1
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Bucataru C, Ciobanasu C. Antimicrobial peptides: Opportunities and challenges in overcoming resistance. Microbiol Res 2024; 286:127822. [PMID: 38986182 DOI: 10.1016/j.micres.2024.127822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/20/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
Antibiotic resistance represents a global health threat, challenging the efficacy of traditional antimicrobial agents and necessitating innovative approaches to combat infectious diseases. Among these alternatives, antimicrobial peptides have emerged as promising candidates against resistant pathogens. Unlike traditional antibiotics with only one target, these peptides can use different mechanisms to destroy bacteria, with low toxicity to mammalian cells compared to many conventional antibiotics. Antimicrobial peptides (AMPs) have encouraging antibacterial properties and are currently employed in the clinical treatment of pathogen infection, cancer, wound healing, cosmetics, or biotechnology. This review summarizes the mechanisms of antimicrobial peptides against bacteria, discusses the mechanisms of drug resistance, the limitations and challenges of AMPs in peptide drug applications for combating drug-resistant bacterial infections, and strategies to enhance their capabilities.
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Affiliation(s)
- Cezara Bucataru
- Alexandru I. Cuza University, Institute of Interdisciplinary Research, Department of Exact and Natural Sciences, Bulevardul Carol I, Nr.11, Iasi 700506, Romania
| | - Corina Ciobanasu
- Alexandru I. Cuza University, Institute of Interdisciplinary Research, Department of Exact and Natural Sciences, Bulevardul Carol I, Nr.11, Iasi 700506, Romania.
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2
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Lu C, Lubin JH, Sarma VV, Stentz SZ, Wang G, Wang S, Khare SD. Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network. Proc Natl Acad Sci U S A 2023; 120:e2303590120. [PMID: 37729196 PMCID: PMC10523478 DOI: 10.1073/pnas.2303590120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 08/14/2023] [Indexed: 09/22/2023] Open
Abstract
Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.
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Affiliation(s)
- Changpeng Lu
- Institute for Quantitative Biomedicine, Rutgers–The State University of New Jersey, Piscataway, NJ08854
| | - Joseph H. Lubin
- Department of Chemistry and Chemical Biology, Rutgers–The State University of New Jersey, Piscataway, NJ08854
| | - Vidur V. Sarma
- Institute for Quantitative Biomedicine, Rutgers–The State University of New Jersey, Piscataway, NJ08854
| | | | - Guanyang Wang
- Department of Statistics, Rutgers–The State University of New Jersey, Piscataway, NJ08854
| | - Sijian Wang
- Institute for Quantitative Biomedicine, Rutgers–The State University of New Jersey, Piscataway, NJ08854
- Department of Statistics, Rutgers–The State University of New Jersey, Piscataway, NJ08854
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers–The State University of New Jersey, Piscataway, NJ08854
- Department of Chemistry and Chemical Biology, Rutgers–The State University of New Jersey, Piscataway, NJ08854
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3
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Li F, Wang C, Guo X, Akutsu T, Webb GI, Coin LJM, Kurgan L, Song J. ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction. Brief Bioinform 2023; 24:bbad372. [PMID: 37874948 DOI: 10.1093/bib/bbad372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/30/2023] [Accepted: 09/29/2023] [Indexed: 10/26/2023] Open
Abstract
Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.
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Affiliation(s)
- Fuyi Li
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Cong Wang
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Geoffrey I Webb
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
| | - Lachlan J M Coin
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, VIC 3000, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Jiangning Song
- Monash Data Futures Institute, Monash University, VIC 3800, Australia
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, VIC 3800, Australia
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4
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Cao R, Hu W, Wei P, Ding Y, Bin Y, Zheng C. FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses. Brief Bioinform 2023; 24:bbad353. [PMID: 37861174 DOI: 10.1093/bib/bbad353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/25/2023] [Accepted: 09/06/2023] [Indexed: 10/21/2023] Open
Abstract
Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.
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Affiliation(s)
- Ruifen Cao
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University
| | - Weiling Hu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University
| | - Pijing Wei
- Institutes of Physical Science and Information Technology, Anhui University
| | - Yun Ding
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University
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5
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Li F, Guo X, Bi Y, Jia R, Pitt ME, Pan S, Li S, Gasser RB, Coin LJ, Song J. Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins. Comput Biol Med 2023; 163:107155. [PMID: 37356289 DOI: 10.1016/j.compbiomed.2023.107155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023]
Abstract
The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools. Thus, advanced in silico methods are needed to identify proteins of this family for subsequent functional annotation efficiently. In this study, we developed the first deep learning-based approach, termed Digerati, for the rapid and accurate identification of PE and PPE family proteins. Digerati was built upon a multipath parallel hybrid deep learning framework, which equips multi-layer convolutional neural networks with bidirectional, long short-term memory, equipped with a self-attention module to effectively learn the higher-order feature representations of PE/PPE proteins. Empirical studies demonstrated that Digerati achieved a significantly better performance (∼18-20%) than alignment-based approaches, including BLASTP, PHMMER, and HHsuite, in both prediction accuracy and speed. Digerati is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE/PPE family members. The webserver and source codes of Digerati are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/Digerati/.
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Affiliation(s)
- Fuyi Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China; Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia.
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Yue Bi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, 3800, Australia
| | - Runchang Jia
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Miranda E Pitt
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia
| | - Shirui Pan
- School of Information and Communication Technology, Griffith University, QLD, 4222, Australia
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Robin B Gasser
- Melbourne Veterinary School, Faculty of Science, The University of Melbourne, VIC, 3010, Australia
| | - Lachlan Jm Coin
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria, 3000, Australia.
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, 3800, Australia.
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6
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Maasch JRMA, Torres MDT, Melo MCR, de la Fuente-Nunez C. Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning. Cell Host Microbe 2023; 31:1260-1274.e6. [PMID: 37516110 DOI: 10.1016/j.chom.2023.07.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/12/2023] [Accepted: 07/06/2023] [Indexed: 07/31/2023]
Abstract
Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.
