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Yang Z, Wu Y, Liu H, He L, Deng X. AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides. J Chem Inf Model 2024; 64:1751-1762. [PMID: 38408296 DOI: 10.1021/acs.jcim.3c02035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
There has been an increasing interest in the use of amyloids for constructing various functional materials. The design of amyloid-associated functional materials requires the identification of the core peptide sequences as the fundamental building block. The existing computational methods are limited in terms of delineating polypeptides, the typical non-Euclidean structural data, and they fail to capture the dynamic interactions between amino acids due to ignoring the contextual information from surrounding amino acids. Here, we first propose the use of a state-of-the-art graph convolutional neural network for predicting the trends of amyloid formation from specific peptide sequences (AMYGNN) by abstracting each polypeptide as a graph, in which the constituting amino acids are viewed as nodes and edges characterizing the connections between pairs of amino acids are established when they meet a given distance threshold (Cα-Cα ≤ 5 Å). Our model achieves high performance with accuracy (0.9208), G-mean (0.9203), MCC (0.8417), and F1 (0.9235) in determining the characteristic peptide sequences to form amyloid. 32 of 534 crucial amino acid properties that greatly contribute to the formation of amyloids are ascertained, and the β-folding-like graph structure of a polypeptide is believed to be essential for the formation of amyloid. Our model enables the mapping of polypeptides with underlying interactions between amino acids and provides a quick and precise predictive framework for directing the construction of amyloid-associated functional materials.
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Affiliation(s)
- Zuojun Yang
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Yuhan Wu
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Hao Liu
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Li He
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Xiaoyuan Deng
- MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China
- Guangdong Provincial Key Laboratory of Laser Life Science, and Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
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2
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Wang D, Jin J, Li Z, Wang Y, Fan M, Liang S, Su R, Wei L. StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides. Bioinformatics 2024; 40:btae057. [PMID: 38305458 PMCID: PMC10904144 DOI: 10.1093/bioinformatics/btae057] [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: 04/17/2023] [Revised: 12/07/2023] [Accepted: 01/30/2024] [Indexed: 02/03/2024] Open
Abstract
MOTIVATION Diabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation across the world. To alleviate the impact of diabetes, researchers have developed the next generation of anti-diabetic drugs, known as dipeptidyl peptidase IV inhibitory peptides (DPP-IV-IPs). However, the discovery of these promising drugs has been restricted due to the lack of effective peptide-mining tools. RESULTS Here, we presented StructuralDPPIV, a deep learning model designed for DPP-IV-IP identification, which takes advantage of both molecular graph features in amino acid and sequence information. Experimental results on the independent test dataset and two wet experiment datasets show that our model outperforms the other state-of-art methods. Moreover, to better study what StructuralDPPIV learns, we used CAM technology and perturbation experiment to analyze our model, which yielded interpretable insights into the reasoning behind prediction results. AVAILABILITY AND IMPLEMENTATION The project code is available at https://github.com/WeiLab-BioChem/Structural-DPP-IV.
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Affiliation(s)
- Ding Wang
- School of Software, Shandong University, Jinan 250101, China
| | - Junru Jin
- School of Software, Shandong University, Jinan 250101, China
| | - Zhongshen Li
- School of Software, Shandong University, Jinan 250101, China
| | - Yu Wang
- School of Software, Shandong University, Jinan 250101, China
| | - Mushuang Fan
- School of Software, Shandong University, Jinan 250101, China
| | - Sirui Liang
- School of Software, Shandong University, Jinan 250101, China
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Leyi Wei
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
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3
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Kandola T, Venkatesan S, Zhang J, Lerbakken BT, Von Schulze A, Blanck JF, Wu J, Unruh JR, Berry P, Lange JJ, Box AC, Cook M, Sagui C, Halfmann R. Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal. eLife 2023; 12:RP86939. [PMID: 37921648 PMCID: PMC10624427 DOI: 10.7554/elife.86939] [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] [Indexed: 11/04/2023] Open
Abstract
A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.
