Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.     

Phylogenetic relationships and historical biogeography of tribes and genera in the subfamily Nymphalinae (Lepidoptera: Nymphalidae)

We infer for the first time the phylogenetic relationships of genera and tribes in the ecologically and evolutionarily well‐studied subfamily Nymphalinae using DNA sequence data from three genes: 1450 bp of cytochrome oxidase subunit I (COI) (in the mitochondrial genome), 1077 bp of elongation factor 1‐alpha (EF1‐α) and 400–403 bp of wingless (both in the nuclear genome). We explore the influence of each gene region on the support given to each node of the most parsimonious tree derived from a combined analysis of all three genes using Partitioned Bremer Support. We also explore the influence of assuming equal weights for all characters in the combined analysis by investigating the stability of clades to different transition/transversion weighting schemes. We find many strongly supported and stable clades in the Nymphalinae. We are also able to identify ‘rogue’ taxa whose positions are weakly supported (the different gene regions are in conflict with each other) and unstable. Our main conclusions are: (1) the tribe Coeini as currently constituted is untenable, and Smyrna, Colobura and Tigridia are part of Nymphalini; (2) ‘Kallimini’ is paraphyletic with regard to Melitaeini and should be split into three tribes: Kallimini s.s., Junoniini and Victorinini; (3) Junoniini, Victorinini, Melitaeini and the newly circumscribed Nymphalini are strongly supported monophyletic groups, and (4) Precis and Junonia are not synonymous or even sister groups. The species Junonia coenia, a model system in developmental biology, clearly belongs in the genus Junonia. A dispersal‐vicariance analysis suggests that dispersal has had a major effect on the distributions of extant species, and three biotic regions are identified as being centres of diversification of three major clades: the Palaearctic for the Nymphalis‐group, the Afrotropics for Junoniini and the Nearctic for Melitaeini. © 2005 The Linnean Society of London, Biological Journal of the Linnean Society, 2005, 86 , 227–251.