Classification of nuclear receptors based on amino acid composition and dipeptide composition

ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms

BACKGROUND

Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic stresses. Therefore, it is crucial to identify TFs associated with abiotic stress response for breeding of abiotic stress tolerant crop cultivars.

METHODS

Based on a machine learning framework, a computational model was envisaged to predict TFs associated with abiotic stress response in plants. To numerically encode TF sequences, four distinct sequence derived features were generated. The prediction was performed using ten shallow learning and four deep learning algorithms. For prediction using more pertinent and informative features, feature selection techniques were also employed.

RESULTS

Using the features chosen by the light-gradient boosting machine-variable importance measure (LGBM-VIM), the LGBM achieved the highest cross-validation performance metrics (accuracy: 86.81%, auROC: 92.98%, and auPRC: 94.03%). Further evaluation of the proposed model (LGBM prediction method + LGBM-VIM selected features) was also done using an independent test dataset, where the accuracy, auROC and auPRC were observed 81.98%, 90.65% and 91.30%, respectively.

CONCLUSIONS

To facilitate the adoption of the proposed strategy by users, the approach was implemented as a prediction server called ASPTF, accessible at https://iasri-sg.icar.gov.in/asptf/. The developed approach and the corresponding web application are anticipated to supplement experimental methods in the identification of transcription factors (TFs) responsive to abiotic stress in plants.

AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest

The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder

Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.

miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning

MicroRNAs (miRNAs) are an essential type of small molecule RNAs that play significant regulatory roles in organisms. Recent studies have demonstrated that small open reading frames (sORFs) harbored in primary miRNAs (pri-miRNAs) can encode small peptides, known as miPEPs. Plant miPEPs can increase the abundance and activity of cognate miRNAs by promoting the transcription of their corresponding pri-miRNAs, thereby modulating plant traits. Biological experiments are the most effective way to accurately identify miPEPs; however, they are time-consuming and expensive. Hence, an efficient computational method for the identification of miPEPs on a large scale is highly desirable. Up to now, there have been no specialized computational tools for identifying miPEPs. In this work, a novel predictor named miPEPPred-FRL based on an adaptive feature representation learning framework that consists of the feature transformation module and the cascade architecture has been proposed. The feature transformation module integrating a newly designed feature selection method and classifier selection rule is developed to convert sequence-based features into primary class and probabilistic features, which are then fed into the improved cascade architecture to obtain more stable and discriminative augmented features. Finally, the augmented features are utilized to construct the final predictor. Cross-validation experiments illustrate that the novel feature selection method and classifier selection rule contribute to boosting the feature representation ability of the framework. Furthermore, the high accuracy of miPEPPred-FRL on independent testing data suggests that it is a trustworthy and valuable tool for the identification of miPEPs.

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction

Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications.

MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention

Identifying drug-target interactions (DTIs) holds significant importance in drug discovery and development, playing a crucial role in various areas such as virtual screening, drug repurposing and identification of potential drug side effects. However, existing methods commonly exploit only a single type of feature from drugs and targets, suffering from miscellaneous challenges such as high sparsity and cold-start problems. We propose a novel framework called MSI-DTI (Multi-Source Information-based Drug-Target Interaction Prediction) to enhance prediction performance, which obtains feature representations from different views by integrating biometric features and knowledge graph representations from multi-source information. Our approach involves constructing a Drug-Target Knowledge Graph (DTKG), obtaining multiple feature representations from diverse information sources for SMILES sequences and amino acid sequences, incorporating network features from DTKG and performing an effective multi-source information fusion. Subsequently, we employ a multi-head self-attention mechanism coupled with residual connections to capture higher-order interaction information between sparse features while preserving lower-order information. Experimental results on DTKG and two benchmark datasets demonstrate that our MSI-DTI outperforms several state-of-the-art DTIs prediction methods, yielding more accurate and robust predictions. The source codes and datasets are publicly accessible at https://github.com/KEAML-JLU/MSI-DTI.

Prediction of matrilineal specific patatin-like protein governing in-vivo maternal haploid induction in maize using support vector machine and di-peptide composition