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Affiliation(s)
- Tej Kandola
- Stowers Institute for Medical ResearchKansas CityUnited States
- The Open UniversityMilton KeynesUnited Kingdom
| | | | - Jiahui Zhang
- Department of Physics, North Carolina State UniversityRaleighUnited States
| | | | | | | | - Jianzheng Wu
- Stowers Institute for Medical ResearchKansas CityUnited States
- Department of Biochemistry and Molecular Biology, University of Kansas Medical CenterKansas CityUnited States
| | - Jay R Unruh
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Paula Berry
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Jeffrey J Lange
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Andrew C Box
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Malcolm Cook
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Celeste Sagui
- Department of Physics, North Carolina State UniversityRaleighUnited States
| | - Randal Halfmann
- Stowers Institute for Medical ResearchKansas CityUnited States
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4
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Bayazit MB, Francois A, McGrail E, Accornero F, Stratton MS. mt-tRNAs in the polymerase gamma mutant heart. THE JOURNAL OF CARDIOVASCULAR AGING 2023; 3:41. [PMID: 38235059 PMCID: PMC10793997 DOI: 10.20517/jca.2023.26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Introduction Mice harboring a D257A mutation in the proofreading domain of the mitochondrial DNA polymerase, Polymerase Gamma (POLG), experience severe metabolic dysfunction and display hallmarks of accelerated aging. We previously reported a mitochondrial unfolded protein response (UPTmt) - like (UPRmt-like) gene and protein expression pattern in the right ventricular tissue of POLG mutant mice. Aim We sought to determine if POLG mutation altered the expression of genes encoded by the mitochondria in a way that might also reduce proteotoxic stress. Methods and Results The expression of genes encoded by the mitochondrial DNA was interrogated via RNA-seq and northern blot analysis. A striking, location-dependent effect was seen in the expression of mitochondrial-encoded tRNAs in the POLG mutant as assayed by RNA-seq. These expression changes were negatively correlated with the tRNA partner amino acid's amyloidogenic potential. Direct measurement by northern blot was conducted on candidate mt-tRNAs identified from the RNA-seq. This analysis confirmed reduced expression of MT-TY in the POLG mutant but failed to show increased expression of MT-TP, which was dramatically increased in the RNA-seq data. Conclusion We conclude that reduced expression of amyloid-associated mt-tRNAs is another indication of adaptive response to severe mitochondrial dysfunction in the POLG mutant. Incongruence between RNA-seq and northern blot measurement of MT-TP expression points towards the existence of mt-tRNA post-transcriptional modification regulation in the POLG mutant that alters either polyA capture or cDNA synthesis in RNA-seq library generation. Together, these data suggest that 1) evolution has distributed mt-tRNAs across the circular mitochondrial genome to allow chromosomal location-dependent mt-tRNA regulation (either by expression or PTM) and 2) this regulation is cognizant of the tRNA partner amino acid's amyloidogenic properties.
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Affiliation(s)
- M. Bilal Bayazit
- Department of Physiology & Cell Biology, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH 43210, USA
- Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Ashley Francois
- Department of Physiology & Cell Biology, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Erin McGrail
- Department of Physiology & Cell Biology, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Federica Accornero
- Department of Physiology & Cell Biology, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH 43210, USA
- Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Matthew S. Stratton
- Department of Physiology & Cell Biology, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH 43210, USA
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5
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Kandola T, Venkatesan S, Zhang J, Lerbakken B, Schulze AV, Blanck JF, Wu J, Unruh J, Berry P, Lange JJ, Box A, Cook M, Sagui C, Halfmann R. Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.20.533418. [PMID: 36993401 PMCID: PMC10055281 DOI: 10.1101/2023.03.20.533418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.
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Affiliation(s)
- Tej Kandola
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
- The Open University, Milton Keyes, MK7 6AA, UK
| | | | - Jiahui Zhang
- Department of Physics, North Carolina State University, Raleigh, NC 27695, USA
| | | | - Alex Von Schulze
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Jillian F Blanck
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Jianzheng Wu
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
- Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Jay Unruh
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Paula Berry
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Jeffrey J Lange
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Andrew Box
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Malcolm Cook
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Celeste Sagui
- Department of Physics, North Carolina State University, Raleigh, NC 27695, USA
| | - Randal Halfmann
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
- Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA
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6
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Zhou Y, Huang Z, Gou Y, Liu S, Yang W, Zhang H, Dzisoo AM, Huang J. AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains. Antib Ther 2023; 6:147-156. [PMID: 37492587 PMCID: PMC10365155 DOI: 10.1093/abt/tbad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 03/30/2023] [Accepted: 04/06/2023] [Indexed: 07/27/2023] Open
Abstract
Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.
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Affiliation(s)
- Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Yushu Gou
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Siqi Liu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Wei Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Hongyu Zhang
- Research and Development, Zhanyuan Therapeutics Ltd., Hangzhou, Zhejiang 310000, China
| | - Anthony Mackitz Dzisoo
- Bioinformatics, Data and Medical Reporting, Arcencsus GmbH, Rostock, Mecklenburg-Vorpommern 18055, Germany
| | - Jian Huang
- To whom correspondence should be addressed. Jian Huang, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 610054, China.
