Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning

Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification

Categorical attributes are common in many classification tasks, presenting certain challenges as the number of categories grows. This situation can affect data handling, negatively impacting the building time of models, their complexity and, ultimately, their classification performance. In order to mitigate these issues, this research proposes a novel preprocessing technique for grouping attribute categories in classification datasets. This approach combines the exact representation of the association between categorical values in a Euclidean space, clustering methods and attribute quality metrics to group similar attribute categories based on their contribution to the classification task. To estimate its effectiveness, the proposal is evaluated within the context of HIV-1 protease cleavage site prediction, where each attribute represents an amino acid that can take multiple possible values. The results obtained on HIV-1 real-world datasets show a significant reduction in the number of categories per attribute, with an average reduction percentage ranging from 74% to 81%. This reduction leads to simplified data representations and improved classification performances compared to not preprocessing. Specifically, improvements of up to 0.07 in accuracy and 0.19 in geometric mean are observed across different datasets and classification algorithms. Additionally, extensive simulations on synthetic datasets with varied characteristics are carried out, providing consistent and reliable results that validate the robustness of the proposal. These findings highlight the capability of the developed method to enhance cleavage prediction, which could potentially contribute to understanding viral processes and developing targeted therapeutic strategies.

Learning peptide properties with positive examples only

Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.

A Protocol to Depict the Proteolytic Processes Using a Combination of Metal-Organic Materials (MOMs), Electron Paramagnetic Resonance (EPR), and Mass Spectrometry (MS)

Proteolysis is a critical biochemical process yet a challenging field to study experimentally due to the self-degradation of a protease and the complex, dynamic degradation steps of a substrate. Mass spectrometry (MS) is the traditional way for proteolytic studies, yet it is challenging when time-resolved, step-by-step details of the degradation process are needed. We recently found a way to resolve the cleavage site, preference/selectivity of cleavage regions, and proteolytic kinetics by combining site-directed spin labeling (SDSL) of protein substrate, time-resolved two-dimensional (2D) electron paramagnetic resonance (EPR) spectroscopy, protease immobilization via metal-organic materials (MOMs), and MS. The method has been demonstrated on a model substrate and protease, yet there is a lack of details on the practical operations to carry out our strategy. Thus, this protocol summarizes the key steps and considerations when carrying out the EPR/MS study on proteolytic processes, which can be generalized to study other protein/polypeptide substrates in proteolysis. Details for the experimental operation and cautions of each step are reported with figures illustrating the concepts. This protocol provides an effective approach to understanding the proteolytic process with the advantages of offering time-resolved, residue-level resolution of structural basis underlying the process. Such information is important for revealing the cleavage site and proteolytic mechanisms of unknown proteases. The advantage of EPR, probing the target substrate regardless of the complexities caused by the proteases and their self-degradation, offers a practically effective, rapid, and easy-to-operate approach to studying proteolysis. Key features • Combining protease immobilization, EPR, spin labeling, and MS experimental methods allows for the analysis of proteolysis process in real time. • Reveals cleavage site, kinetics of product generation, and preference of cleavage regions via time-resolved SDSL-EPR. • MS confirms EPR findings and helps depict the sequences and populations of the cleaved segments in real time. • The demonstrated method can be generalized to other proteins or polypeptide substrates upon proteolysis by other proteases.

Learning Peptide Properties with Positive Examples Only

Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.

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

Background

In 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.

Results

Among 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%).

Conclusions

Logistic 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.

Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach

Background

The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance.

Results

In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.