The mutant matrilineal (mtl) gene encoding patatin-like phospholipase activity is involved in in-vivo maternal haploid induction in maize. Doubling of chromosomes in haploids by colchicine treatment leads to complete fixation of inbreds in just one generation compared to 6-7 generations of selfing. Thus, knowledge of patatin-like proteins in other crops assumes great significance for in-vivo haploid induction. So far, no online tool is available that can classify unknown proteins into patatin-like proteins. Here, we aimed to optimize a machine learning-based algorithm to predict the patatin-like phospholipase activity of unknown proteins. Four different kernels [radial basis function (RBF), sigmoid, polynomial, and linear] were used for building support vector machine (SVM) classifiers using six different sequence-based compositional features (AAC, DPC, GDPC, CTDC, CTDT, and GAAC). A total of 1170 protein sequences including both patatin-like (585 sequences) from various monocots, dicots, and microbes; and non-patatin-like proteins (585 sequences) from different subspecies of Zea mays were analyzed. RBF and polynomial kernels were quite promising in the prediction of patatin-like proteins. Among six sequence-based compositional features, di-peptide composition attained > 90% prediction accuracies using RBF and polynomial kernels. Using mutual information, most explaining dipeptides that contributed the highest to the prediction process were identified. The knowledge generated in this study can be utilized in other crops prior to the initiation of any experiment. The developed SVM model opened a new paradigm for scientists working in in-vivo haploid induction in commercial crops. This is the first report of machine learning of the identification of proteins with patatin-like activity.

ACP-ML: A sequence-based method for anticancer peptide prediction

Cancer is a serious malignant tumor and is difficult to cure. Chemotherapy, as a primary treatment for cancer, causes significant harm to normal cells in the body and is often accompanied by serious side effects. Recently, anti-cancer peptides (ACPs) as a type of protein for treating cancers dominated research into the development of new anti-tumor drugs because of their ability to specifically target and destroy cancer cells. The screening of proteins with cancer-inhibiting properties from a large pool of proteins is key to the development of anti-tumor drugs. However, it is expensive and inefficient to accurately identify protein functions only through biological experiments due to their complex structure. Therefore, we propose a new prediction model ACP-ML to effectively predict ACPs. In terms of feature extraction, DPC, PseAAC, CTDC, CTDT and CS-Pse-PSSM features were used and the most optimal feature set was selected by comparing combinations of these features. Then, a two-step feature selection process using MRMD and RFE algorithms was performed to determine the most crucial features from the most optimal feature set for identifying ACPs. Furthermore, we assessed the classification accuracy of single learning models and different strategies-based ensemble models through ten-fold cross-validation. Ultimately, a voting-based ensemble learning method is developed to predict ACPs. To validate its effectiveness, two independent test sets were used to perform tests, achieving accuracy of 90.891 % and 92.578 % respectively. Compared with existing anticancer peptide prediction algorithms, the proposed feature processing method is more effective, and the proposed ensemble model ACP-ML exhibits stronger generalization capability and higher accuracy.

An integrative machine learning model for the identification of tumor T-cell antigens

The escalating global incidence of cancer poses significant health challenges, underscoring the need for innovative and more efficacious treatments. Cancer immunotherapy, a promising approach leveraging the body's immune system against cancer, emerges as a compelling solution. Consequently, the identification and characterization of tumor T-cell antigens (TTCAs) have become pivotal for exploration. In this manuscript, we introduce TTCA-IF, an integrative machine learning-based framework designed for TTCAs identification. TTCA-IF employs ten feature encoding types in conjunction with five conventional machine learning classifiers. To establish a robust foundation, these classifiers are trained, resulting in the creation of 150 baseline models. The outputs from these baseline models are then fed back into the five classifiers, generating their respective meta-models. Through an ensemble approach, the five meta-models are seamlessly integrated to yield the final predictive model, the TTCA-IF model. Our proposed model, TTCA-IF, surpasses both baseline models and existing predictors in performance. In a comparative analysis involving nine novel peptide sequences, TTCA-IF demonstrated exceptional accuracy by correctly identifying 8 out of 9 peptides as TTCAs. As a tool for screening and pinpointing potential TTCAs, we anticipate TTCA-IF to be invaluable in advancing cancer immunotherapy.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy

BACKGROUND

The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a substitute therapy for inflammatory diseases like rheumatoid arthritis and Alzheimer's. Therefore, the identification of these AIPs is an emerging topic that is equally important.

METHODS

In this work, we have proposed an identification model for AIPs using a voting classifier. We used eight different feature descriptors and five conventional machine-learning classifiers. The eight feature encodings were concatenated to get a hybrid feature set. The five baseline models trained on the hybrid feature set were integrated via a voting classifier. Finally, a feature selection algorithm was used to select the optimal feature set for the construction of our final model, named IF-AIP.

RESULTS

We tested the proposed model on two independent datasets. On independent data 1, the IF-AIP model shows an improvement of 3%-5.6% in terms of accuracies and 6.7%-10.8% in terms of MCC compared to the existing methods. On the independent dataset 2, our model IF-AIP shows an overall improvement of 2.9%-5.7% in terms of accuracy and 8.3%-8.6% in terms of MCC score compared to the existing methods. A comparative performance analysis was conducted between the proposed model and existing methods using a set of 24 novel peptide sequences. Notably, the IF-AIP method exhibited exceptional accuracy, correctly identifying all 24 peptides as AIPs. The source code, pre-trained models, and all datasets are made available at https://github.com/Mir-Saima/IF-AIP.

