Structure-based strategies for drug design and discovery

IEV2Mol: Molecular Generative Model Considering Protein-Ligand Interaction Energy Vectors

Generating drug candidates with desired protein-ligand interactions is a significant challenge in structure-based drug design. In this study, a new generative model, IEV2Mol, is proposed that incorporates interaction energy vectors (IEVs) between proteins and ligands obtained from docking simulations, which quantitatively capture the strength of each interaction type, such as hydrogen bonds, electrostatic interactions, and van der Waals forces. By integrating this IEV into an end-to-end variational autoencoder (VAE) framework that learns the chemical space from SMILES and minimizes the reconstruction error of the SMILES, the model can more accurately generate compounds with the desired interactions. To evaluate the effectiveness of IEV2Mol, we performed benchmark comparisons with randomly selected compounds, unconstrained VAE models (JT-VAE), and compounds generated by RNN models based on interaction fingerprints (IFP-RNN). The results show that the compounds generated by IEV2Mol retain a significantly greater percentage of the binding mode of the query structure than those of the other methods. Furthermore, IEV2Mol was able to generate compounds with interactions similar to those of the input compounds, regardless of structural similarity. The source code and trained models for IEV2Mol, JT-VAE, and IFP-RNN designed for generating compounds active against the DRD2, AA2AR, and AKT1, as well as the data sets (DM-QP-1M, active compounds to each protein, and ChEMBL33) utilized in this study, are released under the MIT License and available at https://github.com/sekijima-lab/IEV2Mol.

Attenuation of Quorum Sensing Mediated Virulence Factors and Biofilm Formation in Pseudomonas Aeruginosa PAO1 by Substituted Chalcones and Flavonols

Flavonoids epitomize structural scaffolds in many biologically active synthetic and natural compounds. They showcase a diverse spectrum of biological activities including anticancer, antidiabetic, antituberculosis, antimalarial, and antibiofilm activities. The antibiofilm activity of a series of new chalcones and flavonols against clinically significant Pseudomonas aeruginosa PAO1 strain was studied. Antivirulence activities were screened by analysing the effect of compounds on the production of virulence factors like pyocyanin, LasA protease, cell surface hydrophobicity, and rhamnolipid. The best ligands towards the quorum sensing proteins LasR, RhlR, and PqsR were recognised using a molecular docking study. The gene expression in P. aeruginosa after treatment with test compounds was evaluated on quorum sensing genes including rhlA, lasB, and pqsE. The antibiofilm potential of chalcones and flavonols was confirmed by the efficient reduction in the production of virulence factors and downregulation of gene expression.

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

A computational approach to identify phytochemicals as potential inhibitor of acetylcholinesterase: Molecular docking, ADME profiling and molecular dynamics simulations

Inhibition of acetylcholinesterase (AChE) is a crucial target in the treatment of Alzheimer's disease (AD). Common anti-acetylcholinesterase drugs such as Galantamine, Rivastigmine, Donepezil, and Tacrine have significant inhibition potential. Due to side effects and safety concerns, we aimed to investigate a wide range of phytochemicals and structural analogues of these compounds. Compounds similar to the established drugs, and phytochemicals were investigated as potential inhibitors for AChE in treating AD. A total of 2,270 compound libraries were generated for further analysis. Initial virtual screening was performed using Pyrx software, resulting in 638 molecules showing higher binding affinities compared to positive controls Tacrine (-9.0 kcal/mol), Donepezil (-7.3 kcal/mol), Galantamine (-8.3 kcal/mol), and Rivastigmine (-6.4 kcal/mol). Subsequently, ADME properties were assessed, including blood-brain barrier permeability and Lipinski's rule of five violations, leading to 88 compounds passing the ADME analysis. Among the rivastigmine analogous, [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate showed interaction with Tyr123, Tyr336, Tyr340, Phe337, Trp285 residues of AChE. Tacrine similar compounds, such as 4-amino-2-styrylquinoline, exhibited bindings with Tyr123, Phe337, Tyr336, Trp285, Trp85, Gly119, and Gly120 residues. A phytocompound (bisdemethoxycurcumin) showed interaction with Trp285, Tyr340, Trp85, Tyr71, and His446 residues of AChE with favourable binding. These findings underscore the potential of these compounds as novel inhibitors of AChE, offering insights into alternative therapeutic avenues for AD. A 100ns simulation analysis confirmed the stability of protein-ligand complex based on the RMSD, RMSF, ligand properties, PCA, DCCM and MMGBS parameters. The investigation suggested 3 ligands as a potent inhibitor of AChE which are [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate, 4-Amino-2-styrylquinoline and bisdemethoxycurcumin. Furthermore, investigation, including in-vitro and in-vivo studies, is needed to validate the efficacy, safety profiles, and therapeutic potential of these compounds for AD treatment.

