Current Drug Metabolism - Volume 20, Issue 3, 2019

Background: Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention. Methods: We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods. Results: Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed. Conclusion: There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Background: Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions. Methods: In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods. Results: We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions. Conclusion: The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Background: Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still a very expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in different fields, such as speech recognition, image classification, bioinformatics, etc. One very important application of these AI techniques is in the field of drug discovery. Methods: We did a large-scale literature search on existing scientific websites (e.g, ScienceDirect, Arxiv) and startup companies to understand current status of machine learning techniques in drug discovery. Results: Our experiments demonstrated that there are different patterns in machine learning fields and drug discovery fields. For example, keywords like prediction, brain, discovery, and treatment are usually in drug discovery fields. Also, the total number of papers published in drug discovery fields with machine learning techniques is increasing every year. Conclusion: The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

Background: The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. Results: In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. Conclusion: This study provides the guide to the development of computational methods for the drug-target interaction prediction.

Background: Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed. Methods: We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc. Results: The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools. Conclusion: Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.

Background: From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue. Methods: We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail. Results: Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved. Conclusion: The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods.

Background: Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification. Methods: Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification. Results: We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs. Conclusion: In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

Background: As molecular chaperones, Heat Shock Proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but are also linked with multiple kinds of diseases. Therefore, HSPs have been regarded as the focus of drug design. Since HSPs from different families play distinct functions, accurately classifying the families of HSPs is the key step to clearly understand their biological functions. In contrast to laborintensive and cost-ineffective experimental methods, computational classification of HSP families has emerged to be an alternative approach. Methods: We reviewed the paper that described the existing datasets of HSPs and the representative computational approaches developed for the identification and classification of HSPs. Results: The two benchmark datasets of HSPs, namely HSPIR and sHSPdb were introduced, which provided invaluable resources for computationally identifying HSPs. The gold standard dataset and sequence encoding schemes for building computational methods of classifying HSPs were also introduced. The three representative web-servers for identifying HSPs and their families were described. Conclusion: The existing machine learning methods for identifying the different families of HSPs indeed yielded quite encouraging results and did play a role in promoting the research on HSPs. However, the number of HSPs with known structures is very limited. Therefore, determining the structure of the HSPs is also urgent, which will be helpful in revealing their functions.

Background: Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450. Objective: This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates. Results: Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors. Conclusion: This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.

Background: Some studies have shown that Human Papillomavirus (HPV) is strongly associated with cervical cancer. As we all know, cervical cancer still remains the fourth most common cancer, affecting women worldwide. Thus, it is both challenging and essential to detect risk types of human papillomaviruses. Methods: In order to discriminate whether HPV type is highly risky or not, many epidemiological and experimental methods have been proposed recently. For HPV risk type prediction, there also have been a few computational studies which are all based on Machine Learning (ML) techniques, but adopt different feature extraction methods. Therefore, we conclude and discuss several classical approaches which have got a better result for the risk type prediction of HPV. Results: This review summarizes the common methods to detect human papillomavirus. The main methods are sequence- derived features, text-based classification, gap-kernel method, ensemble SVM, Word statistical model, position- specific statistical model and mismatch kernel method (SVM). Among these methods, position-specific statistical model get a relatively high accuracy rate (accuracy=97.18%). Word statistical model is also a novel approach, which extracted the information of HPV from the protein “sequence space” with word statistical model to predict high-risk types of HPVs (accuracy=95.59%). These methods could potentially be used to improve prediction of highrisk types of HPVs. Conclusion: From the prediction accuracy, we get that the classification results are more accurate by establishing mathematical models. Thus, adopting mathematical methods to predict risk type of HPV will be the main goal of research in the future.

Background: Nutritional deficiencies are common following Roux-en-Y Gastric Bypass (RYGB). Aetiology is diverse; including non-compliance, altered diet, unresolved preoperative deficiency and differential degrees of post-operative malabsorption occurring as function of length of bypassed intestine. Iron and calcium/vitamin D deficiency occur in up to 50% of patients following RYGB. Currently, treatment strategies recommend the prescription of oral supplements for those who become deficient. Meanwhile, debate exists regarding the absorption capacity of these post-operatively and their efficacy in treating deficiency. Objective: To examine the disposition of oral iron and calcium/vitamin D supplementation following RYGB. Methods: A literature review was carried out using PubMed and Embase. Data from the key interventional studies investigating iron and calcium/vitamin D oral supplement absorption and efficacy following RYGB was summarized. Results: Absorption of both iron and vitamin D/calcium is adversely affected following RYGB. Distribution and metabolism may be altered by the predominance of paracellular absorption pathways which promote unregulated influx into the circulatory system. Overall, studies indicate that current supplementation strategies are efficacious to a degree in treating deficiency following RYGB, generally restoration of optimal status is not achieved. Conclusion: Oral supplement disposition is altered following RYGB. As a result, patients are required to take regimens of oral supplementation indefinitely. The dosage which confers optimum health benefit while avoiding potential toxicity and tolerability issues remains unknown. Novel preparations with improved disposition could help limit the extent of post-RYGB nutritional deficiencies.