Research of the dynamic regulatory mechanism of Compound Danshen Dripping Pills on myocardial infarction based on metabolic trajectory analysis.

BACKGROUND: Myocardial infarction (MI) is a serious cardiovascular disease, which presents different pathophysiological changes with the prolongation of the disease. Compound danshen dripping pills (CDDP) has obvious advantages in MI treatment and widely used in the clinic. However, the current studies were mostly focused on the endpoint of CDDP intervention, lacking the dynamic attention to the disease process. It is of great value to establish a dynamic research strategy focused on the changes in pharmacodynamic substances for guiding clinical medication more precisely. PURPOSE: It is aimed to explore the dynamic regulating pattern of CDDP on MI based on metabolic trajectory analysis, and then clarify the variation characteristic biomarkers and pharmacodynamic substances in the intervention process. METHODS: The MI model was successfully prepared by coronary artery left anterior descending branch ligation, and then CDDP intervention was given for 28 days. Endogenous metabolites and the components of CDDP in serum were measured by LC/MS technique simultaneously to identify dynamic the metabolic trajectory and screen the characteristic pharmacodynamic substances at different points. Finally, network pharmacology and molecular docking techniques were used to simulate the core pharmacodynamic substances and core target binding, then validated at the genetic and protein level by Q-PCR and western blotting technology. RESULTS: CDDP performed typical dynamic regulation features on metabolite distribution, biological processes, and pharmacodynamic substances. During 1-7 days, it mainly regulated lipid metabolism and inflammation, the Phosphatidylcholine (PC(18:1(9Z/18:1(9Z)) and Sphingomyelin (SM(d18:1/23:1(9Z)), SM(d18:1/24:1(15Z)), SM(d18:0/16:1(9Z))) were the main characteristic biomarkers. Lipid metabolism was the mainly regulation pathway during 14-21 days, and the characteristic biomarkers were the Lysophosphatidylethanolamine (LysoPE(0:0/20:0), PE-NMe2(22:1(13Z)/15:0)) and Sphingomyelin (SM(d18:1/23:1(9Z))). At 28 days, in addition to inflammatory response and lipid metabolism, fatty acid metabolism also played the most important role. Correspondingly, Lysophosphatidylcholine (LysoPC(20:0/0:0)), Lysophosphatidylserine (LPS(18:0/0:0)) and Fatty acids (Linoelaidic acid) were the characteristic biomarkers. Based on the results of metabolite distribution and biological process, the characteristic pharmacodynamic substances during the intervention were further identified. The results showed that various kinds of Saponins and Tanshinones as the important active ingredients performed a long-range regulating effect on MI. And the other components, such as Tanshinol and Salvianolic acid B affected Phosphatidylcholine and Sphingomyelin through Relaxin Signaling pathway during the early intervention. Protocatechualdehyde and Rosmarinic acid affected Lysophosphatidylethanolamine and Sphingomyelin through EGFR Tyrosine kinase inhibitor resistance during the late intervention. Tanshinone IIB and Isocryptotanshinone via PPAR signaling pathway affected Lysophosphatidylcholine, Lysophosphatidylserine, and Fatty acids. CONCLUSION: The dynamic regulating pattern was taken as the entry point and constructs the dynamic network based on metabolic trajectory analysis, establishes the dynamic correlation between the drug-derived components and the endogenous metabolites, and elucidates the characteristic biomarkers affecting the changes of the pharmacodynamic indexes, systematically and deeply elucidate the pharmacodynamic substance and mechanism of CDDP on MI. It also enriched the understanding of CDDP and provided a methodological reference for the dynamic analysis of complex systems of TCM.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Investigating the mechanism and efficacy material basis of Xiehuo Xiaoying decoction for treating Graves' disease via thyroid cell apoptosis based on proteomics and molecular docking techniques.