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Affiliation(s)
- Jacqueline R M A Maasch
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo D T Torres
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
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7
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Chen K, Xu M, Lu F, He Y. Development of Matrix Metalloproteinases-Mediated Extracellular Matrix Remodeling in Regenerative Medicine: A Mini Review. Tissue Eng Regen Med 2023; 20:661-670. [PMID: 37160567 PMCID: PMC10352474 DOI: 10.1007/s13770-023-00536-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/25/2023] [Accepted: 03/03/2023] [Indexed: 05/11/2023] Open
Abstract
Extracellular matrix (ECM) components confer biomechanical properties, maintain cell phenotype and mediate tissue homeostasis. ECM remodeling is complex and plays a key role in both physiological and pathological processes. Matrix metalloproteinases (MMPs) are a group of enzymes responsible for ECM degradation and have been accepted as a key regulator in ECM remodeling. In this mini-review, we summarize MMPs categories, functions and the targeted substrates. We then discuss current understanding of the role of MMPs-mediated events, including inflammation reaction, angiogenesis, cellular activities, etc., in ECM remodeling in the context of regenerative medicine.
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Affiliation(s)
- Kaiqi Chen
- Department of Plastic and Cosmetic Surgery, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Mimi Xu
- Department of Plastic and Cosmetic Surgery, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Road, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Feng Lu
- Department of Plastic and Cosmetic Surgery, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Road, Guangzhou, 510515, Guangdong, People's Republic of China.
| | - Yunfan He
- Department of Plastic and Cosmetic Surgery, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Road, Guangzhou, 510515, Guangdong, People's Republic of China.
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8
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Lee YY, Guler M, Chigumba DN, Wang S, Mittal N, Miller C, Krummenacher B, Liu H, Cao L, Kannan A, Narayan K, Slocum ST, Roth BL, Gurevich A, Behsaz B, Kersten RD, Mohimani H. HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search. Nat Commun 2023; 14:4219. [PMID: 37452020 PMCID: PMC10349150 DOI: 10.1038/s41467-023-39905-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023] Open
Abstract
Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.
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Affiliation(s)
- Yi-Yuan Lee
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Cornell University, Ithaca, NY, 14850, USA
| | - Mustafa Guler
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Desnor N Chigumba
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Shen Wang
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Neel Mittal
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | | | | | - Haodong Liu
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Liu Cao
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Aditya Kannan
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | | | - Samuel T Slocum
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Alexey Gurevich
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarbrücken, Germany
- Department of Computer Science, Saarland University, Saarbrücken, Germany
| | - Bahar Behsaz
- Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Roland D Kersten
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
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9
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Wang Y, Tai S, Zhang S, Sheng N, Xie X. PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence. Genes (Basel) 2023; 14:1441. [PMID: 37510345 PMCID: PMC10379012 DOI: 10.3390/genes14071441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/04/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and their core elements accurately is a crucial research area in bioinformatics. At present, models based on machine learning and deep learning have been developed for promoter prediction. However, these models cannot mine the deeper biological information of promoter sequences and consider the complex relationship among promoter sequences. In this work, we propose a novel prediction model called PromGER to predict eukaryotic promoter sequences. For a promoter sequence, firstly, PromGER utilizes four types of feature-encoding methods to extract local information within promoter sequences. Secondly, according to the potential relationships among promoter sequences, the whole promoter sequences are constructed as a graph. Furthermore, three different scales of graph-embedding methods are applied for obtaining the global feature information more comprehensively in the graph. Finally, combining local features with global features of sequences, PromGER analyzes and predicts promoter sequences through a tree-based ensemble-learning framework. Compared with seven existing methods, PromGER improved the average specificity of 13%, accuracy of 10%, Matthew's correlation coefficient of 16%, precision of 4%, F1 score of 6%, and AUC of 9%. Specifically, this study interpreted the PromGER by the t-distributed stochastic neighbor embedding (t-SNE) method and SHAPley Additive exPlanations (SHAP) value analysis, which demonstrates the interpretability of the model.
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Affiliation(s)
- Yan Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Shiwen Tai
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Shuangquan Zhang
- School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Nan Sheng
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Xuping Xie
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
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10
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Matveev EV, Safronov VV, Ponomarev GV, Kazanov MD. Predicting Structural Susceptibility of Proteins to Proteolytic Processing. Int J Mol Sci 2023; 24:10761. [PMID: 37445939 DOI: 10.3390/ijms241310761] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/16/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications.
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Affiliation(s)
- Evgenii V Matveev
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow 117998, Russia
| | - Vyacheslav V Safronov
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Gennady V Ponomarev
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
| | - Marat D Kazanov
- Skolkovo Institute of Science and Technology, Moscow 121205, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, Moscow 127051, Russia
- Dmitry Rogachev National Medical Research Center of Pediatric Hematology, Oncology and Immunology, Moscow 117998, Russia
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey
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11
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Yamaguchi S, Kamino M, Hamamura M, Otsuguro KI. The cytosolic N-terminal region of heterologously-expressed transmembrane channel-like protein 1 (TMC1) can be cleaved in HEK293 cells. PLoS One 2023; 18:e0287249. [PMID: 37352201 PMCID: PMC10289374 DOI: 10.1371/journal.pone.0287249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 06/01/2023] [Indexed: 06/25/2023] Open
Abstract
Transmembrane channel-like protein 1 (TMC1) is a transmembrane protein forming mechano-electrical transduction (MET) channel, which transduces mechanical stimuli into electrical signals at the top of stereocilia of hair cells in the inner ear. As an unexpected phenomenon, we found that the cytosolic N-terminal (Nt) region of heterologously-expressed mouse TMC1 (mTMC1) was localized in nuclei of a small population of the transfected HEK293 cells. This raised the possibility that the Nt region of heterologously-expressed mTMC1 was cleaved and transported into the nucleus. To confirm the cleavage, we performed western blot analyses. The results revealed that at least a fragment of the Nt region was produced from heterologously-expressed mTMC1. Site-directed mutagenesis experiments identified amino acid residues which were required to produce the fragment. The accumulation of the heterologously-expressed Nt fragment into the nuclei depended on nuclear localization signals within the Nt region. Furthermore, a structural comparison showed a similarity between the Nt region of mTMC1 and basic region leucine zipper (bZIP) transcription factors. However, transcriptome analyses using a next-generation sequencer showed that the heterologously-expression of the Nt fragment of mTMC1 hardly altered expression levels of genes. Although it is still unknown what is the precise mechanism and the physiological significance of this cleavage, these results showed that the cytosolic Nt region of heterologously-expressed mTMC1 could be cleaved in HEK293 cells. Therefore, it should be taken into account that the cleavage of Nt region might influence the functional analysis of TMC1 by the heterologous-expression system using HEK293 cells.
