Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites

In-silico screening and identification of potential drug-like compounds for dengue-associated thrombocytopenia from Carica papaya leaf extracts

Dengue virus is a mosquito-borne pathogen that causes a variety of illnesses ranging from mild fever to severe and fatal dengue haemorrhagic fever or dengue shock syndrome. One of the major clinical manifestations of severe dengue infection is thrombocytopenia. The dengue non-structural protein 1 (NS1) is the primary protein that stimulates immune cells via toll-like receptor 4 (TLR4), induces platelets, and promotes aggregation, which could result in thrombocytopenia. The leaf extracts of Carica papaya seem to have therapeutic benefits in managing thrombocytopenia associated with dengue. The present study focuses on understanding the underlying mechanism of the use of papaya leaf extracts in treating thrombocytopenia. We have identified 124 phytocompounds that are present in the papaya leaf extract. The pharmacokinetics, molecular docking, binding free energy calculations, and molecular dynamic simulations were performed to investigate the drug-like properties, binding affinities, and interaction of phytocompounds with NS1 protein as well as the interactions of NS1 with TLR4. Three phytocompounds were found to bind with the ASN130, a crucial amino acid residue in the active site of the NS1 protein. Thus, we conclude that Rutin, Myricetin 3-rhamnoside, or Kaempferol 3-(2''-rhamnosylrutinoside) may serve as promising molecules by ameliorating thrombocytopenia in dengue-infected patients by interfering the interaction of NS1 with TLR4. These molecules can serve as drugs in the management of dengue-associated thrombocytopenia after verifying their effectiveness and assessing the drug potency, through additional in-vitro assays.Communicated by Ramaswamy H. Sarma.

TATA-Binding Protein-Based Virtual Screening of FDA Drugs Identified New Anti-Giardiasis Agents

Parasitic diseases, predominantly prevalent in developing countries, are increasingly spreading to high-income nations due to shifting migration patterns. The World Health Organization (WHO) estimates approximately 300 million annual cases of giardiasis. The emergence of drug resistance and associated side effects necessitates urgent research to address this growing health concern. In this study, we evaluated over eleven thousand pharmacological compounds sourced from the FDA database to assess their impact on the TATA-binding protein (TBP) of the early diverging protist Giardia lamblia, which holds medical significance. We identified a selection of potential pharmacological compounds for combating this parasitic disease through in silico analysis, employing molecular modeling techniques such as homology modeling, molecular docking, and molecular dynamics simulations. Notably, our findings highlight compounds DB07352 and DB08399 as promising candidates for inhibiting the TBP of Giardia lamblia. Also, these compounds and DB15584 demonstrated high efficacy against trophozoites in vitro. In summary, this study identifies compounds with the potential to combat giardiasis, offering the prospect of specific therapies and providing a robust foundation for future research.

Cobdock: an accurate and practical machine learning-based consensus blind docking method

Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

Toxicity of Three Optical Brighteners: Potential Pharmacological Targets and Effects on Caenorhabditis elegans

Optical brighteners (OBs) have become an integral part of our daily lives and culture, with a growing number of applications in various fields. Most industrially produced OBs are derived from stilbene, which has been found in environmental matrices. The main objectives for this work are as follows: first, to identify protein targets for DAST, FB-28, and FB-71, and second, to assess their effects in some behaviors physiologic of Caenorhabditis elegans. To achieve the first objective, each OB was tested against a total of 844 human proteins through molecular docking using AutoDock Vina, and affinities were employed as the main criteria to identify potential target proteins for the OB. Molecular dynamics simulations took and validated the best 25 docking results from two protein databases. The highest affinity was obtained for the Hsp70-1/DAST, CD40 ligand/FB-71, and CD40 ligand/FB-28 complexes. The possible toxic effects that OBs could cause were evaluated using the nematode C. elegans. The lethality, body length, locomotion, and reproduction were investigated in larval stage L1 or L4 of the wild-type strain N2. In addition, transgenic green fluorescent protein (GFP) strains were employed to estimate changes in relative gene expression. The effects on the inhibition of growth, locomotion, and reproduction of C. elegans nematodes exposed to DAST, FB-71, and FB-28 OBs were more noticeable with respect to lethality. Moreover, an interesting aspect in OB was increased the expression of gpx-4 and sod-4 genes associated with oxidative stress indicating a toxic response related to the generation of reactive oxygen species (ROS). In all cases, a clear concentration-response relationship was observed. It is of special attention that the use of OBs is increasing, and their different sources, such as detergents, textiles, plastics, and paper products, must also be investigated to characterize the primary emissions of OBs to the environment and to develop an adequate regulatory framework.

Exploring the composition of protein-ligand binding sites for cancerous inhibitor of PP2A (CIP2A) by inhibitor guided binding analysis: paving a new way for the Discovery of drug candidates against triple negative breast cancer (TNBC)

Triple-negative breast cancer (TNBC) is associated with high-grade invasive carcinoma leading to a 10% to 15% death rate in younger premenopausal women. Targeting cancerous inhibitors of protein phosphatase (CIP2A) has been a highly effective approach for exploring therapeutic drug candidates. Lapatinib, a dual tyrosine kinase inhibitor, has shown promising inhibition properties by inducing apoptosis in TNBC carcinogenesis in vivo. Despite knowledge of the 3D structure of CIP2A, no reports provide insight into CIP2A ligand binding sites. To this effect, we conducted in silico site identification guided by lapatinib binding. Four of the five sites identified were cross-validated, and the stem domain revealed more excellent ligand binding affinity. The binding affinity of lapatinib in these sites was further computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) approach. According to MM/PBSA//200 ns MD simulations, lapatinib exhibited a higher binding affinity against CIP2A in site 2 with ΔG critical values of -37.1 kcal/mol. The steadiness and tightness of lapatinib with CIP2A inside the stem domain disclosed glutamic acid-318 as the culprit amino acid with the highest electrostatic energy. These results provide clear information on the CIP2A domain capable of ligand binding and validate lapatinib as a promising CIP2A inhibitor in TNBC carcinogenesis.

