Binding Free Energy Research Articles

NOVEL HYBRIDS OF QUINOLINE LINKED PYRIMIDINE DERIVATIVES AS CYCLOOXYGENASE INHIBITORS: MOLECULAR DOCKING, ADMET STUDY, AND MD SIMULATION

Objective: Finding novel anti-inflammatory compounds is a crucial sector of research despite the significant advances this field has made. Inefficiency and unfavorable side effects are indeed potential drawbacks of conventional therapy utilizing steroidal or nonsteroidal drugs. This study aims to screen the designed quinoline-linked pyrimidine derivatives as Cyclooxygenase (COX) inhibitors. Methods: In the present study, we assessed the binding interactions of designed quinoline-linked pyrimidine derivatives with COX enzymes using a molecular docking approach. Using Molecular Dynamics (MD) simulations, the compound’s behavior was further investigated and its stability and conformational dynamics were demonstrated. Schrödinger's QikProp program was utilized to analyze the Absorption, Distribution, Metabolism, and Excretion (ADME) properties and toxicity properties were further investigated using Osiris Property Explorer. Additionally, the protein-ligand complexes' binding free energy has been ascertained using the Molecular Mechanics/Generalized Born Surface Area (MM-GBSA) approach, which offered crucial information regarding the strength of their interactions. Results: The designed quinoline-linked pyrimidine derivatives fulfilled the Lipinski Rule of Five and had physicochemical characteristics within acceptable ranges, better ADME properties, and were non-toxic. Among the designed compounds, QPDU1 and QPDT6 showed correspondingly good docking scores for COX-1 and COX-2. QPDT6 was additionally analyzed by MD simulation studies to thoroughly examine the interaction between protein and ligand and their stability. Conclusion: The proposed compounds exhibit strong binding affinities to COX enzymes, stable interactions in MD simulations, and favorable drug-like features. These results support the need for more research and development of these substances as possible anti-inflammatory drugs.

Molecular docking and molecular dynamics of hypoxia-inducible factor (HIF-1alpha): towards potential inhibitors

HIF-1α is a primary regulator in the adaptation of cancer cells to hypoxia. The aim was to find out new inhibitors of the HIF-1α. A molecular dynamic (MD) simulation performed on HIF-1α showed stable dynamic features. Virtual screening of 217 anticancer drugs was performed along with a positive control (2-Methoxyestradiolm, 2-ME2) on an optimized HIF-1α and dynamically simulated structure. Docking results produced two compounds namely pycnidione and nilotinib of high binding affinity −9.34 kcal/mol and −9.04 kcal/mol respectively, whereas 2-ME2 displayed a relatively lower affinity (-6.68 kcal/mol). For the three complexes, MD of 200 ns simulation was run. Data analysis showed that the three medications behaved similarly in the MD simulation. Nilotinib had a lower RMSD and higher SASA than the other complexes. In addition, the Nilotinib-HIF-1α combination had a lower RMSF value, a flatter Rg, and a number of hydrogen bonds similar to other complexes. MM-GBSA analysis revealed that nilotinib, pycnidione and 2-ME2 compounds had free binding energy of −23.77 ± 5.29, −21.85 ± 4.24 and −7.53 ± 6.62 kcal/mol respectively. Nilotinib and pycnidione bind competitively to HIF-1α, with nilotinib showing consistent molecular-dynamic properties. They relatively pass the blood-brain barrier, non-carcinogenic, and have IV-category acute oral toxicity. They have low CYP inhibitory characteristics. Further investigations are therefore warranted to elucidate their implications in hypoxia pathways, cell proliferation, apoptosis, survival, and metastatic potential.

Improving the Performance of Screening MM/PBSA in Protein-Ligand Interactions via Machine Learning

Abstract Accurately estimating protein-ligand binding free energy is crucial for drug design and biophysics, yet remains a challenging task. In this study, we applied the screening molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) method in combination with various machine learning techniques to compute the binding free energies of protein-ligand interactions. Our results demonstrate that machine learning outperforms direct screening MM/PBSA calculations in predicting protein-ligand binding free energies. Notably, the random forest (RF) method exhibited the best predictive performance, with a Pearson correlation coefficient (r p ) of 0.702 and a mean absolute error (MAE) of 1.379 kcal/mol. Furthermore, we analyzed feature importance rankings in the gradient boosting (GB), adaptive boosting (AdaBoost), and RF methods, and found that feature selection significantly impacted predictive performance. In particular, molecular weight (MW) and van der Waals (VDW) energies played a decisive role in the prediction. Overall, this study highlights the potential of combining machine learning methods with screening MM/PBSA for accurately predicting binding free energies in biosystems.