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7
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Yang R, Liu J, Zhang L. ECAmyloid: An amyloid predictor based on ensemble learning and comprehensive sequence-derived features. Comput Biol Chem 2023; 104:107853. [PMID: 36990028 DOI: 10.1016/j.compbiolchem.2023.107853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023]
Abstract
Amyloid fibrils formed by the mis-aggregation of amyloid proteins can lead to neuronal degenerations in the Alzheimer's disease. Predicting amyloid proteins not only contributes to understanding physicochemical properties and formation mechanism of amyloid proteins, but also has significant implications in the amyloid disease treatment and the development of a new purpose for amyloid materials. In this study, an ensemble learning model with sequence-derived features, ECAmyloid, is proposed to identify amyloids. The sequence-derived features including Pseudo Position Specificity Score Matrix (Pse-PSSM), Split Amino Acid Composition (SAAC), Solvent Accessibility (SA), and Secondary Structure Information (SSI) are employed to incorporate sequence composition, evolutionary and structural information. The individual learners of the ensemble learning model are selected by an increment classifier selection strategy. The final prediction results are determined by voting of prediction results of multiple individual learners. In view of the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted to generate positive samples. To eliminate irrelevant features and redundant features, correlation-based feature subset (CFS) selection combined with a heuristic search strategy is performed to obtain the optimal feature subset. Experimental results indicate that the ensemble classifier achieves an accuracy of 98.29%, a sensitivity of 0.992, a specificity of 0.974 on the training dataset using the 10-fold cross validation, far higher than the results obtained by its individual learners. Compared with the original feature set, the accuracy, sensitivity, specificity, MCC, F1-score, G-Mean of the ensemble method trained by the optimal feature subset are improved by 1.05%, 0.012, 0.01, 0.021, 0.011 and 0.011, respectively. Moreover, the comparison results with existing methods on two same independent test datasets demonstrate that the proposed method is an effective and promising predictor for large-scale determination of amyloid proteins. The data and code used to develop ECAmyloid has been shared to Github, and can be freely downloaded at https://github.com/KOALA-L/ECAmyloid.git.
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Affiliation(s)
- Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China
| | - Jiaming Liu
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China
| | - Lina Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
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8
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Xu D, Liu B, Wang J, Zhang Z. Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers. Front Bioeng Biotechnol 2022; 10:998298. [PMID: 36277390 PMCID: PMC9585160 DOI: 10.3389/fbioe.2022.998298] [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: 07/19/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background: In the biotechnology and applied microbiology sectors, artificial intelligence (AI) has been extensively used in disease diagnostics, drug research and development, functional genomics, biomarker recognition, and medical imaging diagnostics. In our study, from 2000 to 2021, science publications focusing on AI in biotechnology were reviewed, and quantitative, qualitative, and modeling analyses were performed. Methods: On 6 May 2022, the Web of Science Core Collection (WoSCC) was screened for AI applications in biotechnology and applied microbiology; 3,529 studies were identified between 2000 and 2022, and analyzed. The following information was collected: publication, country or region, references, knowledgebase, institution, keywords, journal name, and research hotspots, and examined using VOSviewer and CiteSpace V bibliometric platforms. Results: We showed that 128 countries published articles related to AI in biotechnology and applied microbiology; the United States had the most publications. In addition, 584 global institutions contributed to publications, with the Chinese Academy of Science publishing the most. Reference clusters from studies were categorized into ten headings: deep learning, prediction, support vector machines (SVM), object detection, feature representation, synthetic biology, amyloid, human microRNA precursors, systems biology, and single cell RNA-Sequencing. Research frontier keywords were represented by microRNA (2012–2020) and protein-protein interactions (PPIs) (2012–2020). Conclusion: We systematically, objectively, and comprehensively analyzed AI-related biotechnology and applied microbiology literature, and additionally, identified current hot spots and future trends in this area. Our review provides researchers with a comprehensive overview of the dynamic evolution of AI in biotechnology and applied microbiology and identifies future key research areas.
