Differential performance of RoseTTAFold in antibody modeling.

Antibodies are essential to life, and knowing their structures can facilitate the understanding of antibody-antigen recognition mechanisms. Precise antibody structure prediction has been a core challenge for a prolonged period, especially the accuracy of H3 loop prediction. Despite recent progress, existing methods cannot achieve atomic accuracy, especially when the homologous structures required for these methods are not available. Recently, RoseTTAFold, a deep learning-based algorithm, has shown remarkable breakthroughs in predicting the 3D structures of proteins. To assess the antibody modeling ability of RoseTTAFold, we first retrieved the sequences of 30 antibodies as the test set and used RoseTTAFold to model their 3D structures. We then compared the models constructed by RoseTTAFold with those of SWISS-MODEL in a different way, in which we stratified Global Model Quality Estimate (GMQE) into three different ranges. The results indicated that RoseTTAFold could achieve results similar to SWISS-MODEL in modeling most CDR loops, especially the templates with a GMQE score under 0.8. In addition, we also compared the structures modeled by RoseTTAFold, SWISS-MODEL and ABodyBuilder. In brief, RoseTTAFold could accurately predict 3D structures of antibodies, but its accuracy was not as good as the other two methods. However, RoseTTAFold exhibited better accuracy for modeling H3 loop than ABodyBuilder and was comparable to SWISS-MODEL. Finally, we discussed the limitations and potential improvements of the current RoseTTAFold, which may help to further the accuracy of RoseTTAFold's antibody modeling.

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research.

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

IsAb: a computational protocol for antibody design.

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Pain-CKB, A Pain-Domain-Specific Chemogenomics Knowledgebase for Target Identification and Systems Pharmacology Research.

A traditional single-target analgesic, though it may be highly selective and potent, may not be sufficient to mitigate pain. An alternative strategy for alleviation of pain is to seek simultaneous modulation at multiple nodes in the network of pain-signaling pathways through a multitarget analgesic or drug combinations. Here we present a comprehensive pain-domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated computing tools for target identification and systems pharmacology research. Pain-CKB is constructed on the basis of our established chemogenomics technology with new features, including multiple compound support, multicavity protein support, and customizable symbol display. The determination of bioactivity is also revised to avoid the use of complex machine learning models. Our one-stop computing platform describes the chemical molecules, genes, and proteins involved in pain regulation. To date, Pain-CKB has archived 272 analgesics in the market, 84 pain-related targets with 207 available 3D crystal or cryo-EM structures, and 234 662 chemical agents reported for these target proteins. Moreover, Pain-CKB implements user-friendly web-interfaced computing tools and applications for the prediction and analysis of the relevant protein targets and visualization of the outputs, including HTDocking, TargetHunter, BBB permeation predictor, NGL viewer, Spider Plot, etc. The Pain-CKB server is accessible at https://www.cbligand.org/g/pain-ckb.

Mitochondrial structure and function: a new direction for the targeted treatment of chronic liver disease with Chinese herbal medicine.

ETHNOPHARMACOLOGICAL RELEVANCE: Excessive fat accumulation, biological clock dysregulation , viral infections, and sustained inflammatory responses can lead to liver inflammation, fibrosis, and cancer, thus promoting the development of chronic liver disease. A comprehensive understanding of the etiological factors leading to chronic liver disease and the intrinsic mechanisms influencing its onset and progression can aid in identifying potential targets for targeted therapy. Mitochondria, as key organelles that maintain the metabolic homeostasis of the liver, provide an important foundation for exploring therapeutic targets for chronic liver disease. Recent studies have shown that active ingredients in herbal medicines and their natural products can modulate chronic liver disease by influencing the structure and function of mitochondria. Therefore, studying how Chinese herbs target mitochondrial structure and function to treat chronic liver diseases is of great significance. AIM OF THE STUDY: Investigating the prospects of herbal medicine the Lens of chronic liver disease based on mitochondrial structure and function. MATERIALS AND METHODS: A computerized search of PubMed was conducted using the keywords "mitochondrial structure", "mitochondrial function", "mitochondria and chronic liver disease", "botanicals, mitochondria and chronic liver disease".Data from the Web of Science and Science Direct databases were also included. The research findings regarding herbal medicines targeting mitochondrial structure and function for the treatment of chronic liver disease are summarized. RESULTS: A computerized search of PubMed using the keywords "mitochondrial structure", "mitochondrial function", "mitochondria and chronic liver disease", "phytopharmaceuticals, mitochondria, and chronic liver disease", as well as the Web of Science and Science Direct databases was conducted to summarize information on studies of mitochondrial structure- and function-based Chinese herbal medicines for the treatment of chronic liver disease and to suggest that the effects of herbal medicines on mitochondrial division and fusion.The study suggested that there is much room for research on the influence of Chinese herbs on mitochondrial division and fusion. CONCLUSIONS: Targeting mitochondrial structure and function is crucial for herbal medicine to combat chronic liver disease.

