The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides

Computational and Structural Biotechnology Journal, Journal Year: 2024, Volume and Issue: 23, P. 972 - 981

Published: Feb. 12, 2024

Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role innate immune defense against pathogens. Their mechanism of action involves the disruption bacterial cell membranes, causing leakage cellular contents and ultimately leading to death. While AMPs typically lack defined structure solution, they often assume conformation when interacting with membranes. Given this structural flexibility, we investigated whether intrinsically disordered regions (IDRs) AMP-like properties could exhibit antimicrobial activity. We tested 14 from different IDRs predicted have activity that nearly all them did not display anticipated effects. These failed adopt secondary had compromised membrane interactions, resulting hypothesize evolutionary constraints may prevent folding, even membrane-like environments, limiting their potential. Moreover, our research reveals current predictors fail accurately capture features dealing unstructured sequences. Hence, results presented here far-reaching implications for designing improving strategies therapies infectious diseases.

Language: Английский

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deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

PeerJ, Journal Year: 2024, Volume and Issue: 12, P. e17729 - e17729

Published: July 19, 2024

Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components innate immune system, have emerged as a potent solution to AMR due their therapeutic potential. Employing computational methodologies for prompt recognition these indeed unlocks fresh perspectives, thereby potentially revolutionizing drug development. Methods In this study, we developed model named deepAMPNet. This model, which leverages graph neural networks, excels at swift identification AMPs. It employs structures predicted AlphaFold2, encodes residue-level features through bi-directional long short-term memory (Bi-LSTM) protein language and constructs adjacency matrices anchored on amino acids’ contact maps. Results comparative study with other state-of-the-art AMP predictors two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, terms commonly accepted evaluation such AUC, Mcc, sensitivity, specificity, achieved highest or highly comparable performances against predictors. Conclusion interweaves both structural sequence information AMPs, stands high-performance that propels evolution design peptide pharmaceuticals. The data code utilized can be accessed https://github.com/Iseeu233/deepAMPNet .

Language: Английский

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dbAMP 3.0: updated resource of antimicrobial activity and structural annotation of peptides in the post-pandemic era

Nucleic Acids Research, Journal Year: 2024, Volume and Issue: 53(D1), P. D364 - D376

Published: Nov. 14, 2024

Antimicrobial resistance is one of the most urgent global health threats, especially in post-pandemic era. peptides (AMPs) offer a promising alternative to traditional antibiotics, driving growing interest recent years. dbAMP comprehensive database offering extensive annotations on AMPs, including sequence information, functional activity data, physicochemical properties and structural annotations. In this update, has curated data from over 5200 publications, encompassing 33,065 AMPs 2453 antimicrobial proteins 3534 organisms. Additionally, utilizes ESMFold determine three-dimensional structures providing 30,000 that facilitate structure-based insights for clinical drug development. Furthermore, employs molecular docking techniques, 100 docked complexes contribute useful into potential mechanisms AMPs. The toxicity stability are critical factors assessing their as drugs. updated introduced an efficient tool evaluating hemolytic half-life alongside AMP optimization platform designing with high activity, reduced increased stability. freely accessible at https://awi.cuhk.edu.cn/dbAMP/. Overall, represents essential resource analysis design, poised advance strategies

Language: Английский

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PAPreC: A Pipeline for Antigenicity Prediction Comparison Methods across Bacteria

ACS Omega, Journal Year: 2025, Volume and Issue: 10(6), P. 5415 - 5429

Published: Feb. 3, 2025

Antigenicity prediction plays a crucial role in vaccine development, antibody-based therapies, and diagnostic assays, as this predictive approach helps assess the potential of molecular structures to induce recruit immune cells drive antibody production. Several existing methods, which target complete proteins epitopes identified through reverse vaccinology, face limitations regarding input data constraints, feature extraction strategies, insufficient flexibility for model evaluation interpretation. This work presents PAPreC (Pipeline Prediction Comparison), an open-source, versatile workflow (available at https://github.com/YasCoMa/paprec_nx_workflow) designed address these challenges. systematically examines three key factors: selection training sets, methods (including physicochemical descriptors ESM-2 encoder-derived embeddings), diverse classifiers. It provides automated evaluation, interpretability SHapley Additive exPlanations (SHAP) analysis, applicability domain assessments, enabling researchers identify optimal configurations their specific sets. Applying IEDB reference, we demonstrate its effectiveness across ESKAPE pathogen group. A case study involving Pseudomonas aeruginosa Staphylococcus aureus shows that are more suitable different sequence types, embeddings enhance performance. Moreover, our results indicate separate models Gram-positive Gram-negative bacteria not required. offers comprehensive, adaptable, robust framework streamline improve antigenicity bacterial

Language: Английский

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Deep Learning for Antimicrobial Peptides: Computational Models and Databases

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of is both time-consuming and laborious. In recent years, development computational technologies (especially deep learning) has provided new opportunities for peptide prediction. Various models have been proposed predict peptide. this review, we focus on learning We first collected summarized available data resources peptides. Subsequently, existing discussed their limitations challenges. This study aims help biologists design better

Language: Английский

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