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning

Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) have emerged as a promising therapeutic approach against inflammation. Leveraging machine learning methods, we have the opportunity to accelerate the discovery and investigation of these AIPs more effectively. In this study, we proposed an advanced framework by ensemble machine learning and deep learning for AIP prediction. Initially, we constructed three individual models with extremely randomized trees (ET), gated recurrent unit (GRU), and convolutional neural networks (CNNs) with attention mechanism and then used stacking architecture to build the final predictor. By utilizing various sequence encodings and combining the strengths of different algorithms, our predictor demonstrated exemplary performance. On our independent test set, our model achieved an accuracy, MCC, and F1-score of 0.757, 0.500, and 0.707, respectively, clearly outperforming other contemporary AIP prediction methods. Additionally, our model offers profound insights into the feature interpretation of AIPs, establishing a valuable knowledge foundation for the design and development of future anti-inflammatory strategies.

Multi-label classification and features investigation of antimicrobial peptides with various functional classes

The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information

Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.

Computational prediction of allergenic proteins based on multi-feature fusion

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.

PAMPred: A hierarchical evolutionary ensemble framework for identifying plant antimicrobial peptides

Antimicrobial peptides (AMPs) play a crucial role in plant immune regulation, growth and development stages, which have attracted significant attentions in recent years. As the wet-lab experiments are laborious and cost-prohibitive, it is indispensable to develop computational methods to discover novel plant AMPs accurately. In this study, we presented a hierarchical evolutionary ensemble framework, named PAMPred, which consisted of a multi-level heterogeneous architecture to identify plant AMPs. Specifically, to address the existing class imbalance problem, a cluster-based resampling method was adopted to build multiple balanced subsets. Then, several peptide features including sequence information-based and physicochemical properties-based features were fed into the different types of basic learners to increase the ensemble diversity. For boosting the predictive capability of PAMPred, the improved particle swarm optimization (PSO) algorithm and dynamic ensemble pruning strategy were used to optimize the weights at different levels adaptively. Furthermore, extensive ten-fold cross-validation and independent testing experimental results demonstrated that PAMPred achieved excellent prediction performance and generalization ability, and outperformed the state-of-the-art methods. It also indicated that the proposed method could serve as an effective auxiliary tool to identify plant AMPs, which would be conducive to explore the immune regulatory mechanism of plants.

Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism

Cholecystokinin (CCK) can make the human body feel full and has neurotrophic and anti-inflammatory effects. It is beneficial in treating obesity, Parkinson's disease, pancreatic cancer, and cholangiocarcinoma. Traditional biological experiments are costly and time-consuming when it comes to finding and identifying novel CCK-secretory peptides, and there is an urgent need to develop a new computational method to predict new CCK-secretory peptides. This study combines the transfer learning method with the SMILES enumeration data augmentation strategy to solve the data scarcity problem. It establishes a fusion model of the hierarchical attention network (HAN) and bidirectional long short-term memory (BiLSTM), which fully extracts peptide chain features to predict CCK-secretory peptides efficiently. The average accuracy of the proposed method in this study is 95.99%, with an AUC of 98.07%. The experimental results show that the proposed method is significantly superior to other comparative methods in accuracy and robustness. Therefore, this method is expected to be applied to the preliminary screening of CCK-secretory peptides.

A deep learning method for predicting the minimum inhibitory concentration of antimicrobial peptides against Escherichia coli using Multi-Branch-CNN and Attention

Antimicrobial peptides (AMPs) are a promising alternative to antibiotics to combat drug resistance in pathogenic bacteria. However, the development of AMPs with high potency and specificity remains a challenge, and new tools to evaluate antimicrobial activity are needed to accelerate the discovery process. Therefore, we proposed MBC-Attention, a combination of a multi-branch convolution neural network architecture and attention mechanisms to predict the experimental minimum inhibitory concentration of peptides against Escherichia coli. The optimal MBC-Attention model achieved an average Pearson correlation coefficient (PCC) of 0.775 and a root mean squared error (RMSE) of 0.533 (log μM) in three independent tests of randomly drawn sequences from the data set. This results in a 5-12% improvement in PCC and a 6-13% improvement in RMSE compared to 17 traditional machine learning models and 2 optimally tuned models using random forest and support vector machine. Ablation studies confirmed that the two proposed attention mechanisms, global attention and local attention, contributed largely to performance improvement. IMPORTANCE Antimicrobial peptides (AMPs) are potential candidates for replacing conventional antibiotics to combat drug resistance in pathogenic bacteria. Therefore, it is necessary to evaluate the antimicrobial activity of AMPs quantitatively. However, wet-lab experiments are labor-intensive and time-consuming. To accelerate the evaluation process, we develop a deep learning method called MBC-Attention to regress the experimental minimum inhibitory concentration of AMPs against Escherichia coli. The proposed model outperforms traditional machine learning methods. Data, scripts to reproduce experiments, and the final production models are available on GitHub.