Anticancer activity, DFT study, ADMET prediction, and molecular docking of novel α-sulfamidophosphonates

A series of novel α-sulfamidophosphonate derivatives (3a-3 g) were synthesized and evaluated for anticancer activity against different human cancer cell lines (PRI, K562, and JURKAT). The antitumor activity of all compounds using the MTT test remains moderate compared to the standard drug chlorambucil. Compounds 3c and 3 g were found to be more active anticancer agent against PRI and K562 cells with IC50 value 0.056-0.097 and 0.182-0.133 mM, respectively. Molecular docking study related to binding affinity and binding mode analysis showed that synthesized compounds had potential to inhibit glutamate carboxypeptidase II (GCPII). Furthermore, computational analysis was performed through Density Functional Theory (DFT) utilizing the B3LYP 6-31 G (d, p) basis set and the theoretical results were correlated with experimental data. The ADME/toxicity analyses carried out by Swiss ADME and OSIRIS software show that all synthesized molecules exhibited good pharmacokinetics, bioavailability, and had no toxicity profile.

Bovine carbonic anhydrase (bCA) inhibitors: Synthesis, molecular docking and theoretical studies of bisoxadiazole-substituted sulfonamide derivatives

This paper describes the in vitro inhibition potential of bisoxadiazole-substituted sulfonamide derivatives (6a-t) against bovine carbonic anhydrase (bCA) after they were designed through computational analyses and evaluated the predicted interaction via molecular docking. First, in silico ADMET predictions and physicochemical property analysis of the compounds provided insights into solubility and permeability, then density functional theory (DFT) calculations were performed to analyse their ionization energies, nucleophilicity, in vitro electron affinity, dipole moments and molecular interactions under vacuum and dimethyl sulfoxide (DMSO) conditions. After calculating the theoretical inhibition constants, IC50 values determined from enzymatic inhibition were found between 12.93 and 45.77 μM. Molecular docking evaluation revealed favorable hydrogen bonding and π-interactions of the compounds within the bCA active site. The experimentally most active compound, 6p, exhibited the strongest inhibitory activity with a theoretical inhibition constant value of 9.41 nM and H-bonds with Gln91, Thr198, and Trp4 residues and His63 Pi-cation interactions with His63 residues. Overall, the study reveals promising bCA blocking potential for the synthesized derivatives, similar to acetazolamide.

AI is a viable alternative to high throughput screening: a 318-target study

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Identification of potential inhibitor against Leishmania donovani mitochondrial DNA primase through in-silico and in vitro drug repurposing approaches

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking

To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.

Novel N-acylsulfonamides: Synthesis, in silico prediction, molecular docking dynamic simulation, antimicrobial and anti-inflammatory activities

Microbial resistance to drugs currently traded in the market is a serious problem in modern medicine. In this field of research, we synthesized a novel N-acylsulfonamides (NAS) derivatives starting from commercially available compounds; morpholine, isocyanate of chlorosulfonyl and alcohols. The in vitro antimicrobial potential of synthesized compounds was screened against 04 Gram-negative bacteria; Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Acinetobacter baumannii, 02 Gram-positive bacteria: Streptococcus sp, Staphylococcus aureus and 07 yeasts and fungi: Candida albicans, Candida spp, Penicillum spp, Aspegillus sp, Aspergillus flavus, Fusarium sp, and Cladosporium spp. The results of inhibition growth were compared with standard antimicrobial drugs with the goal of exploring their potential antimicrobial activity. In addition, the anti-inflammatory activity of the synthesized compounds was determined in-vitro by protein denaturation method. The obtained bioactivity results were further validated by in silico DFT (Density Functional Theory), ADME (Absorption-Distribution-Métabolisation-Excrétion), molecular docking studies and molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Ensemble determination by NMR data deconvolution