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Affiliation(s)
- Soichiro Yamaguchi
- Laboratory of Physiology, Department of Basic Veterinary Sciences, Faculty of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Maho Kamino
- Laboratory of Pharmacology, Department of Basic Veterinary Sciences, School of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Maho Hamamura
- Laboratory of Pharmacology, Department of Basic Veterinary Sciences, School of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Ken-ichi Otsuguro
- Laboratory of Pharmacology, Department of Basic Veterinary Sciences, Faculty of Veterinary Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
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12
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Schaduangrat N, Anuwongcharoen N, Charoenkwan P, Shoombuatong W. DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists. J Cheminform 2023; 15:50. [PMID: 37149650 PMCID: PMC10163717 DOI: 10.1186/s13321-023-00721-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/08/2023] [Indexed: 05/08/2023] Open
Abstract
Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR ). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds.
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Affiliation(s)
- Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nuttapat Anuwongcharoen
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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13
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MSINGB: A Novel Computational Method Based on NGBoost for Identifying Microsatellite Instability Status from Tumor Mutation Annotation Data. Interdiscip Sci 2023; 15:100-110. [PMID: 36350503 DOI: 10.1007/s12539-022-00544-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 11/11/2022]
Abstract
Microsatellite instability (MSI), a vital mutator phenotype caused by DNA mismatch repair deficiency, is frequently observed in several tumors. MSI is recognized as a critical molecular biomarker for diagnosis, prognosis, and therapeutic selection in several cancers. Identifying MSI status for current gold standard methods based on experimental analysis is laborious, time-consuming, and costly. Although several computational methods based on machine learning have been proposed to identify MSI status, we need to further understand which machine learning model would favor identification for MSI and which feature subset is strongly related to MSI. On this basis, more effective machine learning-based methods can be developed to improve the performance of MSI status identification. In this work, we present MSINGB, an NGBoost-based method for identifying MSI status from tumor somatic mutation annotation data. MSINGB first evaluates the prediction performance of 11 popular machine learning algorithms and 9 deep learning models to identify MSI. Among 20 models, NGBoost, a novel natural gradient boosting method, achieves the overall best performance. MSINGB then introduces two feature selection strategies to find the compact feature subset, which is strongly related to MSI, and employs the SHAP approach to interpreting how selected features impact the model prediction. MSINGB achieves a better prediction performance on both the tenfold cross-validation test and independent test compared with state-of-the-art methods.
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14
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Zhang F, Li M, Zhang J, Shi W, Kurgan L. DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-Annotated Protein Binding Residues. J Mol Biol 2023:167945. [PMID: 36621533 DOI: 10.1016/j.jmb.2023.167945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 12/15/2022] [Accepted: 01/01/2023] [Indexed: 01/07/2023]
Abstract
Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind multiple partners by folding into different conformations and enrichment in different amino acids, the structure-trained and disorder-trained predictors were shown to provide inaccurate results for the other annotation type. A simple consensus-based solution that combines structure- and disorder-trained methods provides limited levels of predictive performance and generates relatively many cross-predictions, where residues that interact with other ligand types are predicted as PBRs. We address this unsolved problem by designing a novel and fast deep-learner, DeepPRObind, that relies on carefully designed modular convolutional architecture and uses innovative aggregate input features. Comparative empirical tests on a low-similarity test dataset reveal that DeepPRObind generates accurate predictions of structured and disordered PBRs and low amounts of cross-predictions, outperforming a comprehensive collection of 12 predictors of PBRs. Given the relatively low runtime of DeepPRObind (40 seconds per protein), we further validate its results based on an analysis of putative PBRs in the yeast proteome, confirming that interactions in disordered regions are enriched among hub proteins. We release DeepPRObind as a convenient web server at https://www.csuligroup.com/DeepPRObind/.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
| | - Wenbo Shi
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.
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15
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Guo X, Li F, Song J. Predicting Pseudouridine Sites with Porpoise. Methods Mol Biol 2023; 2624:139-151. [PMID: 36723814 DOI: 10.1007/978-1-0716-2962-8_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.
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Affiliation(s)
- Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Fuyi Li
- College of Information Engineering, Northwest A&F University, Yangling, China.
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
| | - Jiangning Song
- Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia.
- Monash Data Futures Institute, Monash University, Melbourne, VIC, Australia.
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16
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Henehan GT, Ryan BJ, Kinsella GK. Approaches to Avoid Proteolysis During Protein Expression and Purification. Methods Mol Biol 2023; 2699:77-95. [PMID: 37646995 DOI: 10.1007/978-1-0716-3362-5_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
All cells contain proteases, which hydrolyze the peptide bonds between amino acids of a protein backbone. Typically, proteases are prevented from nonspecific proteolysis by regulation and by their physical separation into different subcellular compartments; however, this segregation is not retained during cell lysis, which is the initial step in any protein isolation procedure. Prevention of proteolysis during protein purification often takes the form of a two-pronged approach: first, inhibition of proteolysis in situ, followed by the early separation of the protease from the protein of interest via chromatographic purification. Protease inhibitors are routinely used to limit the effect of the proteases before they are physically separated from the protein of interest via column chromatography. In this chapter, commonly used approaches to reducing or avoiding proteolysis during protein expression and purification are reviewed.
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Affiliation(s)
- Gary T Henehan
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman, Dublin, Ireland
| | - Barry J Ryan
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman, Dublin, Ireland
| | - Gemma K Kinsella
- School of Food Science and Environmental Health, Technological University Dublin, Grangegorman, Dublin, Ireland.
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17
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Zhang H, Wang Y, Pan Z, Sun X, Mou M, Zhang B, Li Z, Li H, Zhu F. ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA. Brief Bioinform 2022; 23:6747810. [PMID: 36198065 DOI: 10.1093/bib/bbac411] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/04/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022] Open
Abstract
In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.
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Affiliation(s)
- Hanyu Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Honglin Li
- School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.,Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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18
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Onah E, Uzor PF, Ugwoke IC, Eze JU, Ugwuanyi ST, Chukwudi IR, Ibezim A. Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors. BMC Bioinformatics 2022; 23:466. [DOI: 10.1186/s12859-022-05017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.
Results
Among the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).
Conclusions
Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.
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19
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Zhu L, Wang X, Li F, Song J. PreAcrs: a machine learning framework for identifying anti-CRISPR proteins. BMC Bioinformatics 2022; 23:444. [PMID: 36284264 PMCID: PMC9597991 DOI: 10.1186/s12859-022-04986-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. RESULTS Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. CONCLUSIONS In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git .
