ABDpred: Prediction of active antimicrobial compounds using supervised machine learning techniques.

Background Objectives: Discovery of new antibiotics is the need of the hour to treat infectious diseases. An ever-increasing repertoire of multidrug-resistant pathogens poses an imminent threat to human lives across the globe. However, the low success rate of the existing approaches and technologies for antibiotic discovery remains a major bottleneck.

Single-cell analysis reveals altered tumor microenvironments of relapse- and remission-associated pediatric acute myeloid leukemia.

Acute myeloid leukemia (AML) microenvironment exhibits cellular and molecular differences among various subtypes. Here, we utilize single-cell RNA sequencing (scRNA-seq) to analyze pediatric AML bone marrow (BM) samples from diagnosis (Dx), end of induction (EOI), and relapse timepoints. Analysis of Dx, EOI scRNA-seq, and TARGET AML RNA-seq datasets reveals an AML blasts-associated 7-gene signature (CLEC11A, PRAME, AZU1, NREP, ARMH1, C1QBP, TRH), which we validate on independent datasets.

Pediatric T-cell acute lymphoblastic leukemia blast signature and MRD associated immune environment changes defined by single cell transcriptomics analysis.

Different driver mutations and/or chromosomal aberrations and dysregulated signaling interactions between leukemia cells and the immune microenvironment have been implicated in the development of T-cell acute lymphoblastic leukemia (T-ALL). To better understand changes in the bone marrow microenvironment and signaling pathways in pediatric T-ALL, bone marrows collected at diagnosis (Dx) and end of induction therapy (EOI) from 11 patients at a single center were profiled by single cell transcriptomics (10 Dx, 5 paired EOI, 1 relapse). T-ALL blasts were identified by comparison with healthy bone marrow cells.

Machine-learning techniques for the prediction of protein-protein interactions.

Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions.

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains.

Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)-SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.

Interplay between miRNAs and human diseases.

MicroRNAs (miRNAs) are endogenous, non-coding RNAs, which have evoked a great deal of interest due to their importance in many aspects of homeostasis and diseases. MicroRNAs are stable and are essential components of gene regulatory networks. They play a crucial role in healthy individuals and their dysregulations have also been implicated in a wide range of diseases, including diabetes, cardiovascular disease, kidney disease, and cancer.

Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins.

A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins-MYC, APC and MDM2.

LMPID: a manually curated database of linear motifs mediating protein-protein interactions.

Linear motifs (LMs), used by a subset of all protein-protein interactions (PPIs), bind to globular receptors or domains and play an important role in signaling networks. LMPID (Linear Motif mediated Protein Interaction Database) is a manually curated database which provides comprehensive experimentally validated information about the LMs mediating PPIs from all organisms on a single platform. About 2200 entries have been compiled by detailed manual curation of PubMed abstracts, of which about 1000 LM entries were being annotated for the first time, as compared with the Eukaryotic LM resource.

Allergen databases.

In this chapter, five popular allergen databases have been described: (1) Allergome is based on basic and clinical information on allergens causing an IgE-mediated disease; (2) AllergenOnline allows online search of peer-reviewed allergen list; (3) International Union of Immunological Societies Allergen nomenclature subcommittee database contains systematic nomenclature and molecular details of well-characterized allergens; (4) AllFam allows classifying allergens into protein families based on domain information; and (5) SDAP provides in detail structural information of the allergens.