As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we first analyzed two recent studies which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. The re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, present however clear evidences rebutting these conclusions. Next, using metagenomic samples from Manis javanica we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed 4-fold more variations in the bat coronavirus RaTG13 than those in the Manis coronavirus compared to 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.

With the progress of structural biology, the Protein Data Bank (PDB) has witnessed rapid accumulation of experimentally solved protein structures. Since many structures are determined with purification and crystallization additives that are unrelated to a protein's in vivo function, it is nontrivial to identify the subset of protein–ligand interactions that are biologically relevant. We developed the BioLiP2 database (https://zhanggroup.org/BioLiP) to extract biologically relevant protein–ligand interactions from the PDB database. BioLiP2 assesses the functional relevance of the ligands by geometric rules and experimental literature validations. The ligand binding information is further enriched with other function annotations, including Enzyme Commission numbers, Gene Ontology terms, catalytic sites, and binding affinities collected from other databases and a manual literature survey. Compared to its predecessor BioLiP, BioLiP2 offers significantly greater coverage of nucleic acid-protein interactions, and interactions involving large complexes that are unavailable in PDB format. BioLiP2 also integrates cutting-edge structural alignment algorithms with state-of-the-art structure prediction techniques, which for the first time enables composite protein structure and sequence-based searching and significantly enhances the usefulness of the database in structure-based function annotations. With these new developments, BioLiP2 will continue to be an important and comprehensive database for docking, virtual screening, and structure-based protein function analyses.

The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins. There are however no standard and efficient pipeline available for sensitive multiple sequence alignment (MSA) collection. This is particularly challenging when large whole-genome and metagenome databases are involved. We developed DeepMSA, a new open-source method for sensitive MSA construction, which has homologous sequences and alignments created from multi-sources of whole-genome and metagenome databases through complementary hidden Markov model algorithms. The practical usefulness of the pipeline was examined in three large-scale benchmark experiments based on 614 non-redundant proteins. First, DeepMSA was utilized to generate MSAs for residue-level contact prediction by six coevolution and deep learning-based programs, which resulted in an accuracy increase in long-range contacts by up to 24.4% compared to the default programs. Next, multiple threading programs are performed for homologous structure identification, where the average TM-score of the template alignments has over 7.5% increases with the use of the new DeepMSA profiles. Finally, DeepMSA was used for secondary structure prediction and resulted in statistically significant improvements in the Q3 accuracy. It is noted that all these improvements were achieved without re-training the parameters and neural-network models, demonstrating the robustness and general usefulness of the DeepMSA in protein structural bioinformatics applications, especially for targets without homologous templates in the PDB library. https://zhanglab.ccmb.med.umich.edu/DeepMSA/. Supplementary data are available at Bioinformatics online.

Two-dimensional (2D) nanomaterials, especially 2D organic nanomaterials with unprecedentedly diverse and controlled structure, have attracted decent scientific interest. Among the preparation strategies, the top-down approach is one of the considered low-cost and scalable strategies to obtain 2D organic nanomaterials. However, some factors of their layered counterparts limited the development and potential applications of 2D organic nanomaterials, such as type, stability, and strict synthetic conditions of layered counterparts. We report a class of layered solvent knitting hyper-cross-linked microporous polymers (SHCPs) prepared by improving Friedel-Crafts reaction and using dichloroalkane as an economical solvent, stable electrophilic reagent, and external cross-linker at low temperature, which could be used as layered counterparts to obtain previously unknown 2D SHCP nanosheets by method of ultrasonic-assisted solvent exfoliation. This efficient and low-cost strategy can produce previously unreported microporous organic polymers with layered structure and high surface area and gas storage capacity. The pore structure and surface area of these polymers can be controlled by tuning the chain length of the solvent, the molar ratio of AlCl₃, and the size of monomers. Furthermore, we successfully obtain an unprecedentedly high–surface area HCP material (3002 m² g⁻¹), which shows decent gas storage capacity (4.82 mmol g⁻¹ at 273 K and 1.00 bar for CO₂; 12.40 mmol g⁻¹ at 77.3 K and 1.13 bar for H₂). This finding provides an opportunity for breaking the constraint of former knitting methods and opening up avenues for the design and synthesis of previously unknown layered HCP materials.