LDLR protein is a single-chain glycoprotein, widely distributed in various cells and tissues, and is structurally divided into 5 regions, including N-terminal ligand binding domain, EGF-precursor homology domain, O-linked Carbohydrate, transmembrane, and cytoplasmic domains. LDLR regulates plasma cholesterol levels and plays a key role in cholesterol balance in the body. Numerous studies have shown an inverse relationship between cholesterol levels and cancer risk. Cholesterol is an essential structural component of most eukaryotic cell membranes. On the one hand, cholesterol levels are critical for cellular functions, from controlling membrane fluidity and stiffness to directly affecting signal transduction and protein interactions. On the other hand, cholesterol oxidation products, namely oxysterols, have inhibitory effects on cell growth, promote apoptosis, and inhibit antitumor responses. Cancer cells exhibit different metabolic demands for rapid proliferation than non-malignant cells. Therefore, the overproduction of LDLR is an important mechanism for cancer cells to obtain more essential fatty acids and cholesterol through LDLR endocytosis, which can promote the tumor cell growth. Overexpression of LDLR is present in a variety of malignancies and is associated with rapid tumor proliferation. Studies have found that high expression of LDLR is associated with reduced recurrence-free survival in breast cancer patients, suggesting that LDLR is associated with breast cancer. High levels of LDLR expression are also associated with colorectal adenomas and colorectal cancer. LDLR is a prognostic indicator of survival in patients with small cell lung cancer with high LDLR expression. Therefore, in order to achieve the purpose of curing cancer, targeting LDLR as a drug is an excellent choice. CD ComputaBio provides LDLR targeting services to customers to accelerate their research progress.

Figure 1. Domain organization of LDLr and LDLr pathway and its dysregulation by defective mutations. (Asier Benito-Vicente, et al.; 2018)

Our Services

Binding site recognition

• Binding pocket searches using known ligands
• Binding pocket recognition by pharmacophore generation
• Binding pocket identification by fragment screening

Database filtering

• Receptor-based 3D pharmacophore models
• Ligand formation using existing technology (catalysts, etc.)
• Ligand ranking

Ligand optimization

• Qualitative ligand optimization via FragMaps visualization
• Quantitative assessment of the contribution of ligand atoms to binding
• Quantitative estimation of relative ligand affinity
• Quantitative estimation of chemical transformations of bulk ligands

Fragment-Based Ligand Design

• Identification of fragment binding sites
• Estimation of ligand affinity after fragment ligation
• Expansion of fragment types

Our Computational Chemistry Tools

• PyMOL
• Schrödinger Drug Discovery Suite
• CCG MOE
• Dotmatics Vortex
• ChemAxon JChem Suite
• Cresset SPARK

Our Capabilities

In each therapeutic area, CD ComputaBio has accumulated deep expertise in discovery informatics, computational chemistry/molecular modeling, medicinal chemistry, structural biology, in vivo and in vitro pharmacology, and translational science. During the drug discovery process, our team focuses on early lead compounds in different target classes and uses a wide range of techniques, including molecular screening, molecular modeling, medicinal chemistry, structural biology, bioinformatics and computational chemistry, to identify new target the direction of drug development, and then select suitable drug candidates through low-cost, high-efficiency computer simulations to ensure high efficiency and low risk in the later drug development process. Our computational biology team has extensive experience in the research of LDLR targets. Please consult our professional team for details.

References

1. Wattis JA, et al.; Mathematical model for low density lipoprotein (LDL) endocytosis by hepatocytes. Bull Math Biol. 2008, 70(8): 2303-33.
2. Furuya Y, et al.; Low-density lipoprotein receptors play an important role in the inhibition of prostate cancer cell proliferation by statins. Prostate Int. 2016, 4(2): 56-60.
3. Asier Benito-Vicente, et al.; Validation of LDLr Activity as a Tool to Improve Genetic Diagnosis of Familial Hypercholesterolemia: A Retrospective on Functional Characterization of LDLr Variants. Int. J. Mol. Sci. 2018, 19(6), 1676.