Deep learning improves antimicrobial peptide recognition

ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding

BACKGROUND

Antimicrobial resistance is one of our most serious health threats. Antimicrobial peptides (AMPs), effecter molecules of innate immune system, can defend host organisms against microbes and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs. Thus, AMPs are gaining popularity as better substitute to antibiotics. To aid researchers in novel AMPs discovery, we design computational approaches to screen promising candidates.

RESULTS

In this work, we design a deep learning model that can learn amino acid embedding patterns, automatically extract sequence features, and fuse heterogeneous information. Results show that the proposed model outperforms state-of-the-art methods on recognition of AMPs. By visualizing data in some layers of the model, we overcome the black-box nature of deep learning, explain the working mechanism of the model, and find some import motifs in sequences.

CONCLUSIONS

ACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https://github.com/Fuhaoyi/ACEP .

autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences

Deep learning has proven to be a powerful method with applications in various fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can use different deep learning architectures and data sets for rapid modeling. However, the available implementations of neural networks using these toolkits are usually designed for a specific research and are difficult to transfer to other work. Here, we present autoBioSeqpy, a tool that uses deep learning for biological sequence classification. The advantage of this tool is its simplicity. Users only need to prepare the input data set and then use a command line interface. Then, autoBioSeqpy automatically executes a series of customizable steps including text reading, parameter initialization, sequence encoding, model loading, training, and evaluation. In addition, the tool provides various ready-to-apply and adapt model templates to improve the usability of these networks. We introduce the application of autoBioSeqpy on three biological sequence problems: the prediction of type III secreted proteins, protein subcellular localization, and CRISPR/Cas9 sgRNA activity. autoBioSeqpy is freely available with examples at https://github.com/jingry/autoBioSeqpy.

A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction

The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound-target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC50 of the target compounds. The performance of the models was assessed primarily through analysis of the Q2 values produced from runs of differing sample and epoch size; recorded loss values were also reported and graphed. The performance of the models was limited, though multiple changes are proposed for potential improvement of a multi-input recurrent neural network-based screening tool.

A Survey of Network Embedding for Drug Analysis and Prediction

Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks

The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.

DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network

MOTIVATION

Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.

RESULTS

This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units' models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction.

AVAILABILITY AND IMPLEMENTATION

The method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy

Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user's viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.

Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Recent Advances and Computational Approaches in Peptide Drug Discovery

BACKGROUND

Drug design and development is a vast field that requires huge investment along with a long duration for providing approval to suitable drug candidates. With the advancement in the field of genomics, the information about druggable targets is being updated at a fast rate which is helpful in finding a cure for various diseases.

METHODS

There are certain biochemicals as well as physiological advantages of using peptide-based therapeutics. Additionally, the limitations of peptide-based drugs can be overcome by modulating the properties of peptide molecules through various biomolecular engineering techniques. Recent advances in computational approaches have been helpful in studying the effect of peptide drugs on the biomolecular targets. Receptor - ligand-based molecular docking studies have made it easy to screen compatible inhibitors against a target.Furthermore, there are simulation tools available to evaluate stability of complexes at the molecular level. Machine learning methods have added a new edge by enabling accurate prediction of therapeutic peptides.

RESULTS

Peptide-based drugs are expected to take over many popular drugs in the near future due to their biosafety, lower off-target binding chances and multifunctional properties.

CONCLUSION

This article summarises the latest developments in the field of peptide-based therapeutics related to their usage, tools, and databases.

Clinical Applications of Antimicrobial Peptides (AMPs): Where do we Stand Now?

In this era of multi-drug resistance (MDR), antimicrobial peptides (AMPs) are one of the most promising classes of potential drug candidates to combat communicable as well as noncommunicable diseases such as cancers and diabetes. AMPs show a wide spectrum of biological activities which include antiviral, antifungal, anti-mitogenic, anticancer, and anti-inflammatory properties. Apart from these prospective therapeutic potentials, the AMPs can act as food preservatives and immune modulators. Therefore, AMPs have the potential to replace conventional drugs and may gain a significant global drug market share. Although several AMPs have shown therapeutic potential in vitro or in vivo, in most cases they have failed the clinical trial owing to various issues. In this review, we discuss in brief (i) molecular mechanisms of AMPs in various diseases, (ii) importance of AMPs in pharmaceutical industries, (iii) the challenges in using AMPs as therapeutics and how to overcome, (iv) available AMP therapeutics in market, and (v) AMPs under clinical trials. Here, we specifically focus on the therapeutic AMPs in the areas of dermatology, surgery, oncology and metabolic diseases.