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Affiliation(s)
- Dongyu Xu
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Bing Liu
- Department of Bone Oncology, The People’s Hospital of Liaoning Province, Shenyang, Liaoning, China
| | - Jian Wang
- Department of Pathogenic Biology, School of Basic Medicine, China Medical University, Shenyang, Liaoning, China
| | - Zhichang Zhang
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
- *Correspondence: Zhichang Zhang,
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9
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Qing R, Hao S, Smorodina E, Jin D, Zalevsky A, Zhang S. Protein Design: From the Aspect of Water Solubility and Stability. Chem Rev 2022; 122:14085-14179. [PMID: 35921495 PMCID: PMC9523718 DOI: 10.1021/acs.chemrev.1c00757] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Indexed: 12/13/2022]
Abstract
Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.
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Affiliation(s)
- Rui Qing
- State
Key Laboratory of Microbial Metabolism, School of Life Sciences and
Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shilei Hao
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- Key
Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China
| | - Eva Smorodina
- Department
of Immunology, University of Oslo and Oslo
University Hospital, Oslo 0424, Norway
| | - David Jin
- Avalon GloboCare
Corp., Freehold, New Jersey 07728, United States
| | - Arthur Zalevsky
- Laboratory
of Bioinformatics Approaches in Combinatorial Chemistry and Biology, Shemyakin−Ovchinnikov Institute of Bioorganic
Chemistry RAS, Moscow 117997, Russia
| | - Shuguang Zhang
- Media
Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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10
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Charoenkwan P, Kanthawong S, Schaduangrat N, Li’ P, Moni MA, Shoombuatong W. SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides. ACS OMEGA 2022; 7:32653-32664. [PMID: 36120041 PMCID: PMC9476499 DOI: 10.1021/acsomega.2c04305] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies.
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Affiliation(s)
- Phasit Charoenkwan
- Modern
Management and Information Technology, College of Arts, Media and
Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Sakawrat Kanthawong
- Department
of Microbiology, Faculty of Medicine, Khon
Kaen University, Khon Kaen 40002, Thailand
| | - Nalini Schaduangrat
- Center
of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Pietro Li’
- Department
of Computer Science and Technology, University
of Cambridge, Cambridge CB3 0FD, U.K.
| | - Mohammad Ali Moni
- Artificial
Intelligence & Digital Health, School of Health and Rehabilitation
Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, Queensland 4072, Australia
| | - Watshara Shoombuatong
- Center
of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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11
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Charoenkwan P, Ahmed S, Nantasenamat C, Quinn JMW, Moni MA, Lio' P, Shoombuatong W. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep 2022; 12:7697. [PMID: 35546347 PMCID: PMC9095707 DOI: 10.1038/s41598-022-11897-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Saeed Ahmed
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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12
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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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13
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Charoenkwan P, Chiangjong W, Nantasenamat C, Moni MA, Lio’ P, Manavalan B, Shoombuatong W. SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids. Pharmaceutics 2022; 14:pharmaceutics14010122. [PMID: 35057016 PMCID: PMC8779003 DOI: 10.3390/pharmaceutics14010122] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/16/2021] [Accepted: 12/28/2021] [Indexed: 12/13/2022] Open
Abstract
Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs’ functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;
| | - Wararat Chiangjong
- Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand;
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Mohammad Ali Moni
- Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia;
| | - Pietro Lio’
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UK;
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
- Correspondence: (B.M.); (W.S.)
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
- Correspondence: (B.M.); (W.S.)
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14
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Charoenkwan P, Chotpatiwetchkul W, Lee VS, Nantasenamat C, Shoombuatong W. A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides. Sci Rep 2021; 11:23782. [PMID: 34893688 PMCID: PMC8664844 DOI: 10.1038/s41598-021-03293-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/01/2021] [Indexed: 02/08/2023] Open
Abstract
Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.
