DataSheet1_Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery.docx

Computational methods with affordable computational resources are highly desirable for identifying active drug leads from millions of compounds. This requires a model that is both highly efficient and relatively accurate, which cannot be achieved by most of the current methods. In real virtual screening (VS) application scenarios, the desired method should perform much better in selecting active compounds by prediction than by random chance. Here, we systematically evaluate the performance of our previously developed DFCNN model in large-scale virtual screening, and the results show our method has approximately 22 times the success rate compared to the random chance on average with a score cutoff of 0.99. Of the 102 test cases, 10 cases have more than 98 times the success rate of a random guess. Interestingly, in three cases, the prediction success rate is 99 times that of a random guess by a score cutoff of 0.99. This indicates that in most situations after our extremely large-scale VS, the dataset can be reduced 20 to 100 times for the next step of virtual screening based on docking or MD simulation. Furthermore, we have employed an experimental method to verify our computational method by finding several activity inhibitors for Trypsin I Protease. In addition, we also show its proof-of-concept application in de novo drug screening. The results indicate the massive potential of this method in the first step of the real drug development workflow. Moreover, DFCNN only takes about 0.0000225s for one protein–compound prediction on average with 80 Intel CPU cores (2.00 GHz) and 60 GB RAM, which is at least tens of thousands of times faster than AutoDock Vina or Schrödinger high-throughput virtual screening. Additionally, an online webserver based on DFCNN for large-scale screening is available at http://cbblab.siat.ac.cn/DFCNN/index.php for the convenience of the users.</p

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein–peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target

DataSheet_1_Regulatory and functional divergence among members of Ibβfruct2, a sweet potato vacuolar invertase gene controlling starch and glucose content.pdf

Sweet potato [Ipomoea batatas (L.) Lam.] is an important food and industrial crop. Its storage root is rich in starch, which is present in the form of granules and represents the principal storage carbohydrate in plants. Starch content is an important trait of sweet potato controlling the quality and yield of industrial products. Vacuolar invertase encoding gene Ibβfruct2 was supposed to be a key regulator of starch content in sweet potato, but its function and regulation were unclear. In this study, three Ibβfruct2 gene members were detected. Their promoters displayed differences in sequence, activity, and cis-regulatory elements and might interact with different transcription factors, indicating that the three Ibβfruct2 family members are governed by different regulatory mechanisms at the transcription level. Among them, we found that only Ibβfruct2-1 show a high expression level and promoter activity, and encodes a protein with invertase activity, and the conserved domains and three conserved motifs NDPNG, RDP, and WEC are critical to this activity. Only two and six amino acid residue variations were detected in sequences of proteins encoded by Ibβfruct2-2 and Ibβfruct2-3, respectively, compared with Ibβfruct2-1; although not within key motifs, these variations affected protein structure and affinities for the catalytic substrate, resulting in functional deficiency and low activity. Heterologous expression of Ibβfruct2-1 in Arabidopsis decreased starch content but increased glucose content in leaves, indicating Ibβfruct2-1 was a negative regulator of starch content. These findings represent an important advance in understanding the regulatory and functional divergence among duplicated genes in sweet potato, and provide critical information for functional studies and utilization of these genes in genetic improvement.</p

Table_1_Regulatory and functional divergence among members of Ibβfruct2, a sweet potato vacuolar invertase gene controlling starch and glucose content.docx

Sweet potato [Ipomoea batatas (L.) Lam.] is an important food and industrial crop. Its storage root is rich in starch, which is present in the form of granules and represents the principal storage carbohydrate in plants. Starch content is an important trait of sweet potato controlling the quality and yield of industrial products. Vacuolar invertase encoding gene Ibβfruct2 was supposed to be a key regulator of starch content in sweet potato, but its function and regulation were unclear. In this study, three Ibβfruct2 gene members were detected. Their promoters displayed differences in sequence, activity, and cis-regulatory elements and might interact with different transcription factors, indicating that the three Ibβfruct2 family members are governed by different regulatory mechanisms at the transcription level. Among them, we found that only Ibβfruct2-1 show a high expression level and promoter activity, and encodes a protein with invertase activity, and the conserved domains and three conserved motifs NDPNG, RDP, and WEC are critical to this activity. Only two and six amino acid residue variations were detected in sequences of proteins encoded by Ibβfruct2-2 and Ibβfruct2-3, respectively, compared with Ibβfruct2-1; although not within key motifs, these variations affected protein structure and affinities for the catalytic substrate, resulting in functional deficiency and low activity. Heterologous expression of Ibβfruct2-1 in Arabidopsis decreased starch content but increased glucose content in leaves, indicating Ibβfruct2-1 was a negative regulator of starch content. These findings represent an important advance in understanding the regulatory and functional divergence among duplicated genes in sweet potato, and provide critical information for functional studies and utilization of these genes in genetic improvement.</p

A Genetically Encoded Bioluminescent System for Fast and Highly Sensitive Detection of Antibodies with a Bright Green Fluorescent Protein

