The Vaccinia Virus DNA Helicase Structure from Combined Single-Particle Cryo-Electron Microscopy and AlphaFold2 Prediction

AlphaFold2 structures guide prospective ligand discovery

AlphaFold2 (AF2) models have had wide impact, but they have had mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and 5-HT2A receptors, testing hundreds of new molecules and comparing results to docking against the experimental structures. Hit rates were high and similar for the experimental and the AF2 structures, as were affinities. The success of docking against the AF2 models was achieved despite differences in orthosteric residue conformations versus the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based ligand discovery.

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.

AlphaFold2 structures template ligand discovery

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies have cast doubt on their direct usefulness for that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates and affinities from large library docking against AF2 models vs the same screens targeting experimental structures of the same receptors. In retrospective docking screens against the σ2 and the 5-HT2A receptors, the AF2 structures struggled to recapitulate ligands that we had previously found docking against the receptors' experimental structures, consistent with published results. Prospective large library docking against the AF2 models, however, yielded similar hit rates for both receptors versus docking against experimentally-derived structures; hundreds of molecules were prioritized and tested against each model and each structure of each receptor. The success of the AF2 models was achieved despite differences in orthosteric pocket residue conformations for both targets versus the experimental structures. Intriguingly, against the 5-HT2A receptor the most potent, subtype-selective agonists were discovered via docking against the AF2 model, not the experimental structure. To understand this from a molecular perspective, a cryoEM structure was determined for one of the more potent and selective ligands to emerge from docking against the AF2 model of the 5-HT2A receptor. Our findings suggest that AF2 models may sample conformations that are relevant for ligand discovery, much extending the domain of applicability of structure-based ligand discovery.

Structural insight into the assembly and working mechanism of helicase-primase D5 from Mpox virus

The Mpox pandemic, caused by the Mpox virus (or monkeypox virus, MPXV), has gained global attention. The D5 protein, a putative helicase-primase found in MPXV, plays a vital role in viral replication and genome uncoating. Here we determined multiple cryo-EM structures of full-length hexameric D5 in diverse states. These states were captured during ATP hydrolysis while moving along the single-stranded DNA (ssDNA) track. Through comprehensive structural analysis combined with the helicase activity system, we revealed that when the primase domain is truncated or the interaction between the primase and helicase domains is disrupted, the double-stranded DNA (dsDNA) unwinds into ssDNA, suggesting a critical regulatory role of the primase domain. Two transition states bound with ssDNA substrate during unwinding reveals that two ATP molecules were consumed to drive DNA moving forward two nucleotides. Collectively, our findings shed light on the molecular mechanism that links ATP hydrolysis to the DNA unwinding in poxviruses.

Structures and implications of the C962R protein of African swine fever virus

African swine fever virus (ASFV) is highly contagious and can cause lethal disease in pigs. Although it has been extensively studied in the past, no vaccine or other useful treatment against ASFV is available. The genome of ASFV encodes more than 170 proteins, but the structures and functions for the majority of the proteins remain elusive, which hindered our understanding on the life cycle of ASFV and the development of ASFV-specific inhibitors. Here, we report the structural and biochemical studies of the highly conserved C962R protein of ASFV, showing that C962R is a multidomain protein. The N-terminal AEP domain is responsible for the DNA polymerization activity, whereas the DNA unwinding activity is catalyzed by the central SF3 helicase domain. The middle PriCT2 and D5_N domains and the C-terminal Tail domain all contribute to the DNA unwinding activity of C962R. C962R preferentially works on forked DNA, and likely functions in Base-excision repair (BER) or other repair pathway in ASFV. Although it is not essential for the replication of ASFV, C962R can serve as a model and provide mechanistic insight into the replicative primase proteins from many other species, such as nitratiruptor phage NrS-1, vaccinia virus (VACV) and other viruses.

Advances in Structural Virology via Cryo-EM in 2022

In recent years, cryo-electron microscopy (cryo-EM) has emerged as an important standalone technique within structural biology [...].

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.