BaPreS: a software tool for predicting bacteriocins using an optimal set of features

BACKGROUND

Antibiotic resistance is a major public health concern around the globe. As a result, researchers always look for new compounds to develop new antibiotic drugs for combating antibiotic-resistant bacteria. Bacteriocin becomes a promising antimicrobial agent to fight against antibiotic resistance, due to cases of both broad and narrow killing spectra. Sequence matching methods are widely used to identify bacteriocins by comparing them with the known bacteriocin sequences; however, these methods often fail to detect new bacteriocin sequences due to their high diversity. The ability to use a machine learning approach can help find new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. The aim of this work is to develop a machine learning-based software tool called BaPreS (Bacteriocin Prediction Software) using an optimal set of features for detecting bacteriocin protein sequences with high accuracy. We extracted potential features from known bacteriocin and non-bacteriocin sequences by considering the physicochemical and structural properties of the protein sequences. Then we reduced the feature set using statistical justifications and recursive feature elimination technique. Finally, we built support vector machine (SVM) and random forest (RF) models using the selected features and utilized the best machine learning model to implement the software tool.

RESULTS

We applied BaPreS to an established dataset and evaluated its prediction performance. Acquired results show that the software tool can achieve a prediction accuracy of 95.54% for testing protein sequences. This tool allows users to add new bacteriocin or non-bacteriocin sequences in the training dataset to further enhance the predictive power of the tool. We compared the prediction performance of the BaPreS with a popular sequence matching-based tool and a deep learning-based method, and our software tool outperformed both.

CONCLUSIONS

BaPreS is a bacteriocin prediction tool that can be used to discover new highly dissimilar bacteriocins for developing highly effective antibiotic drugs. This software tool can be used with Windows, Linux and macOS operating systems. The open-source software package and its user manual are available at https://github.com/suraiya14/BaPreS .

Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques

Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that's why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers. Actually, a dry lab environment focusing more on computational methods of interaction prediction can be helpful in limiting search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is proposed that called SRX-DTI. In the first stage, combination of various descriptors from protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature vectors. A major challenge in this application is the imbalanced data due to the lack of known interactions, in this regard, in the second stage, the One-SVM-US technique is proposed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection algorithm, coupled with a random forest (RF) classifier is developed to maximize the predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal features. Finally, balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves higher performance than other existing methods in predicting DTIs. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI.

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning

Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules that induce the degradation of target proteins by recruiting an E3 ligase. PROTACs have the potential to inactivate disease-related genes that are considered undruggable by small molecules, making them a promising therapy for the treatment of incurable diseases. However, only a few hundred proteins have been experimentally tested for their amenability to PROTACs, and it remains unclear which other proteins in the entire human genome can be targeted by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved a ROC-AUC of 0.81, an average precision of 0.84, and over 40% sensitivity at a false positive rate of 0.05. When evaluated by an external test set which comprised proteins from different structural folds than those in the training set, the performance of PrePROTAC did not drop significantly, indicating its generalizability. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method, which extends conventional SHAP analysis for original features to an embedding space through in silico mutagenesis. This method allowed us to identify key residues in the protein structure that play critical roles in PROTAC activity. The identified key residues were consistent with existing knowledge. Using PrePROTAC, we identified over 600 novel understudied proteins that are potentially degradable by CRBN and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease.

StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture

One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.

NRPreTo: A Machine Learning-Based Nuclear Receptor and Subfamily Prediction Tool

The nuclear receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo), a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural, and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved the performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at https://github.com/bozdaglab/NRPreTo.

ScerePhoSite: An interpretable method for identifying fungal phosphorylation sites in proteins using sequence-based features

Protein phosphorylation plays a vital role in signal transduction pathways and diverse cellular processes. To date, a tremendous number of in silico tools have been designed for phosphorylation site identification, but few of them are suitable for the identification of fungal phosphorylation sites. This largely hampers the functional investigation of fungal phosphorylation. In this paper, we present ScerePhoSite, a machine learning method for fungal phosphorylation site identification. The sequence fragments are represented by hybrid physicochemical features, and then LGB-based feature importance combined with the sequential forward search method is used to choose the optimal feature subset. As a result, ScerePhoSite surpasses current available tools and shown a more robust and balanced performance. Furthermore, the impact and contribution of specific features on the model performance were investigated by SHAP values. We expect ScerePhoSite to be a useful bioinformatics tool that complements hands-on experiments for the pre-screening of possible phosphorylation sites and facilitates our functional understanding of phosphorylation modification in fungi. The source code and datasets are accessible at https://github.com/wangchao-malab/ScerePhoSite/.

