Molecular Docking Analysis Reveals the Promising Role of Apigenin as a Potential Treatment for Neurological Disorders.

OBJECTIVES: Neurological disorders represent a significant global health challenge, necessitating the exploration of novel therapeutic agents. Apigenin, a natural flavonoid abundantly found in various plants, has garnered attention for its potential neuroprotective properties. In this study, we employed molecular docking simulations to investigate the interaction between apigenin and key molecular targets associated with neurological disorders. METHODS: The molecular docking analysis focused on receptors implicated in neuroinflammation, oxidative stress, and neurotransmission regulation. RESULTS: Our results reveal a high binding affinity of apigenin towards critical targets, including GABA, mACh, nACh, NMDA, 5HTA, AMPA, insulin, and dopamine receptors. The findings suggest that apigenin may exert its neuroprotective effects through multifaceted mechanisms, including anti-inflammatory, antioxidant, and neurotransmission regulatory pathways. Additionally, the absence of adverse binding poses emphasizes the safety profile of apigenin. CONCLUSIONS: This molecular docking study provides valuable insights into the potential therapeutic role of apigenin in mitigating molecular pathways implicated in neurological disorders. Further in vitro and in vivo investigations are warranted to validate and elucidate the neuroprotective mechanisms of apigenin, paving the way for its development as a promising treatment option for various neurological conditions.

Increased throughput in methods for simulating protein ligand binding and unbinding.

By incorporating full flexibility and enabling the quantification of crucial parameters such as binding free energies and residence times, methods for investigating protein-ligand binding and unbinding via molecular dynamics provide details on the involved mechanisms at the molecular level. While these advancements hold promise for impacting drug discovery, a notable drawback persists: their relatively time-consuming nature limits throughput. Herein, we survey recent implementations which, employing a blend of enhanced sampling techniques, a clever choice of collective variables, and often machine learning, strive to enhance the efficiency of new and previously reported methods without compromising accuracy. Particularly noteworthy is the validation of these methods that was often performed on systems mirroring real-world drug discovery scenarios.

Unlocking therapeutic potential of trigonelline through molecular docking as a promising approach for treating diverse neurological disorders.

Neurological disorders pose significant challenges in terms of treatment options, necessitating the exploration of novel therapeutic approaches. Trigonelline, a naturally occurring alkaloid found in various plants, has emerged as a potential treatment option. It has also been reported that trigonelline is involved in several pathways like; Oxidative Stress and Antioxidant, Inflammatory, Neuroprotection and Neurotrophic, Mitochondrial Function and Energy Metabolism. This study aims to investigate the therapeutic potential of trigonelline for diverse neurological disorders using a molecular docking approach. Molecular docking simulations were performed to predict the binding affinity and interaction between trigonelline and target proteins implicated in neurological disorders. The structural requirements for effective binding were also explored. The molecular docking results revealed strong binding interactions and favorable binding affinities between trigonelline and the target proteins involved in diverse neurological disorders like Alzheimer's disease, Parkinson's disease, epilepsy, and depression etc. The predicted binding modes provided insights into the key molecular interactions governing the ligand-protein complexes. The findings suggest that trigonelline holds promise as a therapeutic approach for several neurological disorders. The molecular docking approach employed in this study provides a valuable tool for rational drug design and optimization of trigonelline-based compounds. Further experimental validation and preclinical studies are warranted to confirm the efficacy and safety of trigonelline as a potential treatment option, paving the way for the development of more effective and targeted therapies for neurological disorders.

Synthesis, Characterization, DPPH Radical Scavenging, Urease Enzyme Inhibition, Molecular Docking Simulation, and DFT Analysis of Imine Derivatives of 4-formylpyridine with Selective Detection of Cu+2 Ions.

This study aimed to prepare three imine derivatives (1, 2, and 3) via a condensation reaction of phenyl hydrazine, 2-hydrazino pyridine, and 4-methoxy aniline with 4-formyl pyridine. Electron impact mass spectrometry (EIMS), proton nuclear magnetic resonance (1H-NMR), ultraviolet-visible (UV-Vis) and Fourier transform infrared (FTIR) spectroscopy were utilized for the characterization. The chemosensing properties of [4((2-phenyl hydrazono)methyl) pyridine] (1), [2-(2-(pyridin-4-ylmethylene)hydrazinyl) pyridine] (2), and [4-methoxy-N-yl methylene) aniline] (3) imino bases have been explored for the first time in aqueous media. The photophysical properties of chemosensors (1, 2, and 3) were examined by various cations (Na+, NH4+, Ba+2, Ni+2, Ca+2, Hg+2, Cu+2, Mg+2, Mn+2, and Pd+2). The chemosensor (1) showed very selective binding capability with copper ions at low concentrations (20 µM) without the influence of any other mentioned ions. The maximum complexation was noted with Cu+2 and 1 at pH between 7 to 7.5. The stoichiometry binding ratio between chemosensor (1) and Cu+2 was determined by Job's plot and it was found to be 1:2. The current study explored the use of these Schiff bases for the first time as heterocyclic chemosensors. DPPH radical scavenging, urease enzyme inhibition activities, molecular docking simulation, and density functional theory (DFT) analysis of compounds 1, 2, and 3 were also conducted.

