/SWCNT mass ratios. The surface coverages computed from the MD results varied with oligo composition in a pattern that correlates higher measured yields of nanotube fluorescence with greater surface coverage.

DFT/MM Simulations for Cycloreversion Reaction of Cyclobutane Pyrimidine Dimer with Deprotonated and Protonated E283.

DNA photolyase targets the primary ultraviolet (UV)-induced DNA lesion─cyclobutane pyrimidine dimer (CPD), attaches to it, and catalyzes its dissociation. The catalytic mechanism of DNA photolyase and the role of the conserved residue E283 remain subjects of debate. This study employs two-dimensional potential energy surface maps and minimum free energy paths calculated at the ωB97XD/6-31G/MM level to elucidate these mechanisms. Results suggest that the catalytic process follows a sequential, stepwise reaction in which the C5-C5 and C6-C6 bonds are cleaved in order, facilitated by a protonated E283. Activation free energies for these cleavages are calculated at 4.4 and 4.2 kcal·mol-1, respectively. Protonation of E283 reduces electrostatic repulsion with CPD and forms dual hydrogen bonds with it and provides better solvation, stabilizing the CPD radical anion, particularly during intermediate state. This stabilization renders the initial splitting step exergonic, slows reverse reactions of the C5-C5 bond cleavage and electron transfer, and ensures a high quantum yield. Furthermore, the protonation state of E283 significantly affects the type of bond cleavage. Other residues in the active site were also investigated for their roles in the mechanism.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

On the quaternary structure of human D-3-phosphoglycerate dehydrogenase.

D-3-phosphoglycerate dehydrogenase (PHGDH) catalyzes the NAD+-dependent conversion of D-3-phospho-glycerate to 3-phosphohydroxypyruvate, the first step in the phosphorylated pathway for L-serine (L-Ser) biosynthesis. L-Ser plays different relevant metabolic roles in eukaryotic cells: alterations in L-Ser metabolism have been linked to serious neurological disorders. The human PHGDH (hPHGDH), showing a homotetrameric state in solution, is made of four domains, among which there are two regulatory domains at the C-terminus: the aspartate kinase-chorismate mutase-tyrA prephenate dehydrogenase (ACT) and allosteric substrate-binding (ASB) domains. The structure of hPHGDH was solved only for a truncated, dimeric form harboring the N-terminal end containing the substrate and the cofactor binding domains. A model ensemble of the tetrameric hPHGDH was generated using AlphaFold coupled with molecular dynamics refinement. By analyzing the inter-subunit interactions at the tetrameric interface, the residues F418, L478, P479, R454, and Y495 were selected and their role was studied by the alanine-scanning mutagenesis approach. The F418A variant modifies the putative ASB, slightly alters the activity, the fraction of protein in the tetrameric state, and the protein stability; it seems relevant in dimers' recognition to yield the tetrameric oligomer. On the contrary, the R454A, L478A, P479A, and Y495A variants (ACT domain) determine a loss of the tetrameric assembly, resulting in low stability and misfolding, triggering the aggregation and hampering the activity. The predicted tetrameric interface seems mediated by residues at the ACT domain, and the tetramer formation seems crucial for proper folding of hPHGDH, which, in turn, is essential for both stability and functionality.

Entropy drives the ligand recognition in G-protein-coupled receptor subtypes.

Achieving ligand subtype selectivity within highly homologous subtypes of G-protein-coupled receptor (GPCR) is critical yet challenging for GPCR drug discovery, primarily due to the unclear mechanism underlying ligand subtype selectivity, which hampers the rational design of subtype-selective ligands. Herein, we disclose an unusual molecular mechanism of entropy-driven ligand recognition in cannabinoid (CB) receptor subtypes, revealed through atomic-level molecular dynamics simulations, cryoelectron microscopy structure, and mutagenesis experiments. This mechanism is attributed to the distinct conformational dynamics of the receptor's orthosteric pocket, leading to variations in ligand binding entropy and consequently, differential binding affinities, which culminate in specific ligand recognition. We experimentally validated this mechanism and leveraged it to design ligands with enhanced or ablated subtype selectivity. One such ligand demonstrated favorable pharmacokinetic properties and significant efficacy in rodent inflammatory analgesic models. More importantly, it is precisely due to the high subtype selectivity obtained based on this mechanism that this ligand does not show addictive properties in animal models. Our findings elucidate the unconventional role of entropy in CB receptor subtype selectivity and suggest a strategy for rational design of ligands to achieve entropy-driven subtype selectivity for many pharmaceutically important GPCRs.

