Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find new antibacterial strategies. Natural antimicrobials, known as host defence peptides or antimicrobial peptides, defend host organisms against microbes but most have modest direct antibiotic activity. Enhanced variants have been developed using straightforward design and optimization strategies and are being tested clinically. Here, we describe advanced computer-assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.

Malaria blood stage parasites export a large number of proteins into their host erythrocyte to change it from a container of predominantly hemoglobin optimized for the transport of oxygen into a niche for parasite propagation. To understand this process, it is crucial to know which parasite proteins are exported into the host cell. This has been aided by the PEXEL/HT sequence, a five-residue motif found in many exported proteins, leading to the prediction of the exportome. However, several PEXEL/HT negative exported proteins (PNEPs) indicate that this exportome is incomplete and it remains unknown if and how many further PNEPs exist. Here we report the identification of new PNEPs in the most virulent malaria parasite Plasmodium falciparum. This includes proteins with a domain structure deviating from previously known PNEPs and indicates that PNEPs are not a rare exception. Unexpectedly, this included members of the MSP-7 related protein (MSRP) family, suggesting unanticipated functions of MSRPs. Analyzing regions mediating export of selected new PNEPs, we show that the first 20 amino acids of PNEPs without a classical N-terminal signal peptide are sufficient to promote export of a reporter, confirming the concept that this is a shared property of all PNEPs of this type. Moreover, we took advantage of newly found soluble PNEPs to show that this type of exported protein requires unfolding to move from the parasitophorous vacuole (PV) into the host cell. This indicates that soluble PNEPs, like PEXEL/HT proteins, are exported by translocation across the PV membrane (PVM), highlighting protein translocation in the parasite periphery as a general means in protein export of malaria parasites.

Supplementary data are available at Bioinformatics online.