Engineering the Ligand Specificity of the Human Galectin-1 by Incorporation of Tryptophan Analogues.

Galectin-1 is a ß-galactoside-binding lectin with manifold biological functions. A single tryptophan residue (W68) in its carbohydrate binding site plays a major role in ligand binding and is highly conserved among galectins. To fine tune galectin-1 specificity, we introduced several non-canonical tryptophan analogues at this position of human galectin-1 and analyzed the resulting variants using glycan microarrays. Two variants containing 7-azatryptophan and 7-fluorotryptophan showed a reduced affinity for 3'-sulfated oligosaccharides. Their interaction with different ligands was further analyzed by fluorescence polarization competition assay. Using molecular modeling we provide structural clues that the change in affinities comes from modulated interactions and solvation patterns. Thus, we show that the introduction of subtle atomic mutations in the ligand binding site of galectin-1 is an attractive approach for fine-tuning its interactions with different ligands.

Structural determinants for subnanomolar inhibition of the secreted aspartic protease Sapp1p from Candida parapsilosis.

Pathogenic Candida albicans yeasts frequently cause infections in hospitals. Antifungal drugs lose effectiveness due to other Candida species and resistance. New medications are thus required. Secreted aspartic protease of C. parapsilosis (Sapp1p) is a promising target. We have thus solved the crystal structures of Sapp1p complexed to four peptidomimetic inhibitors. Three potent inhibitors (Ki: 0.1, 0.4, 6.6 nM) resembled pepstatin A (Ki: 0.3 nM), a general aspartic protease inhibitor, in terms of their interactions with Sapp1p. However, the weaker inhibitor (Ki: 14.6 nM) formed fewer nonpolar contacts with Sapp1p, similarly to the smaller HIV protease inhibitor ritonavir (Ki: 1.9 µM), which, moreover, formed fewer H-bonds. The analyses have revealed the structural determinants of the subnanomolar inhibition of C. parapsilosis aspartic protease. Because of the high similarity between Saps from different Candida species, these results can further be used for the design of potent and specific Sap inhibitor-based antimycotic drugs.

Role of water in the determination of protonation states of titratable residues.

Water is the fundamental unit for living being, and its contribution in variety of crucial cellular functions is widely accepted. The presence of water molecules in protein's environment also accounts for structural optimization, in which highly conserved water molecules ensure structural stability of the biomolecule by providing protein-water (solute-solvent) hydrogen-bond interaction networks. Similarly, protonation states and pKa values of individual amino acid residues are also influenced by neighboring water molecules present in the protein's vicinity. In the present study, we have highlighted the role of water molecules in hydrogen-bond optimization, in determining pKa values and protonation states of titratable residues in JH2 domain of JAK2 apo protein. We found that inclusion or exclusion of water molecules while calculating pKa and assigning protonation states to amino acid residues during the molecular system build-up step resulted in slight differences in pKa values of few titratable residues and alternative protonation states of a certain residue. Accordingly, different protonation states of ionizable residues offer differing interaction patterns. Thus, we inferred that the presence of water optimizes the hydrogen-bond interactions by forming direct protein-water interactions and by linking via protein-protein bridging interactions. However, in the absence of water, the interaction pattern is somewhat disrupted. We assume that water molecules could modulate the plausibility of a particular protonation state of titratable residues on the basis of its fit with the local environment, by utilizing some particular hydrogen-bond contacts that would remain unexploited in the absence of water.

Chromatin Compaction Multiscale Modeling: A Complex Synergy Between Theory, Simulation, and Experiment.

Understanding the mechanisms that trigger chromatin compaction, its patterns, and the factors they depend on, is a fundamental and still open question in Biology. Chromatin compacts and reinforces DNA and is a stable but dynamic structure, to make DNA accessible to proteins. In recent years, computational advances have provided larger amounts of data and have made large-scale simulations more viable. Experimental techniques for the extraction and reconstitution of chromatin fibers have improved, reinvigorating theoretical and experimental interest in the topic and stimulating debate on points previously considered as certainties regarding chromatin. A great assortment of approaches has emerged, from all-atom single-nucleosome or oligonucleosome simulations to various degrees of coarse graining, to polymer models, to fractal-like structures and purely topological models. Different fiber-start patterns have been studied in theory and experiment, as well as different linker DNA lengths. DNA is a highly charged macromolecule, making ionic and electrostatic interactions extremely important for chromatin topology and dynamics. Indeed, the repercussions of varying ionic concentration have been extensively examined at the computational level, using all-atom, coarse-grained, and continuum techniques. The presence of high-curvature AT-rich segments in DNA can cause conformational variations, attesting to the fact that the role of DNA is both structural and electrostatic. There have been some tentative attempts to describe the force fields governing chromatin conformational changes and the energy landscapes of these transitions, but the intricacy of the system has hampered reaching a consensus. The study of chromatin conformations is an intrinsically multiscale topic, influenced by a wide range of biological and physical interactions, spanning from the atomic to the chromosome level. Therefore, powerful modeling techniques and carefully planned experiments are required for an overview of the most relevant phenomena and interactions. The topic provides fertile ground for interdisciplinary studies featuring a synergy between theoretical and experimental scientists from different fields and the cross-validation of respective results, with a multi-scale perspective. Here, we summarize some of the most representative approaches, and focus on the importance of electrostatics and solvation, often overlooked aspects of chromatin modeling.

