ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles.

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL:  https://proj.cse.cuhk.edu.hk/aihlab/pronet/.

SSBlazer: a genome-wide nucleotide-resolution model for predicting single-strand break sites.

Single-strand breaks are the major DNA damage in the genome and serve a crucial role in various biological processes. To reveal the significance of single-strand breaks, multiple sequencing-based single-strand break detection methods have been developed, which are costly and unfeasible for large-scale analysis. Hence, we propose SSBlazer, an explainable and scalable deep learning framework for single-strand break site prediction at the nucleotide level. SSBlazer is a lightweight model with robust generalization capabilities across various species and is capable of numerous unexplored SSB-related applications.

Low-dose AAV-CRISPR-mediated liver-specific knock-in restored hemostasis in neonatal hemophilia B mice with subtle antibody response.

AAV-delivered CRISPR/Cas9 (AAV-CRISPR) has shown promising potentials in preclinical models to efficiently insert therapeutic gene sequences in somatic tissues. However, the AAV input doses required were prohibitively high and posed serious risk of toxicity. Here, we performed AAV-CRISPR mediated homology-independent knock-in at a new target site in mAlb 3'UTR and demonstrated that single dose of AAVs enabled long-term integration and expression of hF9 transgene in both adult and neonatal hemophilia B mice (mF9 -/-), yielding high levels of circulating human Factor IX (hFIX) and stable hemostasis restoration during entire 48-week observation period. Furthermore, we achieved hemostasis correction with a significantly lower AAV dose (2 × 109 vg/neonate and 1 × 1010 vg/adult mouse) through liver-specific gene knock-in using hyperactive hF9R338L variant. The plasma antibodies against Cas9 and AAV in the neonatal mice receiving low-dose AAV-CRISPR were negligible, which lent support to the development of AAV-CRISPR mediated somatic knock-in for treating inherited diseases.

Identification of a binding site on soluble RANKL that can be targeted to inhibit soluble RANK-RANKL interactions and treat osteoporosis.

One of the major challenges for discovering protein-protein interaction inhibitors is identifying selective and druggable binding sites at the protein surface. Here, we report an approach to identify a small molecular binding site to selectively inhibit the interaction of soluble RANKL and RANK for designing anti-osteoporosis drugs without undesirable immunosuppressive effects. Through molecular dynamic simulations, we discovered a binding site that allows a small molecule to selectively interrupt soluble RANKL-RANK interaction and without interfering with the membrane RANKL-RANK interaction. We describe a highly potent inhibitor, S3-15, and demonstrate its specificity to inhibit the soluble RANKL-RANK interaction with in vitro and in vivo studies. S3-15 exhibits anti-osteoporotic effects without causing immunosuppression. Through in silico and in vitro experiments we further confirm the binding model of S3-15 and soluble RANKL. This work might inspire structure-based drug discovery for targeting protein-protein interactions.

Long-read PacBio genome sequencing of four environmental saprophytic Sporothrix species spanning the pathogenic clade.

BACKGROUND: The genus Sporothrix belongs to the order Ophiostomatales and contains mainly saprobic soil and plant fungi, although pathogenic species capable of causing human infections are also present. The whole-genomes of disease-causing species have already been sequenced and annotated but no comprehensive genomic resources for environmental Sporothrix species are available, thus limiting our understanding of the evolutionary origin of virulence-related genes and pathogenicity. RESULT: The genome assembly of four environmental Sporothrix species resulted in genome size of ~ 30.9 Mbp in Sporothrix phasma, ~ 35 Mbp in S. curviconia, ~ 38.7 Mbp in S. protearum, and ~ 39 Mbp in S. variecibatus, with a variable gene content, ranging from 8142 (S. phasma) to 9502 (S. variecibatus). The analysis of mobile genetic elements showed significant differences in the content of transposable elements within the sequenced genomes, with the genome of S. phasma lacking several class I and class II transposons, compared to the other Sporothrix genomes investigated. Moreover, the comparative analysis of orthologous genes shared by clinical and environmental Sporothrix genomes revealed the presence of 3622 orthogroups shared by all species, whereas over 4200 genes were species-specific single-copy gene products. Carbohydrate-active enzyme analysis revealed a total of 2608 protein-coding genes containing single and/or multiple CAZy domains, resulting in no statistically significant differences among pathogenic and environmental species. Nevertheless, some families were not found in clinical species. Furthermore, for each sequenced Sporothrix species, the mitochondrial genomes was assembled in a single circular DNA molecule, ranging from 25,765 bp (S. variecibatus) to 58,395 bp (S. phasma). CONCLUSION: In this study, we present four annotated genome assemblies generated using PacBio SMRT sequencing data from four environmental species: S. curviconia, S. phasma, S. protearum and S. variecibatus with the aim to provide a starting point for future comparative genome evolution studies addressing species diversification, ecological/host adaptation and origin of pathogenic lineages within the genus Sporothrix.

Structure-Based Discovery and Structural Basis of a Novel Broad-Spectrum Natural Product against the Main Protease of Coronavirus.

