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.

利用基于蛋白质语言模型的嵌入（ESM-2），我们开发了一个名为 pLMFPPred（蛋白质语言模型功能肽预测器）的工具，用于预测功能性肽并识别有毒肽，同时引入了 SMOTE-TOMEK 数据合成采样和 Shapley 值基于特征选择技术以缓解数据不平衡问题和减少计算成本。在经过验证的独立测试集上，pLMFPPred 分别取得了 0.974、0.99 和 0.974 的准确率、曲线下面积（AUC-ROC）和 F1-Score 值，比当前用于预测功能性肽的方法表现更优。实验结果表明，提出的方法（pLMFPPred）在准确性、曲线下面积（AUC-ROC）和 F1-Score 等方面具有更好的性能，是一种用于预测功能性肽的新的计算方法。

pLMFPPred: 一种准确预测功能肽的新方法，结合了从预训练蛋白质语言模型中嵌入和不平衡学习

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

Antibodies have become an important class of therapeutic agents to treat
human diseases. To accelerate therapeutic antibody discovery, computational
methods, especially machine learning, have attracted considerable interest for
predicting specific interactions between antibody candidates and target
antigens such as viruses and bacteria. However, the publicly available datasets
in existing works have notable limitations, such as small sizes and the lack of
non-binding samples and exact amino acid sequences. To overcome these
limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting
antigen-antibody interactions in the variable domain of heavy chain of heavy
chain antibodies (VHHs), produced from an alpaca immunized with the human
interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure
of VHHs, which facilitates identification of full-length amino acid sequences
by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs
with amino acid sequences. All the antigen-VHH pairs have reliable labels for
binding or non-binding, as generated by a novel labeling method. Furthermore,
via introduction of artificial mutations, AVIDa-hIL6 contains 30 different
mutants in addition to wild-type IL-6 protein. This characteristic provides
opportunities to develop machine learning models for predicting changes in
antibody binding by antigen mutations. We report experimental benchmark results
on AVIDa-hIL6 by using neural network-based baseline models. The results
indicate that the existing models have potential, but further research is
needed to generalize them to predict effective antibodies against unknown
mutants. The dataset is available at this https URL

利用 VHHs 的简单结构，作者建立了一个大规模的数据集 AVIDa-hIL6，以人源性细胞因子 IL-6 为抗原，包含 573,891 个抗原 - VHHs 氨基酸序列对，并且所有抗原 - VHHs 对都有可靠的标签进行分类，这个数据集可以用于预测抗体 - 抗原相互作用，研究人员进一步发现存在潜在的机器学习模型来预测有效的抗体。