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A New Computer Model for Evaluating the Selective Binding Affinity Of pharmaceuticals Article A New Computer Model for Evaluating the Selective Binding Affinity of Phenylalkylamines to T-Type Ca2+ Channels You Lu 1 and Ming Li 2,* 1 Center for Aging, School of Medicine, Tulane University, New Orleans, LA 70112, USA; [email protected] 2 Department of Physiology, School of Medicine, Tulane University, New Orleans, LA 70112, USA * Correspondence: [email protected]; Tel.: +1-504-988-8207 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. Keywords: T-type calcium channel blocker; homology modeling; computer-aid drug design; virtual drug screening; L-type calcium channel 1. Introduction Citation: Lu, Y.; Li, M. A New As the only type of voltage-gated Ca2+ channels that are activated at or near resting Computer Model for Evaluating the membrane potentials, T-type Ca2+ channels (TCCs) play an important role in regulat- Selective Binding Affinity of 2+ ing [Ca ]i homeostasis in a variety of tissues, including pancreatic β-cells and tumor Phenylalkylamines to T-Type Ca2+ cells [1–4]. Therefore, TCC antagonists could be potentially useful for the treatment of Channels. Pharmaceuticals 2021, 14, chronic diseases associated with Ca2+ dysregulation [5–7]. For this reason, it is imperative 141. https://doi.org/10.3390/ to develop more selective TCC antagonists for prospective clinical applications. Since ph14020141 many existing TCC blockers, such as mibefradil, also show inhibitory effects on L-type calcium channels (LCCs), the most important task in developing new TCC blockers is Academic Editor: Osvaldo to enhance their selectivity to TCCs over LCCs. To achieve this, we established TCC– Andrade Santos-Filho phenylalkylamine interaction models based on the specific amino acid sequences in the Received: 27 January 2021 P-loop of TCCs, and α C LCCs for characterizing the drug molecules’ affinities for TCCs Accepted: 8 February 2021 1 Published: 10 February 2021 and LCCs, respectively. TCCs have a close evolutional relationship with LCCs. A recent report from a cryo- α Publisher’s Note: MDPI stays neutral electron microscopy study reveals that the frame of the 1G (Cav3.1) pore domain structure α with regard to jurisdictional claims in is similar to that of 1S (Cav1.1) [8]. This similarity allowed us to confidently adopt the published maps and institutional affil- global structure of the calcium channel CavAb model, constructed based upon Arcobacter iations. butzleri crystallization [9], in the establishment of our TCC model. One of the most remarkable differences between all types of TCCs and LCCs is a lysine residue located adjacent to the critical glutamic acid/aspartic acid residue in domain III. The existence of a positively charged lysine (K3p49) may swing the aspartic acid (D3p50) away from the center of the calcium filter and change the preferred calcium ion and drug binding sites Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. from domains III and IV for LCCs to domains I and IV for TCCs. Therefore, we used the This article is an open access article ZMM molecule modeling program [10–12] to create four-domain TCC models, in which distributed under the terms and the binding affinities of drugs to TCCs and α1C LCC were determined by scoring their free conditions of the Creative Commons energy in binding to the channels [13]. Attribution (CC BY) license (https:// The new TCC models are adopted from a drug–protein interaction framework for creativecommons.org/licenses/by/ modeling CavAb blocking by phenylalkylamines (PAAs) [9]. This is rational because 4.0/). many TCC blockers are PAAs or their derivatives, and because PAAs block CavAb [9,14]. Pharmaceuticals 2021, 14, 141. https://doi.org/10.3390/ph14020141 https://www.mdpi.com/journal/pharmaceuticals Pharmaceuticals 2021, 14, 141 2 of 16 It is proposed that PAAs bind to LCCs in an inverse V-shaped configuration, with the Pharmaceuticals 2021, 14, x FOR PEER REVIEW 2 of 15 ammonium group towards the P-helices, and the nitrile group bound to the calcium ion coordinated by the selectivity filter glutamates in domains III and IV of the LCC [15]. We reason that this is also true for TCC blockers, except that the calcium ion is coordinated many TCC blockers are PAAs or their derivatives, and because PAAs block CavAb [9,14]. in the cavity betweenIt is domains proposed Ithat and PAAs IV, sincebind to the LCCs depolarization in an inverse V-shaped confirmed configuration, that the with pore the domains of CaV3.1ammonium and CaV 1.1group are towards superimposed the P-helices, [8 and]. Asthe anitrile result, group the bound two to rings the