StreaMD: the toolkit for high-throughput molecular dynamics simulations.

Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.

Exploring the Dynamic Interplay of Deleterious Variants on the RAF1-RAP1A Binding in Cancer: Conformational Analysis, Binding Free Energy, and Essential Dynamics.

The RAF1-RAP1A interaction activates the MAPK/ERK pathway which is very crucial in the carcinogenesis process. This protein complex influences tumor formation, proliferation, and metastasis. Understanding aberrant interactions driven by clinical mutations is vital for targeted therapies. Hence, the current study focuses on the screening of clinically reported substitutions in the RAF1 and RAP1A genes using predictive algorithms integrated with all-atoms simulation, essential dynamics, and binding free energy methods. Survival analysis results revealed a strong association between RAF1 and RAP1A expression levels and diminished survival rates in cancer patients across different cancer types. Integrated machine learning algorithms showed that among the 134 mutations reported for these 2 proteins, only 13 and 35 were classified as deleterious mutations in RAF1 and RAP1P, respectively. Moreover, one mutation in RAF1 reported elevated levels of binding between RAF1 and RAP1P while in RAP1A, 7 mutations were reported to increase the binding affinity. The high-binding mutations, P34Q and V60F, were subjected to protein-protein coupling which confirmed the increase in the binding affinity. Wild-type and mutant RAF1-RAP1P bound complexes were subjected to molecular simulation investigation, revealing enhanced structural stability, increased compactness, and stabilized residue fluctuations of the mutant systems in contrast to the wild-type. In addition, hydrogen bonding analysis revealed a variation in the binding paradigm which further underscores the impact of these substitutions on the coupling of RAF1 and RAP1A. Principal component analysis (PCA) and free energy landscape (FEL) evaluation further determined dynamical variations in the wild-type and mutant complexes. Finally, the Gibbs free energy for each complex was estimated and found to be -71.94 ± 0.38 kcal/mol for the wild-type, -95.57 ± 0.37 kcal/mol for the V60F, and -85.76 ± 0.72 kcal/mol for P34Q complex. These findings confirm the effect of these variants on increasing the binding affinity of RAF1 to RAP1P. These mutations can therefore be targeted for cancer therapy to modulate the activity of the MAPK/ERK signaling pathway.

Discovery of potential natural therapeutics targeting cell wall biosynthesis in multidrug-resistant Enterococcus faecalis: a computational perspective.

Identifying therapeutic inhibitors of crucial enzymes involved in the peptidoglycan biosynthesis pathway is pivotal for developing new treatments against multidrug-resistant Enterococcus faecalis V583. MurM, an essential enzyme in this pathway, plays a significant role in the bacterium's cell wall synthesis, making it an attractive druggable target for novel antimicrobial strategies. This study explored the potential of natural compounds as inhibitors of MurM, aiming to discover promising drug candidates that could serve as the foundation for future therapeutic development. The three-dimensional structure of MurM was predicted, optimized, and its binding pocket was analyzed by comparing it with related structures. Over 4,70,000 natural compounds from the COCONUT database were subjected to virtual high-throughput screening (vHTS). The top lead candidates were selected based on their Lipinski's profile, ADME profile, toxicity profile, estimated binding free energy (ΔG) and estimated inhibition constant (Ki). Interaction pattern analysis was used to evaluate the non-covalent interactions between the inhibitors and key residues in MurM's binding pocket. Molecular dynamics simulations were performed over 300 ns to assess the structural stability and impact of these inhibitors on MurM's enzyme. Three lead compounds-CNP0056520, CNP0126952, and CNP0248480-were identified and prioritized with estimated ΔG ranging from - 9.35 to -7.9kcal/mol. Molecular dynamics simulations revealed minimal impact on MurM's overall structure and dynamics, with the candidate inhibitors forming stable protein-ligand complexes. These interactions were supported by several non-covalent interactions between the candidate inhibitors and key residues within MurM's binding pocket. These findings suggest that the identified natural product candidates could serve as promising inhibitors of MurM, potentially leading to novel therapeutics targeting cell wall biosynthesis in multidrug-resistant E. faecalis.