Effective model of protein-mediated interactions in chromatin.

Protein-mediated interactions are ubiquitous in the cellular environment, and particularly in the nucleus, where they are responsible for the structuring of chromatin. We show through molecular-dynamics simulations of a polymer surrounded by binders that the strength of the binder-polymer interaction separates an equilibrium from a nonequilibrium regime. In the equilibrium regime, the system can be efficiently described by an effective model in which the binders are traced out. Even in this case, the polymers display features that are different from those of a standard homopolymer interacting with two-body interactions. We then extend the effective model to deal with the case where binders cannot be regarded as in equilibrium and a new phenomenology appears, including local blobs in the polymer. An effective description of this system can be useful in elucidating the fundamental mechanisms that govern chromatin structuring in particular and indirect interactions in general.

Structure and Dynamics of Type 4a Pili and Type 2 Secretion System Endopili.

Within the highly diverse type four filament (TFF or T4F) superfamily, the machineries of type IVa pili (T4aP) and the type 2 secretion system (T2SS) in diderm bacteria exhibit a substantial sequence similarity despite divergent functions and distinct appearances: T4aP can extend micrometers beyond the outer membrane, whereas the endopili in the T2SS are restricted to the periplasm. The determination of the structure of individual components and entire filaments is crucial to understand how their structure enables them to serve different functions. However, the dynamics of these filaments poses a challenge for their high-resolution structure determination. This review presents different approaches that have been used to study the structure and dynamics of T4aP and T2SS endopili by means of integrative structural biology, cryo-electron microscopy (cryo-EM), and molecular dynamics simulations. Their conserved features and differences are presented. The non-helical stretch in the long-conserved N-terminal helix which is characteristic of all members of the TFF and the impact of calcium on structure, function, and dynamics of these filaments are discussed in detail.

Study of the antidiabetic mechanism of berberine compound on FOXO1 transcription factor through molecular docking and molecular dynamics simulations.

CONTEXT: Diabetes mellitus (DM) is a metabolic disorder disease that causes hyperglycemia conditions and associated with various chronic complications leading to mortality. Due to high toxicity of conventional diabetic drugs, the exploration of natural compounds as alternative diabetes treatments has been widely carried out. Previous in silico studies have highlighted berberine, a natural compound, as a promising alternative in antidiabetic therapy, potentially acting through various pathways, including the inhibition of the FOXO1 transcription factor in the gluconeogenesis pathway. However, the specific mechanism by which berberine interacts with FOXO1 remains unclear, and research in this area is relatively limited. Therefore, this study aims to determine the stability of berberine structure with FOXO1 based on RMSD, RMSF, binding energy, and trajectory analysis to determine the potential of berberine to inhibit the gluconeogenesis pathway. This research was conducted by in silico method with molecular docking using AutoDock4.2 and molecular dynamics study using Amber20, then visualized by VMD. METHODS: Docking between ligand and FOXO1 receptor was carried out with Autodock4.2. For molecular dynamics simulations, the force fields of DNA.OL15, protein.ff14SB, gaff2, and tip3p were used.

Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma.

Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth factor (VEGF) expression. The Vascular Endothelial Growth Factor -2 (VEGF-2) and its receptor was identified as a potential anti-cancer target, and it plays a crucial role in physiology as well as pathology. Inhibition of angiogenesis via blocking the signaling pathway is considered an attractive target. In the present study, 150 FDA-approved drugs have been screened using the concept of drug repurposing against VEGFR-2 by employing the molecular docking, molecular dynamics, grouping data with Machine Learning algorithms, and density functional theory (DFT) approaches. The identified compounds such as Pazopanib, Atogepant, Drosperinone, Revefenacin and Zanubrutinib shown the binding energy - 7.0 to - 9.5 kcal/mol against VEGF receptor in the molecular docking studies and have been observed as stable in the molecular dynamic simulations performed for the period of 500 ns. The MM/GBSA analysis shows that the value ranging from - 44.816 to - 82.582 kcal/mol. Harnessing the machine learning approaches revealed that clustering with K = 10 exhibits the relevance through high binding energy and satisfactory logP values, setting them apart from compounds in distinct clusters. Therefore, the identified compounds are found to be potential to inhibit the VEGFR-2 and the present study will be a benchmark to validate the compounds experimentally.

Molecular Dynamics Simulations in Protein-Protein Docking.

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

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations.

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).