Over the past 20 years, the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and SARS-CoV-2 emerged, causing severe human respiratory diseases throughout the globe. Developing broad-spectrum drugs would be invaluable in responding to new, emerging coronaviruses and to address unmet urgent clinical needs. Main protease (Mpro; also known as 3CLpro) has a major role in the coronavirus life cycle and is one of the most important targets for anti-coronavirus agents. We show that a natural product, noncovalent inhibitor, shikonin, is a pan-main protease inhibitor of SARS-CoV-2, SARS-CoV, MERS-CoV, human coronavirus (HCoV)-HKU1, HCoV-NL63, and HCoV-229E with micromolar half maximal inhibitory concentration (IC50) values. Structures of the main protease of different coronavirus genus, SARS-CoV from the betacoronavirus genus and HCoV-NL63 from the alphacoronavirus genus, were determined by X-ray crystallography and revealed that the inhibitor interacts with key active site residues in a unique mode. The structure of the main protease inhibitor complex presents an opportunity to discover a novel series of broad-spectrum inhibitors. These data provide substantial evidence that shikonin and its derivatives may be effective against most coronaviruses as well as emerging coronaviruses of the future. Given the importance of the main protease for coronavirus therapeutic indication, insights from these studies should accelerate the development and design of safer and more effective antiviral agents. IMPORTANCE The current pandemic has created an urgent need for broad-spectrum inhibitors of SARS-CoV-2. The main protease is relatively conservative compared to the spike protein and, thus, is one of the most promising targets in developing anti-coronavirus agents. We solved the crystal structures of the main protease of SARS-CoV and HCoV-NL63 that bound to shikonin. The structures provide important insights, have broad implications for understanding the structural basis underlying enzyme activity, and can facilitate rational design of broad-spectrum anti-coronavirus ligands as new therapeutic agents.

Protein-RNA interaction prediction with deep learning: structure matters.

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

Enterprise Risk Assessment Based on Machine Learning.

Scientific risk assessment is an important guarantee for the healthy development of an enterprise. With the continuous development and maturity of machine learning technology, it has played an important role in the field of data prediction and risk assessment. This paper conducts research on the application of machine learning technology in enterprise risk assessment. According to the existing literature, this paper uses three machine learning algorithms, i.e., random forest (RF), support vector machine (SVM), and AdaBoost, to evaluate enterprise risk. In the specific implementation, the enterprise's risk assessment indexes are first established, which comprehensively describe the various risks faced by the enterprise through a number of parameters. Then, the three types of machine learning algorithms are trained based on historical data to build a risk assessment model. Finally, for a set of risk indicators obtained under current conditions, the risk index is output through the risk assessment model. In the experiment, some actual data are used to analyze and verify the method, and the results show that the proposed three types of machine learning algorithms can effectively evaluate enterprise risks.

Discovery of new LXRß agonists as glioblastoma inhibitors.

Discovery and optimization of selective liver X receptor ß (LXRß) agonists are challenging due to the high homology of LXRα and LXRß in the ligand-binding domain. There is only one different residue (Val versus Ile) at the ligand-binding pocket of LXRs. With machine learning methods, we identified pan LXR agonists with a novel scaffold (spiro[pyrrolidine-3,3'-oxindole]). Then, we figured out the mechanism of LXR isoform selectivity from co-crystal structures. Based on the mechanism and the new scaffold, LXRß selective agonists were designed and synthesized. This led to the discovery of LXRß agonists 4-7rr, 4-13 and 4-13rr with IC50 values ranging from 1.78 to 6.36 µM against glioblastoma in vitro. Treatment with 50 mg/kg/day of 4-13 for 15 days significantly reduced tumor growth using an in vivo xenograft glioblastoma model.

Discovery of tissue selective liver X receptor agonists for the treatment of atherosclerosis without causing hepatic lipogenesis.

Liver X Receptor (LXR) is a potential drug target for atherosclerosis. One of the major challenges in taking LXR modulators to the clinic is steatosis. It was reported that sterol LXR agonists selectively activate LXR in the intestine and macrophage cells rather than in the liver. We hypothesize that sterol LXR agonists may selectively inhibit atherosclerosis without causing hepatic lipogenesis. Thus, based on LXR structure, 12 sterol compounds were designed and tested in a dual-luciferase reporter gene experiment. It was confirmed that compounds 4 and 6 were LXR agonists. Further experiments demonstrated that compounds 4 and 6 inhibit the formation of macrophage foam cells without inducing triglyceride accumulation in either hepatocytes or adipocytes. In vivo studies demonstrated that compound 4 promotes reverse cholesterol transport without inducing hepatic lipogenesis. Thus, we report that these compounds with sterol scaffolds can be promising leads for the treatment of atherosclerosis without inducing steatosis.

Chemical constituents from Daphne tangutica and their cytotoxicity against nasopharyngeal carcinoma cells.

Two new sesquiterpenoids (1-2), together with 30 known compounds including one sesquiterpenoid (3), six diterpenoids (4-9), fourteen lignans (10-23), and nine other kinds of compounds (24-32), were isolated from the stems of Daphne tangutica Maxim. Their structures were determined through extensive spectroscopic analyses, and the absolute configuration of daphnoid A (1) and B (2) were determined by the experimental and calculated electron circular dichroism (ECD) spectra. All the isolates were evaluated against two human nasopharyngeal carcinoma cells (HONE-1 and SUNE-1). Compound 25 (daphnenone) showed potent cytotoxicity toward HONE-1 and SUNE-1with IC50 values of 2.23 and 1.43 µM, respectively. Further studies indicated that compound 25 exhibited cytotoxic effects by inducing tumor cell apoptosis and arresting the cell cycle at G2/M phases in HONE-1 cells.