Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.
Amitifadine, the only drug ever clinically tested in Phase 3 for treating depression, is a triple reuptake inhibitor (TRI) that simultaneously interacts with human monoamine transporters (MATs) including hSERT, hNET and hDAT. This novel multi-target strategy improves drug efficacy and reduces the toxic side effects of drugs. However, the binding modes accounting for amitifadine's polypharmacological mode of action are still elusive, and extensive exploration of the amitifadine-target interactions between amitifadine and MATs is urgently needed. In this study, a total of 0.63 μs molecular dynamics (MD) simulations with an explicit solvent as well as endpoint binding free energy (BFE) calculation were carried out. MD simulation results identified a shared binding mode involving eleven key residues at the S1 site of MATs for the binding of amitifadine, and the results of the BFE calculations were in good agreement with experimental reports. Moreover, by analyzing the per-residue energy contribution variation at the S1 site of three MATs and additional cross-mutagenesis simulations, the variation in the inhibition ratio of amitifadine between hSERT and two other MATs was discovered to mainly come from non-conserved residues (Y95, I172 and T439 in hNET and Y95, I172, A169 and T439 in hDAT). As the rational inhibition ratio of multi-target drugs among various therapeutic targets was found to be the key to their safety and tolerance, the findings of this study may further facilitate the rational design of more potent but less toxic multi-target antidepressant drugs.
Metabotropic glutamate receptor 5 (mGlu) plays a key role in synaptic information storage and memory, which is a well-known target for a variety of psychiatric and neurodegenerative disorders. In recent years, the increasing efforts have been focused on the design of allosteric modulators, and the negative allosteric modulators (NAMs) are the front-runners. Recently, the architecture of the transmembrane (TM) domain of mGlu receptor has been determined by crystallographic experiment. However, it has been not well understood how the pharmacophores of NAMs accommodated into the allosteric binding site. In this study, molecular dynamics (MD) simulations were performed on mGlu receptor bound with NAMs in preclinical or clinical development to shed light on this issue. In order to identify the key residues, the binding free energies as well as per-residue contributions for NAMs binding to mGlu receptor were calculated. Subsequently, the in silico site-directed mutagenesis of the key residues was performed to verify the accuracy of simulation models. As a result, the shared common features of the studied 5 clinically important NAMs (mavoglurant, dipraglurant, basimglurant, STX107, and fenobam) interacting with 11 residues in allosteric site were obtained. This comprehensive study presented a better understanding of mGlu receptor NAMs binding mechanism, which would be further used as a useful framework to assess and discover novel lead scaffolds for NAMs.
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