Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool.

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool.

NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool.

The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways.

Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing.

Machine learning and data-driven approaches are currently being widely used in drug discovery and development due to their potential advantages in decision-making based on the data leveraged from existing sources. Applying these approaches to drug repurposing (DR) studies can identify new relationships between drug molecules, therapeutic targets and diseases that will eventually help in generating new insights for developing novel therapeutics. In the current study, a dataset of 1671 approved drugs is analyzed using a combined approach involving unsupervised Machine Learning (ML) techniques (Principal Component Analysis (PCA) followed by k-means clustering) and Structure-Activity Relationships (SAR) predictions for DR.

Drug repositioning for anti-tuberculosis drugs: an in silico polypharmacology approach.

Development of potential antitubercular molecules is a challenging task due to the rapidly emerging drug-resistant strains of Mycobacterium tuberculosis (M.tb). Structure-based approaches hold greater benefit in identifying compounds/drugs with desired polypharmacological profiles.

Combined Experimental and Computational Study of the Gelation of Cyclohexane-Based Bis(acyl-semicarbazides) and the Multi-Stimuli-Responsive Properties of Their Gels.

The current study reports the one-step synthesis and gelation properties of cyclohexane-based bis(acyl-semicarbazide) gelators with an additional -NH group incorporated into urea moieties and carrying hydrophobic chains of varying length (C8-C18). The gels exhibited thermoreversibility and could be tuned in the presence of anions at different concentrations in addition their the ultrasound-responsive nature, thus making them multi-stimuli-responsive. The combined experimental and computational study on these gels reveals that the balance between two noncovalent interactions, viz.