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Affiliation(s)
- Lin Zhu
- grid.263488.30000 0001 0472 9649Institute for Advanced Study, Shenzhen University, Shenzhen, China
| | - Xiaoyu Wang
- grid.1002.30000 0004 1936 7857Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800 Australia
| | - Fuyi Li
- grid.1008.90000 0001 2179 088XDepartment of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC Australia
| | - Jiangning Song
- grid.1002.30000 0004 1936 7857Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800 Australia ,grid.1002.30000 0004 1936 7857Monash Data Futures Institute, Monash University, Melbourne, VIC 3800 Australia
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20
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Liu Q, Fang H, Wang X, Wang M, Li S, Coin LJM, Li F, Song J. DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions. Bioinformatics 2022; 38:4053-4061. [PMID: 35799358 DOI: 10.1093/bioinformatics/btac454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/11/2022] [Accepted: 07/06/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Accurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction. RESULTS In this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes. AVAILABILITY AND IMPLEMENTATION The webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Quanzhong Liu
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Honglin Fang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Xiao Wang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Miao Wang
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Shuqin Li
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China
| | - Lachlan J M Coin
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Fuyi Li
- Department of Software Engineering, College of Information Engineering, Northwest A&F University, Yangling 712100, China.,Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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21
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Wang M, Li F, Wu H, Liu Q, Li S. PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest. Interdiscip Sci 2022; 14:697-711. [PMID: 35488998 DOI: 10.1007/s12539-022-00520-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 12/12/2022]
Abstract
Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.
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Affiliation(s)
- Miao Wang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC, 3000, Australia
| | - Hao Wu
- School of Software, Shandong University, Jinan, 250100, Shandong, China
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China.
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China.
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22
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Zeng L, Liu Y, Yu ZG, Liu Y. iEnhancer-DLRA: identification of enhancers and their strengths by a self-attention fusion strategy for local and global features. Brief Funct Genomics 2022; 21:399-407. [PMID: 35942693 DOI: 10.1093/bfgp/elac023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 11/14/2022] Open
Abstract
Identification and classification of enhancers are highly significant because they play crucial roles in controlling gene transcription. Recently, several deep learning-based methods for identifying enhancers and their strengths have been developed. However, existing methods are usually limited because they use only local or only global features. The combination of local and global features is critical to further improve the prediction performance. In this work, we propose a novel deep learning-based method, called iEnhancer-DLRA, to identify enhancers and their strengths. iEnhancer-DLRA extracts local and multi-scale global features of sequences by using a residual convolutional network and two bidirectional long short-term memory networks. Then, a self-attention fusion strategy is proposed to deeply integrate these local and global features. The experimental results on the independent test dataset indicate that iEnhancer-DLRA performs better than nine existing state-of-the-art methods in both identification and classification of enhancers in almost all metrics. iEnhancer-DLRA achieves 13.8% (for identifying enhancers) and 12.6% (for classifying strengths) improvement in accuracy compared with the best existing state-of-the-art method. This is the first time that the accuracy of an enhancer identifier exceeds 0.9 and the accuracy of the enhancer classifier exceeds 0.8 on the independent test set. Moreover, iEnhancer-DLRA achieves superior predictive performance on the rice dataset compared with the state-of-the-art method RiceENN.
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Affiliation(s)
- Li Zeng
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
| | - Yang Liu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
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23
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Bell PA, Scheuermann S, Renner F, Pan CL, Lu HY, Turvey SE, Bornancin F, Régnier CH, Overall CM. Integrating knowledge of protein sequence with protein function for the prediction and validation of new MALT1 substrates. Comput Struct Biotechnol J 2022; 20:4717-4732. [PMID: 36147669 PMCID: PMC9463181 DOI: 10.1016/j.csbj.2022.08.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/07/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Peter A. Bell
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Sophia Scheuermann
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Immunology, Eberhard Karl University Tübingen, 72076 Tübingen, Germany
- Department of Hematology and Oncology, University Hospital Tübingen, Children's Hospital, 72076 Tübingen, Germany
| | - Florian Renner
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
- Molecular Targeted Therapy - Discovery Oncology, Roche Pharma Research & Early Development, F. Hoffmann-La Roche Ltd, 4070 Basel, Switzerland
| | - Christina L. Pan
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Henry Y. Lu
- Department of Pediatrics, British Columbia Children's Hospital, The University of British Columbia, Vancouver, BC V5Z 4H4, Canada
- Department of Experimental Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Stuart E. Turvey
- Department of Pediatrics, British Columbia Children's Hospital, The University of British Columbia, Vancouver, BC V5Z 4H4, Canada
- Department of Experimental Medicine, Faculty of Medicine, The University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Frédéric Bornancin
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
| | - Catherine H. Régnier
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
| | - Christopher M. Overall
- Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
- Corresponding author at: Centre for Blood Research, Life Sciences Centre, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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Vinogradov AA, Chang JS, Onaka H, Goto Y, Suga H. Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning. ACS CENTRAL SCIENCE 2022; 8:814-824. [PMID: 35756369 PMCID: PMC9228559 DOI: 10.1021/acscentsci.2c00223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Indexed: 05/15/2023]
Abstract
Promiscuous post-translational modification (PTM) enzymes often display nonobvious substrate preferences by acting on diverse yet well-defined sets of peptides and/or proteins. Understanding of substrate fitness landscapes for PTM enzymes is important in many areas of contemporary science, including natural product biosynthesis, molecular biology, and biotechnology. Here, we report an integrated platform for accurate profiling of substrate preferences for PTM enzymes. The platform features (i) a combination of mRNA display with next-generation sequencing as an ultrahigh throughput technique for data acquisition and (ii) deep learning for data analysis. The high accuracy (>0.99 in each of two studies) of the resulting deep learning models enables comprehensive analysis of enzymatic substrate preferences. The models can quantify fitness across sequence space, map modification sites, and identify important amino acids in the substrate. To benchmark the platform, we performed profiling of a Ser dehydratase (LazBF) and a Cys/Ser cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis pathway. In both studies, our results point to complex enzymatic preferences, which, particularly for LazBF, cannot be reduced to a set of simple rules. The ability of the constructed models to dissect such complexity suggests that the developed platform can facilitate a wider study of PTM enzymes.