In silico discovery of druggable targets in Citrobacter koseri using echinoderm metabolites and molecular dynamics simulation

Citrobacter koseri causes infection in people who are immunocompromised. Without effective antibiotics, these infections can become severe and life-threatening, so effective drugs are essential to treat these infections. Utilizing subtractive genomics, 2699 ORFs were predicted and translated into amino acid sequences. Metabolic pathway analysis and subcellular localization helped define the roles of key bacterial proteins. Two druggable proteins, WP_012000829.1 and WP_275157394.1, were discovered as promising targets. Alpha Fold provided 3D structures, and a library of 1600 echinoderm metabolites was docked against these proteins, with Ampicillin, Levofloxacin, and Doxycycline as controls. Notably, CMNPD13085 and CMNPD15632 exhibited the highest binding affinities for WP_012000829.1 and WP_275157394.1, respectively. Molecular dynamics simulations and MM-GBSA binding free energy complemented docking results. However, acknowledging the reliance on computational validations, the study emphasizes the need for essential in-vitro research to transform these potential inhibitors into therapeutic drugs.

Computational Screening of Novel Nitroimidazole Candidates: Targeting Key Enzymes of Oral Anaerobes for Anti-Parasitic Potential.

The study focuses on evaluating the parasitic potential of novel metronidazole analogs using computational methods. Specifically, it aims to target key enzymes of oral anaerobes, including UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA) of Fusobacterium nucleatum and DNA topoisomerase (Topo) of Prevotella intermedia. The objective is to assess the pharmacokinetic and toxicity properties of 368 novel nitroimidazole candidates through virtual screening. Additionally, the study aims to determine the binding affinity of the most promising candidates with the target proteins through molecular docking analyses. A combinatorial library of nitroimidazole candidates was constructed, and virtual screening was performed. Molecular docking analyses were conducted to evaluate the binding affinity of selected compounds with MurA and Topo. Further investigation involved molecular dynamic simulation to assess the stability of the compounds within the active sites of MurA and Topo. All selected compounds exhibited activity against both MurA and Topo. Among them, Mnz11, Mnz12, and Mnz15 demonstrated the lowest binding free energies and IC50 values. Molecular dynamic simulation indicated that these three compounds remained stable within the active sites of MurA and Topo, with RMSD values consistently below 2Å. Additionally, the antibacterial potential of the most potent compound, Mnz15, was evaluated against a series of oral microbes. The study concludes that the newly identified nitroimidazole candidates show promise as anti-parasitic agents, based on their activity against key enzymes of oral anaerobes and their pharmacokinetic properties evaluated through computational methods.

Computational investigations of flavonoids as ALDH isoform inhibitors for treatment of cancer

ABSTRACT Human aldehyde dehydrogenases (ALDHs) are a group of 19 isoforms often overexpressed in cancer stem cells (CSCs). These enzymes play critical roles in CSC protection, maintenance, cancer progression, therapeutic resistance, and poor prognosis. Thus, targeting ALDH isoforms offers potential for innovative cancer treatments. Flavonoids, known for their ability to affect multiple cancer-related pathways, have shown anticancer activity by downregulating specific ALDH isoforms. This study aimed to evaluate 830 flavonoids from the PubChem database against five ALDH isoforms (ALDH1A1, ALDH1A2, ALDH1A3, ALDH2, ALDH3A1) using computational methods to identify potent inhibitors. Extra precision (XP) Glide docking and MM-GBSA free binding energy calculations identified several flavonoids with high binding affinities. MD simulation highlighted flavonoids 1, 2, 18, 27, and 42 as potential specific inhibitors for each isoform, respectively. Flavonoid 10 showed high binding affinities for ALDH1A2, ALDH1A3, and ALDH3A1, emerging as a potential multi-ALDH inhibitor. ADMET property evaluation indicated that the promising hits have acceptable drug-like profiles, but further optimization is needed to enhance their therapeutic efficacy and reduce toxicity, making them more effective ALDH inhibitors for future cancer treatment.