Antimicrobial peptide CGA-N12 decreases the Candida tropicalis mitochondrial membrane potential via mitochondrial permeability transition pore

Amino acid sequence from 65th to 76th residue of the N-terminus of Chromogranin A (CGA-N12) is an antimicrobial peptide (AMP). Our previous studies showed that CGA-N12 reduces Candida tropicalis mitochondrial membrane potential. Here, we explored the mechanism that CGA-N12 collapsed the mitochondrial membrane potential by investigations of its action on the mitochondrial permeability transition pore (mPTP) complex of C. tropicalis. The results showed that CGA-N12 induced cytochrome c (Cyt c) leakage, mitochondria swelling and led to polyethylene glycol (PEG) of molecular weight 1000 Da penetrate mitochondria. mPTP opening inhibitors bongkrekic acid (BA) could contract the mitochondrial swelling induced by CGA-N12, but cyclosporin A (CsA) could not. Therefore, we speculated that CGA-N12 could induce C. tropicolis mPTP opening by preventing the matrix-facing (m) conformation of adenine nucleotide transporter (ANT), thereby increasing the permeability of the mitochondrial membrane and resulted in the mitochondrial potential dissipation.

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook.

Database URL: http://bioinfor.imu.edu.cn/raacbook

Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning

Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a short-length (≤30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network. On a balanced benchmark dataset of 188 samples, Deep-AmPEP30 yields an improved performance of 77% in accuracy, 85% in the area under the receiver operating characteristic curve (AUC-ROC), and 85% in area under the precision-recall curve (AUC-PR) over existing machine learning-based methods. To demonstrate its power, we screened the genome sequence of Candida glabrata—a gut commensal fungus expected to interact with and/or inhibit other microbes in the gut—for potential AMPs and identified a peptide of 20 aa (P3, FWELWKFLKSLWSIFPRRRP) with strong anti-bacteria activity against Bacillus subtilis and Vibrio parahaemolyticus. The potency of the peptide is remarkably comparable to that of ampicillin. Therefore, Deep-AmPEP30 is a promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. Our method is available at https://cbbio.cis.um.edu.mo/AxPEP for both individual sequence prediction and genome screening for AMPs.

Progress in the development of antimicrobial peptide prediction tools

Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning

Background

Membrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the results still do not meet the expectations of researchers.

Results

We propose two deep learning models to process sequence information and evolutionary information, respectively. Both models obtained better results than traditional machine learning models. Furthermore, to improve the performance of the sequence information model, we also provide a new vector representation method to replace the one-hot encoding, whose overall success rate improved by 3.81% and 6.55% on two datasets. Finally, a more effective model is obtained by fusing the above two models, whose overall success rate reached 95.68% and 92.98% on two datasets.

Conclusion

The final experimental results show that our method is more effective than existing methods for predicting membrane protein types, which can help laboratory researchers to identify the type of novel membrane proteins.

Antimicrobial peptide identification using multi-scale convolutional network

Background

Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem.

Results

In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy.

Conclusions

Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

Deep learning for mining protein data

The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network

Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In this study, we propose a novel deep learning-based method to predict the health effects of food peptides and elucidate the mode-of-action. In the algorithm, we estimate potential target proteins of food peptides using a multi-task graph convolutional neural network, and predict its health effects using information about therapeutic targets for diseases. We constructed predictive models based on 21,103 peptide-protein interactions involving 10,950 peptides and 2,533 proteins, and applied the models to food peptides (e. g., lactotripeptide, isoleucyltyrosine and sardine peptide) defined in food for specified health use. The models suggested potential effects such as blood-pressure lowering effects, blood glucose level lowering effects, and anti-cancer effects for several food peptides. The interactions of food peptides with target proteins were confirmed by docking simulations.

iDEF-PseRAAC: Identifying the Defensin Peptide by Using Reduced Amino Acid Composition Descriptor

Defensins as 1 of major classes of host defense peptides play a significant role in the innate immunity, which are extremely evolved in almost all living organisms. Developing high-throughput computational methods can accurately help in designing drugs or medical means to defense against pathogens. To take up such a challenge, an up-to-date server based on rigorous benchmark dataset, referred to as iDEF-PseRAAC, was designed for predicting the defensin family in this study. By extracting primary sequence compositions based on different types of reduced amino acid alphabet, it was calculated that the best overall accuracy of the selected feature subset was achieved to 92.38%. Therefore, we can conclude that the information provided by abundant types of amino acid reduction will provide efficient and rational methodology for defensin identification. And, a free online server is freely available for academic users at http://bioinfor.imu.edu.cn/idpf. We hold expectations that iDEF-PseRAAC may be a promising weapon for the function annotation about the defensins protein.

