Identifying antimicrobial peptides in genomes using machine learning

Legana Fingerhut used machine learning to improve predictions of antimicrobial peptides (AMPs) from protein sequences. Her associated framework was the first to specifically address the problem of identifying AMPs from whole-genome data. Her work leads to improved workflows for identifying novel AMPs which advances our understanding of the innate immune system

Bombardier Enables Delivery of Short-Form Bomanins in the Drosophila Toll Response.

Toll mediates a robust and effective innate immune response across vertebrates and invertebrates. In Drosophila melanogaster, activation of Toll by systemic infection drives the accumulation of a rich repertoire of immune effectors in hemolymph, including the recently characterized Bomanins, as well as the classical antimicrobial peptides (AMPs). Here we report the functional characterization of a Toll-induced hemolymph protein encoded by the bombardier (CG18067) gene. Using the CRISPR/Cas9 system to generate a precise deletion of the bombardier transcriptional unit, we found that Bombardier is required for Toll-mediated defense against fungi and Gram-positive bacteria. Assaying cell-free hemolymph, we found that the Bomanin-dependent candidacidal activity is also dependent on Bombardier, but is independent of the antifungal AMPs Drosomycin and Metchnikowin. Using mass spectrometry, we demonstrated that deletion of bombardier results in the specific absence of short-form Bomanins from hemolymph. In addition, flies lacking Bombardier exhibited a defect in pathogen tolerance that we trace to an aberrant condition triggered by Toll activation. These results lead us to a model in which the presence of Bombardier in wild-type flies enables the proper folding, secretion, or intermolecular associations of short-form Bomanins, and the absence of Bombardier disrupts one or more of these steps, resulting in defects in both immune resistance and tolerance

The cationic tetradecapeptide mastoparan as a privileged structure for drug discovery: Enhanced antimicrobial properties of mitoparan analogues modified at position-14

Mastoparan (MP) peptides, distributed in insect venoms, induce a local inflammatory response post envenomation. Most endogenous MPs share common structural elements within a tetradecapeptide sequence that adopts an amphipathic helix whilst traversing biological membranes and when bound to an intracellular protein target. Rational modifications to increase cationic charge density and amphipathic helicity engineered mitoparan (MitP), a mitochondriotoxic bioportide and potent secretagogue. Following intracellular translocation, MitP is accreted by mitochondria thus indicating additional utility as an antimicrobial agent. Hence, the objectives of this study were to compare the antimicrobial activities of a structurally diverse set of cationic cell penetrating peptides, including both MP and MitP sequences, and to chemically engineer analogues of MitP for potential therapeutic applications. Herein, we confirm that, like MP, MitP is a privileged structure for the development of antimicrobial peptides active against both prokaryotic and eukaryotic pathogens. Collectively, MitP and target-selective chimeric analogues are broad spectrum antibiotics, with the Gram-negative A. baumannii demonstrating particular susceptibility. Modifications of MitP by amino acid substitution at position-14 produced peptides, Δ14MitP analogues, with unique pharmacodynamic properties. One example, [Ser14]MitP, lacks both cytotoxicity against human cell lines and mast cell secretory activity yet retains selective activity against the encapsulated yeast C. neoformans

pLMFPPred: a novel approach for accurate prediction of functional peptides integrating embedding from pre-trained protein language model and imbalanced learning

Functional peptides have the potential to treat a variety of diseases. Their good therapeutic efficacy and low toxicity make them ideal therapeutic agents. Artificial intelligence-based computational strategies can help quickly identify new functional peptides from collections of protein sequences and discover their different functions.Using protein language model-based embeddings (ESM-2), we developed a tool called pLMFPPred (Protein Language Model-based Functional Peptide Predictor) for predicting functional peptides and identifying toxic peptides. We also introduced SMOTE-TOMEK data synthesis sampling and Shapley value-based feature selection techniques to relieve data imbalance issues and reduce computational costs. On a validated independent test set, pLMFPPred achieved accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score values of 0.974, 0.99, and 0.974, respectively. Comparative experiments show that pLMFPPred outperforms current methods for predicting functional peptides.The experimental results suggest that the proposed method (pLMFPPred) can provide better performance in terms of Accuracy, Area under the curve - Receiver Operating Characteristics, and F1-Score than existing methods. pLMFPPred has achieved good performance in predicting functional peptides and represents a new computational method for predicting functional peptides.Comment: 20 pages, 5 figures,under revie

Antimicrobial potential of Clostridium and closely related species derived from farm environmental samples : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Manawatū, New Zealand

The exploration of antimicrobial compounds from natural sources such as bacteria, has been fast tracked by the development of antimicrobial resistance to existing antimicrobials and the increasing consumer demand for natural food preservatives. So far, antimicrobial discovery has been biased towards aerobic and facultative anaerobic bacteria and fungi. Strict anaerobes such as Clostridium species have not been thoroughly investigated for their antimicrobial potential. The objective of the current study was to evaluate the antimicrobial potential of Clostridium and closely related species against bacteria associated with food spoilage, food safety, and human health. Tests on culture media inoculated with Clostridium and closely related species from farm samples (conditioned media/CMs) showed various degrees of antimicrobial activity. Farm 4 soil conditioned medium (F4SCM) showed potential for further investigation in the search for potent antimicrobials with its promising antimicrobial activity. Bacterial isolates (FS01, FS2.2, FS03, and FS04) belonging to Clostridium and closely related spp. associated with F4SCM showed antimicrobial potential as evident by culture-based and genome-based methods. F4SCM and FS03CM (CM prepared from FS03) metabolomes showed the presence of several putative antimicrobial metabolites. Among them, 2-hydroxyisocaproic acid (HICA) showed antimicrobial activity against a wide range of bacteria associated with food spoilage and safety indicating its potential as a bio-preservative agent in food products. The cell cytoplasmic membrane is a likely target of the HICA’s antimicrobial activity. Overall, this study demonstrates that anaerobic bacterial species, Clostridium, and closely related species can produce antimicrobial metabolites, that have potential applications in food preservation and human health. The knowledge obtained in this study will help future investigations to identify and characterize antimicrobials from these Clostridium and closely related bacteria and expands the understanding of the potential to produce antimicrobial compounds from the genus Clostridium and closely related species

Machine learning-guided directed evolution for protein engineering

Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

PARCE: Protocol for Amino acid Refinement through Computational Evolution

The in silico design of peptides and proteins as binders is useful for diagnosis and therapeutics due to their low adverse effects and major specificity. To select the most promising candidates, a key matter is to understand their interactions with protein targets. In this work, we present PARCE, an open source Protocol for Amino acid Refinement through Computational Evolution that implements an advanced and promising method for the design of peptides and proteins. The protocol performs a random mutation in the binder sequence, then samples the bound conformations using molecular dynamics simulations, and evaluates the protein-protein interactions from multiple scoring. Finally, it accepts or rejects the mutation by applying a consensus criterion based on binding scores. The procedure is iterated with the aim to explore efficiently novel sequences with potential better affinities toward their targets. We also provide a tutorial for running and reproducing the methodology