Nuclear magnetic resonance (NMR) is the spectroscopic technique of choice for determining molecular conformations in solution at atomic resolution. As solution NMR spectra are rich in structural and dynamic information, the way in which the data should be acquired and handled to deliver accurate ensembles is not trivial. This Review provides a guide to the NMR experiment selection and parametrization process, the generation of viable theoretical conformer pools and the deconvolution of time-averaged NMR data into a conformer ensemble that accurately represents a flexible molecule in solution. In addition to reviewing the key elements of solution ensemble determination of flexible mid-sized molecules, the feasibility and pitfalls of data deconvolution are discussed with a comparison of the performance of representative algorithms.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

DFT calculations, molecular docking, in vitro antimicrobial and antidiabetic studies of green synthesized Schiff bases: as Covid-19 inhibitor

In this investigation, we synthesized Schiff bases 2-(2-methoxyphenoxy)-N-(4-methylbenzylidene)ethanamine, N-(4-methoxybenzylidene)-2-(2-methoxyphenoxy)ethanamine and 2-(2-methoxyphenoxy)-N-(4-nitrobenzylidene)ethanamine from 2-(2-methoxyphenoxy)ethanamine and various aromatic aldehydes by the environmentally friendly sonication method. The B3LYP method with a 6-311++G (d, p) basis set was used in the DFT calculation to obtain the optimized structure of the Schiff base MPEA-NIT. The compounds were tested in vitro for inhibition of bacterial growth (disc well method) and inhibition of α-amylase (starch-iodine method). The compounds tested showed inhibitory activities. In addition, they were subjected to PASS analysis, drug likeness, and bioactivity score predictions using online software. To confirm the experimental findings, molecular docking analyses of synthesized compounds on α-amylase (PDB ID: 1SMD), tRNA threonylcarbamoyladenosine (PDB ID: 5MVR), glycosyl transferase (PDB ID: 6D9T), and peptididoglycan D,D-transpeptidase (PDB ID: 6HZQ) were performed. The emergence of a new coronavirus epidemic necessitates the development of antiviral medications (SARS-CoV-2). Docking active site interactions were investigated to predict compounds' activity against COVID-19 by binding with the SARS-CoV-2 (PDB ID: 6Y84).Communicated by Ramaswamy H. Sarma.

Structure activity relationship (SAR) and anticancer activity of pyrrolidine derivatives: Recent developments and future prospects (A review)

Pyrrolidine molecules are a significant class of synthetic and natural plant metabolites, which show the diversity of pharmacological activities. An extensive variety of synthetic pyrrolidine compounds with numerous derivatization like spirooxindole, thiazole, metal complexes, coumarin, etc have revealed significant anticancer activity. Pyrrolidine molecules are found not only as potential anticancer candidates but also retain the lowest side effects. Depending upon the diverse substitution patterns of the derivatives, these molecules have demonstrated an incredible ability to regulate the various targets to give excellent anti-proliferative activities. Taking these into consideration, efforts have been taken by the scientific fraternity to design and develop a potent anticancer scaffold with negligible side effects. In the present review, we cover the latest advancements in the synthesis of pyrrolidine molecules which have promising anticancer activity toward numerous cancer cell lines. Additionally, it also highlights the effectiveness of derivatives via elucidation of Structural-Activity-Relationship (SAR) which is discussed in detail.

A molecular evolution algorithm for ligand design in DOCK

As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.

A comparative study of 5- fluorouracil, doxorubicin, methotrexate, paclitaxel for their inhibition ability for Mpro of nCoV: Molecular docking and molecular dynamics simulations

A new corona virus (nCoV) is aetiological agent responsible for the viral pneumonia epidemic. Three is no specific therapeutic medicines available for the treatment of this condition and also effective treatment choices are few. In this work author tried to investigate some repurposing drug such as 5- fluorouracil, doxorubicin, methotrexate and paclitaxel against the main protease (Mpro) of nCoV by the computational model. Molecular docking was performed to screen out the best compound and doxorubicin was found to have minimum binding energy −121.89 kcal/mol. To further study, MD simulations were performed at 300 K and the result successfully corroborate the energy obtained by molecular docking. Temperature dependent MD simulation of the best molecule that is doxorubicin obtained from docking result was performed to check the variation in structural changes in Mpro of nCoV at 290 K, 310 K, 320 K and 325 K. It is sound that doxorubicin binds effectively with Mpro of nCoV at 290 K. Further ADME properties of the 5- fluorouracil, doxorubicin, methotrexate and paclitaxel were also evaluated to understand the bioavailability.

