A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI.

Antioxidative, phytochemical and antimicrobial analysis of juices of eight citrus cultivars grown in Pakistan

Comparative nutritional analysis of citrus varieties cultivated in Pakistan has not been reported. Citrus is consumed all over the world due to its taste and also has pharmacological components. The present investigation evaluated the antioxidant, reducing power, total flavonoids and phenolics, DPPH free radical scavenging, protein kinase inhibition, and the antimicrobial activities of eight Pakistani citrus varieties. Grapefruit showed maximum total antioxidant potential (77 µg AAE/100 mg), followed by Kinnow and Shakri. Khatai showed maximum reducing power potential (69.6 µg AAE/100 mg) while Shakri and Grapefruit trailed it. All the varieties showed significant DPPH free radical scavenging activity. Maximum total phenolics in citrus juice were found in Shakri and Kinnow; 26.2 and 25.9 µg GAE/100mg, respectively. Variation in total flavonoids content was observed as Kinnow>Grapefruit>Shakri>Khatai. All the citrus juices showed mild to moderate antibacterial activity, while Mosambi and Malta contained potent antifungal components. HPLC analysis of citrus juices revealed that catechin was present in all citrus genotypes except Kinnow. The study concludes that citrus juices contain strong antioxidative potential, bear protein kinase inhibitors and can be used as cancer chemoprevention and supportive nutrition.

Set-Theoretic Formalism for Treating Ligand-Target Datasets

Data on ligand–target (LT) interactions has played a growing role in drug research for several decades. Even though the amount of data has grown significantly in size and coverage during this period, most datasets remain difficult to analyze because of their extreme sparsity, as there is no activity data whatsoever for many LT pairs. Even within clusters of data there tends to be a lack of data completeness, making the analysis of LT datasets problematic. The current effort extends earlier works on the development of set-theoretic formalisms for treating thresholded LT datasets. Unlike many approaches that do not address pairs of unknown interaction, the current work specifically takes account of their presence in addition to that of active and inactive pairs. Because a given LT pair can be in any one of three states, the binary logic of classical set-theoretic methods does not strictly apply. The current work develops a formalism, based on ternary set-theoretic relations, for treating thresholded LT datasets. It also describes an extension of the concept of data completeness, which is typically applied to sets of ligands and targets, to the local data completeness of individual ligands and targets. The set-theoretic formalism is applied to the analysis of simple and joint polypharmacologies based on LT activity profiles, and it is shown that null pairs provide a means for determining bounds to these values. The methodology is applied to a dataset of protein kinase inhibitors as an illustration of the method. Although not dealt with here, work is currently underway on a more refined treatment of activity values that is based on increasing the number of activity classes.

Profiling of the drug resistance of thousands of Src tyrosine kinase mutants uncovers a regulatory network that couples autoinhibition to the dynamics of the catalytic domain

SUMMARYProtein kinase inhibitors are effective cancer therapies, but acquired resistance often limits clinical efficacy. Despite the cataloguing of numerous resistance mutations with model studies and in the clinic, we still lack a comprehensive understanding of kinase inhibitor resistance. Here, we measured the resistance of thousands of Src tyrosine kinase mutants to a panel of ATP-competitive inhibitors. We found that ATP-competitive inhibitor resistance mutations are distributed throughout Src’s catalytic domain. In addition to inhibitor contact residues, residues that participate in regulating Src’s phosphotransferase activity were prone to the development of resistance. Unexpectedly, a resistance-prone cluster of residues that are on the top face of the N-terminal lobe of the catalytic domain contributes to Src autoinhibition by reducing the dynamics of the catalytic domain, and mutations in this cluster led to resistance by lowering inhibitor affinity and promoting kinase hyperactivation. Together, our studies demonstrate how comprehensive profiling of drug resistance can be used to understand potential resistance pathways and uncover new mechanisms of kinase regulation.