Probing Hydration Patterns in Class-A GPCRs via Biased MD: The A2A Receptor.

Herein, we present a new computational approach for analyzing hydration patterns in biomolecular systems. This protocol aims to efficiently identify regions where structural waters may be located and, in the case of protein-ligand binding, where displacing one or more water molecules could be advantageous in terms of affinity and/or residence time. We validated our approach on the adenosine A2A receptor, a target of significant pharmaceutical relevance. The results of the approach are enriched with an extensive analysis of hydration in A2A and other members of the A-family of GPCRs using available crystallographic evidence and reviewing existing literature. As per the protein-ligand complex case, we conducted a more detailed study of a series of triazine analogues inhibiting A2A. The proposed approach provides results in good agreement with existing data and offers interpretability and simple and fast applicability.

In silico based investigation of dynamic variations in neprilysin (NEP and NEP2) proteins for extracting the point of specificity.

Neprilysin-2 (NEP2) in the central nervous system controls Alzheimer's protein (amyloid-ß) deposition, and prevents its occurrence. However, in the peripheral system, its closest homolog, neutral endopeptidase (NEP), regulates hypertension and heart related diseases. Inhibitors of NEP with a lesser degree of specificity can treat hypertension with an increased risk of cerebral deposition of amyloid-ß. In order to rationalize the point of selectivity, the dynamic behavior of human NEP and NEP2 proteins was monitored by conducting molecular dynamics (MD) simulations. A computationally reliable model of NEP2 was achieved with 79.9%, 19.1% and 0.2% residues in the allowed, additionally allowed and disallowed regions respectively, using as a reference protein. Additionally, molecular docking studies were carried out for a set of five already known inhibitors of NEP and modeled NEP2 to obtain the comparative behaviors of the complexes. MD results highlighted their different responses along with important residues having a part in ligand-protein binding. For substrate and inhibitor binding, Arg664/661 and Zn697/694 were identified as the most conserved residues. High degree flexible transitions during the MD simulations were also observed in loop areas along with active site residues. Energy calculations, hydrogen bonds and their occupancy rates helped to conclude each ligand's potency towards a particular target. In most complexes of hNEP2, the ligands showed weak interactions which might be due to its larger pocket size or huge conformational variations in active site residues upon complexation. In the case of inhibitors of a small size like thiorphan, Arg49 and Arg664 are found to be acting to support the ligand binding in NEP while only Arg661 is acting in NEP2.

Kinetics of protein-ligand unbinding via smoothed potential molecular dynamics simulations.

Drug discovery is expensive and high-risk. Its main reasons of failure are lack of efficacy and toxicity of a drug candidate. Binding affinity for the biological target has been usually considered one of the most relevant figures of merit to judge a drug candidate along with bioavailability, selectivity and metabolic properties, which could depend on off-target interactions. Nevertheless, affinity does not always satisfactorily correlate with in vivo drug efficacy. It is indeed becoming increasingly evident that the time a drug spends in contact with its target (aka residence time) can be a more reliable figure of merit. Experimental kinetic measurements are operatively limited by the cost and the time needed to synthesize compounds to be tested, to express and purify the target, and to setup the assays. We present here a simple and efficient molecular-dynamics-based computational approach to prioritize compounds according to their residence time. We devised a multiple-replica scaled molecular dynamics protocol with suitably defined harmonic restraints to accelerate the unbinding events while preserving the native fold. Ligands are ranked according to the mean observed scaled unbinding time. The approach, trivially parallel and easily implementable, was validated against experimental information available on biological systems of pharmacological relevance.

Structure-based 3D-QSAR models and dynamics analysis of novel N-benzyl pyridinone as p38α MAP kinase inhibitors for anticytokine activity.

A novel series of anticytokine N-benzyl pyridinone derivatives that targets p38α MAP kinase has been analyzed by utilizing a combination of molecular modeling techniques. Statistically significant structure-based 3D-QSAR models were generated for both CoMFA and CoMSIA, and validated through acceptable predictive ability to support both internal and external set of compounds. Structural changes within the protein key backbone residues (Met109 and Gly110), DFG loop position, and side chain movements (Lys53 and Asn114) as resulted by different substituents on these inhibitors were also examined by molecular dynamics simulation. The protocol applied in this study could be helpful to rationalize potent compounds with better inhibitory activity and selectivity profiles against p38α MAP kinase.