Chaos-embedded particle swarm optimization approach for protein-ligand docking and virtual screening

TCMNPAS: a comprehensive analysis platform integrating network formulaology and network pharmacology for exploring traditional Chinese medicine

The application of network formulaology and network pharmacology has significantly advanced the scientific understanding of traditional Chinese medicine (TCM) treatment mechanisms in disease. The field of herbal biology is experiencing a surge in data generation. However, researchers are encountering challenges due to the fragmented nature of the data and the reliance on programming tools for data analysis. We have developed TCMNPAS, a comprehensive analysis platform that integrates network formularology and network pharmacology. This platform is designed to investigate in-depth the compatibility characteristics of TCM formulas and their potential molecular mechanisms. TCMNPAS incorporates multiple resources and offers a range of functions designed for automated analysis implementation, including prescription mining, molecular docking, network pharmacology analysis, and visualization. These functions enable researchers to analyze and obtain core herbs and core formulas from herbal prescription data through prescription mining. Additionally, TCMNPAS facilitates virtual screening of active compounds in TCM and its formulas through batch molecular docking, allowing for the rapid construction and analysis of networks associated with "herb-compound-target-pathway" and disease targets. Built upon the integrated analysis concept of network formulaology and network pharmacology, TCMNPAS enables quick point-and-click completion of network-based association analysis, spanning from core formula mining from clinical data to the exploration of therapeutic targets for disease treatment. TCMNPAS serves as a powerful platform for uncovering the combinatorial rules and mechanism of TCM formulas holistically. We distribute TCMNPAS within an open-source R package at GitHub ( https://github.com/yangpluszhu/tcmnpas ), and the project is freely available at http://54.223.75.62:3838/ .

Uncovering the antimalarial potential of toad venoms through a bioassay-guided fractionation process

Malaria remains to date one of the most devastating parasitic diseases worldwide. The fight against this disease is rendered more difficult by the emergence and spread of drug-resistant strains. The need for new therapeutic candidates is now greater than ever. In this study, we investigated the antiplasmodial potential of toad venoms. The wide array of bioactive compounds present in Bufonidae venoms has allowed researchers to consider many potential therapeutic applications, especially for cancers and infectious diseases. We focused on small molecules, namely bufadienolides, found in the venom of Rhinella marina (L.). The developed bio-guided fractionation process includes a four solvent-system extraction followed by fractionation using flash chromatography. Sub-fractions were obtained through preparative TLC. All samples were characterized using chromatographic and spectrometric techniques and then underwent testing on in vitro Plasmodium falciparum cultures. Two strains were considered: 3D7 (chloroquine-sensitive) and W2 (chloroquine-resistant). This strategy highlighted a promising activity for one compound named resibufogenin. With IC50 values of (29 ± 8) μg/mL and (23 ± 1) μg/mL for 3D7 and W2 respectively, this makes it an interesting candidate for further investigation. A molecular modelling approach proposed a potential binding mode of resibufogenin to Plasmodium falciparum adenine-triphosphate 4 pump as antimalarial drug target.

Comparison of ATP-binding pockets and discovery of homologous recombination inhibitors

The ATP binding sites of many enzymes are structurally related, which complicates their development as therapeutic targets. In this work, we explore a diverse set of ATPases and compare their ATP binding pockets using different strategies, including direct and indirect structural methods, in search of pockets attractive for drug discovery. We pursue different direct and indirect structural strategies, as well as ligandability assessments to help guide target selection. The analyses indicate human RAD51, an enzyme crucial in homologous recombination, as a promising, tractable target. Inhibition of RAD51 has shown promise in the treatment of certain cancers but more potent inhibitors are needed. Thus, we design compounds computationally against the ATP binding pocket of RAD51 with consideration of multiple criteria, including predicted specificity, drug-likeness, and toxicity. The molecules designed are evaluated experimentally using molecular and cell-based assays. Our results provide two novel hit compounds against RAD51 and illustrate a computational pipeline to design new inhibitors against ATPases.

Parallel multi-swarm cooperative particle swarm optimization for protein-ligand docking and virtual screening

BACKGROUND

A high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement.

RESULTS

In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein-ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs.

CONCLUSION

The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein-ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina .

Evidence that nuclear receptors are related to terpene synthases

Ligand-activated nuclear receptors (NRs) orchestrate development, growth, and reproduction across all animal lifeforms - the Metazoa - but how NRs evolved remains mysterious. Given the NR ligands including steroids and retinoids are predominantly terpenoids, we asked whether NRs might have evolved from enzymes that catalyze terpene synthesis and metabolism. We provide evidence suggesting that NRs may be related to the terpene synthase (TS) enzyme superfamily. Based on over 10,000 3D structural comparisons, we report that the NR ligand-binding domain and TS enzymes share a conserved core of seven α-helical segments. In addition, the 3D locations of the major ligand-contacting residues are also conserved between the two protein classes. Primary sequence comparisons reveal suggestive similarities specifically between NRs and the subfamily of cis-isoprene transferases, notably with dehydrodolichyl pyrophosphate synthase and its obligate partner, NUS1/NOGOB receptor. Pharmacological overlaps between NRs and TS enzymes add weight to the contention that they share a distant evolutionary origin, and the combined data raise the possibility that a ligand-gated receptor may have arisen from an enzyme antecedent. However, our findings do not formally exclude other interpretations such as convergent evolution, and further analysis will be necessary to confirm the inferred relationship between the two protein classes.

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

Siteng Fang Reverses Multidrug Resistance in Gastric Cancer: A Network Pharmacology and Molecular Docking Study

Siteng Fang (STF) has been shown to inhibit migration, invasion, and adhesion as well as promote apoptosis in gastric cancer (GC) cells. However, whether it can reverse the multidrug resistance (MDR) of GC to chemotherapy drugs is unknown. Thus, we aimed to elucidate the mechanism of STF in reversing the MDR of GC. The chemical composition of STF and genes related to GC were obtained from the TCMNPAS(TCM Network Pharmacology Analysis System, TCMNPAS) Database, and the targets of the active ingredients were predicted using the Swiss Target Prediction Database. The obtained data were mapped to obtain the key active ingredients and core targets of STF in treating GC. The active component-target network and protein interaction network were constructed by Cytoscape and String database, and the key genes and core active ingredients were obtained. The biological functions and related signal pathways corresponding to the key targets were analyzed and then verified via molecular docking. A total of 14 core active ingredients of STF were screened, as well as 20 corresponding targets, which were mainly enriched in cancer pathway, proteoglycan synthesis, PI3K-AKT signaling pathway, and focal adhesion. Molecular docking showed that the core active ingredients related to MDR, namely quercetin and diosgenin, could bind well to the target. In summary, STF may reverse the MDR of GC and exert synergistic effect with chemotherapeutic drugs. It mediates MDR mainly through the action of quercetin and diosgenin on the PI3K/AKT signaling pathway. These findings are the first to demonstrate the molecular mechanism of STF in reversing MDR in GC, thus providing a direction for follow-up basic research.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

BACKGROUND

The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities.

OBJECTIVE

Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures.

METHODS

We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models.

RESULTS

Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data.

CONCLUSION

Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

Bypassing the requirement for aminoacyl-tRNA by a cyclodipeptide synthase enzyme

Cyclodipeptide synthases (CDPSs) produce a variety of cyclic dipeptide products by utilising two aminoacylated tRNA substrates. We sought to investigate the minimal requirements for substrate usage in this class of enzymes as the relationship between CDPSs and their substrates remains elusive. Here, we investigated the Bacillus thermoamylovorans enzyme, BtCDPS, which synthesises cyclo(l-Leu–l-Leu). We systematically tested where specificity arises and, in the process, uncovered small molecules (activated amino esters) that will suffice as substrates, although catalytically poor. We solved the structure of BtCDPS to 1.7 Å and combining crystallography, enzymatic assays and substrate docking experiments propose a model for how the minimal substrates interact with the enzyme. This work is the first report of a CDPS enzyme utilizing a molecule other than aa-tRNA as a substrate; providing insights into substrate requirements and setting the stage for the design of improved simpler substrates.

Cyclodipeptide synthases recognize a minimalistic substrate to produce cyclic dipeptides in a tRNA-independent manner.

Identification of novel potential ricin inhibitors by virtual screening, molecular docking, molecular dynamics and MM-PBSA calculations: a drug repurposing approach

Ricin is a potent cytotoxin with no available antidote. Its catalytic subunit, RTA, damages the ribosomal RNA (rRNA) of eukaryotic cells, preventing protein synthesis and eventually leading to cell death. The combination between easiness of obtention and high toxicity turns ricin into a potential weapon for terrorist attacks, urging the need of discovering effective antidotes. On this context, we used computational techniques, in order to identify potential ricin inhibitors among approved drugs. Two libraries were screened by two different docking algorithms, followed by molecular dynamics simulations and MM-PBSA calculations in order to corroborate the docking results. Three drugs were identified as potential ricin inhibitors: deferoxamine, leucovorin and plazomicin. Our calculations showed that these compounds were able to, simultaneously, form hydrogen bonds with residues of the catalytic site and the secondary binding site of RTA, qualifying as potential antidotes against intoxication by ricin.Communicated by Ramaswamy H. Sarma.

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking

Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, gwovina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, gwovina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, gwovina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, gwovina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. gwovina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.