Exploring the potential of biologically active phenolic acids from marine natural products as anticancer agents targeting the epidermal growth factor receptor

The epidermal growth factor receptor (EGFR) dimerizes upon ligand bindings to the extracellular domain that initiates the downstream signaling cascades and activates intracellular kinase domain. Thus, activation of autophosphorylation through kinase domain results in metastasis, cell proliferation, and angiogenesis. The main objective of this research is to discover more promising anti-cancer lead compound against EGRF from the phenolic acids of marine natural products using in-silico approaches. Phenolic compounds reported from marine sources are reviewed from previous literatures. Furthermore, molecular docking was carried out using the online tool CB-Dock. The molecules with good docking and binding energies scores were subjected to ADME, toxicity and drug-likeness analysis. Subsequently, molecules from the docking experiments were also evaluated using the acute toxicity and MD simulation studies. Fourteen phenolic compounds from the reported literatures were reviewed based on the findings, isolation, characterized and applications. Molecular docking studies proved that the phenolic acids have good binding fitting by forming hydrogen bonds with amino acid residues at the binding site of EGFR. Chlorogenic acid, Chicoric acid and Rosmarinic acid showed the best binding energies score and forming hydrogen bonds with amino acid residues compare to the reference drug Erlotinib. Among these compounds, Rosmarinic acid showed the good pharmacokinetics profiles as well as acute toxicity profile. The MD simulation study further revealed that the lead complex is stable and could be future drug to treat the cancer disease. Furthermore, in a wet lab environment, both in-vitro and in-vivo testing will be employed to validate the existing computational results.Communicated by Ramaswamy H. Sarma.

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

Selection of optimised ligands by fluorescence-activated bead sorting

The chemistry of aptamers is largely limited to natural nucleotides, and although modifications of nucleic acids can enhance target aptamer affinity, there has not yet been a technology for selecting the right modifications in the right locations out of the vast number of possibilities, because enzymatic amplification does not transmit sequence-specific modification information. Here we show the first method for the selection of specific nucleoside modifications that increase aptamer binding efficacy, using the oncoprotein EGFR as a model target. Using fluorescence-activated bead sorting (FABS), we have successfully selected optimized aptamers from a library of >65 000 variations. Hits were identified by tandem mass spectrometry and validated by using an EGFR binding assay and computational docking studies. Our results provide proof of concept for this novel strategy for the selection of chemically optimised aptamers and offer a new method for rapidly synthesising and screening large aptamer libraries to accelerate diagnostic and drug discovery.

Various concentrations of hesperetin induce different types of programmed cell death in human breast cancerous and normal cell lines in a ROS-dependent manner

The polyphenolic component of citrus fruits, hesperetin (Hst), is a metabolite of hesperidin. In this study, we examined the effect of varying doses and exposure times of hesperetin on MCF-7 and MDA-MB-231 cancer cells, as well as MCF-10A normal cells. By using MTT assay, real-time PCR, western blot, and flow cytometry, we determined the effects of Hst on cell viability, ROS levels, and markers of cell death. Furthermore, molecular docking was used to identify Hst targets that might be involved in ROS-dependent cell death. According to the results, different concentrations of Hst induced different modes of cell death at specific ROS levels. Paraptosis occurred in all cell lines at concentration ranges of IC35 to IC60, and apoptosis occurred at concentrations greater than IC65. In addition, MDA-MB-231 cells were subjected to senescence at sub-toxic doses when treated for a long period of time. When Hst levels were higher, N-acetylcysteine (NAC)'s effect on neutralizing ROS was more pronounced. According to the docking results, Hst may interact with several proteins involved in the regulation of ROS. As an example, the interaction of CCS (Copper chaperone for superoxide dismutase) with Hst might interfere with its chaperone function in folding SOD-1 (superoxide dismutase enzyme), contributing to an increase in cytoplasmic ROS levels. Finally, depending on the ROS level, Hst induces various modes of cell death.

Poria cocos compounds targeting neuropeptide Y1 receptor (Y1R) for weight management: A computational ligand- and structure-based study with molecular dynamics simulations identified beta-amyrin acetate as a putative Y1R inhibitor

