HIV-1 Protease Cleavage Site Prediction Research Articles

Multi-objective optimization with majority voting ensemble of classifiers for prediction of HIV-1 protease cleavage site

HIV-1 protease cleavage site prediction of an amino acid sequence of Human Immune Deficiency Virus (HIV-1) type 1 has been the subject of intense research for decades to increase the AUC value of the prediction without placing much attention to the accuracy metric by many researchers. Knowledge of the substrate specificity of HIV-1 protease has significant application in HIV-1 protease inhibitors development and in studying novel drug targets. Motivated by this, a multi-objective optimization (MOO)-based majority voting ensemble framework combining the outputs from multiple classifiers has been proposed in the current paper to increase both the prediction accuracy and AUC values simultaneously. The optimal set of classifiers that are considered for voting purposes at the time of combining the outputs is determined automatically using the search capability of MOO. Comparatively better results have been attained using various benchmark data sets with average accuracy and AUC (area under the ROC curve) values of 0.92 and 0.96, respectively.

Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors

BackgroundIn most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.ResultsAmong the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).ConclusionsLogistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

Information Fusion for Biological Prediction

Information fusion has become a powerful tool for challenging applications such as biological prediction problems. In this paper, we apply a new information-theoretical fusion technique to HIV-1 protease cleavage site prediction, which is a problem that has been in the focus of much interest and investigation of the machine learning community recently. It poses a difficult classification task due to its high dimensional feature space and a relatively small set of available training patterns. We also apply a new set of biophysical features to this problem and present experiments with neural networks, support vector machines, and decision trees. Application of our feature set results in high recognition rates and concise decision trees, producing manageable rule sets that can guide future experiments. In particular, we found a combination of neural networks and support vector machines to be beneficial for this problem.

Multiobjective evolutionary-based multi-kernel learner for realizing transfer learning in the prediction of HIV-1 protease cleavage sites

Due to the unavailability of adequate patients and expensive labeling cost, many real-world biomedical cases have scarcity in the annotated data. This holds very true for HIV-1 protease specificity problem where only a few experimentally verified cleavage sites are present. The challenge then is to exploit the auxiliary data. However, the problem becomes more complicated when the underlying train and test data are generated from different distributions. To deal with the challenges, we formulate the HIV-1 protease cleavage site prediction problem into a bi-objective optimization problem and solving it by introducing a multiobjective evolutionary-based multi-kernel model. A solution for the optimization problem will lead us to decide the optimal number of base kernels with the best pairing of features. The bi-objective criteria encourage different individual kernels in the ensemble to mitigate the effect of distribution difference in training and test data with the ideal number of base kernels. In this paper, we considered eight different feature descriptors and three different kernel variants of support vector machines to generate the optimal multi-kernel learning model. Non-dominated sorting genetic algorithm-II is employed with bi-objective of achieving a maximum area under the receiver operating characteristic curve simultaneously with a minimum number of features. To validate the effectiveness of the model, the experiments were performed on four HIV-1 protease datasets. The performance comparison with fifteen state-of-the-art techniques on average accuracy and area under curve has been evaluated to justify the improvement of the proposed model. We then analyze Friedman and post hoc tests to demonstrate the significant improvement. The result obtained following the extensive experiment enumerates the bi-objective multi-kernel model performance enhancement on within and cross-learning over the other state-of-the-art techniques.

Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.

It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1′, and p2′ are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

A Consistency-Based Feature Selection Method Allied with Linear SVMs for HIV-1 Protease Cleavage Site Prediction

BackgroundPredicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy.ResultsWe propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations.ConclusionApplying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper.Software availabilityThe software is available at http://ozyer.etu.edu.tr/c-fs-svm.rar.The software can be downloaded at esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar; you will find a readme file which explains how to set the software in order to work.