ETHNOPHARMACOLOGICAL RELEVANCE: For numerous years, the Xiehuo Xiaoying decoction (XHXY), a traditional Chinese medicine formula, has demonstrated substantial promise in treating Graves' disease (GD) in clinical settings, showcasing significant potential. However, the therapeutic mechanism and efficacy material basis of XHXY remains obscure. AIM OF THE STUDY: This work aims to investigate the underlying mechanisms and to study the efficacy material basis of XHXY in anti-GD effect using a combination of TMT quantitative proteomics and molecular docking method. MATERIALS AND METHODS: GD model was initiated by administering Ad-TSH289. Subsequently, the mice underwent a four-week regimen that included oral gavage of XHXY at doses of 17 g/kg·d and 34 g/kg·d, along with intraperitoneal injections of Gentiopicroside (GPS). Utilizing the principles of pharmacological chemistry in traditional Chinese medicine, we employed high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (HPLC-QTOF/MS) to discern prescribed prototype composition of XHXY in serum samples from mouse. TMT proteomics research provided evidence of XHXY's putative targets and important pathways in vivo. The binding activity of probable action targets and prototype composition was detected by molecular docking. Finally, Immunohistochemistry (IHC) and TUNEL staining were used to verify the mechanism of XHXY and GPS in anti-GD. RESULTS: XHXY and GPS alleviated GD by ameliorating the pathological changes and reducing thyroxine and TRAb levels. In mouse serum, a total of 31 prototypical XHXY ingredients were detected, and the majority of these components were from monarch and minister medicine. Proteomics study results indicated that the XHXY may mainly regulate targets including FAS-associated death domain protein (FADD), Apolipoprotein C-III, etc. and main pathways are Apoptosis, Cholesterol metabolism, TNF signalling pathway, etc. Strong binding activity of the prototypical active ingredient and GPS towards FADD, Caspase 8, and Caspase 3 was demonstrated by molecular docking. XHXY and its primary component, GPS, elevated the expression of FADD, Caspase 8, and Caspase 3, and enhance apoptosis in thyroid cells, as lastly validated by TUNEL and IHC staining. CONCLUSIONS: XHXY exhibits a favorable therapeutic effect in treating GD by promoting apoptosis in thyroid cells through the upregulation of FADD, Caspase 8, and Caspase 3 expression. And GPS is the main efficacy material basis for its therapeutic effect in anti-GD.

Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction.

Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph.

Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. Unlike 1D sequences for proteins or 2D graphs for ligands, the 3D graph of protein-ligand complex enables the more accurate representations of the protein-ligand interactions. Benchmark studies have shown that our fusion models FGNN can achieve more accurate prediction of binding affinity than any individual algorithm. The advantages of fusion strategies have been demonstrated in terms of expressive power of data, learning efficiency and model interpretability. Our fusion models show satisfactory performances on diverse data sets, demonstrating their generalization ability. Given the good performances in both binding affinity prediction and virtual screening, our fusion models are expected to be practically applied for drug screening and design. Our work highlights the potential of the fusion graph neural network algorithm in solving complex prediction problems in computational biology and chemistry. The fusion graph neural networks (FGNN) model is freely available in https://github.com/LinaDongXMU/FGNN.

Xiehuo Xiaoying decoction inhibits Tfh cell expansion and promotes Tfr cell amplification to ameliorate Graves' disease.

