Apurinic/apyrimidinic endonuclease 1 (APE1) is an enzyme responsible for the initial step in the base excision repair pathway and is known to be a potential drug target for treating cancers, because its expression is associated with resistance to DNA-damaging anticancer agents. Although several inhibitors already have been identified, the identification of novel kinds of potential inhibitors of APE1 could provide a seed for the development of improved anticancer drugs. For this purpose, we first classified known inhibitors of APE1. According to the classification, we constructed two distinct pharmacophore models. We screened more than 3 million lead-like compounds using the pharmacophores. Hits that fulfilled the features of the pharmacophore models were identified. In addition to the pharmacophore screen, we carried out molecular docking to prioritize hits. Based on these processes, we ultimately identified 1,338 potential inhibitors of APE1 with predicted binding affinities to the enzyme.
Antineoplastic Agents ; Classification ; DNA Repair ; Molecular Docking Simulation

Among all serious diseases globally, diabetes (type 1 and type 2) still poses a major challenge to the world population. Several target proteins have been identified, and the etiology causing diabetes has been reasonably well studied. But, there is still a gap in deciding on the choice of a drug, especially when the target is mutated. Mutations in the KCNJ11 gene, encoding the kir6.2 channel, are reported to be associated with congenital hyperinsulinism, having a major impact in causing type 1 diabetes, and due to the lack of its 3D structure, an attempt has been made to predict the structure of kir6.2, applying fold recognition methods. The current work is intended to investigate the affinity of four phytochemicals namely, curcumin (Curcuma longa), genistein (Genista tinctoria), piperine (Piper nigrum), and pterostilbene (Vitis vinifera) in a normal as well as in a mutant kir6.2 model by adopting a molecular docking methodology. The phytochemicals were docked in both wild and mutated kir6.2 models in two rounds: blind docking followed by ATP-binding pocket-specific docking. From the binding pockets, the common interacting amino acid residues participating strongly within the binding pocket were identified and compared. From the study, we conclude that these phytochemicals have strong affinity in both the normal and mutant kir6.2 model. This work would be helpful for further study of the phytochemicals above for the treatment of type 1 diabetes by targeting the kir6.2 channel.
Congenital Hyperinsulinism ; Curcumin ; Diabetes Mellitus ; Genistein ; Molecular Docking Simulation ; Phytochemicals*

Cathepsin S is a cysteine protease which is closely related to autoimmune diseases,psoriasis and other diseases. In this study,we used virtual screening method to screen compounds,which from the natural product library of traditional Chinese medicine,with potential inhibitory effect on cathepsin S. The work involved in study on inhibitory mechanism of representative compounds,then analysis of the distribution of these compounds in traditional Chinese medicine and the correlation with disease,so as to provide a new drug research and data-base for cathepsin S. The complex crystal structure of cathepsin S,2FQ9,was used to establish the pharmacophore model of cathepsin S inhibitor,and the best pharmacophore model was selected. As a result,fifty compounds were selected from TCMD database. After molecular docking,65 potential inhibitors were identified. Potential inhibitors can produce multiple intermolecular interactions with targets,resulting in inhibition. There are 58 kinds of traditional Chinese medicines which include 65 natural inhibitors. Data collection and analysis of the nature,flavor xing,channel entry and modern pharmacological effects of these traditional Chinese medicines showed that most of them were related to the biological activity of cathepsin S,which supported the validity of the screening results. Cathepsin S has a certain correlation with autoimmune diseases and can be used as a target for further study of traditional Chinese medicine.
Cathepsins ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional ; Molecular Docking Simulation

