ConoServer (http://www.conoserver.org) is a database specializing in the sequences and structures of conopeptides, which are toxins expressed by marine cone snails. Cone snails are carnivorous gastropods, which hunt their prey using a cocktail of toxins that potently subvert nervous system function. The ability of these toxins to specifically target receptors, channels and transporters of the nervous system has attracted considerable interest for their use in physiological research and as drug leads. Since the founding publication on ConoServer in 2008, the number of entries in the database has nearly doubled, the interface has been redesigned and new annotations have been added, including a more detailed description of cone snail species, biological activity measurements and information regarding the identification of each sequence. Automatically updated statistics on classification schemes, three-dimensional structures, conopeptide-bearing species and endoplasmic reticulum signal sequence conservation trends, provide a convenient overview of current knowledge on conopeptides. Transcriptomics and proteomics have began generating massive numbers of new conopeptide sequences, and two dedicated tools have been recently implemented in ConoServer to standardize the analysis of conopeptide precursor sequences and to help in the identification by mass spectrometry of toxins whose sequences were predicted at the nucleic acid level.

With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure–activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4–8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.

α-Conotoxins potently inhibit isoforms of nicotinic acetylcholine receptors (nAChRs), which are essential for neuronal and neuromuscular transmission. They are also used as neurochemical tools to study nAChR physiology and are being evaluated as drug leads to treat various neuronal disorders. A number of experimental studies have been performed to investigate the structure-activity relationships of conotoxin/nAChR complexes. However, the structural determinants of their binding interactions are still ambiguous in the absence of experimental structures of conotoxin-receptor complexes. In this study, the binding modes of α-conotoxin ImI to the α7-nAChR, currently the best-studied system experimentally, were investigated using comparative modeling and molecular dynamics simulations. The structures of more than 30 single point mutants of either the conotoxin or the receptor were modeled and analyzed. The models were used to explain qualitatively the change of affinities measured experimentally, including some nAChR positions located outside the binding site. Mutational energies were calculated using different methods that combine a conformational refinement procedure (minimization with a distance dependent dielectric constant or explicit water, or molecular dynamics using five restraint strategies) and a binding energy function (MM-GB/SA or MM-PB/SA). The protocol using explicit water energy minimization and MM-GB/SA gave the best correlations with experimental binding affinities, with an R² value of 0.74. The van der Waals and non-polar desolvation components were found to be the main driving force for binding of the conotoxin to the nAChR. The electrostatic component was responsible for the selectivity of the various ImI mutants. Overall, this study provides novel insights into the binding mechanism of α-conotoxins to nAChRs and the methodological developments reported here open avenues for computational scanning studies of a rapidly expanding range of wild-type and chemically modified α-conotoxins. Conotoxins are peptide toxins extracted from the venom of carnivorous marine cone snails. Members of the α-conotoxin subfamily potently block nicotinic acetylcholine receptors (nAChRs), which are involved in signal transmission between two neurons or between neurons and muscle fibers. nAChRs are important pharmacological targets due to their involvement in the transmission of pain stimuli and also in numerous neurone diseases and disorders. Their potency and specificity have led to the development of α-conotoxins as drug leads, and also to their use in the investigation of the role of nAChRs in various physiological processes. The most studied conotoxin/nAChR system, ImI/α7, was modeled in this study, and several computational methods were tested for their ability to explain the perturbations observed experimentally after introducing single point mutations into either ImI or the α7 receptor. The aim of this study was to establish a theoretical basis to rapidly identify new α-conotoxin mutants that might have improved specificity and affinity for a given receptor subtype. Furthermore, hundreds of thousands of conotoxins are predicted to exist, and computational methods are needed to help streamline the discovery of their molecular targets.

Natural angiotensin converting enzyme (ACE)-inhibitory peptides, which are derived from marine products, are useful as antihypertensive drugs. Nevertheless, the activities of these natural peptides are relatively low, which limits their applications. The aim of this study was to prepare efficient ACE-inhibitory peptides from sea cucumber-modified hydrolysates by adding exogenous proline according to a facile plastein reaction. When 40% proline (w/w, proline/free amino groups) was added, the modified hydrolysates exhibited higher ACE-inhibitory activity than the original hydrolysates. Among the modified hydrolysates, two novel efficient ACE-inhibitory peptides, which are namely PNVA and PNLG, were purified and identified by a sequential approach combining a sephadex G-15 gel column, reverse-phase high-performance liquid chromatography (RP-HPLC) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS), before we conducted confirmatory studies with synthetic peptides. The ACE-inhibitory activity assay showed that PNVA and PNLG exhibited lower IC₅₀ values of 8.18 ± 0.24 and 13.16 ± 0.39 μM than their corresponding truncated analogs (NVA and NLG), respectively. Molecular docking showed that PNVA and PNLG formed a larger number of hydrogen bonds with ACE than NVA and NLG, while the proline at the N-terminal of peptides can affect the orientation of the binding site of ACE. The method developed in this study may potentially be applied to prepare efficient ACE-inhibitory peptides, which may play a key role in hypertension management.