Targeting RNA structures with small molecules

RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing - by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.

AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges

Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformational state determines substrates, cofactors, and protein binding. Rational drug discovery employs engineered small molecules to selectively interact with proteins to modulate their function. To selectively target a protein and to design small molecules, knowing the protein structure with all its specific conformation is critical. Unfortunately, for a large number of proteins relevant for drug discovery, the three-dimensional structure has not yet been experimentally solved. Therefore, accurately predicting their structure based on their amino acid sequence is one of the grant challenges in biology. Recently, AlphaFold2, a machine learning application based on a deep neural network, was able to predict unknown structures of proteins with an unprecedented accuracy. Despite the impressive progress made by AlphaFold2, nature still challenges the field of structure prediction. In this Perspective, we explore how AlphaFold2 and related methods help make drug design more efficient. Furthermore, we discuss the roles of predicting domain-domain orientations, all relevant conformational states, the influence of posttranslational modifications, and conformational changes due to protein binding partners. We highlight where further improvements are needed for advanced machine learning methods to be successfully and frequently used in the pharmaceutical industry.

Natural therapeutics and nutraceuticals for lung diseases: Traditional significance, phytochemistry, and pharmacology

BACKGROUND

Lung diseases including chronic obstructive pulmonary disease (COPD), infections like influenza, acute respiratory distress syndrome (ARDS), asthma and pneumonia lung cancer (LC) are common causes of sickness and death worldwide due to their remoteness, cold and harsh climatic conditions, and inaccessible health care facilities.

PURPOSE

Many drugs have already been proposed for the treatment of lung diseases. Few of them are in clinical trials and have the potential to cure infectious diseases. Plant extracts or herbal products have been extensively used as Traditional Chinese Medicine (TCM) and Indian Ayurveda. Moreover, it has been involved in the inhibition of certain genes/protiens effects to promote regulation of signaling pathways. Natural remedies have been scientifically proven with remarkable bioactivities and are considered a cheap and safe source for lung disease.

METHODS

This comprehensive review highlighted the literature about traditional plants and their metabolites with their applications for the treatment of lung diseases through experimental models in humans. Natural drugs information and mode of mechanism have been studied through the literature retrieved by Google Scholar, ScienceDirect, SciFinder, Scopus and Medline PubMed resources against lung diseases.

RESULTS

In vitro, in vivo and computational studies have been explained for natural metabolites derived from plants (like flavonoids, alkaloids, and terpenoids) against different types of lung diseases. Probiotics have also been biologically active therapeutics against cancer, anti-inflammation, antiplatelet, antiviral, and antioxidants associated with lung diseases.

CONCLUSION

The results of the mentioned natural metabolites repurposed for different lung diseases especially for SARS-CoV-2 should be evaluated more by advance computational applications, experimental models in the biological system, also need to be validated by clinical trials so that we may be able to retrieve potential drugs for most challenging lung diseases especially SARS-CoV-2.

Material basis research for Echinacea purpurea (L.) Moench against hepatocellular carcinoma in a mouse model through integration of metabonomics and molecular docking

BACKGROUND

Echinacea purpurea (L.) Moench (EP), a well-known "immunostimulant" in the West, is one of the most popular botanicals for patients with cancer. It has been proved to be effective against hepatocellular carcinoma (HCC), while the active ingredients remains unclear.

PURPOSE

This study aimed to investigate the inhibitory effect and interpret the material basis of EP against HCC through metabolomics and molecular docking.

METHODS

Tumor growth, biochemical analysis and pathological changes were detected in HCC-induced mice to evaluate the efficacy of EP. An integrative method combining molecular docking and LC-MS-based metabolomics was performed to evaluate the inhibitory role and screen the material basis of EP against HCC.

