Computer-aided design, structural dynamics analysis, and in vitro susceptibility test of antibacterial peptides incorporating unnatural amino acids against microbial infections
Introduction
Infectious diseases caused by new or previously unrecognized microorganisms are a major problem worldwide. Although the term became part of the journalist's lexicon in the 1990s, emerging infectious diseases have long been recognized as an important outcome of host–pathogen evolution. Because infections may have severe public health consequences, they are a focus of both the popular press and the scientific research [1]. In the past 70 years, antibiotics have been essential in the fight against infectious diseases and have been a contributing factor in the rise in life expectancy. However, with the growing microbial resistance to conventional antimicrobial agents over the past decades, the need for unconventional therapeutic options has become urgent. The World Health Organisations' 2014 report on global surveillance of antimicrobial resistance reveals that antibiotic resistance is no longer a prediction for the future; it is happening right now, across the world, and is putting at risk the ability to treat common infections in both the community and the hospitals [2]. Discovery of new classes of therapeutic strategies to combat antibiotic resistance has long been a great attraction in the medicine community. Many efforts have been directed toward finding alternative antibiotics unaffected by resistance mechanisms.
Antibacterial peptides (ABPs) are a unique and assorted group of molecules produced by living organisms of all types, considered to be part of the host innate immunity. These peptides demonstrate potent antimicrobial activity and are rapidly mobilized to neutralize a broad range of microbes, including viruses, bacteria, protozoa, and fungi. More significantly, the ability of these natural molecules to kill multidrug-resistant microorganisms has gained them considerable attention and clinical interest [3]. ABPs are membrane active polypeptides with important functions in the innate host-defense system of many organisms, which can target and then destabilize the cell membrane of a variety of Gram-negative and Gram-positive bacteria. Although ABPs have become as a promising alternative to traditional antibiotics for treatment of bacterial diseases, many potential problems should be solved before they can be put in clinic and commerce, including low bioavailability, high production costs, toxicity against eukaryotic cells, susceptibility to proteolytic degradation and the development of allergies to these peptides [4]. In recent years, incorporating with unnatural amino acids has been developed as a new and promising strategy to improve the metabolic stability and pharmacokinetic profile of ABPs. ABP incorporating with unnatural amino acids (uABPs) has several advantages over naturally occurring peptides based on factors such as bioavailability, metabolic stability and overall toxicity [5].
Most currently existing methods cannot incorporate unnatural amino acids into natural peptides and often miss potent candidates because they are nonspecific and sensitive to physical conditions. In addition, although a number of experimental techniques such as peptide array have been used to successfully determine antibacterially preferable motifs, given the massive number of unnatural amino acid combinations, the accuracy of the motifs is still limited by the coverage of all possible peptides [6], [7], [8]. Attempts have been made to overcome such experimental issues by computer-aided approaches as linguistic model [9] and quantitative structure–activity relationship [10], which are benefited from the proliferation of known natural and synthetic AMP data and have thus become standard tools in the quest to develop novel peptide agents for treating bacterial infections [11], [12], [13], [14]. Recently, Xiong et al. have successfully combated multidrug resistance in microbial infections by targeting bacterial functional proteome [15] and by using designed antimicrobial peptoids [16], [17]. In the present study, we described a synthetic protocol to rationally develop uABPs by integrating computational modeling and experimental assay. We also performed structural analysis and dynamics simulation to investigate the intermolecular interaction behavior of several potent uABPs with an artificial lipid bilayer that mimics microbial membrane.
Section snippets
Molecular dynamics simulation
Molecular dynamics (MD) simulations of uABP interaction with microbial membrane were performed in GROMACS package [18] using the CHARMM27 force field [19]. The peptides were constructed as random structures and then equilibrated with MD simulations in implicit water environment. An artificial lipid bilayer made up of POPC lipids [20] surrounded by TIP3P water molecules [21] was set, and the initial position of equilibrated peptides was immersed in the water layer and parallel to the lipid
Machine learning regression modeling
The 1491 peptides in the sample set were randomly split into a training set and a test set consisting of 1000 and 491 individuals, respectively. Here, self-organizing map (SOM) [41] was run to project the 1000 training and 491 test peptides into a two-dimensional feature space. As can be seen in Fig. 2, training and test sets can well overlap with each other, indicating a high structural similarity between the training and test peptides. Subsequently, based on 1000 training peptides the machine
Conclusions
An integrated in silico–in vitro discovery of bioactive peptides was described to perform computer-aided rational design of antibacterial peptides incorporating unnatural amino acids (uABPs). In the procedure, statistical regression predictors were built based on prior knowledge and validated rigorously, which were then employed to direct machine iteration optimization of uABPs, attempting to obtain a new uABP population with improved antimicrobial potency. Eight uABPs with top scores were
Conflict of interest
The authors declare no conflict of interest.
Acknowledgments
This work was supported by the First People's Hospital of Jining, the Central Hospital of Jinan and Qilu Hospital.
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