Prediction of transport proteins from sequence information with the deep learning approach

Transport proteins (TPs) are vital to the growth and life of all living things, especially in fields of microbial pathogenesis and drug resistance of tumor cells. Accurately identifying potential TPs remains an important challenge for the advancement of functional genomics. This study aimed to develop a tool for predicting TPs using the deep learning approach. Here, we proposed DeepTP, a convolutional neural network model that uses parallel subnetworks to extract features from protein sequences and uses fully connected layers for TP classification. To train and evaluate the performance of the developed model, datasets were collected from the UniProtKB/Swiss-Prot database. The test results revealed that the proposed model could successfully identify TPs with the AUCROC, accuracy, F-value, and Matthews correlation coefficient of 0.9719, 0.9513, 0.8982, and 0.8679, respectively. By further comparison, DeepTP achieved better performance than other commonly used methods. Analysis of the gradients of prediction score concerning input suggested that DeepTP makes predictions by recognizing the functional domains of TPs. We anticipate that DeepTP will serve as a useful tool for predicting TPs in large-scale genome projects, which will facilitate the discovery of novel TPs.

Disto-TRP: An approach for identifying transient receptor potential (TRP) channels using structural information generated by AlphaFold

The ability to predict 3D protein structures computationally has significantly advanced biological research. The AlphaFold protein structure database, developed by DeepMind, has provided a wealth of predicted protein structures and has the potential to bring about revolutionary changes in the field of life sciences. However, directly determining the function of proteins from their structures remains a challenging task. The Distogram from AlphaFold is used in this study as a novel feature set to identify transient receptor potential (TRP) channels. Distograms feature vectors and pre-trained language model (BERT) features were combined to improve prediction performance for transient receptor potential (TRP) channels. The method proposed in this study demonstrated promising performance on many evaluation metrics. For five-fold cross-validation, the method achieved a Sensitivity (SN) of 87.00%, Specificity (SP) of 93.61%, Accuracy (ACC) of 93.39%, and a Matthews correlation coefficient (MCC) of 0.52. Additionally, on an independent dataset, the method obtained 100.00% SN, 95.54% SP, 95.73% ACC, and an MCC of 0.69. The results demonstrate the potential for using structural information to predict protein function. In the future, it is hoped that such structural information will be incorporated into artificial intelligence networks to explore more useful and valuable functional information in the biological field.

ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types

Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-score_macro, and 0.826 F1-score_micro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.

ProFAB-open protein functional annotation benchmark

As the number of protein sequences increases in biological databases, computational methods are required to provide accurate functional annotation with high coverage. Although several machine learning methods have been proposed for this purpose, there are still two main issues: (i) construction of reliable positive and negative training and validation datasets, and (ii) fair evaluation of their performances based on predefined experimental settings. To address these issues, we have developed ProFAB: Open Protein Functional Annotation Benchmark, which is a platform providing an infrastructure for a fair comparison of protein function prediction methods. ProFAB provides filtered and preprocessed protein annotation datasets and enables the training and evaluation of function prediction methods via several options. We believe that ProFAB will be useful for both computational and experimental researchers by enabling the utilization of ready-to-use datasets and machine learning algorithms for protein function prediction based on Gene Ontology terms and Enzyme Commission numbers. ProFAB is available at https://github.com/kansil/ProFAB and https://profab.kansil.org.

Elucidation of Genome-wide Understudied Proteins targeted by PROTAC-induced degradation using Interpretable Machine Learning

Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules. They induce the degradation of a target protein by recruiting an E3 ligase to the target. The PROTAC can inactivate disease-related genes that are considered as understudied, thus has a great potential to be a new type of therapy for the treatment of incurable diseases. However, only hundreds of proteins have been experimentally tested if they are amenable to the PROTACs. It remains elusive what other proteins can be targeted by the PROTAC in the entire human genome. For the first time, we have developed an interpretable machine learning model PrePROTAC, which is based on a transformer-based protein sequence descriptor and random forest classification to predict genome-wide PROTAC-induced targets degradable by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved ROC-AUC of 0.81, PR-AUC of 0.84, and over 40% sensitivity at a false positive rate of 0.05, respectively. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method to identify positions in the protein structure, which play key roles in the PROTAC activity. The key residues identified were consistent with our existing knowledge. We applied PrePROTAC to identify more than 600 novel understudied proteins that are potentially degradable by CRBN, and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease.