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Affiliation(s)
- Alexander A. Vinogradov
- Department
of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Jun Shi Chang
- Department
of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hiroyasu Onaka
- Department
of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
- Collaborative
Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan
| | - Yuki Goto
- Department
of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Hiroaki Suga
- Department
of Chemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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25
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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26
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Spatial and temporal proteomics reveals the distinct distributions and dynamics of O-GlcNAcylated proteins. Cell Rep 2022; 39:110946. [PMID: 35705054 PMCID: PMC9244862 DOI: 10.1016/j.celrep.2022.110946] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/28/2022] [Accepted: 05/23/2022] [Indexed: 11/30/2022] Open
Abstract
Protein O-GlcNAcylation plays critical roles in many cellular events, and its dysregulation is related to multiple diseases. Integrating bioorthogonal chemistry and multiplexed proteomics, we systematically and site specifically study the distributions and dynamics of protein O-GlcNAcylation in the nucleus and the cytoplasm of human cells. The results demonstrate that O-GlcNAcylated proteins with different functions have distinct distribution patterns. The distributions vary site specifically, indicating that different glycoforms of the same protein may have different distributions. Moreover, we comprehensively analyze the dynamics of O-GlcNAcylated and non-modified proteins in these two compartments, respectively, and the half-lives of glycoproteins in different compartments are markedly different, with the median half-life in the cytoplasm being much longer. In addition, glycoproteins in the nucleus are more dramatically stabilized than those in the cytoplasm under the O-GlcNAcase inhibition. The comprehensive spatial and temporal analyses of protein O-GlcNAcylation provide valuable information and advance our understanding of this important modification. Xu et al. systematically and site specifically study the distribution and dynamics of O-GlcNAcylated proteins in the nucleus and the cytoplasm. O-GlcNAcylated proteins with different functions have distinct distribution patterns. The half-lives of glycoproteins in the two cellular compartments are markedly different, with the much longer median half-life in the cytoplasm.
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27
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Peng X, Wang X, Guo Y, Ge Z, Li F, Gao X, Song J. RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins. Brief Bioinform 2022; 23:6596984. [PMID: 35649392 PMCID: PMC9294422 DOI: 10.1093/bib/bbac215] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/25/2022] [Accepted: 05/06/2022] [Indexed: 11/27/2022] Open
Abstract
RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned on each corresponding target species dataset. This two-stage transfer learning framework can enable the RBP-TSTL model to be effectively trained to learn and improve the prediction performance. Extensive performance benchmarking of the RBP-TSTL models trained using the features generated by the self-supervised pre-trained model and other models trained using hand-crafting encoding features demonstrated the effectiveness of the proposed two-stage knowledge transfer strategy based on the self-supervised pre-trained models. Using the best-performing RBP-TSTL models, we further conducted genome-scale RBP predictions for Homo sapiens, Arabidopsis thaliana, Escherichia coli, and Salmonella and established a computational compendium containing all the predicted putative RBPs candidates. We anticipate that the proposed RBP-TSTL approach will be explored as a useful tool for the characterization of RNA-binding proteins and exploration of their sequence–structure–function relationships.
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Affiliation(s)
- Xinxin Peng
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Xiaoyu Wang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Zongyuan Ge
- Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia.,College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.,KAUST Computational Bioscience Research Center, King Abdullah University of Science and Technology
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia.,Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
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28
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Matrikines as mediators of tissue remodelling. Adv Drug Deliv Rev 2022; 185:114240. [PMID: 35378216 DOI: 10.1016/j.addr.2022.114240] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/21/2022] [Accepted: 03/26/2022] [Indexed: 11/21/2022]
Abstract
Extracellular matrix (ECM) proteins confer biomechanical properties, maintain cell phenotype and mediate tissue repair (via release of sequestered cytokines and proteases). In contrast to intracellular proteomes, where proteins are monitored and replaced over short time periods, many ECM proteins function for years (decades in humans) without replacement. The longevity of abundant ECM proteins, such as collagen I and elastin, leaves them vulnerable to damage accumulation and their host organs prone to chronic, age-related diseases. However, ECM protein fragmentation can potentially produce peptide cytokines (matrikines) which may exacerbate and/or ameliorate age- and disease-related ECM remodelling. In this review, we discuss ECM composition, function and degradation and highlight examples of endogenous matrikines. We then critically and comprehensively analyse published studies of matrix-derived peptides used as topical skin treatments, before considering the potential for improvements in the discovery and delivery of novel matrix-derived peptides to skin and internal organs. From this, we conclude that while the translational impact of matrix-derived peptide therapeutics is evident, the mechanisms of action of these peptides are poorly defined. Further, well-designed, multimodal studies are required.
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29
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Hasan MM, Tsukiyama S, Cho JY, Kurata H, Alam MA, Liu X, Manavalan B, Deng HW. Deepm5C: A deep learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy. Mol Ther 2022; 30:2856-2867. [PMID: 35526094 PMCID: PMC9372321 DOI: 10.1016/j.ymthe.2022.05.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 11/30/2022] Open
Abstract
As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C), plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method to identify RNA m5C sites in the throughout human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature encoding algorithms and a feature derived from word embedding approaches. Afterwards, four variants of deep learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a 1-D convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and formulate the novel testable biological hypothesis.
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Affiliation(s)
- Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA.
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Jae Youl Cho
- Molecular Immunology Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Xiaowen Liu
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea.
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA.
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30
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Zhao B, Kurgan L. Deep Learning in Prediction of Intrinsic Disorder in Proteins. Comput Struct Biotechnol J 2022; 20:1286-1294. [PMID: 35356546 PMCID: PMC8927795 DOI: 10.1016/j.csbj.2022.03.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/04/2022] [Accepted: 03/04/2022] [Indexed: 12/12/2022] Open
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31
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Wang X, Li F, Xu J, Rong J, Webb GI, Ge Z, Li J, Song J. ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning. Brief Bioinform 2022; 23:6530221. [PMID: 35176756 PMCID: PMC8921646 DOI: 10.1093/bib/bbac031] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/10/2022] [Accepted: 01/22/2022] [Indexed: 12/15/2022] Open
Abstract
Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secretion pathways. The non-classical secretion pathway has recently received increasing attention among these secretion pathways, but its exact mechanism remains unclear. Non-classical secreted proteins (NCSPs) are a class of secreted proteins lacking signal peptides and motifs. Several NCSP predictors have been proposed to identify NCSPs and most of them employed the whole amino acid sequence of NCSPs to construct the model. However, the sequence length of different proteins varies greatly. In addition, not all regions of the protein are equally important and some local regions are not relevant to the secretion. The functional regions of the protein, particularly in the N- and C-terminal regions, contain important determinants for secretion. In this study, we propose a new hybrid deep learning-based framework, referred to as ASPIRER, which improves the prediction of NCSPs from amino acid sequences. More specifically, it combines a whole sequence-based XGBoost model and an N-terminal sequence-based convolutional neural network model; 5-fold cross-validation and independent tests demonstrate that ASPIRER achieves superior performance than existing state-of-the-art approaches. The source code and curated datasets of ASPIRER are publicly available at https://github.com/yanwu20/ASPIRER/. ASPIRER is anticipated to be a useful tool for improved prediction of novel putative NCSPs from sequences information and prioritization of candidate proteins for follow-up experimental validation.
