MLACP: machine-learning-based prediction of anticancer peptides.

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN. ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/.

Nuclear receptors are key transcription factors that regulate crucial gene networks responsible for cell growth, differentiation, and homeostasis. Nuclear receptors form a superfamily of phylogenetically related proteins and control functions associated with major diseases (e.g. diabetes, osteoporosis, and cancer). In this study, a novel method has been developed for classifying the subfamilies of nuclear receptors. The classification was achieved on the basis of amino acid and dipeptide composition from a sequence of receptors using support vector machines. The training and testing was done on a non-redundant data set of 282 proteins obtained from the NucleaRDB data base (1). The performance of all classifiers was evaluated using a 5-fold cross validation test. In the 5-fold cross-validation, the data set was randomly partitioned into five equal sets and evaluated five times on each distinct set while keeping the remaining four sets for training. It was found that different subfamilies of nuclear receptors were quite closely correlated in terms of amino acid composition as well as dipeptide composition. The overall accuracy of amino acid composition-based and dipeptide composition-based classifiers were 82.6 and 97.5%, respectively. Therefore, our results prove that different subfamilies of nuclear receptors are predictable with considerable accuracy using amino acid or dipeptide composition. Furthermore, based on above approach, an online web service, NRpred, was developed, which is available at www.imtech.res.in/raghava/nrpred.

iACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention

Anticancer peptides (ACPs) recently have been receiving increasing attention in cancer therapy due to their low consumption, few adverse side effects, and easy accessibility. However, it remains a great challenge to identify anticancer peptides via experimental approaches, requiring expensive and time-consuming experimental studies. In addition, traditional machine-learning-based methods are proposed for ACP prediction mainly depending on hand-crafted feature engineering, which normally achieves low prediction performance. In this study, we propose CACPP (Contrastive ACP Predictor), a deep learning framework based on the convolutional neural network (CNN) and contrastive learning for accurately predicting anticancer peptides. In particular, we introduce the TextCNN model to extract the high-latent features based on the peptide sequences only and exploit the contrastive learning module to learn more distinguishable feature representations to make better predictions. Comparative results on the benchmark data sets indicate that CACPP outperforms all the state-of-the-art methods in the prediction of anticancer peptides. Moreover, to intuitively show that our model has good classification ability, we visualize the dimension reduction of the features from our model and explore the relationship between ACP sequences and anticancer functions. Furthermore, we also discuss the influence of data set construction on model prediction and explore our model performance on the data sets with verified negative samples.

ACP-2DCNN: Deep learning-based model for improving prediction of anticancer peptides using two-dimensional convolutional neural network

The cyclin-dependent kinases (Cdks) are a family of serine/threonine protein kinases whose members are small proteins (∼34–40 kDa) composed of little more than the catalytic core shared by all protein kinases. All Cdks share the feature that their enzymatic activation requires the binding of a regulatory cyclin subunit. Various combination of both cyclin dependent kinases (CDKs) and cyclin proteins are responsible for progression of cell cycle through various phases like G1, S, G2 and M.. CDKs are also essential for proliferation of specialized cells. Realizing the importance of both these proteins in various aspects of life a new efficient computational model has been developed using parameters like hybrid composition for prediction of these proteins. In order to improve the prediction accuracy, we have developed a hybrid module using all features of a protein, which consisted of amino acid composition, dipeptide and pseudo amino acid composition and resulted in an input vector of 450 dimensions (400 dipeptide compositions, 30 pseudo, 20 amino acid compositions of the protein ). The overall prediction accuracy of SVM modules based on dipeptide composition, amino acid and pseudo composition hybrid approach was 99.7914% respectively. The accuracy of all the modules was evaluated using a 10-fold cross-validation technique.

Cancer, a significant global public health issue, resulted in about 10 million deaths in 2022. Anticancer peptides (ACPs), as a category of bioactive peptides, have emerged as a focal point in clinical cancer research due to their potential to inhibit tumor cell proliferation with minimal side effects. However, the recognition of ACPs through wet-lab experiments still faces challenges of low efficiency and high cost. Our work proposes a recognition method for ACPs named ACP-DRL based on deep representation learning, to address the challenges associated with the recognition of ACPs in wet-lab experiments. ACP-DRL marks initial exploration of integrating protein language models into ACPs recognition, employing in-domain further pre-training to enhance the development of deep representation learning. Simultaneously, it employs bidirectional long short-term memory networks to extract amino acid features from sequences. Consequently, ACP-DRL eliminates constraints on sequence length and the dependence on manual features, showcasing remarkable competitiveness in comparison with existing methods.

