■原生动物病原体构成相当大的威胁,导致显著的死亡率和不断发展的耐药性的挑战。这种情况强调了对替代治疗方法的迫切需要。抗微生物肽是有希望的药物开发候选物。然而,缺乏专门针对原生动物病原体的抗菌肽的预测研究。在这项研究中,我们引入了一个成功的基于机器学习的框架,旨在预测潜在的抗原生动物肽对原生动物病原体有效。
■这项研究的主要目的是使用不同的阴性数据集对抗原虫肽进行分类和预测。
■进行了全面的文献综述,以收集实验验证的抗原虫肽,形成我们研究的阳性数据集。为了构建一个健壮的机器学习分类器,合并了多个负面数据集,包括(i)非抗菌药物,(ii)抗病毒,(iii)抗菌,(iv)抗真菌药,和(v)抗微生物肽,不包括靶向原生动物病原体的抗微生物肽。使用pfeature算法提取肽的各种组成特征。两种特征选择方法,SVC-L1和mRMR,用于识别高度相关的特征,这些特征对于区分正面和负面数据集至关重要。此外,五种流行的分类器,即决策树,随机森林,支持向量机,Logistic回归,和XGBoost用于构建有效的决策模型。
■XGBoost在根据mRMR特征选择方法选择的特征对每个阴性数据集中的抗原虫肽进行分类方面最有效。提出的机器学习框架有效区分抗原虫肽(i)非抗菌(ii)抗病毒(iii)抗菌(iv)抗真菌和(v)抗菌,准确率为97.27%,93.64%,86.36%,90.91%,在验证数据集上分别为89.09%。
■这些模型被合并到用户友好的Web服务器中(www.soodlab.com/appred)来预测给定肽的抗原生动物活性。
UNASSIGNED: Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens.
UNASSIGNED: The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets.
UNASSIGNED: A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models.
UNASSIGNED: XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset.
UNASSIGNED: The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.