The ongoing coronavirus disease (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is causing major damages in health and economies worldwide. The development of safe and effective vaccines for COVID-19 is of utmost importance yet none have been licensed to date. One of the strategies for vaccine development utilizes dendritic cells which express class I and class II human leukocyte antigen (HLA) molecules. These HLA molecules present the antigenic peptides to T cells which mediate the immune response. Thus, the study aimed to identify SARS-CoV-2 peptides with potential binding to HLA class I and class II molecules using different bioinformatics tools. SYFPEITHI and IEDB were used to predict epitopes for the most common HLA class I and II alleles among Filipinos. The top predicted epitopes were subjected to de novo and template-based molecular docking. Then, binding energies of the generated peptide-HLA complexes to putative T cell receptors were predicted using a homology modeling approach. Several predicted epitopes showed promising MHC and TCR binding, although results varied considerably between the prediction methods used. In particular, the results of de novo and template-based docking methods did not coincide, the latter of which generated complexes that more closely resemble typical peptide-HLA complexes. The results of this study will be validated by the next stage of the vaccine development project which is the in vitro assessment of the T cell responses elicited by dendritic cells pulsed with the candidate peptides.
Humans ; COVID-19 Vaccines ; Molecular Docking Simulation ; COVID-19

OBJECTIVE: To construct protein functional network according to the physiological process in vivo and functionally based distinct families, to understand biological functions, and to make wise decisions. METHODS: We described here a very effective strategy combining with multiple-docking and protein-ligand binding-affinity fingerprint method to generate bio-functional network and pathway and reveal the protein "unknown" functions and their relationship. RESULTS: Totally 27 sets of proteins and 28 bio-active molecules were used to reconstruct the possible phospholipids metabolic network by computational simulation strategy. The protein-ligand network reconstruction and pathway based drug design showed that the direct interaction investigation might be effective in complex biological system study. CONCLUSION Even for weak and moderate interactions in the real biology system, the relationship between each other can be achieved by fingerprint analysis based on multiple-docking data. The results of these calculations give valuable insight into the pathway and the function relationship among these proteins. This method can be a very useful tool for protein classification, target selection, and inhibitor design.
Ligands ; Molecular Docking Simulation ; Phospholipids ; metabolism ; Proteins ; chemistry

This research is to predict anti-Alzheimer's disease active constituents on the target of acetylcholinesterase(AChE) from Glycyrrhizae Radix et Rhizoma with the help of pharmacophore and molecular docking. AChE ligand-based pharmacophore model was set up and the molecular library of the constituents from Glycyrrhizae Radix et Rhizoma were established by collecting literature. Then the constituents from Glycyrrhizae Radix et Rhizoma were screen for the potential AChE inhibitory potency in silico through matching with the best pharmacophore model. The flexible docking was used to evaluate the interactions between compounds screened from pharmacophore model and AChE protein(PDB ID:4 EY7). The interactions were expressed including but not limited to CDOCKER interaction energy, hydrogen bonds and non-bonding interactions. The molecular library of Glycyrrhizae Radix et Rhizoma contains 44 chemical constituents. As for the pharmacophore model, six kinds of potential AChE inhibitory constituents from Glycyrrhizae Radix et Rhizoma were considered to be the promising compounds according to the results of searching 3 D database of pharmacophore model. The molecular docking was possessed and the interaction patterns were given to show the detail interactions. The compounds screening from the pharmacophore model were consistent with the existing studies to some degree, indicating that the virtual screen protocols of AChE inhibitory constituents from Glycyrrhizae Radix et Rhizoma based on pharmacophore and molecular docking was reliable.
Drugs, Chinese Herbal ; Glycyrrhiza ; Molecular Docking Simulation ; Rhizome ; Triterpenes

