Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

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.

Enzymes play an important role in metabolism that helps in catalyzing bio-chemical reactions. Predicting functions of enzymes by experiments is costly and time consuming. Hence a computational method is required to predict the function of enzymes. This paper presents a supervised machine learning approach to predict the functional classes and subclass of protein sequences including enzymes and non-enzymes based on 857 sequence derived features. This paper used seven sequence derived properties including amino acid composition, dipeptide composition, correlation feature, composition, transition, distribution and pseudo amino acid composition. We have used recursive feature elimination technique (RFE), in order to select optimal number of features. The support vector machine (SVM) has been used to construct a three level model with optimal number of features selected by SVM-RFE, where top (first) level distinguish a query protein as an enzyme or nonenzyme, the next (second) level predicts the enzyme functional class and the last (third) level predict the subfunctional class. The proposed model reported overall accuracy of 97.6%, precision of 97.8%and Matthew Correlation Coefficient (MCC) value of 0.93 for the first level, whereas accuracy of 87.3%, precision of 87.7% and MCC value of 0.84 for second level and accuracy of 85.6%, precision of 87.9% and MCC value of 0.86 for the third level.

A two-layer method based on support vector machines (SVMs) has been developed to distinguish epoxide hydrolases (EHs) from other enzymes and to classify its subfamilies using its primary protein sequences. SVM classifiers were built using three different feature vectors extracted from the primary sequence of EHs: the amino acid composition (AAC), the dipeptide composition (DPC), and the pseudo-amino acid composition (PAAC). Validated by 5-fold cross tests, the first layer SVM classifier can differentiate EHs and non-EHs with an accuracy of 94.2% and has a Matthew's correlation coefficient (MCC) of 0.84. Using 2-fold cross validation, PAAC-based second layer SVM can further classify EH subfamilies with an overall accuracy of 90.7% and MCC of 0.87 as compared to AAC (80.0%) and DPC (84.9%). A program called EHPred has also been developed to assist readers to recognize EHs and to classify their subfamilies using primary protein sequences with greater accuracy.

Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features

The subcellular localization of proteins is very important for characterizing its function in a cell. Accurate prediction of the subcellular locations in computational paradigm has been an active area of interest. Most of the work has been focused on single localization prediction. Only few studies have discussed the multi-target localization, but have not achieved good accuracy so far; in plant sciences, very limited work has been done. Here we report the development of a novel tool Plant-mSubP, which is based on integrated machine learning approaches to efficiently predict the subcellular localizations in plant proteomes. The proposed approach predicts with high accuracy 11 single localizations and three dual locations of plant cell. Several hybrid features based on composition and physicochemical properties of a protein such as amino acid composition, pseudo amino acid composition, auto-correlation descriptors, quasi-sequence-order descriptors and hybrid features are used to represent the protein. The performance of the proposed method has been assessed through a training set as well as an independent test set. Using the hybrid feature of the pseudo amino acid composition, N-Center-C terminal amino acid composition and the dipeptide composition (PseAAC-NCC-DIPEP), an overall accuracy of 81.97 %, 84.75 % and 87.88 % is achieved on the training data set of proteins containing the single-label, single- and dual-label combined, and dual-label proteins, respectively. When tested on the independent data, an accuracy of 64.36 %, 64.84 % and 81.08 % is achieved on the single-label, single- and dual-label, and dual-label proteins, respectively. The prediction models have been implemented on a web server available at http://bioinfo.usu.edu/Plant-mSubP/. The results indicate that the proposed approach is comparable to the existing methods in single localization prediction and outperforms all other existing tools when compared for dual-label proteins. The prediction tool will be a useful resource for better annotation of various plant proteomes.

StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy.

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.

Identifying protein arginine methylation sites using global features of protein sequence coupled with support vector machine optimized by particle swarm optimization algorithm

Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. In this paper, the protein distance matrix image(DMI) is introduced. Based on the protein DMI, two geometric moments derived form each of the protein sequences concerned are adopted for its PseAA. It was demonstrated thru the jackknife cross-validation test that the overall success rate by the new approach was significantly higher than those by the others. The remarkable merit of this approach is that many image recognition tools can be straightforwardly utilized in predicting protein structural classes.

Prediction of G-protein coupled receptors and their subfamilies by incorporating various sequence features into Chou's general PseAAC

Phospholipases, as important lipolytic enzymes, have diverse industrial applications. Regarding the stability of extremophilic archaea's proteins in harsh conditions, analyses of unusual features of their proteins are significantly important for their utilization. This research was accomplished to in silico study of archaeal phospholipases' properties and to develop a pioneering method for distinguishing these enzymes from other archaeal enzymes via machine learning algorithms and Chou's pseudo-amino acid composition concept. The non-redundant sequences of archaeal phospholipases were collected. BioSeq-Analysis sever was used with Support Vector Machine (SVM), Random Forests (RF), Covariance Discrimination (CD), and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) as powerful machine learnings algorithms. Also, different Chou's pseudo-amino acid composition modes were performed and then, 5-fold cross-validation was applied to the sequences. Based on our results, the OET-KNN predictor, with 96% accuracy, yields the best performance in SC-PseAAC mode by 5-fold cross-validation. This predictor also achieved very high values of specificity (95%), sensitivity (96%), Matthews's correlation coefficient (0.92), and accuracy (96%). The present investigation yielded a robust anticipatory model for the archaeal phospholipase prediction utilizing the tenets PseAAC and OET-KNN machine learning algorithm.

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.

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.

In order to predict the types of membrane protein accurately, this paper firstly proposed a fusion feature representation, which contains a more comprehensive information of the original protein sequence by fusing two single feature expressions, pseudo amino acid composition (PseAAC) and dipeptide composition (DipC). Then, we proposed an improved support vector machine (SVM) method by introducing the idea of kernel function to evaluate prediction performance of the new fusion representation. In addition, we have deeply studied the influence of three different kernel functions as well as their kernel parameters on the prediction of membrane protein types. — Through experimental verification, it shows that the proposed integration representation with our improved SVM has a good performance in the prediction of membrane protein types. The final overall prediction accuracy can reach up to 89.64% under the Jackknife test method.

Amino Acid Composition and Biological Value of Proteins In Some Aquatic Plant Species