Poria cocos (PC) is a medicinal herb frequently used in weight-loss clinical trials, however the mechanisms by which its compounds target orexigenic receptors including the neuropeptide Y1 receptor (Y1R) remain largely unknown. This study aimed to screen PC compounds for favourable pharmacokinetics profiles and examine their molecular mechanisms targeting Y1R. Forty-three PC compounds were systematically sought from pharmacological databases and docked with Y1R (PDB: 5ZBQ). By comparing the relative binding affinities, pharmacokinetics and toxicity profiles, we hypothesised that compounds designated PC1 3,4-Dihydroxybenzoic acid, PC8 Vanillic acid, PC40 1-(alpha-L-Ribofuranosyl)uracil, could be potential antagonists as they contact major residues Asn283 and Asp287, similar to various potent Y1R antagonists. In addition, PC21 Poricoic acid B, PC22 Poricoic acid G and PC43 16alpha,25-Dihydroxy-24-methylene-3,4-secolanosta-4(28),7,9(11)-triene-3,21-dioic acid, contacting Asn299, Asp104 and Asp200 proximal to the extracellular surface could also interfere with agonist binding by stabilising the extracellular loop (ECL) 2 of Y1R in a closed position. Owing to their selective interaction with Phe302, an important residue in binding of selective Y1R antagonists, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were proposed as putative antagonists. Following the consensus approach, PC12 beta-Amyrin acetate, PC26 3-Epidehydrotumulosic acid and PC27 Cerevisterol were identified as candidate compounds due to their high affinities (-12.2, -11.0 and -10.8 kcal, respectively), high drug-likeness and low toxicity profiles. Trajectory analyses and energy contributions of PC12-Y1R complex further confirmed their structural stability and favourable binding free energies, highlighting the feasibility and possible development of PC12 beta-Amyrin acetate as a future Y1R inhibitor.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Appraisal of the Possible Role of PPARγ Upregulation by CLA of Probiotic Pediococcus pentosaceus GS4 in Colon Cancer Mitigation

The prevalence of colon cancer (CC) is increasing at the endemic scale, which is accompanied by subsequent morbidity and mortality. Although there have been noteworthy achievements in the therapeutic strategies in recent years, the treatment of patients with CC remains a formidable task. The current study focused on to study role of biohydrogenation-derived conjugated linoleic acid (CLA) of probiotic Pediococcus pentosaceus GS4 (CLA_GS4) against CC, which induced peroxisome proliferator-activated receptor gamma (PPARγ) expression in human CC HCT-116 cells. Pre-treatment with PPARγ antagonist bisphenol A diglycidyl ether has significantly reduced the inhibitory efficacy of enhanced cell viability of HCT-116 cells, suggesting the PPARγ-dependent cell death. The cancer cells treated with CLA/CLA_GS4 demonstrated the reduced level of Prostaglandin E2 PGE₂ in association with reduced COX-2 and 5-LOX expressions. Moreover, these consequences were found to be associated with PPARγ-dependent. Furthermore, delineation of mitochondrial dependent apoptosis with the help of molecular docking LigPlot analysis showed that CLA can bind with hexokinase-II (hHK-II) (highly expressed in cancer cells) and that this association underlies voltage dependent anionic channel to open, thereby causing mitochondrial membrane depolarization, a condition that initiates intrinsic apoptotic events. Apoptosis was further confirmed by annexin V staining and elevation of caspase 1p10 expression. Taken all together, it is deduced that, mechanistically, the upregulation of PPARγ by CLA_GS4 of P. pentosaceus GS4 can alter cancer cell metabolism in association with triggering apoptosis in CC.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment

Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.

AlphaFold2-aware protein-DNA binding site prediction using graph transformer

Protein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information. Based on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformer-based variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm is further confirmed on the independent test set of 181 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 16.4% in area under the precision-recall curve and 11.2% in Matthews correlation coefficient, respectively. We provide the datasets, the predicted structures and the source codes along with the pre-trained models of GraphSite at https://github.com/biomed-AI/GraphSite. The GraphSite web server is freely available at https://biomed.nscc-gz.cn/apps/GraphSite.

Computational approaches for evaluation of isobavachin as potential inhibitor against t877a and w741l mutations in prostate cancer

Prostate cancer is the World's second most common cancer, with the fifth-highest male mortality rate. Point mutations such as T877A and W741L are frequently seen in advanced prostate cancer patients, conferring drug-resistance and hence driving cancer growth. Such occurrence of drug resistance in prostate cancer necessitates designing of suitable ligands to ensure better interactions with the receptors which can block the progression of the disease. The present study focus on the modification of plant-derived flavonoids that might act as inhibitors against such point mutations namely, T877A and W741L. In T877A mutation threonine is substituted by alanine at the 877 codon and W741L mutation, tryptophan is substituted by lysine at the 741 codon in prostate cancer. The study revolved on the aspect of the evaluation of Isobavachin and its derivatives as a potential agent to tackle such point mutations by using the in silico approach. A total of 98 molecular dockings were performed to find the ligand-receptor complexes with the lowest binding energy employing Autodock Software to conduct the blind and site-specific docking. Additionally, ligands were screened for Drug-likeness and toxicity using several tools yielding eight possible drug candidates. Based on the results of Molecular Docking, Drug-likeness, and ADMET testing, ten structures, including six complexes and three receptors were subjected to molecular dynamics simulation of 100 ns covering RMSD, RMSF, Rg, and MM/PBSA. Based on the simulation results, Isobavachin, IsoMod4, and IsoMod7 were concluded to be stable and exhibited potential properties for developing a novel drug to combat prostate cancer and its associated drug-resistance.

