Explore chapters and articles related to this topic
The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
The advantages of screening low-molecular-weight fragment-based libraries compared to traditional higher-molecular-weight chemical libraries include the potential identification of hydrophilic hits in which hydrogen bonding is more likely to contribute to affinity (i.e., enthalpically driven binding). After this, binding affinity can be further enhanced by adding hydrophobic groups (i.e., to promote entropically driven binding). Furthermore, starting with a hydrophilic ligand increases the chances that the final optimized ligand will not be too hydrophobic overall (i.e., log P < 5). Furthermore, a higher ligand efficiency means that the final optimized ligand will most likely have a relatively low molecular weight (i.e., <500). Since, in theory, two to three fragments can be combined to form an optimized ligand, proponents of this methodology claim that screening a fragment library of “n” compounds is equivalent to screening 2n–3n compounds in a traditional library. Also, fragments are less likely to contain sterically blocking groups that interfere with an otherwise favorable ligand–protein interaction.
Nanocarriers as an Emerging Platform for Cancer Therapy
Published in Lajos P. Balogh, Nano-Enabled Medical Applications, 2020
Dan Peer, Jeffrey M. Karp, Seungpyo Hong, Omid C. Farokhzad, Rimona Margalit, Robert Langer
It is generally known that higher binding affinity increases targeting efficacy. However, for solid tumours, there is evidence that high binding affinity can decrease penetration of nanocarriers due to a ‘binding-site barrier’, where the nanocarrier binds to its target so strongly that penetration into the tissue is prevented [16, 21]. In addition to enhanced affinity, multivalent binding effects (or avidity) may also be used to improve targeting. The collective binding in a multivalent interaction is much stronger than monovalent binding. For example, dendrimer nanocarriers conjugated to 3–15 folate molecules showed a 2,500–170,000-fold enhancement in dissociation constants (KD) over free folate when attaching to folate-binding proteins immobilized on a surface. This was attributed to the avidity of the multiple folic acid groups on the periphery of the dendrimers [22].
Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Quantitative analysis of affinity is derived using Position Specific Scoring Matrix (PSSM). Binding-affinity is approximated as the sum of the affinity-score of various matchings of the amino-acids under the assumption that each binding is independent of the other. PSSM optimization is the key to identifying the optimum affinity prediction.
Comprehensive engineering of a therapeutic neutralizing antibody targeting SARS-CoV-2 spike protein to neutralize escape variants
Published in mAbs, 2022
Taichi Kuramochi, Siok Wan Gan, Adrian W.S. Ho, Bei Wang, Nagisa Kageji, Takeru Nambu, Sayaka Iida, Momoko Okuda-Miura, Wei Shan Chia, Chiew Ying Yeo, Dan Chen, Wen-Hsin Lee, Eve Zi Xian Ngoh, Siti Nazihah Mohd Salleh, Cheng-I Wang, Tomoyuki Igawa, Hideaki Shimada
As it was not feasible to generate all possible combinations from the 29 mutations, we adopted a stepwise approach where combinations are incrementally introduced. This strategy is shown in Figure 1c. In the first round of combination, only two mutations were combined. We selectively picked the individual mutations that conferred a large increase of affinity improvement and generated 59 double combination variants. In addition to binding affinity, the physicochemical properties of each variant were assessed by SEC, thermal shift assay (TSA), extracellular matrix (ECM) binding, affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS) and hydrophobic interaction chromatography (HIC). Combination variants that showed additive or synergistic effect in binding affinity as well as acceptable physicochemical properties were shortlisted for further combinations. Conversely, combinations were discarded when there was little improvement in binding affinity or when the affinity improvement was associated with poor physicochemical behavior (Figure 1d). This process was repeated iteratively for nine cycles, with as many as 100 combination variants generated in one cycle until a final combination variant was obtained.
α-Amylase and dipeptidyl peptidase-4 (DPP-4) inhibitory effects of Melicope latifolia bark extracts and identification of bioactive constituents using in vitro and in silico approaches
Published in Pharmaceutical Biology, 2021
Alexandra Quek, Nur Kartinee Kassim, Pei Cee Lim, Dai Chuan Tan, Muhammad Alif Mohammad Latif, Amin Ismail, Khozirah Shaari, Khalijah Awang
In silico molecular docking of compounds, 1–4 and the standard acarbose was carried out in the active site of α-amylase (PDB ID 3BAJ), while the molecular docking of compound 3 and the standard sitagliptin was performed in the active sites of DPP-4 (PDB ID 1X70). The redocking of co-crystallized ligands (acarbose-derived pentasaccharide and sitagliptin) in their respective enzyme receptors was able to replicate the experimental bindings with root-mean-square deviation (RMSD) values of 1.12 and 0.54 Å, respectively (Supplemental Figure S5). The binding affinity of 1, 2, 3, and 4 with α-amylase were −9.3, −6.6, −5.7, and −6.0 kcal/mol, respectively. A lower or more negative value of the binding affinity indicates a stronger binding interaction between the compound and the enzyme. The complex formed between the standard acarbose and α-amylase gave a binding affinity of −9.1 kcal/mol. Meanwhile, the binding affinity of compound 3 with DPP-4 was −5.7 kcal/mol, which was higher than that of the standard sitagliptin (−8.6 kcal/mol). The 2D binding patterns and docking poses of the compounds with α-amylase and DPP-4 enzymes are shown in Figures 2 and 3, respectively. Their 3D interaction diagrams with the enzymes are shown in Supplemental Figures S6 and S7, and is further summarized in Supplemental Tables S1 and S2.
Recent advance in the discovery of tyrosinase inhibitors from natural sources via separation methods
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2021
Xiao-wei Zhang, Guang-li Bian, Pei-ying Kang, Xin-jie Cheng, Kai Yan, Yong-li Liu, Yan-xia Gao, De-qiang Li
Though affinity screening and inhibition profiling both have the obvious advantage of the rapid discovery of TYR inhibitors directly from natural sources without any tedious purification procedures, the two approaches are established based on two different, and in some cases complementary, principles. Affinity screening is based on the binding affinity of molecules to the enzyme regardless of their inhibitory potential. However, the false-positive results are commonly caused in affinity screening by the non-specific affinity between compounds and enzymatic non-active sites or the solid support. While, for the mode of inhibition profiling, the inhibitory activity of compounds was measured according to the catalytic properties of the enzyme, some compounds like tannins have also been shown to result in false-positive results because of their non-specific interactions with the enzyme. Instead of the present strategies using either one, the combination of affinity screening and inhibition profiling would provide an opportunity to narrow down the hits with presumed bioactivity, minimising the risk of false-positive results, the proof-of-concept of which was demonstrated by α-glucosidase inhibitors screening from the crude ethyl acetate extract of Ginkgo biloba71. Undoubtedly, the combined approach provides a powerful tool for the screening of specifically target bioactive compounds with both specific affinity and activity.