Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery DOI Creative Commons
Ahmad Al-Omari,

Yazan H. Akkam,

Ala’a Zyout

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0315477 - e0315477

Опубликована: Дек. 20, 2024

Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that even better eliminating The fundamental transformation in a variety of scientific disciplines, which led the emergence machine learning techniques, has presented significant opportunities for development antimicrobial peptides. Machine and deep used predict peptide efficacy study. main purpose overcome traditional experimental method constraints. Gram-negative bacterium Escherichia coli model organism this investigation assesses 1,360 sequences exhibit anti- E . activity. These peptides’ minimal inhibitory concentrations have been observed be correlated with set 34 physicochemical characteristics. Two distinct methodologies implemented. initial involves utilizing pre-computed attributes as input data machine-learning classification approach. In second method, these features converted into signal images, then transmitted neural network. first methods accuracy 74% 92.9%, respectively. proposed were developed target single microorganism (gram negative ), however, they offered framework could potentially adapted other types antimicrobial, antiviral, anticancer further validation. Furthermore, potential result time cost reductions, well innovative AMP-based treatments. research contributes advancement learning-based AMP drug discovery by generating potent application. implications processing biological computation pharmacology.

Язык: Английский

Stack-AVP: a stacked ensemble predictor based on multi-view information for fast and accurate discovery of antiviral peptides DOI
Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat

и другие.

Journal of Molecular Biology, Год журнала: 2024, Номер unknown, С. 168853 - 168853

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

5
Advances in Machine Learning for Epigenetics and Biomedical Applications DOI
Hao Lin, Hao Lv, Fanny Dao

и другие.

Methods, Год журнала: 2025, Номер 235, С. 53 - 54

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach DOI Creative Commons
Ali Ghulam, Muhammad Arif, Ahsanullah Unar

и другие.

IET Systems Biology, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

Abstract Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy naturally derived peptides in reducing pressure. Hypertension one risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive possessing antihypertensive properties provide considerable potential viable substitutes for conventional pharmaceutical medications. Currently, thorough examination peptide (AHTPs), by using traditional wet‐lab methods highly expensive labours. Therefore, in‐silico approaches especially machine‐learning (ML) algorithms are favourable due saving time cost discovery AHTPs. In this study, novel ML‐based predictor, called StackAHTP was developed predicting accurate AHTPs from sequence only. The proposed method, utilise two types feature descriptors Pseudo‐Amino Acid Composition Dipeptide encode local global hidden information sequences. Furthermore, encoded features serially merged ranked through SHapley Additive explanations (SHAP) algorithm. Then, top fed into three different ensemble classifiers (Bagging, Boosting, Stacking) enhancing prediction performance model. StackAHTPs method achieved superior compare ML (AdaBoost, XGBoost Light Gradient Boosting (LightGBM), Bagging Boosting) on 10‐fold cross validation independent test. experimental outcomes demonstrate that our outperformed existing an accuracy 92.25% F1‐score 89.67% test non‐AHTPs. authors believe research will remarkably contribute large‐scale characterisation accelerate drug process. At https://github.com/ali‐ghulam/StackAHTPs you may find datasets used.

Язык: Английский

Процитировано

0
Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery DOI Creative Commons
Ahmad Al-Omari,

Yazan H. Akkam,

Ala’a Zyout

и другие.

PLoS ONE, Год журнала: 2024, Номер 19(12), С. e0315477 - e0315477

Опубликована: Дек. 20, 2024

Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that even better eliminating The fundamental transformation in a variety of scientific disciplines, which led the emergence machine learning techniques, has presented significant opportunities for development antimicrobial peptides. Machine and deep used predict peptide efficacy study. main purpose overcome traditional experimental method constraints. Gram-negative bacterium Escherichia coli model organism this investigation assesses 1,360 sequences exhibit anti- E . activity. These peptides’ minimal inhibitory concentrations have been observed be correlated with set 34 physicochemical characteristics. Two distinct methodologies implemented. initial involves utilizing pre-computed attributes as input data machine-learning classification approach. In second method, these features converted into signal images, then transmitted neural network. first methods accuracy 74% 92.9%, respectively. proposed were developed target single microorganism (gram negative ), however, they offered framework could potentially adapted other types antimicrobial, antiviral, anticancer further validation. Furthermore, potential result time cost reductions, well innovative AMP-based treatments. research contributes advancement learning-based AMP drug discovery by generating potent application. implications processing biological computation pharmacology.

Язык: Английский

Процитировано

1