A new era of antibody discovery: an in-depth review of AI-driven approaches.

Given their high affinity and specificity for a range of macromolecules, antibodies are widely used in the treatment of autoimmune diseases, cancers, inflammatory diseases, and Alzheimer's disease (AD). Traditional experimental methods are time-consuming, expensive, and labor-intensive. Recent advances in artificial intelligence (AI) technologies provide complementary methods that can reduce the time and costs required for antibody design by minimizing failures and increasing the success rate of experimental tests. In this review, we scrutinize the plethora of AI-driven methodologies that have been deployed over the past 4 years for modeling antibody structures, predicting antibody-antigen interactions, optimizing antibody affinity, and generating novel antibody candidates. We also briefly address the challenges faced in integrating AI-based models with traditional antibody discovery pipelines and highlight the potential future directions in this burgeoning field.

Progress of mitochondrial and endoplasmic reticulum-associated signaling and its regulation of chronic liver disease by Chinese medicine.

The endoplasmic reticulum (ER) is connected to mitochondria through mitochondria-associated ER membranes (MAMs). MAMs provide a framework for crosstalk between the ER and mitochondria, playing a crucial role in regulating cellular calcium balance, lipid metabolism, and cell death. Dysregulation of MAMs is involved in the development of chronic liver disease (CLD). In CLD, changes in MAMs structure and function occur due to factors such as cellular stress, inflammation, and oxidative stress, leading to abnormal interactions between mitochondria and the ER, resulting in liver cell injury, fibrosis, and impaired liver function. Traditional Chinese medicine has shown some research progress in regulating MAMs signaling and treating CLD. This paper reviews the literature on the association between mitochondria and the ER, as well as the intervention of traditional Chinese medicine in regulating CLD.

Traditional herbs: mechanisms to combat cellular senescence.

Cellular senescence plays a very important role in the ageing of organisms and age-related diseases that increase with age, a process that involves physiological, structural, biochemical and molecular changes in cells. In recent years, it has been found that the active ingredients of herbs and their natural products can prevent and control cellular senescence by affecting telomerase activity, oxidative stress response, autophagy, mitochondrial disorders, DNA damage, inflammatory response, metabolism, intestinal flora, and other factors. In this paper, we review the research information on the prevention and control of cellular senescence in Chinese herbal medicine through computer searches of PubMed, Web of Science, Science Direct and CNKI databases.

Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A2A Receptor.

Characterizing the structural basis of ligand recognition of adenosine A2A receptor (AA2AR) will facilitate its rational design and development of small molecules with high affinity and selectivity, as well as optimal therapeutic effects for pain, cancers, drug abuse disorders, etc. In the present work, we applied our reported algorithm, molecular complex characterizing system (MCCS), to characterize the binding features of AA2AR based on its reported 3D structures of protein-ligand complexes. First, we compared the binding score to the reported experimental binding affinities of each compound. Then, we calculated an output example of residue energy contribution using MCCS and compared the results with data obtained from MM/GBSA. The consistency in results indicated that MCCS is a powerful, fast, and accurate method. Sequentially, using a receptor-ligand data set of 57 crystallized structures of AA2ARs, we characterized the binding features of the binding pockets in AA2AR, summarized the key residues that distinguish antagonist from agonist, produced heatmaps of residue energy contribution for clustering various statuses of AA2ARs, explored the selectivity between AA2AR and AA1AR, etc. All the information provided new insights into the protein features of AA2AR and will facilitate its rational drug design.

Structural and in Vitro Functional Characterization of a Menthyl TRPM8 Antagonist Indicates Species-Dependent Regulation.