ETHNOPHARMACOLOGICAL RELEVANCE: Xiehuo Xiaoying decoction (XHXY) has shown great potential in the treatment of GD, but its mechanism remains obscure. Increase of follicular helper T (Tfh) cells and reduction of follicular regulatory T (Tfr) cells contribute to a high thyrotropin receptor antibodies (TRAb) level and possible Graves' disease (GD). Oxidative stress (OS) disrupts T helper cell differentiation and aggravates autoimmunity. AIM OF THE STUDY: This study aimed to investigate whether XHXY decoction can ameliorate autoimmunity in GD via inhibiting OS and regulating Tfh and Tfr cells. MATERIALS AND METHODS: The main XHXY bioactive compounds were identified using high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. GD was induced in the mice through three intramuscular injections of adenovirus expressing the TSH receptor. Then, the mice received oral gavage of XHXY (17 g/kg·d) and 34 g/kg·d) for 4 weeks. OS indicators were assessed. Flow cytometry was used to confirm the proportion of Tfh and Tfr cells in the lymph nodes and spleens of the mice. Cytokine expression levels were determined using enzyme-linked immunosorbent assay. Factors including interleukin-21, B-cell lymphoma-6, and forkhead box P3 (Foxp3) were detected using quantitative polymerase chain reaction. The mRNA and protein expression levels of Kelch-like ECH-associated protein 1 (Keap1), nuclear factor erythroid-2-related factor 2 (Nrf2), and haem oxygenase 1 (HO-1) were detected using quantitative polymerase chain reaction and Western blotting, respectively. RESULTS: Twelve main ingredients of XHXY were identified. XHXY relieved GD by lowering thyroxine (p < 0.01) and TRAb levels (p < 0.01). XHXY ameliorated OS by decreasing the levels of NADPH oxidase 2 (p < 0.05), 4-hydroxynonenal (p < 0.01), and 8-oxo-2'-deoxyguanosine (p < 0.001). It inhibited Tfh cell expansion (p < 0.05), as well as the production of cytokine interleukin -21 (p < 0.01), interleukin -4 (p < 0.01) and transcription factor B-cell lymphoma 6 (p < 0.05). XHXY also induced Tfr cell amplification (p < 0.05), increased the production of interleukin -10 (p < 0.05) and transforming growth factor ß (p < 0.05) and the mRNA levels of Foxp3 (p < 0.05). Finally, the Tfh/Tfr ratio returned to normal. In addition, XHXY activated Nrf2 and HO-1 expression, but inhibited Keap1 activation. CONCLUSIONS: XHXY relieves autoimmunity in GD via inhibiting Tfh cell amplification and Tfr cell reduction, a mechanism which probably involves the Keap1/Nrf2 signaling pathway.

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules.

Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.

XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking.

Prediction of protein-ligand binding affinities is a central issue in structure-based computer-aided drug design. In recent years, much effort has been devoted to the prediction of the binding affinity in protein-ligand complexes using machine learning (ML). Due to the remarkable ability of ML methods in nonlinear fitting, ML-based scoring functions (SFs) can deliver much improved performance on a selected test set, such as the comparative assessment of scoring functions (CASF), when compared to the classical SFs. However, the performance of ML-based SFs heavily relies on the overall similarity of the training set and the test set. To improve the performance and transferability of an SF, we have tried to combine various features including energy terms from X-score and AutoDock Vina, the properties of ligands, and the statistical sequence-related information from either the binding site or the full protein. In conjunction with extreme trees (ET), an ML model, we have developed XLPFE, a new SF. Compared with other tested methods such as X-score, AutoDock Vina, ΔvinaXGB, PSH-ML, or CNN-score, XLPFE achieves consistently better scoring and ranking power for various types of protein-ligand complex structures beyond the CASF, suggesting that XLPFE has superior transferability. In particular, XLPFE performs better with metalloenzymes. With its faster speed, improved accuracy, and better transferability, XLPFE could be usefully applied to a diverse range of protein-ligand complexes.

Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase.

Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.

Prediction of Binding Free Energy of Protein-Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method.

Accurate prediction of protein-ligand binding free energies is important in enzyme engineering and drug discovery. The molecular mechanics/generalized Born surface area (MM/GBSA) approach is widely used to estimate ligand-binding affinities, but its performance heavily relies on the accuracy of its energy components. A hybrid strategy combining MM/GBSA and machine learning (ML) has been developed to predict the binding free energies of protein-ligand systems. Based on the MM/GBSA energy terms and several features associated with protein-ligand interactions, our ML-based scoring function, GXLE, shows much better performance than MM/GBSA without entropy. In particular, the good transferability of the GXLE model is highlighted by its good performance in ranking power for prediction of the binding affinity of different ligands for either the docked structures or crystal structures. The GXLE scoring function and its code are freely available and can be used to correct the binding free energies computed by MM/GBSA.