Generative Topographic Mapping of the Docking Conformational Space

Evaluation of the Mechanism of Yishan Formula in Treating Breast Cancer Based on Network Pharmacology and Experimental Verification

BACKGROUND

Yishan formula (YSF) has a significant effect on the treatment of breast cancer, which can improve the quality of life and prolong the survival of patients with breast cancer; however, its mechanism of action is unknown.

OBJECTIVE

In this study, network pharmacology and molecular docking methods have been used to explore the potential pharmacological effects of the YSF, and the predicted targets have been validated by in vitro experiments.

METHODS

Active components and targets of the YSF were obtained from the TCMSP and Swiss target prediction website. Four databases, namely GeneCards, OMIM, TTD, and DisGeNET, were used to search for disease targets. The Cytoscape v3.9.0 software was utilized to draw the network of drug-component-target and selected core targets. DAVID database was used to analyze the biological functions and pathways of key targets. Finally, molecular docking and in vitro experiments have been used to verify the hub genes.

RESULTS

Through data collection from the database, 157 active components and 618 genes implicated in breast cancer were obtained and treated using the YSF. After screening, the main active components (kaempferol, quercetin, isorhamnetin, dinatin, luteolin, and tamarixetin) and key genes (AKT1, TP53, TNF, IL6, EGFR, SRC, VEGFA, STAT3, MAPK3, and JUN) were obtained. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the YSF could affect the progression of breast cancer by regulating biological processes, such as signal transduction, cell proliferation and apoptosis, protein phosphorylation, as well as PI3K-Akt, Rap1, MAPK, FOXO, HIF-1, and other related signaling pathways. Molecular docking suggested that IL6 with isorhamnetin, MAPK3 with kaempferol, and EGFR with luteolin have strong binding energy. The experiment further verified that YSF can control the development of breast cancer by inhibiting the expression of the hub genes.

CONCLUSION

This study showed that resistance to breast cancer may be achieved by the synergy of multiple active components, target genes, and signal pathways, which can provide new avenues for breast cancer-targeted therapy.

Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking

In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.

Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling

Generative Topographic Mapping (GTM) can be efficiently used to visualize, analyze and model large chemical data. The GTM manifold needs to span the chemical space deemed relevant for a given problem. Therefore, the Frame set (FS) of compounds used for the manifold construction must well cover a given chemical space. Intuitively, the FS size must raise with the size and diversity of the target library. At the same time, the GTM training can be very slow or even becomes technically impossible at FS sizes of the order of 105 compounds - which is a very small number compared to today's commercially accessible compounds, and, especially, to the theoretically feasible molecules. In order to solve this problem, we propose a Parallel GTM algorithm based on the merging of "intermediate" manifolds constructed in parallel for different subsets of molecules. An ensemble of these subsets forms a FS for the "final" manifold. In order to assess the efficiency of the new algorithm, 80 GTMs were built on the FSs of different sizes ranging from 10 to 1.8 M compounds selected from the ChEMBL database. Each GTM was challenged to build classification models for up to 712 biological activities (depending on the FS size). With the novel parallel GTM procedure, we could thus cover the entire spectrum of possible FS sizes, whereas previous studies were forced to rely on the working hypothesis that FS sizes of few thousands of compounds are sufficient to describe the ChEMBL chemical space. In fact, this study formally proves this to be true: a FS containing only 5000 randomly picked compounds is sufficient to represent the entire ChEMBL collection (1.8 M molecules), in the sense that a further increase of FS compound numbers has no benefice impact on the predictive propensity of the above-mentioned 712 activity classification models. Parallel GTM may, however, be required to generate maps based on very large FS, that might improve chemical space cartography of big commercial and virtual libraries, approaching billions of compounds.

Generative topographic mapping in drug design

This is a review article of Generative Topographic Mapping (GTM) - a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces - and its specific applications in Drug Design (chemical space cartography, compound library design and analysis, virtual screening, pharmacological profiling, de novo drug design, conformational space & docking interaction cartography, etc.) Written by chemoinformaticians for potential users among medicinal chemists and biologists, the article purposely avoids all underlying mathematics. First, the GTM concept is intuitively explained, based on the strong analogies with the rather popular Self-Organizing Maps (SOMs), which are well established library analysis tools. GTM is basically a fuzzy-logics-based generalization of SOMs. The second part of the review, some of published GTM applications in drug design are briefly revisited.