Molecular similarity of MDR inhibitors

Everyone is free to re-use the published material if proper accreditation/citation of the original publication is given. http://creativecommons.org/licences/by/3.0/The molecular similarity of multidrug resistance (MDR) inhibitors was evaluated using the point centred atom charge approach in an attempt to find some common features of structurally unrelated inhibitors. A series of inhibitors of bacterial MDR were studied and there is a high similarity between these in terms of their shape, presence and orientation of aromatic ring moieties. A comparison of the lipophilic properties of these molecules has also been conducted suggesting that this factor is important in MDR inhibition.Peer reviewe

In silico structural evaluation of short cationic antimicrobial peptides

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Cationic peptides with antimicrobial properties are ubiquitous in nature and have been studied for many years in an attempt to design novel antibiotics. However, very few molecules are used in the clinic so far, sometimes due to their complexity but, mostly, as a consequence of the unfavorable pharmacokinetic profile associated with peptides. The aim of this work is to investigate cationic peptides in order to identify common structural features which could be useful for the design of small peptides or peptido-mimetics with improved drug-like properties and activity against Gram negative bacteria. Two sets of cationic peptides (AMPs) with known antimicrobial activity have been investigated. The first reference set comprised molecules with experimentally-known conformations available in the protein databank (PDB), and the second one was composed of short peptides active against Gram negative bacteria but with no significant structural information available. The predicted structures of the peptides from the first set were in excellent agreement with those experimentally-observed, which allowed analysis of the structural features of the second group using computationally-derived conformations. The peptide conformations, either experimentally available or predicted, were clustered in an “all vs. all” fashion and the most populated clusters were then analyzed. It was confirmed that these peptides tend to assume an amphipathic conformation regardless of the environment. It was also observed that positively-charged amino acid residues can often be found next to aromatic residues. Finally, a protocol was evaluated for the investigation of the behavior of short cationic peptides in the presence of a membrane-like environment such as dodecylphosphocholine (DPC) micelles. The results presented herein introduce a promising approach to inform the design of novel short peptides with a potential antimicrobial activity.Peer reviewedFinal Published versio

In silico and in vitro approaches to develop Dimethylarginine dimethylaminohydrolase-1 inhibitors

Introduction: Dimethylarginine dimethylaminohydrolases (DDAH) metabolise the endogenous nitric oxide synthase (NOS) inhibitors: asymmetric dimethylarginine (ADMA) and monomethylarginine1. In sepsis excessive nitric oxide partially contributes to acute circulatory failure, and pharmacological DDAH1 inhibition has been proposed in order to increase methylarginines and reduce NO levels 2. The SR257 arginine analogue, with NG-methoxyethyl substituent, inhibits DDAH1 with an IC50 22 µM without directly inhibiting NOSs1,3. Methods: Acyclic and cyclic NG,NG-disubstituted arginines were made as previously described4 using Katritzky’s synthesis preparing trisubstituted guanidines from di-(benzotriazol-1-yl)methanimine5. Molecular docking was employed to explore interactions of these NG,NG-disubstituted arginines with human DDAH1 (PDB 2JAJ) using Glide (Schroedinger6) and Autodock47. The published SR257 ligand was used to define the binding site with both software tools. Recombinant human DDAH1 activity was measured using colorometric citrulline assay8 containing ADMA (100 µM), sodium phosphate (10 mM pH7.4); with symmetric dimethylarginine (100 µM), not a substrate for DDAH1, as blank. Experiments were carried out in duplicate, and repeated on at least 3 separate occasions. Results: Recombinant DDAH1 activity was reduced to less than 25% of control (ADMA substrate, 100 µM) in the presence of 100 µM piperidinyl, methoxyethyl/methyl, N-methylpiperazinyl, with morpholinyl and pyrrolidinyl substituents reducing activity to less than 10% of control. The in silico Glide docking score and predicted Autodock4 binding energy for human DDAH1 (PDB, 2JAJ) for the known SR257 DDAH1 inhibitor and NG,NG-disubstituted arginines are shown in the table: Conclusion: Both Autodock4 and Glide docking predicted higher binding energies for morpholinyl, pyrrolidinyl and piperinyl than the known SR257 compound. In vitro assays confirmed these NG,NG-disubstituted arginines reduced DDAH1 activity. There was variation between Glide and Autodock4 in the docking predictions for methoxyethyl/methyl and N-methylpiperazinyl. In silico prediction of DDAH1-ligand interactions may assist in the future design and development of novel NG,NG-disubstituted arginines. References: 1 Leiper, J. et al. (2007) Nat Med. 13:198-203. 2 Wang, Z et al. (2014) Biochem J. 460:309 3 Rossiter, S. et al. (2005) J Med Chem. 48:4670-4678. 4 Morfill, C et al. (2012) http://www.pA2online.org/abstracts/Vol10Issue4abst197P.pdf 5 Katritzky, A et al. (2000) J. Org. Chem. 65: 8080-8082. 6 Friesner, RA et al. (2006) J Med Chem. 49:6177-6196. 7 Morris, GM et al. (2009) J. Comp. Chem. 16:2785-91. 8 Knipp, M & Vasak, M (2000) Anal Biochem 286:257-64

Deep learning for novel antimicrobial peptide design

Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic. The need for new antibiotics is high, while the arsenal of available agents is decreasing, especially for the treatment of infections by Gram-negative bacteria like Escherichia coli. Antimicrobial peptides (AMPs) are offering a promising route for novel antibiotic development and deep learning techniques can be utilised for successful AMP design. In this study, a long short-term memory (LSTM) generative model and a bidirectional LSTM classification model were constructed to design short novel AMP sequences with potential antibacterial activity against E. coli. Two versions of the generative model and six versions of the classification model were trained and optimised using Bayesian hyperparameter optimisation. These models were used to generate sets of short novel sequences that were classified as antimicrobial or non-antimicrobial. The validation accuracies of the classification models were 81.6–88.9% and the novel AMPs were classified as antimicrobial with accuracies of 70.6–91.7%. Predicted three-dimensional conformations of selected short AMPs exhibited the alpha-helical structure with amphipathic surfaces. This demonstrates that LSTMs are effective tools for generating novel AMPs against targeted bacteria and could be utilised in the search for new antibiotics leads