Binding of toluidine blue-myristic acid derivative to cucurbit[7]uril and human serum albumin: computational and biophysical insights towards a biosupramolecular assembly

A new toluidine blue-myristic acid photosensitizer derivate (TBOMyr) was investigated as a design molecule to bind simultaneously to cucurbit[7]uril (CB[7]) and human serum albumin (HSA) with the aim of constructing a biosupramolecular assembly. Molecular docking and dynamics calculations revealed the main supramolecular and bio-molecular interactions of TBOMyr with the macrocycle or the protein, respectively. The addition of the negatively charged myristic acid-like tail resulted in a unique conformation of the CB[7] complex where the phenothiazine core was included in the cavity of CB[7], leaving the fatty acid portion free to interact with the protein. A favorable ternary interaction between TBOMyr, CB[7] and HSA was suggested by the calculations, and an experimental binding affinity in the order of 10⁵ M^-1 was determined for the TBOMyr@CB[7] complex with HSA. The new TBOMyr derivative could find applications in photodynamic therapy benefiting from the biosupramolecular interactions as a transport system.

Molecular Docking as a Tool to Examine Organic Cation Sorption to Organic Matter

Molecular docking simulations were performed to examine the structural effects of organic cations on their sorption to organic matter. A set of benzylamine compounds was used to assess the sorption trends arising from the systematic structural differences between ring or nitrogen substituents. Binding simulations were performed using AutoDock 4.2 with Schulten's proposed soil organic matter as a representative organic matter structure. The calculated binding energies for the sorbate compounds correlated strongly with the measured sorption energies for Pahokee peat, indicating that the simulated binding energies and their associated sorbate orientations were representative of the experimental conditions. Graphical docking orientations showed primary, secondary, and tertiary aminium compounds to form hydrogen-bond interactions with deprotonated carboxylic acid groups in a pocket of the organic matter structure. Quaternary ammonium compounds formed pi-pi or cation-pi interactions with the aromatic groups elsewhere in the same organic matter pocket. Ring substituents showed no clear trends in sorption energies with the substituent group type for primary aminium compounds. Rather, substituent groups altered the simulated van der Waals, electrostatic, hydrogen-bond, and desolvation energy contributions to the overall sorption energies, in part because of the variations in docking orientations between compounds. Increasing methyl substitution of the aminium nitrogen group was associated with an increase in van der Waals energy contributions and a decrease in electrostatic energy contributions to the overall compound sorption energies because of aminium charge delocalization into methyl substituents and steric hindrance from methyl substituents to form specific interactions. The findings illustrate how molecular docking can be used to explore the effects of organic cation structure on sorption interactions with organic matter.

In silico evidence of beauvericin antiviral activity against SARS-CoV-2

Background

Scientists are still battling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus 2019 (COVID-19) pandemic so human lives can be saved worldwide. Secondary fungal metabolites are of intense interest due to their broad range of pharmaceutical properties. Beauvericin (BEA) is a secondary metabolite produced by the fungus Beauveria bassiana. Although promising anti-viral activity has previously been reported for BEA, studies investigating its therapeutic potential are limited.

Methods

The objective of this study was to assess the potential usage of BEA as an anti-viral molecule via protein–protein docking approaches using MolSoft.

Results

In-silico results revealed relatively favorable binding energies for BEA to different viral proteins implicated in the vital life stages of this virus. Of particular interest is the capability of BEA to dock to both the main coronavirus protease (Pockets A and B) and spike proteins. These results were validated by molecular dynamic simulation (Gromacs). Several parameters, such as root-mean-square deviation/fluctuation, the radius of gyration, H-bonding, and free binding energy were analyzed. Computational analyses revealed that interaction of BEA with the main protease pockets in addition to the spike glycoprotein remained stable.

Conclusion

Altogether, our results suggest that BEA might be considered as a potential competitive and allosteric agonist inhibitor with therapeutic options for treating COVID-19 pending in vitro and in vivo validation.

IAF, QGF, and QDF Peptides Exhibit Cholesterol-Lowering Activity through a Statin-like HMG-CoA Reductase Regulation Mechanism: In Silico and In Vitro Approach

In this study, in silico approaches are employed to investigate the binding mechanism of peptides derived from cowpea β-vignin and HMG-CoA reductase. With the obtained information, we designed synthetic peptides to evaluate their in vitro enzyme inhibitory activity. In vitro, the total protein extract and <3 kDa fraction, at 5000 µg, support this hypothesis (95% and 90% inhibition of HMG-CoA reductase, respectively). Ile-Ala-Phe, Gln-Gly-Phe, and Gln-Asp-Phe peptides were predicted to bind to the substrate binding site of HMGCR via HMG-CoAR. In silico, it was established that the mechanism of HMG-CoA reductase inhibition largely entailed mimicking the interactions of the decalin ring of simvastatin and via H-bonding; in vitro studies corroborated the predictions, whereby the HMG-CoA reductase activity was decreased by 69%, 77%, and 78%, respectively. Our results suggest that Ile-Ala-Phe, Gln-Gly-Phe, and Gln-Asp-Phe peptides derived from cowpea β-vignin have the potential to lower cholesterol synthesis through a statin-like regulation mechanism.

