Protein Remote Homology Detection and Fold Recognition Based on Sequence-Order Frequency Matrix

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Protein remote homology recognition using local and global structural sequence alignment

Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms.

Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model

Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods that combine the various features to improve predictive performance remain the challenge problems. In this study, we proposed two novel algorithms: AWMG and EMfold. AWMG used a novel predictor based on the multi-view learning framework for fold recognition. Each view was treated as the intermediate representation of the corresponding data source of proteins, including the evolutionary information and the retrieval information. AWMG calculated the auto-weight for each view respectively and constructed the latent subspace which contains the common information shared by different views. The marginalized constraint was employed to enlarge the margins between different folds, improving the predictive performance of AWMG. Furthermore, we proposed a novel ensemble method called EMfold, which combines two complementary methods AWMG and DeepSS. The later method was a template-based algorithm using the SPARKS-X and DeepFR programs. EMfold integrated the advantages of template-based assignment and machine learning classifier. Experimental results on the two widely datasets (LE and YK) showed that the proposed methods outperformed some state-of-the-art methods, indicating that AWMG and EMfold are useful tools for protein fold recognition.

Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores

Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment scores between query-template protein pairs and the other is the machine learning method based on the feature representation and classifier. These two approaches have their own advantages and disadvantages. Can we combine these methods to establish more accurate predictors for protein fold recognition? In this study, we made an initial attempt and proposed two novel algorithms: TSVM-fold and ESVM-fold. TSVM-fold was based on the Support Vector Machines (SVMs), which utilizes a set of pairwise sequence similarity scores generated by three complementary template-based methods, including HHblits, SPARKS-X, and DeepFR. These scores measured the global relationships between query sequences and templates. The comprehensive features of the attributes of the sequences were fed into the SVMs for the prediction. Then the TSVM-fold was further combined with the HHblits algorithm so as to improve its generalization ability. The combined method is called ESVM-fold. Experimental results in two rigorous benchmark datasets (LE and YK datasets) showed that the proposed methods outperform some state-of-the-art methods, indicating that the TSVM-fold and ESVM-fold are efficient predictors for protein fold recognition.

Exploring novel and potent cell penetrating peptides in the proteome of SARS-COV-2 using bioinformatics approaches

Among various delivery systems for vaccine and drug delivery, cell-penetrating peptides (CPPs) have been known as a potent delivery system because of their capability to penetrate cell membranes and deliver some types of cargoes into cells. Several CPPs were found in the proteome of viruses such as Tat originated from human immunodeficiency virus-1 (HIV-1), and VP22 derived from herpes simplex virus-1 (HSV-1). In the current study, a wide-range of CPPs was identified in the proteome of SARS-CoV-2, a new member of coronaviruses family, using in silico analyses. These CPPs may play a main role for high penetration of virus into cells and infection of host. At first, we submitted the proteome of SARS-CoV-2 to CellPPD web server that resulted in a huge number of CPPs with ten residues in length. Afterward, we submitted the predicted CPPs to C2Pred web server for evaluation of the probability of each peptide. Then, the uptake efficiency of each peptide was investigated using CPPred-RF and MLCPP web servers. Next, the physicochemical properties of the predicted CPPs including net charge, theoretical isoelectric point (pI), amphipathicity, molecular weight, and water solubility were calculated using protparam and pepcalc tools. In addition, the probability of membrane binding potential and cellular localization of each CPP were estimated by Boman index using APD3 web server, D factor, and TMHMM web server. On the other hand, the immunogenicity, toxicity, allergenicity, hemolytic potency, and half-life of CPPs were predicted using various web servers. Finally, the tertiary structure and the helical wheel projection of some CPPs were predicted by PEP-FOLD3 and Heliquest web servers, respectively. These CPPs were divided into: a) CPP containing tumor homing motif (RGD) and/or tumor penetrating motif (RXXR); b) CPP with the highest Boman index; c) CPP with high half-life (~100 hour) in mammalian cells, and d) CPP with +5.00 net charge. Based on the results, we found a large number of novel CPPs with various features. Some of these CPPs possess tumor-specific motifs which can be evaluated in cancer therapy. Furthermore, the novel and potent CPPs derived from SARS-CoV-2 may be used alone or conjugated to some sequences such as nuclear localization sequence (NLS) for vaccine and drug delivery.

Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation

As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted features, which depict the characteristics of different protein folds; however, effective feature extraction methods still represent the bottleneck for further performance improvement of protein fold recognition. As a powerful feature extractor, deep convolutional neural network (DCNN) can automatically extract discriminative features for fold recognition without human intervention, which has demonstrated an impressive performance on protein fold recognition. Despite the encouraging progress, DCNN often acts as a black box, and as such, it is challenging for users to understand what really happens in DCNN and why it works well for protein fold recognition. In this study, we explore the intrinsic mechanism of DCNN and explain why it works for protein fold recognition using a visual explanation technique. More specifically, we first trained a VGGNet-based DCNN model, termed VGGNet-FE, which can extract fold-specific features from the predicted protein residue-residue contact map for protein fold recognition. Subsequently, based on the trained VGGNet-FE, we implemented a new contact-assisted predictor, termed VGGfold, for protein fold recognition; we then visualized what features were extracted by each of the convolutional layers in VGGNet-FE using a deconvolution technique. Furthermore, we visualized the high-level semantic information, termed fold-discriminative region, of a predicted contact map from the localization map obtained from the last convolutional layer of VGGNet-FE. It is visually confirmed that VGGNet-FE could effectively extract distinct fold-discriminative regions for different types of protein folds, thereby accounting for the improved performance of VGGfold for protein fold recognition. In summary, this study is of great significance for both understanding the working principle of DCNNs in protein fold recognition and exploring the relationship between the predicted protein contact map and protein tertiary structure. This proposed visualization method is flexible and applicable to address other DCNN-based bioinformatics and computational biology questions. The online web server of VGGfold is freely available at http://csbio.njust.edu.cn/bioinf/vggfold/.

ReFold-MAP: Protein remote homology detection and fold recognition based on features extracted from profiles

Protein remote homology detection and protein fold recognition are two important tasks in protein structure and function prediction. There are three kinds of methods in this field, including the discriminative methods, the alignment methods, and the ranking methods. In this study, a new discriminative method called ReFold-MAP is proposed. The proposed method extracts comprehensive features based on three profile-based features: Motif-PSSM, ACC-PSSM, and PDT-profile. We call these features as MAP features, which incorporate the structural motif kernel information, the evolutionary information, and the sequence information. The experiments prove that ReFold-MAP outperforms other approaches. Therefore, ReFold-MAP will be a useful tool for protein remote homology detection and fold recognition.

RFPR-IDP: reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating both fully ordered proteins and disordered proteins

As an important type of proteins, intrinsically disordered proteins/regions (IDPs/IDRs) are related to many crucial biological functions. Accurate prediction of IDPs/IDRs is beneficial to the prediction of protein structures and functions. Most of the existing methods ignore the fully ordered proteins without IDRs during training and test processes. As a result, the corresponding predictors prefer to predict the fully ordered proteins as disordered proteins. Unfortunately, these methods were only evaluated on datasets consisting of disordered proteins without or with only a few fully ordered proteins, and therefore, this problem escapes the attention of the researchers. However, most of the newly sequenced proteins are fully ordered proteins in nature. These predictors fail to accurately predict the ordered and disordered proteins in real-world applications. In this regard, we propose a new method called RFPR-IDP trained with both fully ordered proteins and disordered proteins, which is constructed based on the combination of convolution neural network (CNN) and bidirectional long short-term memory (BiLSTM). The experimental results show that although the existing predictors perform well for predicting the disordered proteins, they tend to predict the fully ordered proteins as disordered proteins. In contrast, the RFPR-IDP predictor can correctly predict the fully ordered proteins and outperform the other 10 state-of-the-art methods when evaluated on a test dataset with both fully ordered proteins and disordered proteins. The web server and datasets of RFPR-IDP are freely available at http://bliulab.net/RFPR-IDP/server.

Fold-LTR-TCP: protein fold recognition based on triadic closure principle

As an important task in protein structure and function studies, protein fold recognition has attracted more and more attention. The existing computational predictors in this field treat this task as a multi-classification problem, ignoring the relationship among proteins in the dataset. However, previous studies showed that their relationship is critical for protein homology analysis. In this study, the protein fold recognition is treated as an information retrieval task. The Learning to Rank model (LTR) was employed to retrieve the query protein against the template proteins to find the template proteins in the same fold with the query protein in a supervised manner. The triadic closure principle (TCP) was performed on the ranking list generated by the LTR to improve its accuracy by considering the relationship among the query protein and the template proteins in the ranking list. Finally, a predictor called Fold-LTR-TCP was proposed. The rigorous test on the LE benchmark dataset showed that the Fold-LTR-TCP predictor achieved an accuracy of 73.2%, outperforming all the other competing methods.

MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks

Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the characteristics of protein folds. However, the feature extraction methods are still the bottleneck of the performance improvement of these methods. In this paper, we proposed two new feature extraction methods called MotifCNN and MotifDCNN to extract more discriminative fold-specific features based on structural motif kernels to construct the motif-based convolutional neural networks (CNNs). The pairwise sequence similarity scores calculated based on fold-specific features are then fed into support vector machines to construct the predictor for fold recognition, and a predictor called MotifCNN-fold has been proposed. Experimental results on the benchmark dataset showed that MotifCNN-fold obviously outperformed all the other competing methods. In particular, the fold-specific features extracted by MotifCNN and MotifDCNN are more discriminative than the fold-specific features extracted by other deep learning techniques, indicating that incorporating the structural motifs into the CNN is able to capture the characteristics of protein folds.

Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods

The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.

A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features

To gain insight into the malfunction of the Golgi apparatus and its relationship to various genetic and neurodegenerative diseases, the identification of sub-Golgi proteins, both cis-Golgi and trans-Golgi proteins, is of great significance. In this study, a state-of-art random forests sub-Golgi protein classifier, rfGPT, was developed. The rfGPT used 2-gap dipeptide and split amino acid composition for the feature vectors and was combined with the synthetic minority over-sampling technique (SMOTE) and an analysis of variance (ANOVA) feature selection method. The rfGPT was trained on a sub-Golgi protein sequence data set (137 sequences), with sequence identity less than 25%. For the optimal rfGPT classifier with 93 features, the accuracy (ACC) was 90.5%; the Matthews correlation coefficient (MCC) was 0.811; the sensitivity (Sn) was 92.6%; and the specificity (Sp) was 88.4%. The independent testing scores for the rfGPT were ACC = 90.6%; MCC = 0.696; Sn = 96.1%; and Sp = 69.2%. Although the independent testing accuracy was 4.4% lower than that for the best reported sub-Golgi classifier trained on a data set with 40% sequence identity (304 sequences), the rfGPT is currently the top sub-Golgi protein predictor utilizing feature vectors without any position-specific scoring matrix and its derivative features. Therefore, the rfGPT is a more practical tool, because no sequence alignment is required with tens of millions of protein sequences. To date, the rfGPT is the Golgi classifier with the best independent testing scores, optimized by training on smaller benchmark data sets. Feature importance analysis proves that the non-polar and aliphatic residues composition, the (aromatic residues) + (non-polar, aliphatic residues) dipeptide and aromatic residues composition between NH2-termial and COOH-terminal of protein sequences are the three top biological features for distinguishing the sub-Golgi proteins.

MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy–defined energy

Recursive feature elimination (RFE), as one of the most popular feature selection algorithms, has been extensively applied to bioinformatics. During the training, a group of candidate subsets are generated by iteratively eliminating the least important features from the original features. However, how to determine the optimal subset from them still remains ambiguous. Among most current studies, either overall accuracy or subset size (SS) is used to select the most predictive features. Using which one or both and how they affect the prediction performance are still open questions. In this study, we proposed MinE-RFE, a novel RFE-based feature selection approach by sufficiently considering the effect of both factors. Subset decision problem was reflected into subset-accuracy space and became an energy-minimization problem. We also provided a mathematical description of the relationship between the overall accuracy and SS using Gaussian Mixture Models together with spline fitting. Besides, we comprehensively reviewed a variety of state-of-the-art applications in bioinformatics using RFE. We compared their approaches of deciding the final subset from all the candidate subsets with MinE-RFE on diverse bioinformatics data sets. Additionally, we also compared MinE-RFE with some well-used feature selection algorithms. The comparative results demonstrate that the proposed approach exhibits the best performance among all the approaches. To facilitate the use of MinE-RFE, we further established a user-friendly web server with the implementation of the proposed approach, which is accessible at http://qgking.wicp.net/MinE/. We expect this web server will be a useful tool for research community.