Computational Analysis of S1PR1 SNPs Reveals Drug Binding Modes Relevant to Multiple Sclerosis Treatment

Background/Objectives: Multiple sclerosis (MS) is an autoimmune disorder of the central nervous system (CNS) characterized by myelin and axonal damage with a globally rising incidence. While there is no known cure for MS, various disease-modifying treatments (DMTs) exist, including those targeting Sphingosine-1-Phosphate Receptors (S1PRs), which play important roles in immune response, CNS function, and cardiovascular regulation. This study focuses on understanding how nonsynonymous single nucleotide polymorphisms (rs1299231517, rs1323297044, rs1223284736, rs1202284551, rs1209378712, rs201200746, and rs1461490142) in the S1PR1’s active site affect the binding of endogenous ligands, as well as different drugs used in MS management. Methods: Extensive molecular dynamics simulations and linear interaction energy (LIE) calculations were employed to predict binding affinities and potentially guide future personalized medicinal therapies. The empirical parameters of the LIE method were optimized using the binding free energies calculated from experimentally determined IC50 values. These optimized parameters were then applied to calculate the binding free energies of S1P to mutated S1PR1, which correlated well with experimental values, confirming their validity for assessing the impact of SNPs on S1PR1 binding affinities. Results: The binding free energies varied from the least favorable −8.2 kcal/mol for the wild type with ozanimod to the most favorable −16.7 kcal/mol for the combination of siponimod with the receptor carrying the F2055.42L mutation. Conclusions: We successfully demonstrated the differences in the binding modes, interactions, and affinities of investigated MS drugs in connection with SNPs in the S1PR1 binding site, resulting in several viable options for personalized therapies depending on the present mutations.

An in silico approach uncovering the competency of oncolytic human adenovirus 52 for targeted breast cancer virotherapy

Breast cancer remains a major health threat throughout the world specifically in women above 30 years of age however, it is rarely known to affect men as well. It is characterized by the abnormal division of cells in the breast tissue resulting in the development of breast malignancies. Various risk factors contributing to breast cancer include age, family history, genetic mutations (chiefly in BRCA1 and BRCA2 genes) along with hormonal imbalances (oestrogen, progesterone, HER2). Early detection which can be obtained through frequent rounds of self-examination, mammographic scanning, and clinical assessment plays a crucial role in the prevention of the disease. In addition, appropriate diagnosis assists in better therapeutic responses. This study highlights the considerable health risks associated with the conventional treatment procedures which arise and increased demand of advanced, secure, and risk-free treatment alternatives. Oncolytic viruses are potentially apparent for the aim of improving cancer therapeutics with reduced side effects. These viruses act as the fundamental therapeutic agent themselves that selectively target and kill malignant cells without harm to healthy tissues. The key objective of the research is to provide evidence that Human Adenovirus 52 is a potent oncolytic virus and to highlight its capacity to target and eliminate cancer cells with precision while causing the least amount of harm to healthy tissues. Validating the in-silico method entails evaluating the precision and dependability of the computational modelling by contrasting the in-silico predictions with the findings from the experiments rank as the secondary objective. The workflow of this research utilized in-silico computational drug designing approaches including retrieval of tertiary structures of both the target Breast Cancer Type 1 Susceptibility Protein (BRCA1) and the viral Human Adenovirus 52 protein, their validation generating Ramachandran Plots determining favoured amino acid residue angles and prediction of their active residues. Furthermore, the study focused on the molecular dynamics docking of proteins, interpretation of molecular interactions between the docked complex, as well as the assessment of the molecular dynamic simulations (MD) in addition to their MMGBSA binding energy calculations. A successful docking between BRCA1 and Adenovirus protein provided a significant score of 329.2 +/- 24.3, furthermore, MD simulations showed a high RMSD peak at 2.8 Å, RMSF were maximum at 3.5 Å with highest protein–protein interaction, the radius of gyration was stable throughout the simulation representing elastic stability along with a high energy interaction value of - 7882 kCal/mol. Moreover, the MMGBSA calculation results showed a notable release of binding free energy of - 68.96 kCal/mol demonstrating effective bond formation between the docked complex. These findings propose the effectiveness of Human Adenovirus 52 to treat cancer. The selected oncolytic Human Adenovirus 52 is a potential candidate for the target specific treatment of breast cancer through virotherapy. This computer-aided drug discovery presents significant potential in targeting cancer cells and would assist in the development of potent drug reagents for the cancer therapy.