Redesigning Arenicin-1, an Antimicrobial Peptide from the Marine Polychaeta Arenicola marina, by Strand Rearrangement or Branching, Substitution of Specific Residues, and Backbone Linearization or Cyclization

Arenicin-1, a β-sheet antimicrobial peptide isolated from the marine polychaeta Arenicola marina coelomocytes, has a potent, broad-spectrum microbicidal activity and also shows significant toxicity towards mammalian cells. Several variants were rationally designed to elucidate the role of structural features such as cyclization, a certain symmetry of the residue arrangement, or the presence of specific residues in the sequence, in its membranolytic activity and the consequent effect on microbicidal efficacy and toxicity. The effect of variations on the structure was probed using molecular dynamics simulations, which indicated a significant stability of the β-hairpin scaffold and showed that modifying residue symmetry and β-strand arrangement affected both the twist and the kink present in the native structure. In vitro assays against a panel of Gram-negative and Gram-positive bacteria, including drug-resistant clinical isolates, showed that inversion of the residue arrangement improved the activity against Gram-negative strains but decreased it towards Gram-positive ones. Variants with increased symmetry were somewhat less active, whereas both backbone-cyclized and linear versions of the peptides, as well as variants with R→K and W→F replacement, showed antimicrobial activity comparable with that of the native peptide. All these variants permeabilized both the outer and the inner membranes of Escherichia coli, suggesting that a membranolytic mechanism of action was maintained. Our results indicate that the arenicin scaffold can support a considerable degree of variation while maintaining useful biological properties and can thus serve as a template for the elaboration of novel anti-infective agents.

An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies

BACKGROUND

Antimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AMPs and their derivatives can greatly improve the immunity and breeding survival rate of aquatic products. It is highly desirable to develop computational tools for rapidly and accurately identifying AMPs and their functional types, for the purpose of helping design new and more effective antimicrobial agents.

RESULTS

In this study, we made an attempt to develop an advanced machine learning based computational approach, MAMPs-Pred, for identification of AMPs and its function types. Initially, SVM-prot 188-D features were extracted that were subsequently used as input to a two-layer multi-label classifier. In specific, the first layer is to identify whether it is an AMP by applying RF classifier, and the second layer addresses the multi-type problem by identifying the activites or function types of AMPs by applying PS-RF and LC-RF classifiers. To benchmark the methods,the MAMPs-Pred method is also compared with existing best-performing methods in literature and has shown an improved identification accuracy.

CONCLUSIONS

The results reported in this study indicate that the MAMP-Pred method achieves high performance for identifying AMPs and its functional types.The proposed approach is believed to supplement the tools and techniques that have been developed in the past for predicting AMPs and their function types.

Characterization and identification of antimicrobial peptides with different functional activities

In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.

Toward computer-made artificial antibiotics

Merging concepts from synthetic biology and computational biology may yield antibiotics that are less likely to elicit resistance than existing drugs and that yet can fight drug-resistant infections. Indeed, computer-guided strategies coupled with massively parallel high-throughput experimental methods represent a new paradigm for antibiotic discovery. Infections caused by multidrug-resistant microorganisms are increasingly deadly. In the current post-antibiotic era, many of these infections cannot be treated with our existing antimicrobial arsenal. Furthermore, we may have already exhausted the category of large molecules produced in nature having antimicrobial activity: the antibiotic scaffolds we have discovered so far may represent the majority of those that exist. The rise in drug-resistant bacteria and lack of new antibiotic classes clearly call for out-of-the-box strategies. Recent advances in computational synthetic biology have enabled the development of antimicrobials. New molecular descriptors and genetic and pattern recognition algorithms are powerful tools that bring us a step closer to developing efficient antibiotics. We review several computational tools for drug design and a number of recently generated antibiotic candidates, with an emphasis on peptide-based molecules. Design strategies can generate a diversity of synthetic antimicrobial peptides, which may help to mitigate the spread of resistance and combat multidrug-resistant microorganisms.

DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions

MOTIVATION

Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep-learning (DL) methods have been shown to generate more accurate prediction models than 'shallow' methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs.

RESULTS

We developed DeepGSR, an optimized DL architecture for the prediction of different types of GSRs. The performance of the DeepGSR structure is evaluated on the recognition of polyadenylation signals (PAS) and translation initiation sites (TIS) of different organisms: human, mouse, bovine and fruit fly. The results show that DeepGSR outperformed the state-of-the-art methods, reducing the classification error rate of the PAS and TIS prediction in the human genome by up to 29% and 86%, respectively. Moreover, the cross-organisms and genome-wide analyses we performed, confirmed the robustness of DeepGSR and provided new insights into the conservation of examined GSRs across species.

AVAILABILITY AND IMPLEMENTATION

DeepGSR is implemented in Python using Keras API; it is available as open-source software and can be obtained at https://doi.org/10.5281/zenodo.1117159.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Alternative splicing events expand molecular diversity of camel CSN1S2 increasing its ability to generate potentially bioactive peptides

In a previous study on camel milk from Kazakhstan, we reported the occurrence of two unknown proteins (UP1 and UP2) with different levels of phosphorylation. Here we show that UP1 and UP2 are isoforms of camel αs2-CN (αs2-CNsv1 and αs2-CNsv2, respectively) arising from alternative splicing events. First described as a 178 amino-acids long protein carrying eight phosphate groups, the major camel αs2-CN isoform (called here αs2-CN) has a molecular mass of 21,906 Da. αs2-CNsv1, a rather frequent (35%) isoform displaying a higher molecular mass (+1,033 Da), is present at four phosphorylation levels (8P to 11P). Using cDNA-sequencing, αs2-CNsv1 was shown to be a variant arising from the splicing-in of an in-frame 27-nucleotide sequence encoding the nonapeptide ENSKKTVDM, for which the presence at the genome level was confirmed. αs2-CNsv2, which appeared to be present at 8P to 12P, was shown to include an additional decapeptide (VKAYQIIPNL) revealed by LC-MS/MS, encoded by a 3'-extension of exon 16. Since milk proteins represent a reservoir of biologically active peptides, the molecular diversity generated by differential splicing might increase its content. To evaluate this possibility, we searched for bioactive peptides encrypted in the different camel αs2-CN isoforms, using an in silico approach. Several peptides, putatively released from the C-terminal part of camel αs2-CN isoforms after in silico digestion by proteases from the digestive tract, were predicted to display anti-bacterial and antihypertensive activities.

A putative antimicrobial peptide from Hymenoptera in the megaplasmid pSCL4 of Streptomyces clavuligerus ATCC 27064 reveals a singular case of horizontal gene transfer with potential applications

Streptomyces clavuligerus is a Gram-positive bacterium that is a high producer of secondary metabolites with industrial applications. The production of antibiotics such as clavulanic acid or cephamycin has been extensively studied in this species; nevertheless, other aspects, such as evolution or ecology, have received less attention. Furthermore, genes that arise from ancient events of lateral transfer have been demonstrated to be implicated in important functions of host species. This approximation discovered relevant genes that genomic analyses overlooked. Thus, we studied the impact of horizontal gene transfer in the S. clavuligerus genome. To perform this task, we applied whole-genome analysis to identify a laterally transferred sequence from different domains. The most relevant result was a putative antimicrobial peptide (AMP) with a clear origin in the Hymenoptera order of insects. Next, we determined that two copies of these genes were present in the megaplasmid pSCL4 but absent in the S. clavuligerus ATCC 27064 chromosome. Additionally, we found that these sequences were exclusive to the ATCC 27064 strain (and so were not present in any other bacteria) and we also verified the expression of the genes using RNAseq data. Next, we used several AMP predictors to validate the original annotation extracted from Hymenoptera sequences and explored the possibility that these proteins had post-translational modifications using peptidase cleavage prediction. We suggest that Hymenoptera AMP-like proteins of S. clavuligerus ATCC 27064 may be useful for both species adaptation and as an antimicrobial molecule with industrial applications.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).