Phenothiazines as dual inhibitors of SARS-CoV-2 main protease and COVID-19 inflammation

COVID-19, caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2), currently has no treatment for acute infection. The main protease (Mpro) of SARS-CoV-2 is an essential enzyme for viral replication and an attractive target for disease intervention. The phenothiazine moiety has demonstrated drug versatility for biological systems, including inhibition of butyrylcholinesterase, a property important in the cholinesterase anti-inflammatory cascade. Nineteen phenothiazine drugs were investigated using in silico modelling techniques to predict binding energies and inhibition constants (Ki values) with SARS-CoV-2 Mpro. Because most side-effects of phenothiazines are due to interactions with various neurotransmitter receptors and transporters, phenothiazines with few such interactions were also investigated. All compounds were found to bind to the active site of SARS-CoV-2 Mpro and showed Ki values ranging from 1.30 to 52.4 µM in a rigid active site. Nine phenothiazines showed inhibition constants <10 µM. The compounds with limited interactions with neurotransmitter receptors and transporters showed micromolar (µM) Ki values. Docking results were compared with remdesivir and showed similar interactions with key residues Glu-166 and Gln-189 in the active site. This work has identified several phenothiazines with limited neurotransmitter receptor and transporter interactions and that may provide the dual action of inhibiting SARS-CoV-2 Mpro to prevent viral replication and promote the release of anti-inflammatory cytokines to curb viral-induced inflammation. These compounds are promising candidates for further investigation against SARS-CoV-2.

DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilizes 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another data set SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 1st, 2018, until February 28th, 2020, for ligand binding site (LBS) detection. DeepPocket's results on various binding site data sets and SC6K highlight its better performance over current state-of-the-art methods and good generalization ability over novel structures.

Interference of Polydatin/Resveratrol in the ACE2:Spike Recognition during COVID-19 Infection. A Focus on Their Potential Mechanism of Action through Computational and Biochemical Assays

In the search for new therapeutic strategies to contrast SARS-CoV-2, we here studied the interaction of polydatin (PD) and resveratrol (RESV)-two natural stilbene polyphenols with manifold, well known biological activities-with Spike, the viral protein essential for virus entry into host cells, and ACE2, the angiotensin-converting enzyme present on the surface of multiple cell types (including respiratory epithelial cells) which is the main host receptor for Spike binding. Molecular Docking simulations evidenced that both compounds can bind Spike, ACE2 and the ACE2:Spike complex with good affinity, although the interaction of PD appears stronger than that of RESV on all the investigated targets. Preliminary biochemical assays revealed a significant inhibitory activity of the ACE2:Spike recognition with a dose-response effect only in the case of PD.

Phosphate binding sites prediction in phosphorylation-dependent protein-protein interactions

MOTIVATION

Phosphate binding plays an important role in modulating protein-protein interactions, which are ubiquitous in various biological processes. Accurate prediction of phosphate binding sites is an important but challenging task. Small size and diversity of phosphate binding sites lead to a substantial challenge for developing accurate prediction methods.

RESULTS

Here we present the phosphate binding site predictor (PBSP), a novel and accurate approach to identifying phosphate binding sites from protein structures. PBSP combines an energy-based ligand-binding sites identification method with reverse focused docking using a phosphate probe. We show that PBSP outperforms not only general ligand binding sites predictors but also other existing phospholigand-specific binding sites predictors. It achieves ∼95% success rate for top 10 predicted sites with an average Matthews correlation coefficient (MCC) value of 0.84 for successful predictions. PBSP can accurately predict phosphate binding modes, with average position error of 1.4 Å and 2.4 Å in bound and unbound datasets, respectively. Lastly, visual inspection of the predictions is conducted. Reasons for failed predictions are further analyzed and possible ways to improve the performance are provided. These results demonstrate a novel and accurate approach to phosphate binding sites identification in protein structures.

AVAILABILITY

The software and benchmark datasets are freely available at http://web.pkusz.edu.cn/wu/PBSP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pharmacophore-Based Screening & Modification of Amiloride Analogs for targeting the NhaP-type Cation-Proton Antiporter in Vibrio cholerae

Structural and mutational analysis of Vc-NhaP2 identified a putative cation binding pocket formed by antiparallel extended regions of two transmembrane segments (TMSs V/XII) along with TMS VI. Molecular Dynamics (MD) simulations suggested that the flexibility of TMS-V/XII is crucial for the intra-molecular conformational events in Vc-NhaP2. In this study, we developed some putative Vc-NhaP2 inhibitors from Amiloride analogs (AAs). Molecular docking of the modified AAs revealed promising binding. The four selected drugs potentially interacted with functionally important amino acid residues located on the cytoplasmic side of TMS VI, the extended chain region of TMS V and TMS XII and the loop region between TMSs VIIII and IX. Molecular dynamics simulations revealed that binding of the selected drugs can potentially destabilize the Vc-NhaP2 and alters the flexibility of the functionally important TMS VI. The work presents the utility of in silico approaches for the rational identification of potential targets and drugs that could target NhaP2 cation proton antiporter to control Vibrio cholerae. The goal is to identify potential drugs that can be validated in future experiments.

Experimental and computational investigation on the binding of anticancer drug gemcitabine with bovine serum albumin

This study reports the experimental and computational investigation on the binding of a common anticancer drug, gemcitabine, with the model plasma protein, bovine serum albumin (BSA). Several experimental and computational methods, such as intrinsic and synchronous fluorescence, UV-visible, and circular dichroism spectroscopies, consensus molecular docking and molecular dynamics simulation have been employed to elucidate the binding mechanism. Gemcitabine altered the UV-visible spectrum of BSA, which is a clear indication of the complex formation between them. The visual inspection of observed fluorescence quenching results at λ_ex = 280 nm and 295 nm has shown the substantial involvement of tyrosine residue, even larger than tryptophan. However, after the correction of inner filter effect of the observed data, it became clear that tyrosine has a negligible role in quenching. A 20-fold decrease in quenching constant was found in the corrected data, as compared to the observed data at λ_ex = 280 nm. There was a 1:1 weak binding between BSA and gemcitabine accompanied by dynamic quenching. The secondary structure of BSA remained almost intact in the presence of gemcitabine. The primary binding site of gemcitabine inside BSA was the drug binding site 2 or DS II, which is located in the subdomain 3 A. MD Simulation results suggested that gemcitabine doesn't affect or deviate the structure of BSA upon interaction throughout 100 ns time period. The dominating intermolecular forces were hydrophobic forces and hydrogen bonding. A small change in the frontier molecular orbitals of gemcitabine was also observed after its binding with BSA.Communicated by Ramaswamy H. Sarma.

Withanone from Withania somnifera Attenuates SARS-CoV-2 RBD and Host ACE2 Interactions to Rescue Spike Protein Induced Pathologies in Humanized Zebrafish Model

Purpose

SARS-CoV-2 engages human ACE2 through its spike (S) protein receptor binding domain (RBD) to enter the host cell. Recent computational studies have reported that withanone and withaferin A, phytochemicals found in Withania somnifera, target viral main protease (M^Pro) and host transmembrane TMPRSS2, and glucose related protein 78 (GRP78), respectively, implicating their potential as viral entry inhibitors. Absence of specific treatment against SARS-CoV-2 infection has encouraged exploration of phytochemicals as potential antivirals.

Aim

This study aimed at in silico exploration, along with in vitro and in vivo validation of antiviral efficacy of the phytochemical withanone.

Methods

Through molecular docking, molecular dynamic (MD) simulation and electrostatic energy calculation the plausible biochemical interactions between withanone and the ACE2-RBD complex were investigated. These in silico observations were biochemically validated by ELISA-based assays. Withanone-enriched extract from W. somnifera was tested for its ability to ameliorate clinically relevant pathological features, modelled in humanized zebrafish through SARS-CoV-2 recombinant spike (S) protein induction.

Results

Withanone bound efficiently at the interacting interface of the ACE2-RBD complex and destabilized it energetically. The electrostatic component of binding free energies of the complex was significantly decreased. The two intrachain salt bridge interactions (K31-E35) and the interchain long-range ion-pair (K31-E484), at the ACE2-RBD interface were completely abolished by withanone, in the 50 ns simulation. In vitro binding assay experimentally validated that withanone efficiently inhibited (IC₅₀=0.33 ng/mL) the interaction between ACE2 and RBD, in a dose-dependent manner. A withanone-enriched extract, without any co-extracted withaferin A, was prepared from W. somnifera leaves. This enriched extract was found to be efficient in ameliorating human-like pathological responses induced in humanized zebrafish by SARS-CoV-2 recombinant spike (S) protein.

Conclusion

In conclusion, this study provided experimental validation for computational insight into the potential of withanone as a potent inhibitor of SARS-CoV-2 coronavirus entry into the host cells.

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.

Luteolin and abyssinone II as potential inhibitors of SARS-CoV-2: an in silico molecular modeling approach in battling the COVID-19 outbreak

BACKGROUND

At present, the entire world is in a war against COVID-19 pandemic which has gradually led us toward a more compromised "new normal" life. SARS-CoV-2, the pathogenic microorganism liable for the recent COVID-19 outbreak, is extremely contagious in nature resulting in an unusual number of infections and death globally. The lack of clinically proven therapeutic intervention for COVID-19 has dragged the world's healthcare system into the biggest challenge. Therefore, development of an efficient treatment scheme is now in great demand. Screening of different biologically active plant-based natural compounds could be a useful strategy for combating this pandemic. In the present research, a collection of 43 flavonoids of 7 different classes with previously recorded antiviral activity was evaluated via computational and bioinformatics tools for their impeding capacity against SARS-CoV-2. In silico drug likeness, pharmacophore and Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) profile analysis of the finest ligands were carried out using DataWarrior, DruLiTo and admetSAR programs, respectively. Molecular docking was executed by AutoDock Vina, while molecular dynamics simulation of the target protein-ligand bound complexes was done using nanoscalable molecular dynamics and visual molecular dynamics software package. Finally, the molecular target analysis of the selected ligands within Homo sapiens was conducted with SwissTargetPredcition web server.

RESULTS

Out of the forty-three flavonoids, luteolin and abyssinone II were found to develop successful docked complex within the binding sites of target proteins in terms of lowest binding free energy and inhibition constant. The root mean square deviation and root mean square fluctuation values of the docked complex displayed stable interaction and efficient binding between the ligands and target proteins. Both of the flavonoids were found to be safe for human use and possessed good drug likeness properties and target accuracy.

CONCLUSIONS

Conclusively, the current study proposes that luteolin and abyssinone II might act as potential therapeutic candidates for SARS-CoV-2 infection. In vivo and in vitro experiments, however, should be taken under consideration to determine the efficiency and to demonstrate the mechanism of action.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

CB-Dock: a web server for cavity detection-guided protein-ligand blind docking

As the number of elucidated protein structures is rapidly increasing, the growing data call for methods to efficiently exploit the structural information for biological and pharmaceutical purposes. Given the three-dimensional (3D) structure of a protein and a ligand, predicting their binding sites and affinity are a key task for computer-aided drug discovery. To address this task, a variety of docking tools have been developed. Most of them focus on docking in the preset binding sites given by users. To automatically predict binding modes without information about binding sites, we developed a user-friendly blind docking web server, named CB-Dock, which predicts binding sites of a given protein and calculates the centers and sizes with a novel curvature-based cavity detection approach, and performs docking with a popular docking program, Autodock Vina. This method was carefully optimized and achieved ~70% success rate for the top-ranking poses whose root mean square deviation (RMSD) were within 2 Å from the X-ray pose, which outperformed the state-of-the-art blind docking tools in our benchmark tests. CB-Dock offers an interactive 3D visualization of results, and is freely available at http://cao.labshare.cn/cb-dock/.