RESULTS

EP significantly suppressed tumor growth and decreased the levels of AFP. Histological analysis showed that wide areas of necrosis in the EP-treated tumors that were almost absent in those in model group. Serum metabolomics results revealed EP could significantly improve 12 serum different metabolites induced by HCC, which were involved into phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism. Then, 5 related genes were selected out to be the key targets of EP against HCC based on Metscape. 22 identified compounds were docked through Sybyl-X. The herb-compound-gene-metabolic pathways network (HCGMN) was constructed to reveal the associations between EP and HCC. Finally, 19 compounds were screened as promising active ingredients of EP against HCC.

CONCLUSION

The results showed that the approach integrating of metabonomics and molecular docking is a powerful strategy to obtain the active ingredients from plants.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

Accelerating Molecular Dynamics Enrichments of High-Affinity Ligands for Proteins

Molecular docking algorithms are used to seek the most active compounds from a pool of ligands. In principle, molecular dynamics (MD) simulations with accurate physical potentials and sampling could yield better enrichments, but they are computationally expensive. Here, we describe a method called MELD-Bracket that utilizes biased replica exchange ladders in MD in order to compete different ligands against each other within a fast bracket style "binding tournament". MELD-Bracket finds best-binders rapidly when ligands are well separated in their binding affinities.

Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations

The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.

VTR: A Web Tool for Identifying Analogous Contacts on Protein Structures and Their Complexes

Evolutionarily related proteins can present similar structures but very dissimilar sequences. Hence, understanding the role of the inter-residues contacts for the protein structure has been the target of many studies. Contacts comprise non-covalent interactions, which are essential to stabilize macromolecular structures such as proteins. Here we show VTR, a new method for the detection of analogous contacts in protein pairs. The VTR web tool performs structural alignment between proteins and detects interactions that occur in similar regions. To evaluate our tool, we proposed three case studies: we 1) compared vertebrate myoglobin and truncated invertebrate hemoglobin; 2) analyzed interactions between the spike protein RBD of SARS-CoV-2 and the cell receptor ACE2; and 3) compared a glucose-tolerant and a non-tolerant β-glucosidase enzyme used for biofuel production. The case studies demonstrate the potential of VTR for the understanding of functional similarities between distantly sequence-related proteins, as well as the exploration of important drug targets and rational design of enzymes for industrial applications. We envision VTR as a promising tool for understanding differences and similarities between homologous proteins with similar 3D structures but different sequences. VTR is available at http://bioinfo.dcc.ufmg.br/vtr.

Construction and activity evaluation of novel dual-target (SE/CYP51) anti-fungal agents containing amide naphthyl structure

With the increase of fungal infection and drug resistance, it is becoming an urgent task to discover the highly effective antifungal drugs. In the study, we selected the key ergosterol bio-synthetic enzymes (Squalene epoxidase, SE; 14 α-demethylase, CYP51) as dual-target receptors to guide the construction of novel antifungal compounds, which could achieve the purpose of improving drug efficacy and reducing drug-resistance. Three different series of amide naphthyl compounds were generated through the method of skeleton growth, and their corresponding target products were synthesized. Most of compounds displayed the obvious biological activity against different Candida spp. and Aspergillus fumigatus. Among of them, target compounds 14a-2 and 20b-2 not only possessed the excellent broad-spectrum anti-fungal activity (MIC₅₀, 0.125-2 μg/mL), but also maintained the anti-drug-resistant fungal activity (MIC₅₀, 1-4 μg/mL). Preliminary mechanism study revealed the compounds (14a-2, 20b-2) could block the bio-synthetic pathway of ergosterol by inhibiting the dual-target (SE/CYP51) activity, and this finally caused the cleavage and death of fungal cells. In addition, we also discovered that compounds 14a-2 and 20b-2 with low toxic and side effects could exert the excellent therapeutic effect in mice model of fungal infection, which was worthy for further in-depth study.

