Text mining for modeling of protein complexes enhanced by machine learning

Artifical intelligence: a virtual chemist for natural product drug discovery

Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.

Pan-Cancer Analysis of PGAM1 and Its Experimental Validation in Uveal Melanoma Progression

Phosphoglycerate mutase 1 (PGAM1) is a key enzyme regulating cancer glycolysis. However, the expression and function of PGAM1 in uveal melanoma (UVM) are unknown and systematic analysis is lacking. This study performed a comprehensive analysis of PGAM1 expression across 33 cancer types in multiple public databases. Results demonstrated PGAM1 is aberrantly overexpressed in most tumors compared to normal tissues, and this overexpression is associated with poor prognosis, advanced tumor staging, and aggressive clinical phenotypes in multiple cancers including UVM, lung, breast and bladder carcinomas. In addition, PGAM1 expression positively correlated with infiltration levels of tumor-promoting immune cells including macrophages, NK cells, myeloid dendritic cells, etc. Further experiments showed that PGAM1 was overexpressed in UVM cell lines and tissues, and it was positively associated with a poor prognosis of UVM patients. And knockdown of PGAM1 inhibited migration/invasion and induced apoptosis in UVM cells, followed by decreased levels of PD-L1, Snail, and BCl-2 and increased levels of E-cadherin. Additionally, the correlation analysis and molecular docking results suggest that PGAM1 could interact with PD-L1, Snail and BCl-2. Thus, PGAM1 may promote UVM pathogenesis via modulating immune checkpoint signaling, EMT and apoptosis. Collectively, this study reveals PGAM1 as a valuable prognostic biomarker and potential therapeutic target in aggressive cancers including UVM.

Integrative Analysis of the Role of TP53 in Human Pan-Cancer

Tumor protein P53 (TP53) is an important tumor suppressor gene in humans. Under normal circumstances, TP53 can help repair mutated genes, or promote the death of cells with severe gene mutations (specifically, TP53 prevents cells from arrest in the G1/S phase when deoxyribonucleic acid (DNA) is damaged and promotes apoptosis if not repaired), and prevents normal cells from becoming malignant cells. TP53 mutations affect its tumor suppressor function, leading to the development of malignant tumors. In this study, using a public database, we explored the pan-cancer expression of TP53, its impact on patient survival and prognosis, the types of gene mutations, its correlation with immunity, and its regulation of other transcription factors and micro RNA (miRNA). The docking sites of therapeutic drugs and key amino acid sites of action provide a basis for future targeted therapies. TP53 has important biological functions in the human body. This study provides a theoretical basis for clinical TP53 gene therapy.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.