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Affiliation(s)
- Xiaoyu Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jing Xu
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Jia Rong
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Geoffrey I Webb
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Zongyuan Ge
- Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia
| | - Jian Li
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Jiangning Song
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
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32
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Yu Y, Wang R, Teo RD. Machine Learning Approaches for Metalloproteins. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27041277. [PMID: 35209064 PMCID: PMC8878495 DOI: 10.3390/molecules27041277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 01/10/2023]
Abstract
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.
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Affiliation(s)
- Yue Yu
- Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan, Jiangsu 215316, China;
- Department of Physics, Duke University, Durham, NC 27708, USA
| | - Ruobing Wang
- Department of Chemistry, Duke University, Durham, NC 27708, USA;
| | - Ruijie D. Teo
- Department of Chemistry, Duke University, Durham, NC 27708, USA;
- UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Correspondence:
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33
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Kabir M, Nantasenamat C, Kanthawong S, Charoenkwan P, Shoombuatong W. Large-scale comparative review and assessment of computational methods for phage virion proteins identification. EXCLI JOURNAL 2022; 21:11-29. [PMID: 35145365 PMCID: PMC8822302 DOI: 10.17179/excli2021-4411] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/29/2021] [Indexed: 12/11/2022]
Abstract
Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.
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Affiliation(s)
- Muhammad Kabir
- School of Systems and Technology, Department of Computer Science, University of Management and Technology, Lahore, Pakistan, 54770
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Sakawrat Kanthawong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand, 40002
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand, 50200
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
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34
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Han K, Cao P, Wang Y, Xie F, Ma J, Yu M, Wang J, Xu Y, Zhang Y, Wan J. A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning. Front Pharmacol 2022; 12:814858. [PMID: 35153767 PMCID: PMC8835726 DOI: 10.3389/fphar.2021.814858] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 12/20/2021] [Indexed: 01/01/2023] Open
Abstract
Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.
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Affiliation(s)
- Ke Han
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
- College of Pharmacy, Harbin University of Commerce, Harbin, China
- *Correspondence: Ke Han, ; Jie Wan,
| | - Peigang Cao
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Yu Wang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Fang Xie
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jiaqi Ma
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Mengyao Yu
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jianchun Wang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Yaoqun Xu
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Yu Zhang
- Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China
| | - Jie Wan
- Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin, China
- *Correspondence: Ke Han, ; Jie Wan,
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35
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Zhang M, Jia C, Li F, Li C, Zhu Y, Akutsu T, Webb GI, Zou Q, Coin LJM, Song J. Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction. Brief Bioinform 2022; 23:6502561. [PMID: 35021193 PMCID: PMC8921625 DOI: 10.1093/bib/bbab551] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 11/12/2021] [Accepted: 11/30/2021] [Indexed: 01/13/2023] Open
Abstract
Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.
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Affiliation(s)
| | - Cangzhi Jia
- Corresponding authors: Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. E-mail: ; Lachlan J.M. Coin, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia. E-mail: ; Quan Zou, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. E-mail: ; Cangzhi Jia, School of Science, Dalian Maritime University, Dalian 116026, China. E-mail:
| | | | | | | | | | - Geoffrey I Webb
- Department of Data Science and Artificial Intelligence, Monash University, Melbourne, VIC 3800, Australia,Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Quan Zou
- Corresponding authors: Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. E-mail: ; Lachlan J.M. Coin, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia. E-mail: ; Quan Zou, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. E-mail: ; Cangzhi Jia, School of Science, Dalian Maritime University, Dalian 116026, China. E-mail:
| | - Lachlan J M Coin
- Corresponding authors: Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. E-mail: ; Lachlan J.M. Coin, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia. E-mail: ; Quan Zou, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. E-mail: ; Cangzhi Jia, School of Science, Dalian Maritime University, Dalian 116026, China. E-mail:
| | - Jiangning Song
- Corresponding authors: Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. E-mail: ; Lachlan J.M. Coin, Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia. E-mail: ; Quan Zou, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China. E-mail: ; Cangzhi Jia, School of Science, Dalian Maritime University, Dalian 116026, China. E-mail:
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Li F, Guo X, Xiang D, Pitt ME, Bainomugisa A, Coin LJ. Computational analysis and prediction of PE_PGRS proteins using machine learning. Comput Struct Biotechnol J 2022; 20:662-674. [PMID: 35140886 PMCID: PMC8804200 DOI: 10.1016/j.csbj.2022.01.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/09/2022] [Accepted: 01/18/2022] [Indexed: 12/18/2022] Open
Abstract
PEPPER is the first machine learning-based predictor for PE_PGRS proteins. PEPPER is based on lightGBM and various sequence and physicochemical features. PEPPER can identify PE_PGRS proteins rapidly and accurately. The webserver of PEPPER and stand-alone tool are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/PEPPER/.
Mycobacterium tuberculosis genome comprises approximately 10% of two families of poorly characterised genes due to their high GC content and highly repetitive nature. The largest sub-group, the proline-glutamic acid polymorphic guanine-cytosine-rich sequence (PE_PGRS) family, is thought to be involved in host response and disease pathogenicity. Due to their high genetic variability and complexity of analysis, they are typically disregarded for further research in genomic studies. There are currently limited online resources and homology computational tools that can identify and analyse PE_PGRS proteins. In addition, they are computational-intensive and time-consuming, and lack sensitivity. Therefore, computational methods that can rapidly and accurately identify PE_PGRS proteins are valuable to facilitate the functional elucidation of the PE_PGRS family proteins. In this study, we developed the first machine learning-based bioinformatics approach, termed PEPPER, to allow users to identify PE_PGRS proteins rapidly and accurately. PEPPER was built upon a comprehensive evaluation of 13 popular machine learning algorithms with various sequence and physicochemical features. Empirical studies demonstrated that PEPPER achieved significantly better performance than alignment-based approaches, BLASTP and PHMMER, in both prediction accuracy and speed. PEPPER is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE_PGRS proteins.
