GPCR-MPredictor: multi-level prediction of G protein-coupled receptors using genetic ensemble

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery

The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process "faster, smarter and cheaper," we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy

Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is extracting valuable features and filtering out redundancy. To achieve a more efficient classification model, we put the feature synonym problem into consideration and create a new method based on functional word clustering and integration. Through evaluating the evolution correlation between features using the transition scores in mature molecular substitution matrices, candidate features are clustered into synonym groups. Each group of the clustered features is then integrated and represented by a unique key functional word. These retained key functional words are used to form a feature knowledge base. The original GPCR sequences are then transferred into feature vectors based on a feature re-extraction strategy according to the features in the knowledge base before the training and testing stage. We create multiple machine learning models based on Naïve Bayesian (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The established model is applied to classify two public data sets containing 8354 and 12,731 GPCRs, respectively. These models achieve significant performance in almost all evaluation criteria in comparison with state-of-the art. This work demonstrated the potential of the novel feature extraction strategy and provided an effective theoretical design for the hierarchical classification of GPCRs.

EMCBOW-GPCR: A method for identifying G-protein coupled receptors based on word embedding and wordbooks

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An computational method was developed to identify G-protein coupled receptors.

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Three word-embedding models and a bag-of-words model are used to extract original features.

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A high accuracy was achieved by using fusion information.

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A powerful tool was established.

G Protein-Coupled Receptors (GPCRs) are one of the largest membrane protein receptor family in human, which are also important targets for many drugs. Thence, it’s of great significance to judge whether a protein is a GPCR or not. However, identifying GPCRs by experimental methods is very expensive and time-consuming. As more and more GPCR primary sequences are accumulated, it’s feasible to develop a computational model to predict GPCRs precisely and quickly. In this paper, a novel method called EMCBOW-GPCR has been proposed to improve the accuracy of identifying GPCRs based on natural language processing (NLP). For representing GPCRs, three word-embedding models and a bag-of-words model are used to extract original features. Then, the original features are thrown into a Deep-learning algorithm to extract features further and reduce the dimension. Finally, the obtained features are fed into Extreme Gradient Boosting. As shown with the results comparison, the overall prediction metrics of EMCBOW-GPCR are higher than the state of the arts. In order to be convenient for more researchers to use EMCBOW-GPCR, the method and source code have been opened in github, which are available at https://github.com/454170054/EMCBOW-GPCR, and a user-friendly web-server for EMCBOW-GPCR has been established at http://www.jci-bioinfo.cn/emcbowgpcr.

Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space

GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately for each of the family, subfamily, and sub-subfamily level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose DeepHier, a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function. However, the identification accuracies of these in silico methods and their false discovery rate have not yet been assessed so far, which greatly limits the usage of these algorithms. Herein, a comprehensive comparison of the performances among four popular prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these methods was systematically assessed by four standard statistical indexes based on the independent test datasets of 93 functional protein families defined by UniProtKB keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model organisms (Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Mycobacterium tuberculosis). As a result, the substantially higher sensitivity of SVM and BLAST was observed compared with that of PNN and KNN. However, the machine learning algorithms (PNN, KNN and SVM) were found capable of substantially reducing the false discovery rate (SVM < PNN < KNN). In sum, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

WISH, a novel CFEM GPCR is indispensable for surface sensing, asexual and pathogenic differentiation in rice blast fungus

We have selected and characterized a unique Conserved Fungal-specific Extra-cellular Membrane-spanning (CFEM) domain containing PTH11 like G-protein coupled receptor (GPCR), which is responsible for Water wettability, Infection, Surface sensing and Hyper-conidiation (WISH). The pathogenicity gene WISH is predicted to encode a novel seven transmembrane protein in the rice blast fungus, Magnaporthe oryzae, one of the deadliest pathogens of rice. We generated knockout mutants through a homologous recombination-based method to understand the function of the gene. These mutants are nonpathogenic due to a defect in sensing hydrophobic surface and appressorium differentiation. The mutant failed to undergo early events of pathogenesis, and appressorium development is diminished on inductive hydrophobic surface and was unable to penetrate susceptible rice leaves. The Δwish mutant did not develop any appressorium, suggesting that WISH protein is required for appressorium morphogenesis and is also involved in host surface recognition. We examined various aspects of pathogenesis and the results indicated involvement of WISH in preventing autolysis of vegetative hyphae, determining surface hydrophobicity and maintenance of cell-wall integrity. WISH gene from M. oryzae strain B157 complemented the Δwish mutant, indicating functional authenticity. Exogenous activation of cellular signaling failed to suppress the defects in Δwish mutants. These findings suggest that WISH GPCR senses diverse extracellular signals to play multiple roles and might have effects on PTH11 and MPG1 genes especially as an upstream effector of appressorium differentiation. It is for the first time that a typical GPCR containing seven transmembrane helices involved in the early events of plant pathogenesis of M. oryzae has been functionally characterized.

SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

Eukaryotic G protein-coupled receptors as descendants of prokaryotic sodium-translocating rhodopsins

Abstract

Microbial rhodopsins and G-protein coupled receptors (GPCRs, which include animal rhodopsins) are two distinct (super) families of heptahelical (7TM) membrane proteins that share obvious structural similarities but no significant sequence similarity. Comparison of the recently solved high-resolution structures of the sodium-translocating bacterial rhodopsin and various Na⁺-binding GPCRs revealed striking similarity of their sodium-binding sites. This similarity allowed us to construct a structure-guided sequence alignment for the two (super)families, which highlighted their evolutionary relatedness. Our analysis supports a common underlying molecular mechanism for both families that involves a highly conserved aromatic residue playing a pivotal role in rotation of the 6th transmembrane helix.

Reviewers

This article was reviewed by Oded Beja, G. P. S. Raghava and L. Aravind.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0091-4) contains supplementary material, which is available to authorized users.

Applying random forest and subtractive fuzzy c-means clustering techniques for the development of a novel G protein-coupled receptor discrimination method using pseudo amino acid compositions

G protein-coupled receptors (GPCRs) constitute the largest superfamily of integral membrane proteins (IMPs) and they tremendously contribute in the flow of information into cells. In this study, the random forest (RF) and the subtractive fuzzy c-means clustering (SBC) methods have been used to determine the importance of input variables and discriminate GPCRs from non-GPCRs using twenty amino acid and fifty pseudo amino acid compositions derived from GPCR sequences. The studied dataset was retrieved from the UniProt/SWISSPROT database and consists of 1000 GPCR and 1000 non-GPCR reviewed sequences. The top ranked RF-SBC-based model discriminates GPCRs and non-GPCRs successfully with the accuracy, sensitivity, specificity and Matthew's coefficient correlation (MCC) rates of 99.15%, 99.60%, 98.70% and 0.983%, respectively. These rates were obtained from averaged values of 5-fold cross validation using only twenty four out of fifty pseudo amino acid composition features. The results show that the proposed RF-SBC-based model outperforms other existing algorithms in terms of the evaluated performance criteria. The webserver for the proposed algorithm is available at http://brcinfo.shinyapps.io/GPCRIden.

A multi-fold string kernel for sequence classification

A novel framework is proposed to classify biological sequences using a kernel. It considers the topological information along with the primary structural information. The widely used string kernel for sequence classification does not take into account the structural information which might be available for biological sequences. The proposed kernels incorporate the additional structural information and thus make the features more informative.

DNA-LCEB: a high-capacity and mutation-resistant DNA data-hiding approach by employing encryption, error correcting codes, and hybrid twofold and fourfold codon-based strategy for synonymous substitution in amino acids

Data-hiding in deoxyribonucleic acid (DNA) sequences can be used to develop an organic memory and to track parent genes in an offspring as well as in genetically modified organism. However, the main concerns regarding data-hiding in DNA sequences are the survival of organism and successful extraction of watermark from DNA. This implies that the organism should live and reproduce without any functional disorder even in the presence of the embedded data. Consequently, performing synonymous substitution in amino acids for watermarking becomes a primary option. In this regard, a hybrid watermark embedding strategy that employs synonymous substitution in both twofold and fourfold codons of amino acids is proposed. This work thus presents a high-capacity and mutation-resistant watermarking technique, DNA-LCEB, for hiding secret information in DNA of living organisms. By employing the different types of synonymous codons of amino acids, the data storage capacity has been significantly increased. It is further observed that the proposed DNA-LCEB employing a combination of synonymous substitution, lossless compression, encryption, and Bose-Chaudary-Hocquenghem coding is secure and performs better in terms of both capacity and robustness compared to existing DNA data-hiding schemes. The proposed DNA-LCEB is tested against different mutations, including silent, miss-sense, and non-sense mutations, and provides substantial improvement in terms of mutation detection/correction rate and bits per nucleotide. A web application for DNA-LCEB is available at http://111.68.99.218/DNA-LCEB.

Protein sub-nuclear localization prediction using SVM and Pfam domain information

The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139.227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server.

Bioinformatics tools for predicting GPCR gene functions

The automatic classification of GPCRs by bioinformatics methodology can provide functional information for new GPCRs in the whole 'GPCR proteome' and this information is important for the development of novel drugs. Since GPCR proteome is classified hierarchically, general ways for GPCR function prediction are based on hierarchical classification. Various computational tools have been developed to predict GPCR functions; those tools use not simple sequence searches but more powerful methods, such as alignment-free methods, statistical model methods, and machine learning methods used in protein sequence analysis, based on learning datasets. The first stage of hierarchical function prediction involves the discrimination of GPCRs from non-GPCRs and the second stage involves the classification of the predicted GPCR candidates into family, subfamily, and sub-subfamily levels. Then, further classification is performed according to their protein-protein interaction type: binding G-protein type, oligomerized partner type, etc. Those methods have achieved predictive accuracies of around 90 %. Finally, I described the future subject of research of the bioinformatics technique about functional prediction of GPCR.

WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .