FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm

Protein-ligand docking is an essential process that has accelerated drug discovery. How to accurately and effectively optimize the predominant position and orientation of ligands in the binding pocket of a target protein is a major challenge. This paper proposed a novel ligand binding pose search method called FWAVina based on the fireworks algorithm, which combined the fireworks algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon local search method adopted in AutoDock Vina to address the pose search problem in docking. The FWA was used as a global optimizer to rapidly search promising poses, and the Broyden-Fletcher-Goldfarb-Shannon method was incorporated into FWAVina to perform an exact local search. FWAVina was developed and tested on the PDBbind and DUD-E datasets. The docking performance of FWAVina was compared with the original Vina program. The results showed that FWAVina achieves a remarkable execution time reduction of more than 50 % than Vina without compromising the prediction accuracies in the docking and virtual screening experiments. In addition, the increase in the number of ligand rotatable bonds has almost no effect on the efficiency of FWAVina. The higher accuracy, faster convergence and improved stability make the FWAVina method a better choice of docking tool for computer-aided drug design. The source code is available at https://github.com/eddyblue/FWAVina/.     

A merged molecular docking,ADME-T and dynamics approaches towards the genus of Arisaema as herpes simplex virus type 1 and type 2 inhibitors

An attempt toward screening of phytoconstituents (Arisaema genus) against herpes viruses (HSV-1 and HSV-2) was carried out using in silico approaches. Human HSV-1 and HSV-2 are accountable for cold sores genital herpes, respectively. Two drug targets, namely thymidine kinase (TK; PDB: 2ki5) serine protease (PDB: 1at3) were selected for HSV-1 and HSV-2. Initially, molecular docking tool was employed to screened apex hits phytoconstituents against herpes infections. ADME-T studies of top ranked were also further highlighted to achieve their effectiveness. Following, molecular dynamics studies were also examined to further optimize the stability of ligands. Glide scores and binding interactions of phytoconstituents were compared with Acyclovir, the main drug used in treatment of HSV, the screened top hits exhibited more glide scores and better binding for both HSV-1 and HSV-2 receptors. Additionally, ADME-T showed an ideal range for top hits while molecular dynamics results also illustrated stability of models. Ultimately, the whole efforts reveal to top three most promising hits for HSV-1 (39, 21, 19) and HSV-2 (20, 51, 19) receptors which can be explored further in wet lab experiments as promising agents against HSV infections.     

Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies,molecular docking,synthesis and biological testing

Spread of multidrug‐resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the development of new effective inhibitory compounds with selective molecular mechanism of action and low toxicity. The goal of this work is to identify more potent molecules active against E. coli strains by using machine learning, docking studies, synthesis and biological evaluation. A set of predictive QSAR models was built with two publicly available structurally diverse data sets, including recent data deposited in PubChem. The predictive ability of these models tested by a 5-fold cross-validation, resulted in balanced accuracies (BA) of 59–98% for the binary classifiers. Test sets validation showed that the models could be instrumental in predicting the antimicrobial activity with an accuracy (with BA = 60–99 %) within the applicability domain. The models were applied to screen a virtual chemical library, which was designed to have activity against resistant E. coli strains. The eight most promising compounds were identified, synthesized and tested. All of them showed the different levels of anti-E. coli activity and acute toxicity. The docking results have shown that all studied compounds are potential DNA gyrase inhibitors through the estimated interactions with amino acid residues and magnesium ion in the enzyme active center The synthesized compounds could be used as an interesting starting point for further development of drugs with low toxicity and selective molecular action mechanism against resistant E. coli strains. The developed QSAR models are freely available online at OCHEM http://ochem.eu/article/112525 and can be used to virtual screening of potential compounds with anti-E. coli activity.     