Author Summary

Many human diseases remain incurable because disease-causing genes cannot by selectively and effectively targeted by small molecules. Proteolysis-targeting chimera (PROTAC), an organic compound that binds to both a target and a degradation-mediating E3 ligase, has emerged as a promising approach to selectively target disease-driving genes that are not druggable by small molecules. Nevertheless, not all of proteins can be accommodated by E3 ligases, and be effectively degraded. Knowledge on the degradability of a protein will be crucial for the design of PROTACs. However, only hundreds of proteins have been experimentally tested if they are amenable to the PROTACs. It remains elusive what other proteins can be targeted by the PROTAC in the entire human genome. In this paper, we propose an intepretable machine learning model PrePROTAC that takes advantage of powerful protein language modeling. PrePROTAC achieves high accuracy when evaluated by an external dataset which comes from different gene families from the proteins in the training data, suggesting the generalizability of PrePROTAC. We apply PrePROTAC to the human genome, and identify more than 600 understudied proteins that are potentially responsive to the PROTAC. Furthermore, we design three PROTAC compounds for novel drug targets associated with Alzheimer's disease.

HyperVR: a hybrid deep ensemble learning approach for simultaneously predicting virulence factors and antibiotic resistance genes

Infectious diseases emerge unprecedentedly, posing serious challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce and cause damage to host cells, and antibiotic resistance genes (ARGs) allow pathogens to evade otherwise curable treatments. Simultaneous identification of VFs and ARGs can save pathogen surveillance time, especially in situ epidemic pathogen detection. However, most tools can only predict either VFs or ARGs. Few tools that predict VFs and ARGs simultaneously usually have high false-negative rates, are sensitive to the cutoff thresholds and can only identify conserved genes. For better simultaneous prediction of VFs and ARGs, we propose a hybrid deep ensemble learning approach called HyperVR. By considering both best hit scores and statistical gene sequence patterns, HyperVR combines classical machine learning and deep learning to simultaneously and accurately predict VFs, ARGs and negative genes (neither VFs nor ARGs). For the prediction of individual VFs and ARGs, in silico spike-in experiment (the VFs and ARGs in real metagenomic data), and pseudo-VFs and -ARGs (gene fragments), HyperVR outperforms the current state-of-the-art prediction tools. HyperVR uses only gene sequence information without strict cutoff thresholds, hence making prediction straightforward and reliable.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE

BACKGROUND

Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work.

RESULTS

In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects.

CONCLUSIONS

DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .

Deep Multi-Label Joint Learning for RNA and DNA-Binding Proteins Prediction

The recognition of DNA- (DBPs) and RNA-binding proteins (RBPs) is not only conducive to understanding cell function, but also a challenging task. Previous studies have shown that these proteins are usually considered separately due to different binding domains. In addition, due to the high similarity between DBPs and RBPs, it is possible for DBPs predictor to predict RBPs as DBPs, and vice versa, which leads to high cross-prediction rate. In this study, we creatively propose a novel deep multi-label joint learning framework to leverage the relationship between multiple labels and binding proteins. First, a multi-label variant network is designed to explore multi-scale context hidden information. Then, multi-label Long Short-Term Memory (multiLSTM) is used to mine the potential relationship between labels. Finally, the calibrated hidden features from variant network are considered for different levels of joint learning so that multiLSTM can better explore the correlation between them. Extensive experiments are also carried out to compare the proposed method with other existing methods. Furthermore, we also provide further insights into the importance of the relevant bioanalysis of proteins obtained from our model and summarize these binding proteins that are significantly related to a disease. Our method is freely available at http://39.108.90.186/dmlj.

AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information

Introduction

Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in antibody therapeutics is the experimental identification of antibody-antigen interactions, which is generally time-consuming, costly, and laborious. Although some computational methods have been proposed to screen potential antibodies, the dependence on 3D structures still limits the application of these methods.

Methods

Here, we developed a deep learning-assisted prediction method (i.e., AbAgIntPre) for fast identification of antibody-antigen interactions that only relies on amino acid sequences. A Siamese-like convolutional neural network architecture was established with the amino acid composition encoding scheme for both antigens and antibodies.

Results and Discussion

The generic model of AbAgIntPre achieved satisfactory performance with the Area Under Curve (AUC) of 0.82 on a high-quality generic independent test dataset. Besides, this approach also showed competitive performance on the more specific SARS-CoV dataset. We expect that AbAgIntPre can serve as an important complement to traditional experimental methods for antibody screening and effectively reduce the workload of antibody design. The web server of AbAgIntPre is freely available at http://www.zzdlab.com/AbAgIntPre.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches

Background

The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches.

