A New Computer Model for Evaluating the Selective Binding Affinity of Phenylalkylamines to T-Type Ca2+ Channels

Abstract

To establish a computer model for evaluating the binding affinity of phenylalkylamines (PAAs) to T-type Ca2+ channels (TCCs), we created new homology models for both TCCs and a L-type calcium channel (LCC). We found that PAAs have a high affinity for domains I and IV of TCCs and a low affinity for domains III and IV of the LCC. Therefore, they should be considered as favorable candidates for TCC blockers. The new homology models were validated with some commonly recognized TCC blockers that are well characterized. Additionally, examples of the TCC blockers created were also evaluated using these models.

Seeking the exclusive binding region of phenylalkylamine derivatives on human T‐type calcium channels via homology modeling and molecular dynamics simulation approach

“…In comparison, the mibefradil is less likely to place at the center of the channel pore after binding to the amino acid (Figure 2C). The predicted binding affinities between testing drugs and TCCs from 2.67 to 25.99 mM (Figure 2D) are matched to other published studies 14,38–40 …”

“…The predicted binding affinities between testing drugs and TCCs from 2.67 to 25.99 mM (Figure 2D) are matched to other published studies. 14,[38][39][40]…”

“…FEP analysis finds in water a mutation caused total Gibbs-free energy to increase by 43.83,40.52, and 36.9 kcal/mol on α 1 G, α 1 H, and α 1 I, respectively. In vacuum, these numbers become 73.96, 43.58, and 62.27 kcal/mol on α 1 G, α 1 H, and α 1 I, respectively.…”

CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization.

“…Most methods define complexity as a function of the presence of features deemed to be complex or infrequent such as chiral centres, uncommon moieties, or unusual molecular fragments. One of the most popular measures of SA (Omolabi et al, 2021;Basu et al, 2020;Lu and Li, 2021;Imrie et al, 2021a;Humbeck et al, 2018), SAscore (Ertl and Schuffenhauer, 2009) is a complexity-based method that uses the rarity of fragments found in PubChem (Kim et al, 2016) and a set of predefined properties such as the ring complexity or the number of stereo centres to calculate its score. Another commonly used SA score, SCScore (Coley et al, 2018) employs an indirect estimation of complexity assuming that the complexity of the reactants is never larger than the complexity of the products.…”

“…Due primarily to their simplicity and speed, SAscore and SCScore have been used extensively across drug development pipelines including for compound screening (e.g., Omolabi et al, 2021;Basu et al, 2020;Lu and Li, 2021;Huang et al, 2019), dataset preparation (e.g., Imrie et al, 2021b;Humbeck et al, 2018) and molecule generation/optimization (e.g., Leguy et al, 2020;Zhou et al, 2019;Khemchandani et al, 2020a;Green et al, 2020). SAScore is one of the most popular metrics for biasing or discarding potentially infeasible compounds in methods for computational generation of de novo molecules (e.g., Yassine et al, 2021;Imrie et al, 2020;Prykhodko et al, 2019;Leguy et al, 2020;Khemchandani et al, 2020b).…”