Empirical comparison and analysis of machine learning-based approaches for druggable protein identification.

Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of druggable proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of druggable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valuable guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.

3D interconnected porous HA scaffolds with SiO2 additions: effect of SiO2 content and macropore size on the viability of human osteoblast cells.

3D interconnected porous scaffolds of HA and HA with various additions of SiO2 were fabricated using a polymeric template technique, to make bioceramic scaffolds consisting of macrostructures of the interconnected macropores. Three different sizes of the polyurethane template were used in the fabrication process to form different size interconnected macropores, to study the effect of pore size on human osteoblast cell viability. The template used allowed fabrication of scaffolds with pore sizes of 45, 60, and 75 ppi, respectively. Scanning microscopy was used extensively to observe the microstructure of the sintered samples and the characteristics of cells growing on the HA surfaces of the interconnected macropores. It has been clearly demonstrated that the SiO2 addition has influenced both the phase transformation of HA to TCP (ß-TCP and α-TCP) and also affected the human osteoblast cell viability grown on these scaffolds.

Biological activities of extracts from endophytic fungi isolated from Garcinia plants.

Sixty-five crude extracts from 51 selected endophytic fungi isolated from Garcinia species were tested for various bioactivities. Eighty per cent of the fungal extracts from fermentation broths and mycelia displayed bioactivities: antimycobacterial (76.9%), antimalarial (14.1%), antiviral (16.7%), antioxidant (22.2%), antiproliferation (11.1% against NCI-H187 and 12.7% against KB cells), and cytotoxicity to Vero cells (40.0%). Based on internal transcribed spacer rRNA sequence analysis, 15 bioactive isolates were identified as Aspergillus, Botryosphaeria, Curvularia, Fusicoccum, Guignardia, Muscodor, Penicillium, Pestalotiopsis, and Phomopsis spp. One isolate (N24) was matched with an unidentified fungal endophyte. These results indicate that endophytic fungi isolated from Garcinia plants in Thailand are potential sources of various bioactive natural products.