Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

The orderly migration of various white blood cell types to inflammatory sites is a highly regulated process that involves a diversity of adhesion and signaling molecules. This cellular influx is initiated by relatively low affinity interactions that allow for leukocytes to roll along the vascular surfa ce. This rolling phe­ nomenon is mediated by adhesive interactions between lectin containing ad­ hesion molecules, termed selectins, on both the vascular endothelium and leukocytes, and car bohydrate ligands immobilized on mucin-like scaffolds. This adhesion allows for a rapid recognition of various cell types under the conditions of vascular flow, with the result that inflammatory cells are spe­

Interactions and the initiation of the inflammatory response

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics ...

Metabolomics for Investigating Physiological and Pathophysiological Processes

Changing patterns of infectious disease

Epidermal Growth Factor Receptor (EGFR) is a well-characterized cancer drug target. In the past, several QSAR models have been developed for predicting inhibition activity of molecules against EGFR. These models are useful to a limited set of molecules for a particular class like quinazoline-derivatives. In this study, an attempt has been made to develop prediction models on a large set of molecules (~3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole. We train, test and validate our classification models on a dataset called EGFR10 that contains 508 inhibitors (having inhibition activity IC50 less than 10 nM) and 2997 non-inhibitors. Our Random forest based model achieved maximum MCC 0.49 with accuracy 83.7% on a validation set using 881 PubChem fingerprints. In this study, frequency-based feature selection technique has been used to identify best fingerprints. It was observed that PubChem fingerprints FP380 (C(~O) (~O)), FP579 (O = C-C-C-C), FP388 (C(:C) (:N) (:N)) and FP 816 (ClC1CC(Br)CCC1) are more frequent in the inhibitors in comparison to non-inhibitors. In addition, we created different datasets namely EGFR100 containing inhibitors having IC50 < 100 nM and EGFR1000 containing inhibitors having IC50 < 1000 nM. We trained, test and validate our models on datasets EGFR100 and EGFR1000 datasets and achieved and maximum MCC 0.58 and 0.71 respectively. In addition, models were developed for predicting quinazoline and pyrimidine based EGFR inhibitors. In summary, models have been developed on a large set of molecules of various classes for discriminating EGFR inhibitors and non-inhibitors. These highly accurate prediction models can be used to design and discover novel EGFR inhibitors. In order to provide service to the scientific community, a web server/standalone EGFRpred also has been developed (
 http://crdd.osdd.net/oscadd/egfrpred/
 ). This article was reviewed by Dr Murphy, Prof Wang and Dr. Eisenhaber.

/pdf/qsar-based-model-for-discriminating-egfr-inhibitors-and-non-15hi814of2.pdf

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.

In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life. In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides. In summary, this study describes a web server ‘HLP’ that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (
 http://www.imtech.res.in/raghava/hlp/
 ).

/pdf/designing-of-peptides-with-desired-half-life-in-intestine-3t3r11uti2.pdf

Designing of peptides with desired half-life in intestine-like environment

Background
Benzylisoquinoline is the structural backbone of many alkaloids with a wide variety of structures including papaverine, noscapine, codeine, morphine, apomorphine, berberine, protopine and tubocurarine. Many benzylisoquinoline alkaloids have been reported to show therapeutic properties and to act as novel medicines. Thus it is important to collect and compile benzylisoquinoline alkaloids in order to explore their usage in medicine.

https://catbull.com/alamut/Bibliothek/1471-2210-10-4.pdf

BIAdb: A curated database of benzylisoquinoline alkaloids

Background
Identification of drug-like molecules is one of the major challenges in the field of drug discovery. Existing approach like Lipinski rule of 5 (Ro5), Operea have their own limitations. Thus, there is a need to develop computational method that can predict drug-likeness of a molecule with precision. In addition, there is a need to develop algorithm for screening chemical library for their drug-like properties.

/pdf/drugmint-a-webserver-for-predicting-and-designing-of-drug-2ez1q2jio5.pdf

DrugMint: a webserver for predicting and designing of drug-like molecules

Background: The emergence of drug resistant tuberculosis poses a serious concern globally and researchers are in rigorous search for new drugs to fight against these dreadful bacteria. Recently, the bacterial GlmU protein, involved in peptidoglycan, lipopolysaccharide and techoic acid synthesis, has been identified as an important drug target. A unique C-terminal disordered tail, essential for survival and the absence of gene in host makes GlmU a suitable target for inhibitor design. Results: This study describes the models developed for predicting inhibitory activity (IC50) of chemical compounds against GlmU protein using QSAR and docking techniques. These models were trained on 84 diverse compounds (GlmU inhibitors) taken from PubChem BioAssay (AID 1376). These inhibitors were docked in the active site of the C-terminal domain of GlmU protein (2OI6) using the AutoDock. A QSAR model was developed using docking energies as descriptors and achieved maximum correlation of 0.35/0.12 (r/r 2 ) between actual and predicted pIC50. Secondly, QSAR models were developed using molecular descriptors calculated using various software packages and achieved maximum correlation of 0.77/0.60 (r/r 2 ). Finally, hybrid models were developed using various types of descriptors and achieved high correlation of 0.83/0.70 (r/r 2 ) between predicted and actual pIC50. It was observed that some molecular descriptors used in this study had high correlation with pIC50. We screened chemical libraries using models developed in this study and predicted 40 potential GlmU inhibitors. These inhibitors could be used to develop drugs against Mycobacterium tuberculosis. Conclusion: These results demonstrate that docking energies can be used as descriptors for developing QSAR models. The current work suggests that docking energies based descriptors could be used along with commonly used molecular descriptors for predicting inhibitory activity (IC50) of molecules against GlmU. Based on this study an open source platform, http://crdd.osdd.net/raghava/gdoq, has been developed for predicting inhibitors GlmU.

/pdf/a-web-server-for-predicting-inhibitors-against-bacterial-22vzaa12rg.pdf

Deepak Singla

Papers

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest.

Designing of peptides with desired half-life in intestine-like environment

BIAdb: A curated database of benzylisoquinoline alkaloids

DrugMint: a webserver for predicting and designing of drug-like molecules

A web server for predicting inhibitors against bacterial target GlmU protein