Synthesis, Characterization and Computational Studies on (E)-4-(1-((2-hydroxyphenyl)imi no) hexyl)-5-methyl-2-phenyl-2,4-dihydro-3H-pyrazol-3-one

A coupling precursor, 4-hexanoyl-5-methyl-2-phenyl-2,4-dihydro-3H-pyrazol-3-one, 1 synthesized by the reaction of 3-methyl-1-phenyl-1H-pyrazol-5-one with hexanoyl chloride was reacted with 2-aminophenol to synthesize (E)-4-(1-((2-hydroxyphenyl)imino)hexyl)-5-methyl-2-phenyl-2,4-dihydro-3H-pyrazol-3-one, 1a. The compounds were characterized by conventional spectroscopic techniques, 1H and 13C NMR, UV-Visible, FTIR spectroscopy and single crystal X-ray crystallograpic structural determinations and computational techniques. To augment the experimental data, DFT calculations were carried. Molecular dockings were used to predict the interactions between, 1a with some biological targets. The binding free energies in the Plasmepsin II active sites indicated anti−malarial activity against P. falciparum in epidermal growth factor receptor (EGFR) inhibition, anti-inflammatory properties against human peroxiredoxin 5, and anticancer properties. With binding energies less than −5.00 kcal/mol, the results indicate that 1a is a potential drug target for anti-inflammatory, anti-malarial and anti-cancer properties.

Identification and anticoagulant mechanisms of novel factor XIa inhibitory peptides by virtual screening of a in silico generated deep-sea peptide database

The objective of this study was to identify novel anticoagulant peptides from the deep-sea using multiple in silico methods, and to investigate their inhibitory activity and molecular mechanisms. A deep-sea peptide database was firstly constructed by performing virtual proteolysis on protein sequences from animals inhabiting deep-sea hydrothermal vents and cold seeps. Candidate anticoagulant peptides were identified through molecular docking and binding free energy screening against FXIa as the target. Two novel anticoagulant peptides, PRNIF (IC50 = 0.67 mM) and GNDRCL (IC50 = 1.52 mM), were identified, and their anticoagulant activities were verified in vitro. PRNIF was demonstrated to be a noncompetitive inhibitor of FXIa, and caused significant prolongation of the thrombin time (TT) and the activated partial thromboplastin time (APTT), whereas GNDRCL markedly prolonged only APTT. Molecular dynamics simulations demonstrated considerable conformational shifts of both anticoagulant peptides when bound to the active sites of FXIa. The lowest energy binding poses of the FXIa-peptide complexes for PRNIF and GNDRCL exhibited comparable numbers of hydrogen bonds and binding free energies. However, occupancy analysis revealed completely distinct stability characteristics of the hydrogen bond interactions. The conserved residue Asp569 in the S1 pocket of FXIa formed strong and stable hydrogen bonds as well as a salt bridge with the arginine residues of PRNIF, which were not observed in the FXIa-GNDRCL complex. To our knowledge, PRNIF represented the first FXIa inhibitory peptide derived from the deep-sea, which may contribute to the development and utilization of deep-sea peptides resources. Two deep-sea peptides may potentially serve as an alternative food-derived ingredient that could be utilized for thrombosis prevention.

Structural, spectroscopic, and biological properties of an asymmetric Cu (II) Schiff-base complex: Synthesis, DNA/BSA interactions, and antimicrobial potential with experimental and computational insights

A copper (II) complex with the asymmetric bidentate Schiff-base ligand (E)-2-(((4-chlorophenyl) imino) methyl) phenol was synthesized and characterized using FTIR and UV–VIS spectroscopy. Photoluminescence studies were conducted on both the copper (II) complex and the free ligand. XRD analysis revealed the copper (II) complex crystallizes in the monoclinic system, space group P21/c. The complex structure was optimized using DFT calculations with B3LYP/6-31G+(d,p) and Lanl2dz basis sets, with QTAIM and NBO analyses confirming complex formation. The energy gap of the copper (II) complex was determined to be 3.701 eV (alpha state) and 2.764 eV (beta state). MEP analysis of the optimized structure indicated a hydrophobic environment around the nitrogen atom, with the molecule exhibiting overall hydrophobicity. The chlorine atoms were identified as potential nucleophilic sites. The antimicrobial efficacy of a Schiff base copper (II) complex was evaluated against selected pathogenic bacteria (B. subtilis, B. megaterum, K. pneumoniae, E. coli) and the fungus Candida albicans. Among these C. albicans for complex showed larger zones (11–15.5 mm) at higher concentrations such as 2 and 2.5 mg/mL. Molecular docking studies suggested strong binding affinity between the complex and the target protein, this was further supported by obtained root means square deviations, root means square fluctuations, radius of gyration and hydrogen bond values during simulations. The binding free energy between BSA and the copper (II) complex was calculated as −8.7 kcal/mol, indicating significant interaction. However, only weak van der Waals interactions were observed between the complex and DNA. Toxicity studies revealed the copper (II) complex had minimal irritant effects on ocular and cutaneous tissues, with a low lethal dose. In comparison, Tricresyl phosphate showed moderate to severe ocular irritancy and modest cutaneous irritancy.