Targeting the actin nucleation promoting factor WASp provides a therapeutic approach for hematopoietic malignancies

Cancer cells depend on actin cytoskeleton rearrangement to carry out hallmark malignant functions including activation, proliferation, migration and invasiveness. Wiskott–Aldrich Syndrome protein (WASp) is an actin nucleation-promoting factor and is a key regulator of actin polymerization in hematopoietic cells. The involvement of WASp in malignancies is incompletely understood. Since WASp is exclusively expressed in hematopoietic cells, we performed in silico screening to identify small molecule compounds (SMCs) that bind WASp and promote its degradation. We describe here one such identified molecule; this WASp-targeting SMC inhibits key WASp-dependent actin processes in several types of hematopoietic malignancies in vitro and in vivo without affecting naïve healthy cells. This small molecule demonstrates limited toxicity and immunogenic effects, and thus, might serve as an effective strategy to treat specific hematopoietic malignancies in a safe and precisely targeted manner.

Cancer cells proliferate and invade via cytoskeletal proteins such as WASp, exclusively expressed in hematopoietic cells. Here the authors show a specific small molecule compound inhibiting cancer cell activity by WASp degradation and demonstrating its therapeutic potential in vitro and in vivo.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Computational evidence of new putative allosteric sites in the acetylcholinesterase receptor

Acetylcholinesterase (AChE), with a rigid structure and buried active site at the end of a deep narrow gorge, is interesting enough to solve the paradox between high catalytic activity and unavailability of the active site in treatment of Alzheimer's disease (AD). In this way, the blind docking process is performed on an ensemble of AChE structures created with molecular dynamics (MD) simulations to survey the whole space of AChE to find multiple access pathways to the active site and ranking them based on their affinity scores. Our results show that there are other allosteric binding sites in the protein structure whose inhibition, can affect protein function by disrupting the release of the Acetylcholine (AC) degradation products. In this study, inhibitory activities of Hybride14 and two natural compounds (Papaverine and Palmatine) were evaluated for all possible allosteric sites via docking method. The results confirmed the non-competitive inhibition mechanism. The best binding mode for these inhibitors and efficacy of hydrogen bonds and hydrophobic interactions on inhibitory activities of ligands were also disclosed. Furthermore, our studies provide significant molecular insight for AChE inhibition that could aid in the development of new drugs for AD's treatment.

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

Overview of Viral Pneumonia Associated With Influenza Virus, Respiratory Syncytial Virus, and Coronavirus, and Therapeutics Based on Natural Products of Medicinal Plants

Viral pneumonia has been a serious threat to global health, especially now we have dramatic challenges such as the COVID-19 pandemic. Approximately six million cases of community-acquired pneumonia occur every year, and over 20% of which need hospital admission. Influenza virus, respiratory virus, and coronavirus are the noteworthy causative agents to be investigated based on recent clinical research. Currently, anaphylactic reaction and inflammation induced by antiviral immunity can be incriminated as causative factors for clinicopathological symptoms of viral pneumonia. In this article, we illustrate the structure and related infection mechanisms of these viruses and the current status of antiviral therapies. Owing to a set of antiviral regiments with unsatisfactory clinical effects resulting from side effects, genetic mutation, and growing incidence of resistance, much attention has been paid on medicinal plants as a natural source of antiviral agents. Previous research mainly referred to herbal medicines and plant extracts with curative effects on viral infection models of influenza virus, respiratory virus, and coronavirus. This review summarizes the results of antiviral activities of various medicinal plants and their isolated substances, exclusively focusing on natural products for the treatment of the three types of pathogens that elicit pneumonia. Furthermore, we have introduced several useful screening tools to develop antiviral lead compounds.

Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19

AIMS

To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications.

BACKGROUND

SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally.

OBJECTIVE

To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs.

METHODS

A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol.

RESULTS

Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications.

CONCLUSION

Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

Molecular Binding and Simulation Studies of Staphylococcus aureus Superantigens with Flavonoid Compounds

BACKGROUND

Superantigens of Staphylococcus aureus namely enterotoxin A, exfoliative toxin A, and Toxic shock syndrome toxin-1 cause detrimental effects on the cells of the immune system.

METHODS

In this work, the toxins were downloaded from the Protein DataBank database and energies were minimized using KoBaMIN server. Forty flavonoids compounds were identified by pubchem compound database through extensive literature study and their 3D structures were obtained by submitting SMILES to CORINA tool. Based on Lipinski's rule of five, the molecules were filtered that resulted in 27 compounds. Molecular docking was performed for identifying the binding and interaction sites of flavonoids with the toxins using Autodock 4.