Integrating Molecular Docking and Molecular Dynamics Simulations

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

YesU from Bacillus subtilis preferentially binds fucosylated glycans

The interaction of carbohydrate-binding proteins (CBPs) with their corresponding glycan ligands is challenging to study both experimentally and computationally. This is in part due to their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates, as exists in nucleic acids and proteins. We recently described a function-prediction technique called SPOT-Struc that identifies CBPs by global structural alignment and binding-affinity prediction. Here we experimentally determined the carbohydrate specificity and binding affinity of YesU (RCSB PDB ID: 1oq1), an uncharacterized protein from Bacillus subtilis that SPOT-Struc predicted would bind high mannose-type glycans. Glycan array analyses however revealed glycan binding patterns similar to those exhibited by fucose (Fuc)-binding lectins, with SPR analysis revealing high affinity binding to Lewis^x and lacto-N-fucopentaose III. Structure based alignment of YesU revealed high similarity to the legume lectins UEA-I and GS-IV, and docking of Lewis^x into YesU revealed a complex structure model with predicted binding affinity of −4.3 kcal/mol. Moreover the adherence of B. subtilis to intestinal cells was significantly inhibited by Le^x and Le^y but by not non-fucosylated glycans, suggesting the interaction of YesU to fucosylated glycans may be involved in the adhesion of B. subtilis to the gastrointestinal tract of mammals.

In vitro inhibition potential of mono-n-octyl phthalate on Mycobacterium tuberculosis H37Ra: Possibility of binding to mycobacterial PknB-An in silico approach

Fatty acids of specific chain lengths have been shown to inhibit the growth of Mycobacterium tuberculosis. In the present study, specific synthetic aromatic derivatives of n-octyl esters were investigated for their property to inhibit the growth of M. tuberculosis H37Ra. Agar well diffusion assay indicated that the crude synthetic derivatives obtained by the esterification of phthalic acid (PA) and n-octanol exhibited antimycobacterial activity. Further, the activity was authenticated with the Miroplate Alamar Blue Assay (MABA). Subsequently, the active component was purified by bioactivity guided chromatographic fractionation. The structure of the synthetic derivative was deduced by UV-Vis, FT-IR, LC-MS, GC-mass spectrometry, and NMR spectroscopy. Molecular docking and molecular dynamic simulation (MDS) were performed with Autodock 4.0 and GROMACS 5.1.2 softwares, respectively. It was found that mono-n-octyl phthalate (MOP) exhibited antimycobacterial activity with a MIC of 20 μg/mL, and not by any other related compounds, including di-n-octyl phthalate, PA, phthalic anhydride, and n-octanol. Binding of MOP with protein kinase B can participate in the binding cavity region, which was previously reported. Subsequently, we authenticate the stability with MDS. This is first report on the inhibition of M. tuberculosis growth by MOP.

In silico targeting of non-structural 4B protein from dengue virus 4 with spiropyrazolopyridone: study of molecular dynamics simulation, ADMET and virtual screening

Dengue fever is one of the most prevalent disease in tropical and sub-tropical regions of the world. According to the World Health Organisation (WHO), approximately 3.5 billion people have been affected with dengue fever. Four serotypes of dengue virus (DENV) i.e. DENV1, DENV2, DENV3 and DENV4 have up to 65% genetic variations among themselves. dengue virus 4 (DENV4) was first reported from Amazonas, Brazil and is spreading perilously due to lack of awareness of preventive measures, as it is the least targeted serotype. In this study, non-structural protein 4B of dengue virus 4 (DENV4-NS4B) is computationally characterised and simulations are performed including solvation, energy minimizations and neutralisation for the refinement of predicted model of the protein. The spiropyrazolopyridone is considered as an effective drug against NS4B of DENV2, therefore, a total of 91 different analogues of spiropyrazolopyridone are used to analyse their inhibitory action against DENV4-NS4B. These compounds are docked at the binding site with various binding affinities, representing their efficacy to block the binding pocket of the protein. Pharmacological and pharmacokinetic assessment performed on these inhibitors shows that these are suitable candidates to be used as a drug against the dengue fever. Among all these 91 compounds, Analogue-I and Analogue-II are analysed to be the most effective inhibitor having potential to be used as drugs against dengue virus.

Toward a generalized computational workflow for exploiting transient pockets as new targets for small molecule stabilizers: Application to the homogentisate 1,2-dioxygenase mutants at the base of rare disease Alkaptonuria

Alkaptonuria (AKU) is an inborn error of metabolism where mutation of homogentisate 1,2-dioxygenase (HGD) gene leads to a deleterious or misfolded product with subsequent loss of enzymatic degradation of homogentisic acid (HGA) whose accumulation in tissues causes ochronosis and degeneration. There is no licensed therapy for AKU. Many missense mutations have been individuated as responsible for quaternary structure disruption of the native hexameric HGD. A new approach to the treatment of AKU is here proposed aiming to totally or partially rescue enzyme activity by targeting of HGD with pharmacological chaperones, i.e. small molecules helping structural stability. Co-factor pockets from oligomeric proteins have already been successfully exploited as targets for such a strategy, but no similar sites are present at HGD surface; hence, transient pockets are here proposed as a target for pharmacological chaperones. Transient pockets are detected along the molecular dynamics trajectory of the protein and filtered down to a set of suitable sites for structural stabilization by mean of biochemical and pharmacological criteria. The result is a computational workflow relevant to other inborn errors of metabolism requiring rescue of oligomeric, misfolded enzymes.