Synthesis,in-silico antiviral screening and in-vitro antibacterial screening of a novel chalcone derivatives

A new series of novel chalcones was synthesized and subjected to screening of theoretical molecular and biological properties. For evaluating the theoretical molecular properties of these molecules Molinspiration and Osiris software were used. It was concluded from data that the majority of molecules exhibited theoretical molecular and biological properties similar to that of standard drugs. Role of Hemagglutinin is vital during the attack of virus on cells so Hemagglutinin inhibitors may act as potent antiviral agents. Considering this fact in-silico studies were performed using the SwissDock screening engine on Hemagglutinin target PDB code 1HGH. Hemagglutinin inhibition potential in terms of binding affinity was expressed as ΔG values ranging from −8.71 kcal/mol to −7.39 kcal/mol. Compound IIIm showed maximum binding affinity with ΔG value −8.71 kcal/mol followed by compound IIIj ΔG value −8.31 kcal/mol. It's prudent from ΔG values that compounds may act as potent antiviral agents. Compounds were also screened for in-vitro antibacterial activity against five pathogenic strains. Most of the compounds exhibited low to moderate activity against strains under study. Compound IIIn demonstrated good activity against four pathogenic strains with highest zone of inhibition of 16 mm against K. pneumoniae and S. typhi.     

Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Intrinsically disordered proteins or intrinsically disordered regions (IDPs) have gained much attention in recent years due to their vital roles in biology and prevalence in various human diseases. Although IDPs are perceived as attractive therapeutic targets, rational drug design targeting IDPs remains challenging because of their conformational heterogeneity. Here, we propose a hierarchical computational strategy for IDP drug virtual screening (IDPDVS) and applied it in the discovery of p53 transactivation domain I (TAD1) binding compounds. IDPDVS starts from conformation sampling of the IDP target, then it combines stepwise conformational clustering with druggability evaluation to identify potential ligand binding pockets, followed by multiple docking screening runs and selection of compounds that can bind multi-conformations. p53 is an important tumor suppressor and restoration of its function provides an opportunity to inhibit cancer cell growth. TAD1 locates at the N-terminus of p53 and plays key roles in regulating p53 function. No compounds that directly bind to TAD1 have been reported due to its highly disordered structure. We successfully used IDPDVS to identify two compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells. Our study demonstrates that IDPDVS is an efficient strategy for IDP drug discovery and p53 TAD1 can be directly targeted by small molecules.

A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.     

Design,synthesis and characterization of a series of 6-substituted-4-hydroxy-1-(2-substitutedthiazol-4-yl)quinolin-2(1H)-one derivatives and evaluation of their in vitro anticancer and antibacterial activity

The current research work deals with the design, synthesis and characterization of a series of 6-substituted-4-hydroxy-1-(2-substitutedthiazol-4-yl)quinolin-2(1H)-one derivatives [III(a-d)(1–3)] and evaluation of their in-vitro anticancer activity against MDA-MB (Breast cancer) and A549 (Lung cancer) cell lines based upon MTT assay and in-vitro antibacterial by the measurement of zone of inhibition and determining the Minimum Inhibitory Concentration (MIC). All the synthesized compounds were characterized by UV, IR, ¹H NMR and ¹³C NMR spectral data.Molecular docking studies of the title compounds were carried out using Molegro Virtual Docker (MVD-2013, 6.0) software. The synthesized compounds exhibited well conserved hydrogen bond interactions with one or more amino acid residues in the active pocket of EGFRK tyrosine kinase domain (PDB ID: 1m17) for docking study on anticancer activity and S. aureus DNA Gyrase domain complexed with a ciprofloxacin inhibitor (PDB ID: 2XCT) for antibacterial docking study. All synthesized derivatives were potent against A549 (Lung cancer) cell line as compared to MDA-MB (Breast cancer) cell line. Compound 2-(4-(4-hydroxy-6-methyl-2-oxoquinolin-1(2H)-yl)thiazol-2-yl)hydrazin-1-ium iodide (IIId-2) was found to be the most cytotoxic as compared to the other synthesized derivatives, with IC₅₀ values of 346.12 μg/mL against A549 (Lung cancer) cell line, however all synthesized derivatives were found to be a poor antibacterial agent when compared with standard ciprofloxacin.Thus, the synthesized derivatives possessed a potential to bind with some of the residues of the active site and can be further developed into potential pharmacological agents.     

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning

Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis routes to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes at least 4500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity.