RESULTS AND CONCLUSION

The docked complexes were then subjected to molecular dynamics simulation using Gromacs. The analysis revealed the stability of the complexes as indicated by three hydrogen bonds formed during the simulation time period of 20 ns.

Evaluation of potency of the selected bioactive molecules from Indian medicinal plants with MPro of SARS-CoV-2 through in silico analysis

Background

The recent outbreak of novel SARs CoVid-2 across the globe and absence of specific drug against this virus lead the scientific community to look into some alternative indigenous treatments. India as a hub of ayurvedic and medicinal plants can shed light on its treatment using specific active bio-molecules from these plants.

Objectives

Keeping our herbal resources in mind we were interested to inquire whether some phytochemicals from Indian spices and medicinal plants can be used as alternative therapeutic agents in contrast to synthetic drugs.

Materials and methods

We used in-silico molecular docking approach to test whether bioactive molecules of herbal origin such as Hyperoside, Nimbaflavone, Ursolic acid, 6-gingerol, 6-shogaol& 6-paradol, Curcumin, Catechins&Epigallocatechin, α-Hederin, Piperine could bind and potentially block theM^proenzyme of Sars-CoV-2 virus.

Results

Ursolic acid showed the highest docking score (-8.7 kcal/mol) followed by Hyperoside (-8.6kcal/mol), α-Hederin (-8.5 kcal/mol) and Nimbaflavone (-8.0kcal/mol). Epigallocatechin, Catechins, and Curcumin also exhibited high binding affinity (Docking score -7.3, -7.1 and -7.1 kcal/mol) with the M^pro. Rest of the tested phytochemicals exhibited moderate binding and inhibitory effects.

Conclusion

This finding provides a basis for biochemical assay on Sars-CoV-2 virus.

Utilization of a complex arrangement approach for spectroscopic examination with Eosin Y of various cephalosporins in their pure or pharmaceutical dosage forms, and in human plasma

Approved, basic, effective and successful spectroscopic strategies (spectrophotometric and spectrofluorimetric) were created to measure seven cephalosporins: cefpiramide (I), cefuroxime (II), cefoxitin (III), ceftazidime (IV), cefpirome (V), ceftobiprole (VI), and ceftriaxone (VII). These strategies used a two-fold complex arrangement response for the drug amino groups with Eosin Y (EY). The examined drugs were determined spectrophotometrically at 542-550 nm in acetic acid derivative buffer. The examined drugs were determined spectrofluorimetrically by measuring their quenching effect on EY local fluorescence at 545 nm after excitation at 305 nm. The absorbance-intensity plots were rectilinear over the ranges 20-100, 10-130, 20-220, 30-230, 10-210, 20-180 and 10-130 μg ml^-1 for I, II, III, IV, V, VI, and VII samples, respectively. The fluorescence-intensity plots were rectilinear over the ranges 0.5-1.5, 0.1-0.9, 0.3-1.5, 0.5-2.5, 0.1-0.9, 0.5-2.5 and 0.1-1.0 μg ml^-1 for I, II, III, IV, V, VI, and VII samples, respectively. The recommended materials were certified as adhering to International Council for Harmonisation (ICH) guidelines and were used to examine the tested drugs in different dosage forms and in human plasma tests. The approved materials matched the reference materials.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule-along with any bound ligand(s)-within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method-coupled with our XModeScore method for experimental tautomer/protomer state determination-to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

Structural Basis of Potential Inhibitors Targeting SARS-CoV-2 Main Protease

The Coronavirus disease-19 (COVID-19) pandemic is still devastating the world causing significant social, economic, and political chaos. Corresponding to the absence of globally approved antiviral drugs for treatment and vaccines for controlling the pandemic, the number of cases and/or mortalities are still rising. Current patient management relies on supportive treatment and the use of repurposed drugs as an indispensable option. Of a crucial role in the viral life cycle, ongoing studies are looking for potential inhibitors to the main protease (Mpro) of severe acute respiratory syndrome Coronavirus -2 (SARS-CoV-2) to tackle the pandemic. Although promising results have been achieved in searching for drugs inhibiting the Mpro, work remains to be done on designing structure-based improved drugs. This review discusses the structural basis of potential inhibitors targeting SARS-CoV-2 Mpro, identifies gaps, and provides future directions. Further, compounds with potential Mpro based antiviral activity are highlighted.