Lauric acid and myristic acid from Allium sativum inhibit the growth of Mycobacterium tuberculosis H37Ra: in silico analysis reveals possible binding to protein kinase B

CONTEXT

The bulb of Allium sativum Linn (Alliaceae) has numerous medicinal values. Though the petroleum ether extract of the bulb has shown to exhibit antimycobacterial activity, the phytochemical(s) responsible for this inhibitory activity is not known.

OBJECTIVE

To characterize the bioactive compounds in the petroleum ether extract of Allium sativum (garlic) that inhibit the growth of Mycobacterium tuberculosis H37Ra.

MATERIALS AND METHODS

Bioactivity-guided fractionation was employed to isolate the bioactive compounds. Antimycobacterial activity was evaluated by well-diffusion method and microplate alamar blue assay (MABA). Infrared spectroscopy, mass spectrometry and nuclear magnetic resonance spectroscopy were used to characterize the bioactive compounds. Autodock was used to obtain information on molecular recognition, and molecular dynamics simulation was performed using GROMACS.

RESULTS

The bioactive compounds that inhibited the growth of M. tuberculosis H37Ra were found to be lauric acid (LA) and myristic acid (MA). The minimal inhibitory concentration of LA and MA was found to be 22.2 and 66.7 μg/mL, respectively. In silico analysis revealed that these fatty acids could bind at the cleft between the N-terminal and C-terminal lobes of the cytosolic domain of serine/threonine protein kinase B (PknB).

DISCUSSION AND CONCLUSION

The inhibition activity was dependent on the alkyl chain length of the fatty acid, and the amino acid residues involved in binding to fatty acid was found to be conserved across the Pkn family of proteins. The study indicates the possibility of using fatty acid derivatives, involving Pkn family of proteins, to inhibit the signal transduction processes in M. tuberculosis.

Broad Spectrum Anti-Influenza Agents by Inhibiting Self-Association of Matrix Protein 1

The matrix protein 1 (M1) of influenza A virus (IAV) exists as a three-dimensional oligomeric structure in mature virions with high sequence conservation across different IAV subtypes, which makes it a potential broad spectrum antiviral target. We hypothesized that impairing self-association of M1 through a small molecule 'wedge', which avidly binds to an M1-M1 interface, would result in a completely new class of anti-influenza agents. To establish this proof-of-principle, we performed virtual screening on a library of >70,000 commercially available small molecules that resulted in several plausible 'wedges'. Biophysical studies showed that the best molecule bound the M1 protein potently and weakened M1-M1 self-association. Most importantly, the agent reduced the thickness of the M1 layer in mature virions and inhibited in ovo propagation of multiple IAV strains including H1N1, pandemic H1N1, H3N2 and H5N1, which supports the "wedge" hypothesis. These results demonstrate that M1 is a promising druggable target for the discovery of a completely new line of broad spectrum anti-IAV agents.

Small-molecule binding sites to explore protein-protein interactions in the cancer proteome

The Cancer Genome Atlas (TCGA) offers an unprecedented opportunity to identify small-molecule binding sites on proteins with overexpressed mRNA levels that correlate with poor survival. Here, we analyze RNA-seq and clinical data for 10 tumor types to identify genes that are both overexpressed and correlate with patient survival. Protein products of these genes were scanned for binding sites that possess shape and physicochemical properties that can accommodate small-molecule probes or therapeutic agents (druggable). These binding sites were classified as enzyme active sites (ENZ), protein-protein interaction sites (PPI), or other sites whose function is unknown (OTH). Interestingly, the overwhelming majority of binding sites were classified as OTH. We find that ENZ, PPI, and OTH binding sites often occurred on the same structure suggesting that many of these OTH cavities can be used for allosteric modulation of enzyme activity or protein-protein interactions with small molecules. We discovered several ENZ (PYCR1, QPRT, and HSPA6) and PPI (CASC5, ZBTB32, and CSAD) binding sites on proteins that have been seldom explored in cancer. We also found proteins that have been extensively studied in cancer that have not been previously explored with small molecules that harbor ENZ (PKMYT1, STEAP3, and NNMT) and PPI (HNF4A, MEF2B, and CBX2) binding sites. All binding sites were classified by the signaling pathways to which the protein that harbors them belongs using KEGG. In addition, binding sites were mapped onto structural protein-protein interaction networks to identify promising sites for drug discovery. Finally, we identify pockets that harbor missense mutations previously identified from analysis of TCGA data. The occurrence of mutations in these binding sites provides new opportunities to develop small-molecule probes to explore their function in cancer.

AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms

MOTIVATION

The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms.

RESULTS

We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects.

AVAILABILITY AND IMPLEMENTATION

http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Charting a Path to Success in Virtual Screening

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis

Background

Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands.

Results

We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS.

Conclusion

The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0518-z) contains supplementary material, which is available to authorized users.

An overview of the prediction of protein DNA-binding sites

Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

Systems pharmacology and molecular docking strategies prioritize natural molecules as cardioprotective agents

Multidimensional prioritization of cardioprotective natural compounds.