The aim of this paper was to explore the potential molecular mechanism of Banxia Xiexin Decoction in the treatment of colon cancer through pharmacology network and molecular docking methods. The chemical constituents and action targets of 7 herbs from Banxia Xiexin Decoction were collected by using TCMSP database,Chinese Pharmacopoeia and literatures consultation. GeneCards database was used to predict the potential targets of colon cancer. GO biological process analysis and KEGG pathway enrichment analysis of the disease and drug intersection targets were carried out through DAVID database. "Component-target-pathway" network and protein-protein interaction(PPI) network were construction by using Cytoscape and STRING database,and then the core components and targets of Banxia Xiexin Decoction in the treatment of colon cancer were selected according to the topological parameters. Finally, Autodock Vina was used to realize the molecular docking of core components and key targets. The prediction results showed that there were 190 active compounds and 324 corresponding targets for Banxia Xiexin Decoction,involving 74 potential targets for colon cancer. Cytoscape topology analysis revealed 11 key targets such as STAT3,TP53,AKT1,TNF,IL6 and SRC, as well as 10 core components such as quercetin,β-sitosterol,baicalein,berberine,and 6-gingerol.In bioinformatics enrichment analysis, 679 GO terms and 106 KEGG pathways were obtained, mainly involving PI3 K-AKT signaling pathway,TNF signaling pathway and TP53 signaling pathway. The results of molecular docking showed that baicalein,berberine,licochalcone A and 6-gingerol had a high affinity with SRC,STAT3,TNF and IL6. The results suggested that Banxia Xiexin Decoction could play an anti-colon cancer effect by inhibiting cell proliferation, regulating cell cycle, inducing apoptosis and anti-inflammatory function. The study revealed the multi-components,multi-targets and multi-pathways molecular mechanism of Banxia Xiexin Decoction,which could provide scientific basis and research ideas for the clinical application of Banxia Xiexin Decoction and the treatment of colon cancer with compound Chinese medicines.
Colonic Neoplasms ; Drugs, Chinese Herbal ; Humans ; Molecular Docking Simulation ; Technology

The computer information technology that has penetrated into every aspect of our lives, can not only assist the screening of drugs, but also simulate the effect of drugs. At present, computer-aided technologies have been used to screen aptamers, which play an important role in improving the screening efficiency and screening high affinity binding aptamers. This review summarized the screening methods of aptamers through computer-aided sequence evaluation, structural analysis and molecular docking.
Aptamers, Nucleotide ; Computers ; Molecular Docking Simulation ; SELEX Aptamer Technique/methods*

This study aims to explore the targets of ginsenosides in brain based on drug affinity responsive target stability(DARTS) technology. Specifically, DARTS technology was combined with label-free liquid chromatography tandem mass spectrometry(LC-MS) to screen out the proteins in the brain that might interact with ginsenosides. Based on the screening results, adenylate kinase 1(AK1) was selected for further confirmation. First, the His-AK1 fusion protein was yielded successively through the construction of recombinant prokaryotic expression vector, expression of target protein, and purification of the fusion protein. Biolayer interferometry(BLI) was employed to detect the direct interaction of Rg_1, Re, Rb_1, Rd, Rh_2, F1, Rh_1, compound K(CK), 25-OH-PPD, protopanaxa-diol(PPD), and protopanaxatriol(PPT) with AK1, thereby screening the ginsenoside monomer or sapogenin that had strong direct interaction with the suspected target protein AK1. Then, the BLI was used to further determine the kinetic parameters for the binding of PPD(strongest interaction with AK1) to His-AK1 fusion protein. Finally, molecular docking technology was applied to analyze the binding properties between the two. With DARTS and LC-MS, multiple differential proteins were screened out, and AK1 was selected based on previous research for target verification. Fusion protein His-AK1 was obtained by prokaryotic expression, and the response(nm) of Re, Rg_1, Rd, Rb_1, Rh_1, Rh_2, F1, PPT, PPD, 25-OH-PPD, and CK with His-AK1 was respectively 0.003 1, 0.001 9, 0.042 8, 0.022 2, 0.013 4, 0.037 3, 0.013 9, 0.030 7, 0.140 2, 0.016 0, and 0.040 8. The K_(on), K_(off), and K_D values of PPD and His-AK1 were determined by the BLI as 1.22×10~2 mol~(-1)·L·s~(-1), 1.04×10~(-2) s~(-1), 8.52×10~(-5) mol·L~(-1). According to the molecular docking result, PPD bound to AK1 with the absolute value of the docking score of 3.438, and hydrogen bonds mainly formed between the two. Thus, AK1 is one of the protein action sites of ginsenosides in the brain. The direct interaction between ginsenoside metabolite PPD and AK1 is the strongest.
Brain/metabolism* ; Chromatography, Liquid ; Ginsenosides ; Molecular Docking Simulation ; Technology

Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.
Computational Biology ; Drug Design ; Drug Discovery ; High-Throughput Screening Assays ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Quantitative Structure-Activity Relationship