Quantum chemical studies on molecular structure, AIM, ELF, RDG and antiviral activities of hybrid hydroxychloroquine in the treatment of COVID-19: Molecular docking and DFT calculations

Structure-activity relationships for hydroxychloroquine compound and its derivatives resulted in a potent antiviral activity. Where hydroxychloroquine derivatives showed an apparent efficacy against coronavirus related pneumonia. For this reason, the current study is focused on the structural properties of hydroxychloroquine and hydroxychloroquine sulfate. Optimized structures of these molecules have been reported by using DFT method at B3LYP/6-31G* level of theory. The geometric were determined and compared with the experimental crystal structure. The intra and intermolecular interactions which exist within these compounds are analyzed by different methods namely the topological analysis AIM, ELF and the reduced gradient of the density. These approaches make it possible in particular to study the properties of hydrogen bonds. The highest occupied molecular orbital and the lowest unoccupied molecular orbital energy levels are constructed and the corresponding frontier energy gaps are determined to realize the charge transfer within the molecule. The densities of state diagrams were determined to calculate contributions to the molecular orbitals. The molecular electrostatic potential surfaces are determined to give a visual representation of charge distribution of these ligands and to provide information linked to electrophilic and nucleophilic sites localization. Finally, these derivatives were evaluated for the inhibition of COVID-19 activity by using the molecular docking method.

Characterisation of trehalose-6-phosphate phosphatases from bacterial pathogens

The trehalose biosynthesis pathway has recently received attention for therapeutic intervention combating infectious diseases caused by bacteria, helminths or fungi. Trehalose-6-phosphate phosphatase (TPP) is a key enzyme of the most common trehalose biosynthesis pathway and a particularly attractive target owing to the toxicity of accumulated trehalose-6-phosphate in pathogens. Here, we characterised TPP-like proteins from bacterial pathogens implicated in nosocomial infections in terms of their steady-state kinetics as well as pH- and metal-dependency of their enzymatic activity. Analysis of the steady-state kinetics of recombinantly expressed enzymes from Acinetobacter baumannii, Corynebacterium diphtheriae and Pseudomonas stutzeri yielded similar kinetic parameters as those of other reported bacterial TPPs. In contrast to nematode TPPs, the divalent metal ion appears to be bound only weakly in the active site of bacterial TPPs, allowing the exchange of the resident magnesium ion with other metal ions. Enzymatic activity comparable to the wild-type enzyme was observed for the TPP from P. stutzeri with manganese, cobalt and nickel. Analysis of the enzymatic activity of S. maltophilia TPP active site mutants provides evidence for the involvement of four canonical aspartate residues as well as a strictly conserved histidine residue of TPP-like proteins from bacteria in the enzyme mechanism. That histidine residue is a member of an interconnected network of five conserved residues in the active site of bacterial TPPs which likely constitute one or more functional units, directly or indirectly cooperating to enhance different aspects of the catalytic activity.

Novel naphthylamide derivatives as dual-target antifungal inhibitors: Design, synthesis and biological evaluation

Fungal infections have become a serious medical problem due to the high infection rate and the frequent emergence of drug resistance. Squalene epoxidase (SE) and 14α-demethylase (CYP51) are considered as the important antifungal targets, they can show the synergistic effect on antifungal therapy. In the study, a series of active fragments were screened through the method of De Novo Link, and these active fragments with the higher Ludi_Scores were selected, which can show the obvious binding ability with the dual targets (SE, CYP51). Subsequently, three series of target compounds with naphthyl amide scaffolds were constructed by connecting these core fragments, and their structures were synthesized. Most of compounds showed the antifungal activity in the treatment of pathogenic fungi. It was worth noting that compounds 10b-5 and 17a-2 with the excellent broad-spectrum antifungal properties also exhibited the obvious antifungal effects against drug-resistant fungi. Preliminary mechanism study has proved these target compounds can block the biosynthesis of ergosterol by inhibiting the activity of dual targets (SE, CYP51). Furthermore, target compounds 10-5 and 17a-2 with low toxicity side effects also demonstrated the excellent pharmacological effects in vivo. The molecular docking and ADMET prediction were performed, which can guide the optimization of subsequent lead compounds.