A method for fast and highly sensitive detection of antibodies in serum would greatly facilitate the early diagnosis of disease and infection and dose optimization of therapeutic antibody. Bioluminescence detection with LUMABS (renamed mNeonG-LUMABS, where mNeonG is short for mNeonGreen) sensors based on bioluminescence resonance energy transfer (BRET) between blue-emitting luciferase Nluc and green fluorescent protein (FP) mNeonGreen has been demonstrated to enable fast detection of antibodies directly in serum with reasonable sensitivity. However, some mNeonG-LUMABS sensors exhibit low sensitivity, and thus, sensitivity improvement remains imperative. Here, we report a bright green FP, Clover4, obtained by structure-guided mutagenesis of green FP Clover. Despite similar brightness and fluorescence spectra of Clover and mNeonGreen, Clover4-LUMABS sensors exhibit a largely increased dynamic range (maximum 20-fold) and much lower limit of detection (LOD) (maximum 5.6-fold), most likely because Clover4 is positioned in a more parallel orientation to Nluc in LUMABS. Due to modular design, Clover4-LUMABS offers a general BRET system for fast and highly sensitive antibody detection in serum

Inhibition of autophagosome-lysosome fusion by ginsenoside Ro via the ESR2-NCF1-ROS pathway sensitizes esophageal cancer cells to 5-fluorouracil-induced cell death via the CHEK1-mediated DNA damage checkpoint

<p>Modulation of autophagy has been increasingly regarded as a promising cancer therapeutic approach. In this study, we screened several ginsenosides extracted from <i>Panax ginseng</i> and identified ginsenoside Ro (Ro) as a novel autophagy inhibitor. Ro blocked the autophagosome-lysosome fusion process by raising lysosomal pH and attenuating lysosomal cathepsin activity, resulting in the accumulation of the autophagosome marker MAP1LC3B/LC3B and SQSTM1/p62 (sequestosome 1) in various esophageal cancer cell lines. More detailed studies demonstrated that Ro activated ESR2 (estrogen receptor 2), which led to the activation of NCF1/p47^PHOX (neutrophil cytosolic factor 1), a subunit of NADPH oxidase, and subsequent reactive oxygen species (ROS) production. Treatment with siRNAs or inhibitors of the ESR2-NCF1-ROS axis, such as N-acetyl-L-cysteine (NAC), diphenyleneiodonium chloride (DPI), apocynin (ACN), Tiron, and Fulvestrant apparently decreased Ro-induced LC3B-II, GFP-LC3B puncta, and SQSTM1, indicating that ROS instigates autophagic flux inhibition triggered by Ro. More importantly, suppression of autophagy by Ro sensitized 5-fluorouracil (5-Fu)-induced cell death in chemoresistant esophageal cancer cells. 5-Fu induced prosurvival autophagy, and by inhibiting such autophagy, siRNAs against <i>BECN1/beclin 1, ATG5, ATG7</i>, and <i>LC3B</i> enhanced 5-Fu-induced autophagy-associated and apoptosis-independent cell death. We observed that Ro potentiates 5-Fu cytotoxicity via delaying CHEK1 (checkpoint kinase 1) degradation and downregulating DNA replication process, resulting in the delayed DNA repair and the accumulation of DNA damage. In summary, these data suggest that Ro is a novel autophagy inhibitor and could function as a potent anticancer agent in combination therapy to overcome chemoresistance.</p

Gypenoside L, Isolated from Gynostemma pentaphyllum, Induces Cytoplasmic Vacuolation Death in Hepatocellular Carcinoma Cells through Reactive-Oxygen-Species-Mediated Unfolded Protein Response

Exploring novel anticancer agents that can trigger non-apoptotic or non-autophagic cell death is urgent for cancer treatment. In this study, we screened and identified an unexplored anticancer activity of gypenoside L (Gyp-L) isolated from Gynostemma pentaphyllum. We showed that treatment with Gyp-L induces non-apoptotic and non-autophagic cytoplasmic vacuolation death in human hepatocellular carcinoma (HCC) cells. Mechanically, Gyp-L initially increased the intracellular reactive oxygen species (ROS) levels, which, in turn, triggered protein ubiquitination and unfolded protein response (UPR), resulting in Ca²⁺ release from endoplasm reticulum (ER) inositol trisphosphate receptor (IP₃R)-operated stores and finally cytoplasmic vacuolation and cell death. Interruption of the ROS–ER–Ca²⁺ signaling pathway by chemical inhibitors significantly prevented Gyp-L-induced vacuole formation and cell death. In addition, Gyp-L-induced ER stress and vacuolation death required new protein synthesis. Overall, our works provide strong evidence for the anti-HCC activity of Gyp-L and suggest a novel therapeutic option by Gyp-L through the induction of a unconventional ROS–ER–Ca²⁺-mediated cytoplasmic vacuolation death in human HCC