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Katuwawala A, Zhao B, Kurgan L. DisoLipPred: accurate prediction of disordered lipid-binding residues in protein sequences with deep recurrent networks and transfer learning. Bioinformatics 2021; 38:115-124. [PMID: 34487138 DOI: 10.1093/bioinformatics/btab640] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/05/2021] [Accepted: 09/02/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Intrinsically disordered protein regions interact with proteins, nucleic acids and lipids. Regions that bind lipids are implicated in a wide spectrum of cellular functions and several human diseases. Motivated by the growing amount of experimental data for these interactions and lack of tools that can predict them from the protein sequence, we develop DisoLipPred, the first predictor of the disordered lipid-binding residues (DLBRs). RESULTS DisoLipPred relies on a deep bidirectional recurrent network that implements three innovative features: transfer learning, bypass module that sidesteps predictions for putative structured residues, and expanded inputs that cover physiochemical properties associated with the protein-lipid interactions. Ablation analysis shows that these features drive predictive quality of DisoLipPred. Tests on an independent test dataset and the yeast proteome reveal that DisoLipPred generates accurate results and that none of the related existing tools can be used to indirectly identify DLBR. We also show that DisoLipPred's predictions complement the results generated by predictors of the transmembrane regions. Altogether, we conclude that DisoLipPred provides high-quality predictions of DLBRs that complement the currently available methods. AVAILABILITY AND IMPLEMENTATION DisoLipPred's webserver is available at http://biomine.cs.vcu.edu/servers/DisoLipPred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Akila Katuwawala
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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38
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Dyer RP, Weiss GA. Making the cut with protease engineering. Cell Chem Biol 2021; 29:177-190. [PMID: 34921772 PMCID: PMC9127713 DOI: 10.1016/j.chembiol.2021.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 07/30/2021] [Accepted: 11/29/2021] [Indexed: 12/30/2022]
Abstract
Proteases cut with enviable precision and regulate diverse molecular events in biology. Such qualities drive a seemingly inexhaustible appetite for proteases with new activities and capabilities. Comprising 25% of the total industrial enzyme market, proteases appear in consumer goods, such as detergents, textile processing, and numerous foods; additionally, proteases include 25 US Food and Drug Administration-approved medicines and various research tools. Recent advances in protease engineering strategies address target specificity, catalytic efficiency, and stability. This guide to protease engineering surveys best practices and emerging strategies. We further highlight gaps and flexibilities inherent to each system that suggest opportunities for new technology development along with engineered proteases to solve challenges in proteomics, protein sequencing, and synthetic gene circuits.
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Affiliation(s)
- Rebekah P Dyer
- Department of Molecular Biology and Biochemistry, University of California, Irvine, 1102 NS-2, Irvine, CA 92697-2025, USA
| | - Gregory A Weiss
- Department of Chemistry, University of California, Irvine, 1102 NS-2, Irvine, CA 92697-2025, USA; Department of Molecular Biology and Biochemistry, University of California, Irvine, 1102 NS-2, Irvine, CA 92697-2025, USA; Department of Pharmaceutical Sciences, University of California, Irvine, 1102 NS-2, Irvine, CA 92697-2025, USA.
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Zhang F, Zhao B, Shi W, Li M, Kurgan L. DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning. Brief Bioinform 2021; 23:6461158. [PMID: 34905768 DOI: 10.1093/bib/bbab521] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/30/2021] [Accepted: 11/14/2021] [Indexed: 12/14/2022] Open
Abstract
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Bi Zhao
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - Wenbo Shi
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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Wang X, Yan R, Wang Y. Computational identification of human ubiquitination sites using convolutional and recurrent neural networks. Mol Omics 2021; 17:948-955. [PMID: 34515266 DOI: 10.1039/d0mo00183j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Ubiquitination is a very important protein post-translational modification in humans, which is closely related to many human diseases such as cancers. Although some methods have been elegantly proposed to predict human ubiquitination sites, the accuracy of these methods is generally not very satisfactory. In order to improve the prediction accuracy of human ubiquitination sites, we propose a new ensemble method HUbipPred, which takes the binary encoding and physicochemical properties of amino acids as training features, and integrates two intensively trained convolutional neural networks and two recurrent neural networks to build the model. Finally, HUbiPred achieves AUC values of 0.852 and 0.844 in five-fold cross-validation and independent tests, respectively, which greatly improves the prediction accuracy compared to previous predictors. We also analyze the physicochemical properties of amino acids around ubiquitination sites, study the important roles of architectures (i.e., convolution, long short-term memory (LSTM) and fully connected hidden layers) in the networks for prediction performance, and also predict potential ubiquitination sites in humans using HUbiPred. The training and test datasets, predicted human ubiquitination sites, and source codes of HUbiPred are publicly available at https://github.com/amituofo-xf/HUbiPred.
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Affiliation(s)
- Xiaofeng Wang
- College of Mathematics and Computer Sciences, Shanxi Normal University, Linfen 041004, China.
| | - Renxiang Yan
- School of Biological Sciences and Engineering, Fujian Key Laboratory of Marine Enzyme Engineering, Fuzhou University, Fuzhou 350002, China.
| | - Yongji Wang
- College of Life Sciences, Shanxi Normal University, Linfen 041000, China
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Staem5: A novel computational approachfor accurate prediction of m5C site. MOLECULAR THERAPY. NUCLEIC ACIDS 2021; 26:1027-1034. [PMID: 34786208 PMCID: PMC8571400 DOI: 10.1016/j.omtn.2021.10.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/27/2021] [Accepted: 10/06/2021] [Indexed: 12/25/2022]
Abstract
5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.
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Zhu Y, Yin S, Zheng J, Shi Y, Jia C. O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine. J Bioinform Comput Biol 2021; 20:2150029. [PMID: 34806952 DOI: 10.1142/s0219720021500293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal-Wallis (K-W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.
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Affiliation(s)
- Yan Zhu
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
| | - Shuwan Yin
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
| | - Jia Zheng
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
| | - Yixia Shi
- School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, P. R. China
| | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, P. R. China
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43
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Li F, Dong S, Leier A, Han M, Guo X, Xu J, Wang X, Pan S, Jia C, Zhang Y, Webb GI, Coin LJM, Li C, Song J. Positive-unlabeled learning in bioinformatics and computational biology: a brief review. Brief Bioinform 2021; 23:6415313. [PMID: 34729589 DOI: 10.1093/bib/bbab461] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/27/2021] [Accepted: 10/07/2021] [Indexed: 12/14/2022] Open
Abstract
Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.
