APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures.

Good knowledge of a peptide's tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides.

Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.

ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides.

The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.

The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.

Structural and positional studies of the antimicrobial peptide brevinin-1BYa in membrane-mimetic environments.

Brevinin-1BYa (FLPILASLAAKFGPKLFCLVTKKC), first isolated from skin secretions of the foothill yellow-legged frog Rana boylii, shows broad-spectrum activity, being particularly effective against opportunistic yeast pathogens. The structure of brevinin-1BYa was investigated in various solution and membrane-mimicking environments by proton nuclear magnetic resonance (1 H-NMR) spectroscopy and molecular modelling. The peptide does not possess a secondary structure in aqueous solution. In a 33% 2,2,2-trifluoroethanol (TFE-d3 )-H2 O solvent mixture, as well as in membrane-mimicking sodium dodecyl sulfate and dodecylphosphocholine micelles, the peptide's structure is characterised by a flexible helix-hinge-helix motif, with the hinge located at the Gly13 /Pro14 residues, and the two α-helices extending from Pro3 to Phe12 and from Pro14 to Thr21 . Positional studies involving the peptide in sodium dodecyl sulfate and dodecylphosphocholine micelles using 5-doxyl-labelled stearic acid and manganese chloride paramagnetic probes show that the peptide's helical segments lie parallel to the micellar surface, with the residues on the hydrophobic face of the amphipathic helices facing towards the micelle core and the hydrophilic residues pointing outwards, suggesting that the peptide exerts its biological activity by a non-pore-forming mechanism.

Insights into conformation and membrane interactions of the acyclic and dicarba-bridged brevinin-1BYa antimicrobial peptides.

Brevinin-1BYa is a 24-amino acid residue host-defense peptide, first isolated from skin secretions of the foothill yellow-legged frog Rana boylii. The peptide is of interest, as it shows broad-spectrum antimicrobial activity, and is particularly effective against opportunistic yeast pathogens. Its potential for clinical use, however, is hindered by its latent haemolytic activity. The structures of two analogues, the less haemolytic [C18S,C24S]brevinin-1BYa and the more potent cis-dicarba-brevinin-1BYa, were investigated in various solution and membrane-mimicking environments by [Formula: see text] spectroscopy and molecular modelling techniques. Neither peptide possesses a secondary structure in aqueous solution. In both the membrane-mimicking sodium dodecyl sulphate micelles and 33% 2,2,2-trifluoroethanol ([Formula: see text] solvent mixture, the peptides' structures are characterised by two [Formula: see text]-helices connected by a flexible hinge located at the [Formula: see text] residues. With the aid of molecular dynamics simulations and paramagnetic probes, it was determined that the peptides' helical segments lie parallel to the micellar surface, with their hydrophobic residues facing towards the micelle core and the hydrophilic residues pointing outwards, suggesting that both peptides exert their biological activity by a non-pore-forming mechanism. Unlike that of the dicarba analogue, the C-terminus of the acyclic peptide is only weakly associated with the micellar surface and is in direct contact with the surrounding aqueous solvent.

NMR model structure of the antimicrobial peptide maximin 3.

Maximin 3 is a 27-residue-long cationic antimicrobial peptide found in the skin secretion and brain of the Chinese red-belly toad Bombina maxima. The peptide is of biological interest as it possesses anti-HIV activity, not found in the other maximin peptides, in addition to antimicrobial, antitumor and spermicidal activities. The three-dimensional structure of maximin 3 was obtained in a 50/50% water/2,2,2-trifluoroethanol-d3 mixture using two-dimensional NMR spectroscopy. Maximin 3 was found to adopt an α-helical structure from residue G1 to A22, and a coil structure with a helical propensity in the C-terminal tail. The peptide is amphipathic, showing a clear separation between polar and hydrophobic residues. Interactions with sodium dodecyl sulfate micelles, a widely used bacterial membrane-mimicking environment, were modeled using molecular dynamics simulations. The peptide maintained an α-helical conformation, occasionally displaying a flexibility around residues G9 and G16, which is likely responsible for the peptide's low haemolytic activity. It is found to preferentially adopt a position parallel to the micellar surface, establishing a number of hydrophobic and electrostatic interactions with it.