HIV-1 Protease Cleavage Site Prediction Based on Two-Stage Feature Selection Method

The HIV-1 protease inhibitor plays an important role in the therapy of AIDS. The research on HIV-1 protease’s cleavage site will be useful to found new therapeutic targets. To predict the HIV-1 protease specific site, we apply Amino Acid Index(AAIndex)’s 531 amino acid’s parameter of chemical and physical to present the structure of peptide sample. And based on two stage feature selection method , 57 features are selected from original 4248 features. By using four kernel function of support vector machine(SVM), HIV-1 protease specific site’s model is built. Our research showed the modeling by the kernel function of NormalizePolyKernel had the higher prediction rate than other three kernel function. As a result, the accuracy rate of prediction achieves 93.947% and 93.684% for corss validation test and an independent set test, respectively.

A new approach for HIV-1 protease cleavage site prediction combined with feature selection

Acquired immunodeficiency syndrome (AIDS) is a fatal disease which highly threatens the health of human being. Human immunodeficiency virus (HIV) is the pathogeny for this disease. Investigating HIV-1 protease cleavage sites can help researchers find or develop protease inhibitors which can restrain the replication of HIV-1, thus resisting AIDS. Feature selection is a new approach for solving the HIV-1 protease cleavage site prediction task and it’s a key point in our research. Comparing with the previous work, there are several advantages in our work. First, a filter method is used to eliminate the redundant features. Second, besides traditional orthogonal encoding (OE), two kinds of newly proposed features extracted by conducting principal component analysis (PCA) and non-linear Fisher transformation (NLF) on AAindex database are used. The two new features are proven to perform better than OE. Third, the data set used here is largely expanded to 1922 samples. Also to improve prediction performance, we conduct parameter optimization for SVM, thus the classifier can obtain better prediction capability. We also fuse the three kinds of features to make sure comprehensive feature representation and improve prediction performance. To effectively evaluate the prediction performance of our method, five parameters, which are much more than previous work, are used to conduct complete comparison. The experimental results of our method show that our method gain better performance than the state of art method. This means that the feature selection combined with feature fusion and classifier parameter optimization can effectively improve HIV-1 cleavage site prediction. Moreover, our work can provide useful help for HIV-1 protease inhibitor developing in the future.

HIV-1 Protease Cleavage Site Prediction Based on Two-Stage Feature Selection Method

Knowledge of the mechanism of HIV protease cleavage specificity is critical to the design of specific and effective HIV inhibitors. Searching for an accurate, robust, and rapid method to correctly predict the cleavage sites in proteins is crucial when searching for possible HIV inhibitors. In this article, HIV-1 protease specificity was studied using the correlation-based feature subset (CfsSubset) selection method combined with Genetic Algorithms method. Thirty important biochemical features were found based on a jackknife test from the original data set containing 4,248 features. By using the AdaBoost method with the thirty selected features the prediction model yields an accuracy of 96.7% for the jackknife test and 92.1% for an independent set test, with increased accuracy over the original dataset by 6.7% and 77.4%, respectively. Our feature selection scheme could be a useful technique for finding effective competitive inhibitors of HIV protease.

A new feature encoding scheme for HIV-1 protease cleavage site prediction

HIV-1 protease has been the subject of intense research for deciphering HIV-1 virus replication process for decades. Knowledge of the substrate specificity of HIV-1 protease will enlighten the way of development of HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, various feature encoding techniques and machine learning algorithms have been used frequently. In this paper, a new feature amino acid encoding scheme is proposed to predict HIV-1 protease cleavage sites. In the proposed method, we combined orthonormal encoding and Taylor’s venn-diagram. We used linear support vector machines as the classifier in the tests. We also analyzed our technique by comparing some feature encoding techniques. The tests are carried out on PR-1625 and PR-3261 datasets. Experimental results show that our amino acid encoding technique leads to better classification performance than other encoding techniques on a standalone classifier.

