IGTCM: An integrative genome database of traditional Chinese medicine plants.

Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM-related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM-related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non-redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG-mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM-related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

ACFIS: a web server for fragment-based drug discovery.

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/.