Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.Phosphoinositides (PIs) are a family of eight lipids consisting of phosphatidylinositol (PtdIns) and its seven phosphorylated forms. PIs have important regulatory functions in the cell including lipid signaling, protein transport, and membrane trafficking. Yeast has been recognized as a eukaryotic model system to study lipid-protein interactions. Hundreds of yeast PI-binding proteins have been identified, but this research knowledge remains scattered. Besides, the complete PI-binding spectrum and potential PI-binding domains have not been interlinked. No comprehensive databases are available to support the lipid-protein interaction research on phosphoinositides. Here we constructed the first knowledgebase of Yeast Phosphoinositide-Binding Proteins (YPIBP), a repository consisting of 679 PI-binding proteins collected from high-throughput proteome-array and lipid-array studies, QuickGO, and a rigorous literature mining. The YPIBP also contains protein domain information in categories of lipid-binding domains, lipid-related domains and other domains. The YPIBP provides search and browse modes along with two enrichment analyses (PI-binding enrichment analysis and domain enrichment analysis). An interactive visualization is given to summarize the PI-domain-protein interactome. Finally, three case studies were given to demonstrate the utility of YPIBP. The YPIBP knowledgebase consolidates the present knowledge and provides new insights of the PI-binding proteins by bringing comprehensive and in-depth interaction network of the PI-binding proteins. YPIBP is available at http//cosbi7.ee.ncku.edu.tw/YPIBP/.Mammalian apurinic/apyrimidinic (AP) endonuclease 1 (APE1) has versatile enzymatic functions, including redox, endonuclease, and exonuclease activities. APE1 is thus broadly associated with pathways in DNA repair, cancer cell growth, and drug resistance. Unlike its AP site-specific endonuclease activity in Base excision repair (BER), the 3'-5' exonucleolytic cleavage of APE1 using the same active site exhibits complex substrate selection patterns, which are key to the biological functions. This work aims to integrate molecular structural information and biocatalytic properties to deduce the substrate recognition mechanism of APE1 as an exonuclease and make connection to its diverse functionalities in the cell. In particular, an induced space-filling model emerges in which a bridge-like structure is formed by Arg177 and Met270 (RM bridge) upon substrate binding, causing the active site to adopt a long and narrow product pocket for hosting the leaving group of an AP site or the 3'-end nucleotide. Rather than distinguishing bases as other exonucleases, the hydrophobicity and steric hindrance due to the APE1 product pocket provides selectivity for substrate structures, such as matched or mismatched blunt-ended dsDNA, recessed dsDNA, gapped dsDNA, and nicked dsDNA with 3'-end overhang shorter than 2 nucleotides. These dsDNAs are similar to the native substrates in BER proofreading, BER for trinucleotide repeats (TNR), Nucleotide incision repair (NIR), DNA single-strand breaks (SSB), SSB with damaged bases, and apoptosis. Integration of in vivo studies, in vitro biochemical assays, and structural analysis is thus essential for linking the APE1 exonuclease activity to the specific roles in cellular functions.Mutations in leucine-rich repeat kinase 2 (LRRK2) are a frequent cause of autosomal dominant Parkinson's disease (PD) and have been associated with familial and sporadic PD. XL413 ic50 Reducing the kinase activity of LRRK2 is a promising therapeutic strategy since pathogenic mutations increase the kinase activity. Several small-molecule LRRK2 inhibitors are currently under investigation for the treatment of PD. However, drug discovery and development are always accompanied by high costs and a risk of late failure. The use of already approved drugs for a new indication, which is known as drug repositioning, can reduce the cost and risk. In this study, we applied a structure-based drug repositioning approach to identify new LRRK2 inhibitors that are already approved for a different indication. In a large-scale structure-based screening, we compared the protein-ligand interaction patterns of known LRRK2 inhibitors with protein-ligand complexes in the PDB. The screening yielded 6 drug repositioning candidates. Two of these candidates, Sunitinib and Crizotinib, demonstrated an inhibition potency (IC50) and binding affinity (Kd) in the nanomolar to micromolar range. While Sunitinib has already been known to inhibit LRRK2, Crizotinib is a novel LRRK2 binder. Our results underscore the potential of structure-based methods for drug discovery and development. In light of the recent breakthroughs in cryo-electron microscopy and structure prediction, we believe that structure-based approaches like ours will grow in importance.