Coding of amino acids by texture descriptors

In this paper we propose a new feature extractor for peptide/protein classification based on the calculation of texture descriptors. Representing a peptide/protein using a matrix descriptor, instead of a vector, allows to deal with the peptide/protein as an image and to use texture descriptors for representation purposes. A matrix descriptor, which is a squared matrix of the dimension of the peptide/protein, is obtained considering a partial ordering of the amino acids of the peptide/protein according to their value of a given physicochemical property. Each matrix descriptor is considered as a texture image and several texture descriptors are considered to obtain a compact representation which is scale invariant (i.e. independent on the length of the peptide\protein). The texture descriptors tested in this work are: local binary patterns (LBP), discrete cosine transform (DCT) and Daubechies wavelets. The experimental section reports several tests, aimed at supporting our ideas, performed on the following datasets: vaccine dataset for the predictions of peptides that bind human leukocyte antigens; human immunodeficiency virus (HIV-1) protease cleavage site prediction dataset and membrane proteins type dataset. The experimental results confirm the usefulness of the novel descriptors: the performance obtained by our system on the three difficult datasets is quite high, indicating that the proposed method is a feasible system for extracting information from peptides and proteins. The performance obtained by each of the three texture descriptors calculated from the matrix-based representation, and coupled to a support vector machine classifier, is lower than the performance obtained by other vector-based descriptors based on physicochemical properties proposed in the literature. Anyway the new descriptors bring different information and our tests show that the texture descriptors and the vector-based descriptors can be combined to improve the overall performance of the system. In particular the proposed approach improves the state-of-the-art results in two out of three tested problems (HIV-1 protease cleavage site prediction dataset and membrane proteins type dataset).

Variable context Markov chains for HIV protease cleavage site prediction

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

A Further Step Toward an Optimal Ensemble of Classifiers for Peptide Classification, a Case Study: HIV Protease

The focuses of this work are: to propose a novel method for building an ensemble of classifiers for peptide classification based on substitution matrices; to show the importance to select a proper set of the parameters of the classifiers that build the ensemble of learning systems. The HIV-1 protease cleavage site prediction problem is here studied. The results obtained by a blind testing protocol are reported, the comparison with other state-of-the-art approaches, based on ensemble of classifiers, allows to quantify the performance improvement obtained by the systems proposed in this paper. The simulation based on experimentally determined protease cleavage data has demonstrated the success of these new ensemble algorithms. Particularly interesting it is to note that also if the HIV-1 protease cleavage site prediction problem is considered linearly separable we obtain the best performance using an ensemble of non-linear classifiers.

Machine learning for HIV-1 protease cleavage site prediction

Recently, several works have approached the HIV-1 protease specificity problem by applying a number of classifier creation and combination methods, known as ensemble methods, from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective comparison. For the first time we have made an extensive study on methods for feature extraction, feature transformation and multiclassifier systems (MCS) in the problem of HIV-1 protease. In this work we report an experimental comparison on several learning systems coupled with different feature representations. We confirm previous results stating that linear classifiers obtain higher performance than non-linear classifiers using orthonormal encoding, but we also show that using Karhunen–Loeve transform the performance of neural networks are comparable to one of linear support vector machines. Finally we propose a new hierarchical approach that, for the first time, combines ideas derived from the machine learning methodologies and from a knowledge base of this particular problem. This approach proves to be a successful attempt to obtain a drastically error reduction with respect to the performance of linear classifiers: the error rate decreases from 9.1% using linear-SVM to 6.6% using our new hierarchical classifier based on some pattern rules.

A reliable method for HIV-1 protease cleavage site prediction

Recently, several works have approached the HIV-1 protease specificity problem by applying techniques from machine learning. In this work, an encoding scheme based on the BLOSUM50 matrix is investigated. We show that combining a linear discriminant classifier and radial basis function support vector machine we obtain performance higher than previously published in the literature.

Comparison among feature extraction methods for HIV-1 protease cleavage site prediction

Recently, several works have approached the HIV-1 protease specificity problem by applying a number of methods from the field of machine learning. However, it is still difficult for researchers to choose the best method due to the lack of an effective comparison. For the first time we have made an extensive study on methods for feature extraction for the problem of HIV-1 protease. We show that a fusion of classifiers trained in different feature spaces permits to obtain a drastically error reduction with respect to the performance of the state-of-the-art.