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.

The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER outperforms AlphaFold2 and AlphaFold3 on both single-domain and multidomain proteins. Large-scale folding experiments further show that D-I-TASSER could fold 81% of protein domains and 73% of full-chain sequences in the human proteome with results highly complementary to recently released models by AlphaFold2. These results highlight a new avenue to integrate deep learning with classical physics-based folding simulations for high-accuracy protein structure and function predictions that are usable in genome-wide applications.

Significance: More than 80% of eukaryotic proteins and 67% of prokaryotic proteins contain multiple domains. Due to the technical difficulties in structural biology, however, 65.3% of solved proteins in the Protein Data Bank contain only single-domain structures. Similarly, most computational approaches are optimized for single-domain structure predictions. We propose a pipeline for assembling domain models into full-length structures with interdomain orientations constrained by an analogous structure alignment search. Large-scale benchmark tests showed an unprecedented ability of DEMO in modeling multidomain structures, with a success rate significantly beyond that of the state-of-the-art approaches built on linker modeling. This development helps bridge the significant gap between the increasingly improved ability of individual domain structure determination and the extremely high demands of community for multidomain structure models.

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

The emergence of deep learning, particularly AlphaFold, has revolutionized static protein structure prediction, marking a transformative milestone in structural biology. However, protein function is not solely determined by static three-dimensional structures but is fundamentally governed by dynamic transitions between multiple conformational states. This shift from static to multi-state representations is crucial for understanding the mechanistic basis of protein function and regulation. This review outlines the fundamental concepts of protein dynamic conformations, surveys recent computational advances in modeling these dynamics in the post-AlphaFold era, and highlights key challenges, including data limitations, methodological constraints, and evaluation metrics. We also discuss potential strategies to address these challenges and explore future research directions to deepen our understanding of protein dynamics and their functional implications. This work aims to provide insights and perspectives to facilitate the ongoing development of protein conformation studies in the era of artificial intelligence-driven structural biology.