calcium of the ion flexible PAA moleculecoordinated [15] will by the make selectivity hydrogen filter glutamates bonding in contactsdomains III with and IV the of the mobile LCC [15]. side We chains of relevant aminoreason that acids this is from also true domains for TCC I blockers, and IV except of TCCs. that the This calcium strategy ion is coordinated allowed in the cavity between domains I and IV, since the depolarization confirmed that the pore us to create computerdomains models of Ca forV3.1 simulating and CaV1.1 are the superimposed interactions [8]. betweenAs a result, drugs the two and rings channel of the flex- receptors for LCC andible TCCs,PAA molecule respectively. [15] will make hydrogen bonding contacts with the mobile side chains of relevant amino acids from domains I and IV of TCCs. This strategy allowed us to create 2. Results computer models for simulating the interactions between drugs and channel receptors for LCC and TCCs, respectively. 2.1. Homology Modeling of TCCs and a1C LCC Using the bacterial2. Results calcium CavAb open channel 3D structure as the input, ZMM generated the first template2.1. Homology of Modeling the calcium of TCCs channel, and α1C LCC which was then modified with S5-P- loop-S6 segments of α1C,Using and theα bacterial1G, α1H, calcium and αCa1vIAb (Table open 1channel) to create 3D structure corresponding as the input, protein ZMM gen- structures of Cav1.2erated LCC andthe first Ca templatev3.1, Ca ofv 3.2,the calcium and Ca chav3.3nnel, TCCs, which respectively was then modified (Figure with1 S5-P-loop-A–D ). For cross-validationS6 of segments ZMM-generated of α1C, and α 3D1G, α structure1H, and α1I models,(Table 1) to we create also corresponding performed protein ab initio struc- tures of Cav1.2 LCC and Cav3.1, Cav3.2, and Cav3.3 TCCs, respectively (Figure 1A–D). For α α modeling of 1C andcross-validation1G calcium of ZMM-generated channels. Since 3D struct thereure ismodels, a considerable we also performed overlap ab initio of PAA inhibition betweenmodeling LCC of α1 andC and TCCs α1G calcium [9], the channels. allosteric Since structuresthere is a considerable of LCC overlap and TCCs of PAA are more likely to beinhibition similar. between Comparing LCC andtwo TCCs different [9], the allo homologysteric structures modeling of LCC and tools, TCCsZMM are more likely to be similar. Comparing two different homology modeling tools, ZMM generates generates more consistent 3D models of the domain III S5-P-loop-S6 segment of α1C and more consistent 3D models of the domain III S5-P-loop-S6 segment of α1C and α1G (Figure α G (Figure1E,F) than the ab initio method (Figure1G,H). 1 1E,F) than the ab initio method (Figure 1G,H). A B C D E F G H Figure 1. Top views of homology modeling results from ZMM for α1C, α1G, α1H, and α1I. (A–D) FigureThe structures 1. Top views of ofα homology1C, α1G, modelingα1H, and resultsα1 I,from respectively; ZMM for α1C, blue, α1G, α green,1H, and brown,α1I. (A–D and) The yellowstructures represent of α1C, αdomains1G, α1H, and I, II,α1I, III, respectively; and IV,respectively; blue, green, brown, the fourand yellow selectivity-determining represent domains I, II, aminoIII, and acidsIV, respectively; (glutamic the acid four or selectivity-determining amino acids (glutamic acid or aspartic acid) in the P-loop are colored red and displayed as spheres; ZMMaspartic generates acid) more in the consistent P-loop 3D are structure colored than red the and ab displayedinitio modeling as spheres;method for ZMM α1C and generates α1G; (E) the more predicted consistent 3D structure3D structure of the α than1C domain the abIII generated initio modeling by ZMM, the method glutamic for acidα1 isC represented and α1G; by (E red) the spheres; predicted (F) the predicted 3D structure 3D structure of the α1G domain III generated by ZMM, the lysine is represented by red spheres; (G) the most representative of the α C domain III generated by ZMM, the glutamic acid is represented by red spheres; (F) the structure selected1 by Calibur clustering analysis [16] of α1C domain III, the glutamic acid is represented by red spheres; (predictedH) the most 3Drepresentative structure structure of the αselected1G domain by Calibur III generatedclustering analysis by ZMM, of α1G thedomain lysine III, the is representedlysine is represented by red by red spheres. spheres; (G) the most representative structure selected by Calibur clustering analysis [16] of α1C domain III, the glutamic acid is represented by red spheres; (H) the most representative structure selected by Calibur clustering analysis of α1G domain III, the lysine is represented by red spheres.
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