The first Conus genome assembly reveals a primary genetic central dogma of conopeptides in C. betulinus.

Although there are various Conus species with publicly available transcriptome and proteome data, no genome assembly has been reported yet. Here, using Chinese tubular cone snail (C. betulinus) as a representative, we sequenced and assembled the first Conus genome with original identification of 133 genome-widely distributed conopeptide genes. After integration of our genomics, transcriptomics, and peptidomics data in the same species, we established a primary genetic central dogma of diverse conopeptides, assuming a rough number ratio of ~1:1:1:10s for the total genes: transcripts: proteins: post-translationally modified peptides. This ratio may be special for this worm-hunting Conus species, due to the high diversity of various Conus genomes and the big number ranges of conopeptide genes, transcripts, and peptides in previous reports of diverse Conus species. Only a fraction (45.9%) of the identified conotopeptide genes from our achieved genome assembly are transcribed with transcriptomic evidence, and few genes individually correspond to multiple transcripts possibly due to intraspecies or mutation-based variances. Variable peptide processing at the proteomic level, generating a big diversity of venom conopeptides with alternative cleavage sites, post-translational modifications, and N-/C-terminal truncations, may explain how the 133 genes and ~123 transcripts can generate thousands of conopeptides in the venom of individual C. betulinus. We also predicted many conopeptides with high stereostructural similarities to the putative analgesic ω-MVIIA, addiction therapy AuIB and insecticide ImI, suggesting that our current genome assembly for C. betulinus is a valuable genetic resource for high-throughput prediction and development of potential pharmaceuticals.

Effective descriptions of molecular structures and the quantitative structure-activity relationship studies.

In this research, we found CoMFA alone could not obtain sufficiently a strong equation to allow confident prediction for aminobenzenes. When some other parameter, such as heat of molecular formation of the compounds, was introduced into the CoMFA model, the results were improved greatly. It gives us a hint that a better description for molecular structures will yield a better prediction model, and this hint challenged us to look for another method--the projection areas of molecules in 3D space for 3D-QSAR. It is surprising that much better results than that obtained by using CoMFA were achieved. Besides the CoMFA analysis, multiregression analysis and neural network methods for building the models were used in this paper.