Methods

We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control.

Results

From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside.

Conclusions

Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03686-y.

Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast

Background

The need to mitigate and substitute the use of fossil fuels as the main energy matrix has led to the study and development of biofuels as an alternative. Second-generation (2G) ethanol arises as one biofuel with great potential, due to not only maintaining food security, but also as a product from economically interesting crops such as energy-cane. One of the main challenges of 2G ethanol is the inefficient uptake of pentose sugars by industrial yeast Saccharomyces cerevisiae, the main organism used for ethanol production. Understanding the main drivers for xylose assimilation and identify novel and efficient transporters is a key step to make the 2G process economically viable.

Results

By implementing a strategy of searching for present motifs that may be responsible for xylose transport and past adaptations of sugar transporters in xylose fermenting species, we obtained a classifying model which was successfully used to select four different candidate transporters for evaluation in the S. cerevisiae hxt-null strain, EBY.VW4000, harbouring the xylose consumption pathway. Yeast cells expressing the transporters SpX, SpH and SpG showed a superior uptake performance in xylose compared to traditional literature control Gxf1.

Conclusions

Modelling xylose transport with the small data available for yeast and bacteria proved a challenge that was overcome through different statistical strategies. Through this strategy, we present four novel xylose transporters which expands the repertoire of candidates targeting yeast genetic engineering for industrial fermentation. The repeated use of the model for characterizing new transporters will be useful both into finding the best candidates for industrial utilization and to increase the model’s predictive capabilities.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02153-7.

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.

RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.

Identifying SNARE Proteins Using an Alignment-Free Method Based on Multiscan Convolutional Neural Network and PSSM Profiles

Background: SNARE proteins play a vital role in membrane fusion and cellular physiology and pathological processes. Many potential therapeutics for mental diseases or even cancer based on SNAREs are also developed. Therefore, there is a dire need to predict the SNAREs for further manipulation of these essential proteins, which demands new and efficient approaches. Methods: Some computational frameworks were proposed to tackle the hurdles of biological methods, which take plenty of time and budget to conduct the identification of SNAREs. However, the performances of existing frameworks were insufficiently satisfied, as they failed to retain the SNARE sequence order and capture the mass hidden features from SNAREs. This paper proposed a novel model constructed on the multiscan convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to address these limitations. We employed and trained our model on the benchmark dataset with fivefold cross-validation and two different independent datasets. Results: Overall, the multiscan CNN was cross-validated on the training set and excelled in the SNARE classification reaching 0.963 in AUC and 0.955 in AUPRC. On top of that, with the sensitivity, specificity, accuracy, and MCC of 0.842, 0.968, 0.955, and 0.767, respectively, our proposed framework outperformed previous models in the SNARE recognition task. Conclusions: It is truly believed that our model can contribute to the discrimination of SNARE proteins and general proteins.

Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack

Accurate and efficient identification of anti-inflammatory peptides (AIPs) is crucial for the treatment of inflammation. Here, we proposed a two-layer stacking ensemble model, AIPStack, to effectively predict AIPs. At first, we constructed a new dataset for model building and validation. Then, peptide sequences were represented by hybrid features, which were fused by two amino acid composition descriptors. Next, the stacking ensemble model was constructed by random forest and extremely randomized tree as the base-classifiers and logistic regression as the meta-classifier to receive the outputs from the base-classifiers. AIPStack achieved an AUC of 0.819, accuracy of 0.755, and MCC of 0.510 on the independent set 3, which were higher than other AIP predictors. Furthermore, the essential sequence features were highlighted by the Shapley Additive exPlanation (SHAP) method. It is anticipated that AIPStack could be used for AIP prediction in a high-throughput manner and facilitate the hypothesis-driven experimental design.

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AIPStack model was developed for the prediction of anti-inflammatory peptides

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The hybrid features were used to describe the peptide sequences

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The proposed model AIPStack outperformed existing ones

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SHAP was used to highlight the essential features required for AIP prediction

CRISPRCasStack: a stacking strategy-based ensemble learning framework for accurate identification of Cas proteins