TRPM8 antagonists derived from its cognate ligand, (-)-menthol, are underrepresented. We determine the absolute stereochemistry of a well-known TRPM8 antagonist, (-)-menthyl 1, using VCD and 2D NMR. We explore 1 for its antagonist effects of the human TRPM8 (hTRPM8) orthologue to uncover species-dependent inhibition versus rat channels. (-)-Menthyl 1 inhibits menthol- and icilin-evoked Ca2+ responses at hTRPM8 with IC50 values of 805 ± 200 nM and 1.8 ± 0.6 µM, respectively, while more potently inhibiting agonist responses at the rat orthologue (rTRPM8 IC50 (menthol) = 117 ± 18 nM, IC50 (icilin) = 521 ± 20 nM). Whole-cell patch-clamp recordings of hTRPM8 confirm the 1 inhibition of menthol-stimulated currents, with an IC50 of 700 ± 200 nM. We demonstrate that 1 possesses ≥400-fold selectivity for hTRPM8 versus hTRPA1/hTRPV1. (-)-menthyl 1 can be used as a novel chemical tool to study hTRPM8 pharmacology and differences in species commonly used in drug discovery.

Differential expression and bioinformatic analysis of circRNA in nonalcoholic steatohepatitis cirrhosis.

AIM: This study investigates the expression profile of circRNA in nonalcoholic steatohepatitis (NASH) cirrhosis and identifies the underlying pathogenesis of core genes of NASH cirrhosis. METHODS: The GEO 134146 dataset was obtained from GEO database. EdgeR software was used to analyze the differential expression of circRNA between NASH cirrhosis samples and normal samples, and Starbase and miRWalk databases were used to predict the targeted miRNA and mRNA. The protein-protein interaction network of these target genes was established by searching the string database of interacting genes, Cytoscape and Mcode analysis. In addition, David and Omicshare were used to analyze the functional enrichment and pathway enrichment of target genes. RESULTS: We evaluated 99 differentially expressed circRNAs, 27 of which were up-regulated, and 72 were down-regulated. A regulatory network consisting of 10 circRNAs, 30 miRNAs, and 1217 mRNAs was further constructed. The differential expression of circRNA is closely related to the functions of "target gene transcriptional regulation", "protein binding", "serine/threonine kinase", etc. The difference in circRNA is mainly related to the "MAPK" signaling pathway and the "FoxO" signaling pathway. CONCLUSIONS: This study confirmed the abnormal regulation of circRNA in NASH cirrhosis. Bioinformatic analysis showed that abnormal expression of circRNA might be related to the occurrence and development of NASH cirrhosis.

Binding Characterization of GPCRs-Modulator by Molecular Complex Characterizing System (MCCS).

Increasing attention has been devoted to allosteric modulators as the preferred therapeutic agents for their colossal advantages such as higher selectivity, fewer side effects, and lower toxicity since they bind at allosteric sites that are topographically distinct from the classic orthosteric sites. However, the allosteric binding pockets are not conserved and there are no cogent methods to comprehensively characterize the features of allosteric sites with the binding of modulators. To overcome this limitation, our lab has developed a novel algorithm that can quantitatively characterize the receptor-ligand binding feature named Molecular Complex Characterizing System (MCCS). To illustrate the methodology and application of MCCS, we take G protein coupled receptors (GPCRs) as an example. First, we summarized and analyzed the reported allosteric binding pockets of class A GPCRs using MCCS. Sequentially, a systematic study was conducted between cannabinoid receptor type 1 (CB1) and its allosteric modulators, where we used MCCS to analyze the residue energy contribution and the interaction pattern. Finally, we validated the predicted allosteric binding site in CB2 via MCCS in combination with molecular dynamics (MD) simulation. Our results demonstrate that the MCCS program is advantageous in recapitulating the allosteric regulation pattern of class A GPCRs of the reported pockets as well as in predicting potential allosteric binding pockets. This MCCS program can serve as a valuable tool for the discovery of small-molecule allosteric modulators for class A GPCRs.

Pain Chemogenomics Knowledgebase (Pain-CKB) for Systems Pharmacology Target Mapping and Physiologically Based Pharmacokinetic Modeling Investigation of Opioid Drug-Drug Interactions.

More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.