The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.

Cancer remains a major global health burden, and conventional treatments such as surgery, radiotherapy, and chemotherapy are often limited by systemic toxicity, drug resistance, and high cost. Anticancer peptides (ACPs) have emerged as promising therapeutic candidates because of their selective activity against tumor cells; however, experimental identification of ACPs is labor-intensive and time-consuming, and peptide activity may vary across tissue contexts. Although many computational models have been developed for general ACP prediction, tissue-specific ACP classification remains insufficiently explored. In this study, we developed ACP-Boost, a tissue-aware machine learning framework for tissue-specific ACP classification across nine cancer-related tissues: blood, brain, breast, cervix, colon, liver, lung, prostate, and skin. Experimentally validated peptide records were integrated from CancerPPD2 and DCTPep, followed by preprocessing, redundancy control, and tissue annotation. Peptide sequences were encoded into 473-dimensional feature vectors comprising amino acid composition (AAC), dipeptide composition (DPC), physicochemical property composition (PCP), and pseudo-amino acid composition (PseAAC). To address class imbalance while preserving the original sequence distribution, we formulated the task as a one-versus-rest classification problem, applied group-aware train-test splitting based on peptide sequences, and emphasized evaluation metrics suitable for imbalanced data. We systematically compared five machine learning algorithms, including support vector machine, random forest, logistic regression, k-nearest neighbors, and XGBoost. Among them, XGBoost showed the most stable overall performance across tissues. The results indicate that peptide sequence-derived descriptors contain measurable tissue-associated signals, although predictive separability remains moderate for several cancer types. Feature importance analysis further suggested that both shared charge-related properties and tissue-dependent sequence descriptors contribute to model discrimination. Overall, this study provides a comparative computational framework for tissue-specific ACP classification and highlights the importance of incorporating biological heterogeneity into peptide prediction tasks.

Chapter 6 - Multiview learning with shallow 1D-CNN for anticancer activity classification of therapeutic peptides

The dramatic rise in cancer incidence, alongside treatment deficiencies, has elevated cancer to the second-leading cause of death globally. The increasing morbidity and mortality of this disease can be traced back to a number of causes, including treatment-related side effects, drug resistance, inadequate curative treatment and tumor relapse. Recently, anti-cancer bioactive peptides (ACPs) have emerged as a potential therapeutic choice within the pharmaceutical arsenal due to their high penetration, specificity and fewer side effects. In this contribution, we present a general overview of the literature concerning the conformational structures, modes of action and membrane interaction mechanisms of ACPs, as well as provide recent examples of their successful employment as targeting ligands in cancer treatment. The use of ACPs as a diagnostic tool is summarized, and their advantages in these applications are highlighted. This review expounds on the main approaches for peptide synthesis along with their reconstruction and modification needed to enhance their therapeutic effect. Computational approaches that could predict therapeutic efficacy and suggest ACP candidates for experimental studies are discussed. Future research prospects in this rapidly expanding area are also offered.

Cancer is a well-known dreadful killer of human being’s health, which has led to countless deaths and misery. Traditional treatment can also affect the normal cells while killing cancer cells. Meanwhile, physical or chemical techniques are costly and inefficient. Fortunately, anticancer peptides are a promising treatment, with specifically targeted, low production cost and other advantages. In order to effectively identify the anticancer peptides, we proposed a stacking heterogeneous ensemble learning model, ACP-SE, for predicting anticancer peptides. More specifically, to fully exploit protein sequence information, we developed an efficient feature representation approach by integrating binary profile feature and conjoint triad feature. Then we use a stacking ensemble strategy to combine the three heterogeneous classifiers and get the final prediction results. It was demonstrated that the proposed ACP-SE remarkably outperformed other comparison methods.

Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/.

Mastoparan is a membranolytic anti-cancer peptide that works synergistically with gemcitabine in a mouse model of mammary carcinoma

Proteome of monocled cobra (Naja kaouthia) venom and potent anti breast cancer peptide from trypsin hydrolyzate of the venom protein