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Affiliation(s)
- Fuyi Li
- Monash University, Australia
| | | | - André Leier
- Department of Genetics, UAB School of Medicine, USA
| | - Meiya Han
- Department of Biochemistry and Molecular Biology, Monash University, Australia
| | | | - Jing Xu
- Computer Science and Technology from Nankai University, China
| | - Xiaoyu Wang
- Department of Biochemistry and Molecular Biology and Biomedicine Discovery Institute, Monash University, Australia
| | - Shirui Pan
- University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Cangzhi Jia
- College of Science, Dalian Maritime University, Australia
| | - Yang Zhang
- Northwestern Polytechnical University, China
| | - Geoffrey I Webb
- Faculty of Information Technology at Monash University, Australia
| | - Lachlan J M Coin
- Department of Clinical Pathology, University of Melbourne, Australia
| | - Chen Li
- Biomedicine Discovery Institute and Department of Biochemistry of Molecular Biology, Monash University, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute, Monash University, Melbourne, Australia
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44
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Wang X, Li F, Qiu W, Xu B, Li Y, Lian X, Yu H, Zhang Z, Wang J, Li Z, Xue W, Zhu F. SYNBIP: synthetic binding proteins for research, diagnosis and therapy. Nucleic Acids Res 2021; 50:D560-D570. [PMID: 34664670 PMCID: PMC8728148 DOI: 10.1093/nar/gkab926] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/13/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022] Open
Abstract
The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.
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Affiliation(s)
- Xiaona Wang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Wenqi Qiu
- Department of Surgery, HKU-SZH & Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Binbin Xu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yanlin Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Hongyan Yu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Zhao Zhang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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45
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Liu T, Chen J, Zhang Q, Hippe K, Hunt C, Le T, Cao R, Tang H. The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite. Curr Med Chem 2021; 29:807-821. [PMID: 34636289 DOI: 10.2174/0929867328666211005140625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/28/2021] [Accepted: 08/15/2021] [Indexed: 11/22/2022]
Abstract
Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.
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Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Jiamao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University. United States
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University. United States
| | - Thu Le
- Department of Computer Science, Pacific Lutheran University. United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University. United States
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
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46
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Melo MCR, Maasch JRMA, de la Fuente-Nunez C. Accelerating antibiotic discovery through artificial intelligence. Commun Biol 2021; 4:1050. [PMID: 34504303 PMCID: PMC8429579 DOI: 10.1038/s42003-021-02586-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
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Affiliation(s)
- Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline R M A Maasch
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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Jia C, Zhang M, Fan C, Li F, Song J. Formator: Predicting Lysine Formylation Sites Based on the Most Distant Undersampling and Safe-Level Synthetic Minority Oversampling. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1937-1945. [PMID: 31804942 DOI: 10.1109/tcbb.2019.2957758] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Lysine formylation is a reversible type of protein post-translational modification and has been found to be involved in a myriad of biological processes, including modulation of chromatin conformation and gene expression in histones and other nuclear proteins. Accurate identification of lysine formylation sites is essential for elucidating the underlying molecular mechanisms of formylation. Traditional experimental methods are time-consuming and expensive. As such, it is desirable and necessary to develop computational methods for accurate prediction of formylation sites. In this study, we propose a novel predictor, termed Formator, for identifying lysine formylation sites from sequences information. Formator is developed using the ensemble learning (EL) strategy based on four individual support vector machine classifiers via a voting system. Moreover, the most distant undersampling and Safe-Level-SMOTE oversampling techniques were integrated to deal with the data imbalance problem of the training dataset. Four effective feature extraction methods, namely bi-profile Bayes (BPB), k-nearest neighbor (KNN), amino acid physicochemical properties (AAindex), and composition and transition (CTD) were employed to encode the surrounding sequence features of potential formylation sites. Extensive empirical studies show that Formator achieved the accuracy of 87.24 and 74.96 percent on jackknife test and the independent test, respectively. Performance comparison results on the independent test indicate that Formator outperforms current existing prediction tool, LFPred, suggesting that it has a great potential to serve as a useful tool in identifying novel lysine formylation sites and facilitating hypothesis-driven experimental efforts.
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Eckersley A, Ozols M, O'Connor C, Bell M, Sherratt MJ. Predicting and characterising protein damage in the extracellular matrix. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY 2021. [DOI: 10.1016/j.jpap.2021.100055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021; 18:527-556. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text]. EXPERT OPINION Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).
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Affiliation(s)
- Luís Perpetuo
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Université Toulouse III, Toulouse, France
| | - Rita Ferreira
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Francisco Amado
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Adelino Leite-Moreira
- UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
| | - Artur M S Silva
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Rui Vitorino
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro.,LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro.,UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
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Liang X, Li F, Chen J, Li J, Wu H, Li S, Song J, Liu Q. Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification. Brief Bioinform 2021; 22:bbaa312. [PMID: 33316035 PMCID: PMC8294543 DOI: 10.1093/bib/bbaa312] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/30/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022] Open
Abstract
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. Therefore, it is desirable to summarize the advantages and disadvantages of the existing methods, provide useful insights and suggestions for the development and improvement of novel computational tools to characterize and identify ACPs. With this in mind, we firstly comprehensively investigate 16 state-of-the-art predictors for ACPs in terms of their core algorithms, feature encoding schemes, performance evaluation metrics and webserver/software usability. Then, comprehensive performance assessment is conducted to evaluate the robustness and scalability of the existing predictors using a well-prepared benchmark dataset. We provide potential strategies for the model performance improvement. Moreover, we propose a novel ensemble learning framework, termed ACPredStackL, for the accurate identification of ACPs. ACPredStackL is developed based on the stacking ensemble strategy combined with SVM, Naïve Bayesian, lightGBM and KNN. Empirical benchmarking experiments against the state-of-the-art methods demonstrate that ACPredStackL achieves a comparative performance for predicting ACPs. The webserver and source code of ACPredStackL is freely available at http://bigdata.biocie.cn/ACPredStackL/ and https://github.com/liangxiaoq/ACPredStackL, respectively.
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Affiliation(s)
- Xiao Liang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Centre for Data Science, Monash University, Melbourne, VIC 3800, Australia
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
| | - Jinxiang Chen
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Junlong Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Hao Wu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Centre for Data Science, Monash University, Melbourne, VIC 3800, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
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