Discovery and development of biopeptides are time-consuming, laborious, and dependent on various factors. Data-driven computational methods, especially machine learning (ML) approach, can rapidly and efficiently predict the utility of therapeutic peptides. ML methods offer an array of tools that can accelerate and enhance decision making and discovery for well-defined queries with ample and sophisticated data quality. Various ML approaches, such as support vector machines, random forest, extremely randomized tree, and more recently deep learning methods, are useful in peptide-based drug discovery. These approaches leverage the peptide data sets, created via high-throughput sequencing and computational methods, and enable the prediction of functional peptides with increased levels of accuracy. The use of ML approaches in the development of peptide-based therapeutics is relatively recent; however, these techniques are already revolutionizing protein research by unraveling their novel therapeutic peptide functions. In this review, we discuss several ML-based state-of-the-art peptide-prediction tools and compare these methods in terms of their algorithms, feature encodings, prediction scores, evaluation methodologies, and software utilities. We also assessed the prediction performance of these methods using well-constructed independent data sets. In addition, we discuss the common pitfalls and challenges of using ML approaches for peptide therapeutics. Overall, we show that using ML models in peptide research can streamline the development of targeted peptide therapies.

Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening

Motivation Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and its activity from the given peptides is one of the challenging tasks in immunoinformatics, which is essential for drug development and basic research. Although there are a few computational methods that have been proposed for this aspect, none of them are able to identify HLPs and their activities simultaneously. Results In this study, we proposed a two-layer prediction framework, called HLPpred-Fuse, that can accurately and automatically predict both hemolytic peptides (HLPs or non-HLPs) as well as HLPs activity (high and low). More specifically, feature representation learning scheme was utilized to generate 54 probabilistic features by integrating six different machine learning classifiers and nine different sequence-based encodings. Consequently, the 54 probabilistic features were fused to provide sufficiently converged sequence information which was used as an input to extremely randomized tree for the development of two final prediction models which independently identify HLP and its activity. Performance comparisons over empirical cross-validation analysis, independent test and case study against state-of-the-art methods demonstrate that HLPpred-Fuse consistently outperformed these methods in the identification of hemolytic activity. Availability and implementation For the convenience of experimental scientists, a web-based tool has been established at http://thegleelab.org/HLPpred-Fuse. Contact glee@ajou.ac.kr or watshara.sho@mahidol.ac.th or bala@ajou.ac.kr. Supplementary information Supplementary data are available at Bioinformatics online.

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.

DNA N6-adenine methylation (6mA) is an epigenetic modification in prokaryotes and eukaryotes. Identifying 6mA sites in rice genome is important in rice epigenetics and breeding, but non-random distribution and biological functions of these sites remain unclear. Several machine-learning tools can identify 6mA sites but show limited prediction accuracy, which limits their usability in epigenetic research. Here, we developed a novel computational predictor, called the Sequence-based DNA N6-methyladenine predictor (SDM6A), which is a two-layer ensemble approach for identifying 6mA sites in the rice genome. Unlike existing methods, which are based on single models with basic features, SDM6A explores various features, and five encoding methods were identified as appropriate for this problem. Subsequently, an optimal feature set was identified from encodings, and corresponding models were developed individually using support vector machine and extremely randomized tree. First, all five single models were integrated via ensemble approach to define the class for each classifier. Second, two classifiers were integrated to generate a final prediction. SDM6A achieved robust performance on cross-validation and independent evaluation, with average accuracy and Matthews correlation coefficient (MCC) of 88.2% and 0.764, respectively. Corresponding metrics were 4.7%–11.0% and 2.3%–5.5% higher than those of existing methods, respectively. A user-friendly, publicly accessible web server ( http://thegleelab.org/SDM6A ) was implemented to predict novel putative 6mA sites in rice genome.

SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome.

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

Evaluation of different computational methods on 5-methylcytosine sites identification.

Quality Assessment (QA) plays an important role in protein structure prediction. Traditional protein QA methods suffer from searching databases or comparing with other models for making predictions, which usually fail. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure at each time-step, without using any database. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

AngularQA: Protein Model Quality Assessment with LSTM Networks

Artificial Intelligence (AI) technologies have been successfully applied to many fields, such as object detection and speech recognition. Among these applications, few consider cases where some feature values are missing or unreliable, such as in the prediction of job-hopping patterns where many profiles are incomplete, even though these missing features may be important for businesses (e.g. human resources and management). In this paper, we propose a novel AI technology, Sequential Optimization of Naive Bayesian (SONB), which not only makes predictions, but also learns the underlying pattern and automatically estimates missing or unreliable feature values. We analyzed several important job-hopping features and applied it to predict job-hopping patterns on many incomplete profiles. Our experiment shows SONB accurately estimates missing values and achieves state-of-the-art performance. In addition, the accuracy of deep learning is improved by 3% on the new dataset generated by SONB over the raw data. In summary, we introduce a novel AI technology for forecasting, which could also be used to estimate missing values in the input data. It is applied to a large (20,185,365 employee profiles) dataset and successfully predicts job-hopping patterns for employees based on their profiles, which could be a valuable resource for businesses.

Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping

Correctly predicting the complex three-dimensional structure of a protein from its sequence would allow for a superior understanding of the function of specific proteins with many applications. We propose a novel method aimed to tackle a crucial step in the protein prediction problem, assessing the quality of generated predictions. Unlike traditional methods, our method, to the best of our knowledge, is the first to analyse the topology of the predicted structure. We found that our new representation provided accurate information regarding the location of the protein's backbone. Using this information, we implemented a novel algorithm based on convolutional neural network (CNN) to predict GDT_TS score for given protein models. Our method has shown promising results - overall correlation of 0.41 on CASP12 dataset. Future work will aim to implement additional features into our representation. The software is freely available at GitHub: https://github.com/caorenzhi/TopQA.

TopQA: a topological representation for single-model protein quality assessment with machine learning

https://biorxiv.org/content/biorxiv/early/2019/02/25/560995.full-text.pdf

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AngularQA: Protein Model Quality Assessment with LSTM Networks

Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping

TopQA: a topological representation for single-model protein quality assessment with machine learning

AngularQA: Protein Model Quality Assessment with LSTM Networks