The retrosynthetic accessibility score (RAscore) is based on AI driven retrosynthetic planning, and is useful for rapid scoring of synthetic feasability and pre-screening of large datasets of virtual/generated molecules.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

dbHDPLS: A database of human disease-related protein-ligand structures

Protein-ligand complexes perform specific functions, most of which are related to human diseases. The database, called as human disease-related protein-ligand structures (dbHDPLS), collected 8833 structures which were extracted from protein data bank (PDB) and other related databases. The database is annotated with comprehensive information involving ligands and drugs, related human diseases and protein-ligand interaction information, with the information of protein structures. The database may be a reliable resource for structure-based drug target discoveries and druggability predictions of protein-ligand binding sites, drug-disease relationships based on protein-ligand complex structures. It can be publicly accessed at the website: http://DeepLearner.ahu.edu.cn/web/dbDPLS/.     

SCOUT: A new algorithm for the inference of pseudo-time trajectory using single-cell data

Single cell technology is a powerful tool to reveal intercellular heterogeneity and discover cellular developmental processes. When analyzing the complexity of cellular dynamics and variability, it is important to construct a pseudo-time trajectory using single-cell expression data to reflect the process of cellular development. Although a number of computational and statistical methods have been developed recently for single-cell analysis, more effective and efficient methods are still strongly needed. In this work we propose a new method named SCOUT for the inference of single-cell pseudo-time ordering with bifurcation trajectories. We first propose to use the fixed-radius near neighbors algorithms based on cell densities to find landmarks to represent the cell states, and employ the minimum spanning tree (MST) to determine the developmental branches. We then propose to use the projection of Apollonian circle or a weighted distance to determine the pseudo-time trajectories of single cells. The proposed algorithm is applied to one synthetic and two realistic single-cell datasets (including single-branching and multi-branching trajectories) and the cellular developmental dynamics is recovered successfully. Compared with other popular methods, numerical results show that our proposed method is able to generate more robust and accurate pseudo-time trajectories. The code of the method is implemented in Python and available at https://github.com/statway/SCOUT.     

Molecular docking,HOMO-LUMO and quantum chemical computation analysis of anti-glyoximehydrazone derivatives containing pyrazolone moiety and their transition metal complexes

In this study, in order to obtain biologically active compounds, a series of anti-glyoximehydrazone ligands bearing vic-dioxime, hydrazone, and pyrazole moieties and their (O•••H–O) bridged nickel(II), cobalt(II) and copper(II) metal complexes were prepared. Further, the molecular docking studies were carried out on those ligands and their nickel(II), cobalt(II) and copper(II) metal complexes to analyze the interaction with EGFR Kinase domain complexed with tak-285 (PDB ID: 3POZ) and human androgen receptor T877A mutant (PDB ID:2OZ7). In addition, the compounds were optimized by using B3LYP/6-311G+(d,p) level of Density Functional Theory (DFT) to evaluate the HOMO–LUMO contours and quantum chemical parameters. Also, bioactivity analysis were performed.Metal complexes had higher binding affinities against 3POZ and 2OZ7. The most promising compounds for 3POZ were nickel(II) and copper(II) metal complexes. However, for the 2OZ7 target receptor, cobalt(II) and copper(II) metal complexes were the possible hit compounds. Furthermore, cobalt(II) metal complex of ligand two was found to be the most reactive one among others. Moreover, it had the highest ω which is related to a potent higher electrophilic character. It was determined that all the compounds had moderate bioactivity.In conclusion, nickel(II), cobalt(II), and copper(II) complexes could be powerful hit compounds for anti-cancer drug discovery studies.     

predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance

Formylation is one of the newly discovered post-translational modifications in lysine residue which is responsible for different kinds of diseases. In this work, a novel predictor, named predForm-Site, has been developed to predict formylation sites with higher accuracy. We have integrated multiple sequence features for developing a more informative representation of formylation sites. Moreover, decision function of the underlying classifier have been optimized on skewed formylation dataset during prediction model training for prediction quality improvement. On the dataset used by LFPred and Formator predictor, predForm-Site achieved 99.5% sensitivity, 99.8% specificity and 99.8% overall accuracy with AUC of 0.999 in the jackknife test. In the independent test, it has also achieved more than 97% sensitivity and 99% specificity. Similarly, in benchmarking with recent method CKSAAP_FormSite, the proposed predictor significantly outperformed in all the measures, particularly sensitivity by around 20%, specificity by nearly 30% and overall accuracy by more than 22%. These experimental results show that the proposed predForm-Site can be used as a complementary tool for the fast exploration of formylation sites. For convenience of the scientific community, predForm-Site has been deployed as an online tool, accessible at http://103.99.176.239:8080/predForm-Site.     

Effective single-cell clustering through ensemble feature selection and similarity measurements

Single-cell RNA sequencing technologies have revolutionized biomedical research by providing an effective means to profile gene expressions in individual cells. One of the first fundamental steps to perform the in-depth analysis of single-cell sequencing data is cell type classification and identification. Computational methods such as clustering algorithms have been utilized and gaining in popularity because they can save considerable resources and time for experimental validations. Although selecting the optimal features (i.e., genes) is an essential process to obtain accurate and reliable single-cell clustering results, the computational complexity and dropout events that can introduce zero-inflated noise make this process very challenging. In this paper, we propose an effective single-cell clustering algorithm based on the ensemble feature selection and similarity measurements. We initially identify the set of potential features, then measure the cell-to-cell similarity based on the subset of the potentials through multiple feature sampling approaches. We construct the ensemble network based on cell-to-cell similarity. Finally, we apply a network-based clustering algorithm to obtain single-cell clusters. We evaluate the performance of our proposed algorithm through multiple assessments in real-world single-cell RNA sequencing datasets with known cell types. The results show that our proposed algorithm can identify accurate and consistent single-cell clustering. Moreover, the proposed algorithm takes relative expression as input, so it can easily be adopted by existing analysis pipelines. The source code has been made publicly available at https://github.com/jeonglab/scCLUE.     

Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus

There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention.In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network.GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.     

TTRMDB: A database for structural and functional analysis on the impact of SNPs over transthyretin (TTR) using bioinformatic tools

Hereditary Transthyretin-associated amyloidosis (ATTR) is an autosomal dominant protein-folding disorder with adult-onset caused by mutation of transthyretin (TTR). TTR is characterized by extracellular deposition of amyloid, leading to loss of autonomy and finally, death. More than 100 distinct mutations in TTR gene have been reported from variable age of onset, clinical expression and penetrance data. Besides, the cure for the disease remains still obscure. Further, the prioritizing of mutations concerning the characteristic features governing the stability and pathogenicity of TTR mutant proteins remains unanswered, to date and thus, a complex state of study for researchers. Herein, we provide a full report encompassing the effects of every reported mutant model of TTR protein about the stability, functionality and pathogenicity using various computational tools. In addition, the results obtained from our study were used to create TTRMDB (Transthyretin mutant database), which could be easy access to researchers at http://vit.ac.in/ttrmdb.     

CrystalGrower: a generic computer program for Monte Carlo modelling of crystal growth

A Monte Carlo crystal growth simulation tool, CrystalGrower, is described which is able to simultaneously model both the crystal habit and nanoscopic surface topography of any crystal structure under conditions of variable supersaturation or at equilibrium. This tool has been developed in order to permit the rapid simulation of crystal surface maps generated by scanning probe microscopies in combination with overall crystal habit. As the simulation is based upon a coarse graining at the nanoscopic level features such as crystal rounding at low supersaturation or undersaturation conditions are also faithfully reproduced. CrystalGrower permits the incorporation of screw dislocations with arbitrary Burgers vectors and also the investigation of internal point defects in crystals. The effect of growth modifiers can be addressed by selective poisoning of specific growth sites. The tool is designed for those interested in understanding and controlling the outcome of crystal growth through a deeper comprehension of the key controlling experimental parameters.

Generic in silico methodology – CrystalGrower – for simulating crystal habit and nanoscopic surface topology to determine crystallisation free energies.     

Identification of β-strand mediated protein–protein interaction inhibitors using ligand-directed fragment ligation

β-Strand mediated protein–protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-Strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach, using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and in situ screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

Dynamic ligation screening is used to identify acylhydrazone-linked peptide-fragment hybrids which bind to the SHANK1 PDZ domain with comparable affinity to the native GKAP peptide as shown by biophysical and structural analyses.