ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors

Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.     

SETE: Sequence-based Ensemble learning approach for TCR Epitope binding prediction

Predicting the binding of T cell receptors (TCRs) to epitopes plays a vital role in the immunotherapy, because it guides the development of therapeutic vaccines and cancer treatments. Many prediction methods attempted to explain the relationship between TCR repertoires from different aspects such as the V(D)J gene locus and the biophysical features of amino acids molecules, but the extraction of these features is time consuming and the performance of these models are limited. Few studies have investigated how k-mers formed by adjacent amino acids in TCR sequences direct the epitope recognition, and the specific mechanism of TCR epitope binding is still unclear. Motivated by these, we presented SETE (Sequence-based Ensemble learning approach for TCR Epitope binding prediction), a novel model to predict the TCR epitope binding accurately. The model deconstructed the CDR3β sequence to short amino acid chains as features and learned the pattern of them between different TCR repertoires with gradient boosting decision tree algorithm. Experiments have demonstrated that SETE can be helpful in predicting the TCRs’ corresponding epitopes and it outperforms other state-of-the-art methods in predicting the epitope specificity of TCR on VDJdb data set. The source codes have been uploaded at https://github.com/wonanut/SETE for academic usage only.     

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/.     

HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage

Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.     

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.     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

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.     

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.     

RepEx: A web server to extract sequence repeats from protein and DNA sequences

Evolution builds up new genetic material from existing ones, not in random, but in highly ordered and eloquent patterns. Most of these sequence repeats are revelatory of valuable information contributing to areas of disease research and function of macromolecules, to name a few. In the age of next generation genome sequencing, rapid and efficient extraction of all unbiased sequence repeats from macromolecules is the need of the hour. In view of this reckoning, an online web-based computing server, RepEx, has been developed to extract and display all possible repeats for DNA and protein sequences. Apart from exact or identical repeats, the server has been designed adeptly to identify and extract degenerate, inverted, everted and mirror repeats from both DNA and protein sequences. The server has striking output displays, featuring interactive graphs and comprehensive output files. In addition, RepEx has been accoutered with an easy-to-use interface and search filters to facilitate a user-defined query or search and is freely available and accessible via the World Wide Web at http://bioserver2.physics.iisc.ac.in/RepEx/.     

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.     

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.     

Absorption and fluorescence spectra of open-chain tetrapyrrole pigments–bilirubins,biliverdins, phycobilins,and synthetic analogues

Open-chain tetrapyrroles are ubiquitous and abundant in living organisms (algae, animals, bacteria, and plants), including examples such as bilirubin, biliverdin, phycocyanobilin, phycoerythrobilin, and urobilin. The open-chain tetrapyrroles, collectively termed bilins, arise from biosynthesis or degradation of tetrapyrrole macrocycles. Bilins are now known to play a wide variety of biological roles encompassing light-harvesting (in phycobiliproteins), photomorphogenesis, signaling, and redox chemistry. The absorption spectra of bilins spans the ultraviolet (UV), visible, to near-infrared (NIR) regions depending on the degree of conjugation, thereby providing a wide range of colors from red/orange to blue/green. The fluorescence intensity of bilins is often quite low and hence fewer spectra are available, but can be increased substantially by structural rigidification, as evidenced by the wide use of biliproteins as fluorescent labels. The present article describes a database of absorption and fluorescence spectra of bilins from natural and synthetic origins for 220 compounds (270 absorption and 13 fluorescence spectral traces). Spectral traces of bilins published over the past ∼50 years have been digitized and assembled along with information concerning solvent, photochemical properties (molar absorption coefficient and fluorescence quantum yield), and literature references. The spectral traces (xy-coordinate data files) can be viewed, downloaded, and accessed at www.photochemcad.com. The accessibility of spectral traces in digital format should facilitate identification and quantitative calculations of interest in diverse scientific areas.     

Efficient utilization on PSSM combining with recurrent neural network for membrane protein types prediction

Position-Specific Scoring Matrix (PSSM) is an excellent feature extraction method that was proposed early in protein classifying prediction, but within the restriction of feature shape in PSSM, researchers make a lot attempts to process it so that PSSM can be input to the traditional machine learning algorithms. These processes drop information provided by PSSM in a way thus the feature representation is limited. Moreover, the high-dimensional feature representation of PSSM makes it incompatible with other feature extraction methods. We use the PSSM as the input of Recurrent Neural Network without any post-processing, the amino acids in protein sequences are regarded as time step in RNN. This way takes full advantage of the information that PSSM provides. In this study, the PSSM is input to the model directly and the internal information of PSSM is fully utilized, we propose an end-to-end solution and achieve state-of-the-art performance. Ultimately, the exploration of how to combine PSSM with traditional feature extraction methods is carried out and achieve slightly improved performance. Our network architecture is implemented in Python and is available at https://github.com/YellowcardD/RNN-for-membrane-protein-types-prediction.     

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.     

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.     

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype

Genotype plays a significant role in determining characteristics in an organism and genotype calling has been greatly accelerated by sequencing technologies. Furthermore, most parametric statistical models are unable to effectively call genotype, which is influenced by the size of structural variations and the coverage fluctuations of sequencing data. In this study, we propose a new method for calling deletions’ genotypes from the next-generation data, called Cnngeno. Cnngeno can convert sequencing data into images and classifies the genotypes from these images using the convolutional neural network(CNN). Moreover, Cnngeno adopted the convolutional bootstrapping strategy to improve the anti-noisy label’s ability. The results show that Cnngeno performs better in terms of precision for calling genotype when compared with other existing methods. The Cnngeno is an open-source method, available at https://github.com/BRF123/Cnngeno.     

Fluorogenic Trp(redBODIPY) cyclopeptide targeting keratin 1 for imaging of aggressive carcinomas

Keratin 1 (KRT1) is overexpressed in squamous carcinomas and associated with aggressive pathologies in breast cancer. Herein we report the design and preparation of the first Trp-based red fluorogenic amino acid, which is synthetically accessible in a few steps and displays excellent photophysical properties, and its application in a minimally-disruptive labelling strategy to prepare a new fluorogenic cyclopeptide for imaging of KRT1+ cells in whole intact tumour tissues.

Trp(redBODIPY) is the first red-emitting Trp-based amino acid for the preparation of fluorogenic peptides with retention of target binding affinity.