CRISPR-Cas system is an adaptive immune system widely found in most bacteria and archaea to defend against exogenous gene invasion. One of the most critical steps in the study of exploring and classifying novel CRISPR-Cas systems and their functional diversity is the identification of Cas proteins in CRISPR-Cas systems. The discovery of novel Cas proteins has also laid the foundation for technologies such as CRISPR-Cas-based gene editing and gene therapy. Currently, accurate and efficient screening of Cas proteins from metagenomic sequences and proteomic sequences remains a challenge. For Cas proteins with low sequence conservation, existing tools for Cas protein identification based on homology cannot guarantee identification accuracy and efficiency. In this paper, we have developed a novel stacking-based ensemble learning framework for Cas protein identification, called CRISPRCasStack. In particular, we applied the SHAP (SHapley Additive exPlanations) method to analyze the features used in CRISPRCasStack. Sufficient experimental validation and independent testing have demonstrated that CRISPRCasStack can address the accuracy deficiencies and inefficiencies of the existing state-of-the-art tools. We also provide a toolkit to accurately identify and analyze potential Cas proteins, Cas operons, CRISPR arrays and CRISPR-Cas locus in prokaryotic sequences. The CRISPRCasStack toolkit is available at https://github.com/yrjia1015/CRISPRCasStack.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

ACP-check: An anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy

The anticancer peptide is an emerging anticancer drug that has become an effective alternative to chemotherapy and targeted therapy due to fewer side effects and resistance. The traditional biological experimental method for identifying anticancer peptides is a time-consuming and complicated process that hinders large-scale, rapid, and effective identification. In this paper, we propose a model based on a bidirectional long short-term memory network and multi-features fusion, called ACP-check, which employs a bidirectional long short-term memory network to extract time-dependent information features from peptide sequences, and combines them with amino acid sequence features including binary profile feature, dipeptide composition, the composition of k-spaced amino acid group pairs, amino acid composition, and sequence-order-coupling number. To verify the performance of the model, six benchmark datasets are selected, including ACPred-Fuse, ACPred-FL, ACP240, ACP740, main and alternate datasets of AntiCP2.0. In terms of Matthews correlation coefficients, ACP-check obtains 0.37, 0.82, 0.80, 0.75, 0.56, and 0.86 on six datasets respectively, which is an improvement by 2%-86% than existing state-of-the-art anticancer peptides prediction methods. Furthermore, ACP-check achieves prediction accuracy with 0.91, 0.91, 0.90, 0.87, 0.78, and 0.93 respectively, which increases range from 1%-49%. Overall, the comparison experiment shows that ACP-check can accurately identify anticancer peptides by sequence-level information. The code and data are available at http://www.cczubio.top/ACP-check/.

ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites

Lysine glutarylation is a post-translational modification (PTM) that plays a regulatory role in various physiological and biological processes. Identifying glutarylated peptides using proteomic techniques is expensive and time-consuming. Therefore, developing computational models and predictors can prove useful for rapid identification of glutarylation. In this study, we propose a model called ProtTrans-Glutar to classify a protein sequence into positive or negative glutarylation site by combining traditional sequence-based features with features derived from a pre-trained transformer-based protein model. The features of the model were constructed by combining several feature sets, namely the distribution feature (from composition/transition/distribution encoding), enhanced amino acid composition (EAAC), and features derived from the ProtT5-XL-UniRef50 model. Combined with random under-sampling and XGBoost classification method, our model obtained recall, specificity, and AUC scores of 0.7864, 0.6286, and 0.7075 respectively on an independent test set. The recall and AUC scores were notably higher than those of the previous glutarylation prediction models using the same dataset. This high recall score suggests that our method has the potential to identify new glutarylation sites and facilitate further research on the glutarylation process.

Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network

Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.

BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides

Blood-brain barrier (BBB) is a major barrier to drug delivery into the brain in the treatment of central nervous system (CNS) diseases. Blood-brain barrier penetrating peptides (BBPs), a class of peptides that can cross BBB through various mechanisms without damaging BBB, are effective drug candidates for CNS diseases. However, identification of BBPs by experimental methods is time-consuming and laborious. To discover more BBPs as drugs for CNS disease, it is urgent to develop computational methods that can quickly and accurately identify BBPs and non-BBPs. In the present study, we created a training dataset that consists of 326 BBPs derived from previous databases and published manuscripts and 326 non-BBPs collected from UniProt, to construct a BBP predictor based on sequence information. We also constructed an independent testing dataset with 99 BBPs and 99 non-BBPs. Multiple machine learning methods were compared based on the training dataset via a nested cross-validation. The final BBP predictor was constructed based on the training dataset and the results showed that random forest (RF) method outperformed other classification algorithms on the training and independent testing dataset. Compared with previous BBP prediction tools, the RF-based predictor, named BBPpredict, performs considerably better than state-of-the-art BBP predictors. BBPpredict is expected to contribute to the discovery of novel BBPs, or at least can be a useful complement to the existing methods in this area. BBPpredict is freely available at http://i.uestc.edu.cn/BBPpredict/cgi-bin/BBPpredict.pl.

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.