Unveiling molecular mechanism underlying inhibition of human islet amyloid polypeptide fibrillation by benzene carboxylic acid-peptide conjugate

Type 2 diabetes (T2D) is linked to the apoptosis of insulin-producing β-cells due to the aberrant fibrillation of the human islet amyloid polypeptide (hIAPP or amylin) to cytotoxic aggregates. Profit et al. generated conjugates (C1–C7) by appending benzene carboxylic acids of varying charges to the N-terminal of hIAPP22-29 fragment peptide NFGAILSS to modulate hIAPP fibrillation and cytotoxicity. C5 (4 µM) derived by conjugating low-cost, commercially available benzene-1,2,4,5-tetracarboxylic acid known as pyromellitic acid to NFGAILSS completely abolishes the hIAPP (40 µM) self-assembly as noted in the thioflavin T (ThT) fluorescence assay. The circular dichroism (CD) spectra highlighted that C5 stabilized hIAPP in a distinctive conformation and blocked its conformational switching to amyloidogenic β-sheet structure. C5 possessing a charge-dense pyromellitic acid moiety appended on the N-terminal region of self-recognition hIAPP fragment sequence NFGAILSS created significant interest as it effectively inhibited hIAPP fibrillation and possessed lower molecular mass, smaller size, and charge as compared to hIAPP aggregation inhibitor EEEENFGAILSS (P10). However, it remains unclear how C5 traps hIAPP into a unique conformation that abolishes its self-aggregation propensity. Thus, molecular dynamics (MD) simulations have been employed to illuminate the conformational transitions and structural changes in hIAPP on the inclusion of C5. C5 displayed high-affinity binding interactions to hIAPP (ΔGbinding = −37.11 ± 3.72 kcal/mol) with major contributions from the van der Waals and electrostatic interactions. Furthermore, residue-specific binding free energy analysis depicted high-affinity binding interactions of C5 with Phe15, His18, Asp21, and Tyr37 of hIAPP that play a crucial role in hIAPP fibrillation. The negatively charged carboxylate groups of pyromellitic acid moiety of C5 displayed interactions with the key residues of hIAPP as noted in the conformational clustering analysis. Notably higher sampling of helix from 24.00 ± 0.84 % in hIAPP to 34.20 ± 1.92 % in hIAPP–C5 is consistent with the CD studies, which depicted that C5 trapped hIAPP monomer in a conformation that blocked its self-association. MD simulations illuminated the molecular mechanism and atomistic details of the C5 interactions with hIAPP, which are responsible for its inhibitory activity against hIAPP fibrillation and alleviating hIAPP aggregates-induced cytotoxicity.

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

To facilitate drug discovery and development for the treatment of osteoporosis. With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB1 cannabinoid ligands.

The substantial increase in the number of active and inactive-state CB1 receptor experimental structures has provided opportunities for CB1 drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB1 receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (rMM/GBSA = 0.433 - 0.652 vs. rMM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energyminimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB1 receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors. The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30ns production simulations using GROMACS 2018 at 300K and 1atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.

Probing the Molecular Basis of Aminoacyl-Adenylate Affinity With Mycobacterium tuberculosis Leucyl-tRNA Synthetase Employing Molecular Dynamics, Umbrella Sampling Simulations and Site-Directed Mutagenesis.

