A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/.

Polygonumins B, C and D, derivative compounds of polygonumins A, were isolated from the stem of Polygonum minus. Based on NMR results, the structure of polygonumins derivatives is comprised of four phenylpropanoid units and a sucrose unit, with a similar structure to polygonumins A. However, the structural differences between polygonumins B (1), C (2) and D (3) can be distinguished based on the existence of methoxy, ethanoyl and hydroxyl groups and protons which bind to C-4, C-4′ and C-3″. Interestingly, these bioactive compounds showed various medicinal properties based on our investigation on antioxidant, anticholinesterase and anti-HIV-1 protease activities. The IC₅₀ value of DPPH and ABTS (antioxidant activities) was in the following descending order: polygonumins B > polygonumins C > polygonumins A > polygonumins D. In addition, almost similar pattern of antioxidant activity was observed for anti-acetylcholinesterase activity based on its IC₅₀ value in descending order: polygonumins B > polygonumins C > polygonumins D > polygonumins A. On the other hand, polygonumins C and D showed inhibition of HIV-1 protease activity more than the positive control, pepstatin A. Finally, molecular docking studies on AChE and BChE proteins were carried out in order to gain insight into the mode of interactions between these compounds and the active residues for both enzymes. These remarkable findings indicate that these compounds have potential to be developed as targeted drugs for Alzheimer’s disease or as anti-HIV drugs.

Glucosinolates (GSLs) and cyanogenic glycosides (CGs) fulfil functions in plant defence and have been reported to be anticancer agents. Generally, GSL-containing plants do not produce CG, and vice versa, CG-containing plants do not synthesise GSLs. However, the production of both GSL and CG compounds was observed in Carica papaya. Additionally, several studies found both GSL glucotropaeolin and CG prunasin in papaya leaves. The advancement of genome technologies can be explored to elucidate the gene functions and other molecular discoveries in plants that might relate to GSLs and CGs. This review aims to discuss the complex interplay of the rare events whereby these two compounds (GSL and CG) co-occur in a bifurcation pathway in papaya. To our knowledge, this is the first review that highlights novel GSL and CG genes in papaya. Furthermore, species-specific pathways in papaya are also discussed and comprehensively described. The transcription factors involved in regulating GSL and CG biosynthesis pathways are also discussed, accompanied by relevant bioinformatic approaches that can help discover potential regulatory genes that control the production of prunasin and glucotropaeolin in papaya.

Machine learning-based protein mutational effect prediction is widely used in protein engineering and pathogenicity prediction, but training data scarcity remains a major challenge due to high costs of experimental measurements. A previous study proposed data augmentation using computational estimates by molecular simulation. However, this approach has been limited to predicting mutational effects on thermostability. Here, we present a new data augmentation method that combines molecular simulation with zero-shot prediction computed by protein language models. These computational estimates serve as ‘weak’ training data to supplement experimental training data. Our method dynamically adjusts the weight and inclusion of weak training data based on available experimental training data. This reduces potential negative impacts of weak training data while extending applicability to diverse protein properties such as binding affinity and enzymatic activity. Benchmark tests demonstrate that our method improves prediction accuracy particularly when experimental training data are scarce. These results indicate the capability of our approach to advance protein engineering and pathogenicity prediction in small data regimes.