Leucyl-tRNA synthetase (LeuRS) is clinically validated molecular target for antibiotic development. Recently, we have reported several classes of small-molecular inhibitors targeting aminoacyl-adenylate binding site of Mycobacterium tuberculosis LeuRS with antibacterial activity. In this work, we performed in silico site-directed mutagenesis of M. tuberculosis LeuRS synthetic site in order to identify the most critical amino acid residues for the interaction with substrate and prove binding modes of inhibitors. We carried out 20-ns molecular dynamics (MD) simulations and used umbrella sampling (US) method for the calculation of the binding free energy (ΔGb) of leucyl-adenylate with wild-type and mutated forms of LeuRS. According to molecular modeling results, it was found that His89, Tyr93, and Glu660 are essential amino acid residues both for aminoacyl-adenylate affinity and hydrogen bond formation. We have selected His89 for experimental site-directed mutagenesis since according to our previous molecular docking results this amino acid residue was predicted to be important for inhibitor interaction in adenine-binding region. We obtained recombinant mutant M. tuberculosis LeuRS H89A. Using aminoacylation assay we have found that the mutation of His89 to Ala in the active site of M. tuberculosis LeuRS results in significant decrease of inhibitory activity for compounds belonging to three different chemical classes-3-phenyl-5-(1-phenyl-1H-[1,2,3]triazol-4-yl)-[1,2,4]oxadiazoles, N-benzylidene-N'-thiazol-2-yl-hydrazines, and 1-oxo-1H-isothiochromene-3-carboxylic acid (4-phenyl-thiazol-2-yl)-amide derivatives. Therefore, the interaction with His89 should be taken into account during further M. tuberculosis LeuRS inhibitors development and optimization.

Discovery of Cyclic Peptide Inhibitors Targeted on TNFα-TNFR1 from Computational Design and Bioactivity Verification

Activating tumor necrosis factor receptor 1 (TNFR1) with tumor necrosis factor alpha (TNFα) is one of the key pathological mechanisms resulting in the exacerbation of rheumatoid arthritis (RA) immune response. Despite various types of drugs being available for the treatment of RA, a series of shortcomings still limits their application. Therefore, developing novel peptide drugs that target TNFα-TNFR1 interaction is expected to expand therapeutic drug options. In this study, the detailed interaction mechanism between TNFα and TNFR1 was elucidated, based on which, a series of linear peptides were initially designed. To overcome its large conformational flexibility, two different head-to-tail cyclization strategies were adopted by adding a proline-glycine (GP) or cysteine-cysteine (CC) to form an amide or disulfide bond between the N-C terminal. The results indicate that two cyclic peptides, R1_CC4 and α_CC8, exhibit the strongest binding free energies. α_CC8 was selected for further optimization using virtual mutations through in vitro activity and toxicity experiments due to its optimal biological activity. The L16R mutant was screened, and its binding affinity to TNFR1 was validated using ELISA assays. This study designed a novel cyclic peptide structure with potential anti-inflammatory properties, possibly bringing an additional choice for the treatment of RA in the future.

A New Proton Transfer Complex Between 3,4-Diaminopyridine Drug and 2,6-Dichloro-4-nitrophenol: Synthesis, Spectroscopic Characterization, DFT Studies, DNA Binding Analysis, and Antitumor Activity

The proton transfer (PT) complexation reaction between 3,4-diaminopyridine (3,4-DAP), an important drug, and 2,6-dichloro-4-nitrphenole (DCNP) was investigated experimentally and theoretically. The experimental results indicated a chemical reaction occurred because of a hydrogen bonding, followed by proton transfer from the DCNP to the 3,4-DAP in different polar media. The Benesi–Hildebrand equation was used to estimate the formation constant (Kf), molar absorptivity (εPT), and other physical parameters. The formed PT complex was characterized using FTIR, 1H, and 13C NMR spectra. In addition, the nanocrystalline structure, particle sizes, and surface morphology of the complex were investigated by XRD and SEM-EDX. The structure of the 1:1 PT complex was calculated theoretically in the gas phase and the presence of solvent effects. Using TD-DFT calculations, the band observed at 406 nm (Calc. 379.5 nm) and 275 nm (Calc. 272.3 nm) could be assigned to the HOMO→LUMO transition (99%), and HOMO→L+3 transition (87%), respectively. The DNA binding ability of the PT complex was investigated, revealing an intercalative binding mechanism with a binding constant Kb of 4.6 × 104 M−1. Based on the results of the Ct-DNA binding study, the binding free energy of the PT complex with the receptor of human DNA (PDB ID:1BNA) is found to be −7.2 kcal/mol. The cytotoxic effects of the PT complex were evaluated on selected cancer cell lines, demonstrating significant antitumor activity against A-549 and MCF-7 cancer cell lines.