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Affiliation(s)
- Phasit Charoenkwan
- grid.7132.70000 0000 9039 7662Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200 Thailand
| | - Warot Chotpatiwetchkul
- grid.419784.70000 0001 0816 7508Applied Computational Chemistry Research Unit, Department of Chemistry, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520 Thailand
| | - Vannajan Sanghiran Lee
- grid.10347.310000 0001 2308 5949Department of Chemistry, Centre of Theoretical and Computational Physics, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Chanin Nantasenamat
- grid.10223.320000 0004 1937 0490Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700 Thailand
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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15
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Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Lio' P, Manavalan B, Shoombuatong W. StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. Methods 2021; 204:189-198. [PMID: 34883239 DOI: 10.1016/j.ymeth.2021.12.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
The development of efficient and effective bioinformatics tools and pipelines for identifying peptides with dipeptidyl peptidase IV (DPP-IV) inhibitory activities from large-scale protein datasets is of great importance for the discovery and development of potential and promising antidiabetic drugs. In this study, we present a novel stacking-based ensemble learning predictor (termed StackDPPIV) designed for identification of DPP-IV inhibitory peptides. Unlike the existing method, which is based on single-feature-based methods, we combined five popular machine learning algorithms in conjunction with ten different feature encodings from multiple perspectives to generate a pool of various baseline models. Subsequently, the probabilistic features derived from these baseline models were systematically integrated and deemed as new feature representations. Finally, in order to improve the predictive performance, the genetic algorithm based on the self-assessment-report was utilized to determine a set of informative probabilistic features and then used the optimal one for developing the final meta-predictor (StackDPPIV). Experiment results demonstrated that StackDPPIV could outperform its constituent baseline models on both the training and independent datasets. Furthermore, StackDPPIV achieved an accuracy of 0.891, MCC of 0.784 and AUC of 0.961, which were 9.4%, 19.0% and 11.4%, respectively, higher than that of the existing method on the independent test. Feature analysis demonstrated that our feature representations had more discriminative ability as compared to conventional feature descriptors, which highlights the combination of different features was essential for the performance improvement. In order to implement the proposed predictor, we had built a user-friendly online web server at http://pmlabstack.pythonanywhere.com/StackDPPIV.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - 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
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, the University of Queensland St Lucia, QLD 4072, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, UK
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
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16
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Malik AA, Chotpatiwetchkul W, Phanus-Umporn C, Nantasenamat C, Charoenkwan P, Shoombuatong W. StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors. J Comput Aided Mol Des 2021; 35:1037-1053. [PMID: 34622387 DOI: 10.1007/s10822-021-00418-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/17/2021] [Indexed: 01/07/2023]
Abstract
Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.
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Affiliation(s)
- Aijaz Ahmad Malik
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Warot Chotpatiwetchkul
- Applied Computational Chemistry Research Unit, Department of Chemistry, School of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand
| | - Chuleeporn Phanus-Umporn
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, 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 of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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17
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Wei L, Ye X, Xue Y, Sakurai T, Wei L. ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism. Brief Bioinform 2021; 22:6209691. [PMID: 33822870 DOI: 10.1093/bib/bbab041] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/11/2021] [Accepted: 01/28/2021] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxicity of peptides within the vast number of candidate peptides. RESULTS In this study, we proposed ATSE, a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural networks and attention mechanism. More specifically, it consists of four modules: (i) a sequence processing module for converting peptide sequences to molecular graphs and evolutionary profiles, (ii) a feature extraction module designed to learn discriminative features from graph structural information and evolutionary information, (iii) an attention module employed to optimize the features and (iv) an output module determining a peptide as toxic or non-toxic, using optimized features from the attention module. CONCLUSION Comparative studies demonstrate that the proposed ATSE significantly outperforms all other competing methods. We found that structural information is complementary to the evolutionary information, effectively improving the predictive performance. Importantly, the data-driven features learned by ATSE can be interpreted and visualized, providing additional information for further analysis. Moreover, we present a user-friendly online computational platform that implements the proposed ATSE, which is now available at http://server.malab.cn/ATSE. We expect that it can be a powerful and useful tool for researchers of interest.
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Affiliation(s)
- Lesong Wei
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577
| | - Yuyang Xue
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China
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18
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Nilamyani AN, Auliah FN, Moni MA, Shoombuatong W, Hasan MM, Kurata H. PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features. Int J Mol Sci 2021; 22:2704. [PMID: 33800121 PMCID: PMC7962192 DOI: 10.3390/ijms22052704] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/15/2022] Open
Abstract
Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.
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Affiliation(s)
- Andi Nur Nilamyani
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
| | - Firda Nurul Auliah
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
| | - Mohammad Ali Moni
- WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, Sydney, NSW 2052, Australia;
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (A.N.N.); (F.N.A.)
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19
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Auliah FN, Nilamyani AN, Shoombuatong W, Alam MA, Hasan MM, Kurata H. PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations. Int J Mol Sci 2021; 22:ijms22042120. [PMID: 33672741 PMCID: PMC7924619 DOI: 10.3390/ijms22042120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/12/2021] [Accepted: 02/18/2021] [Indexed: 12/30/2022] Open
Abstract
Pupylation is a type of reversible post-translational modification of proteins, which plays a key role in the cellular function of microbial organisms. Several proteomics methods have been developed for the prediction and analysis of pupylated proteins and pupylation sites. However, the traditional experimental methods are laborious and time-consuming. Hence, computational algorithms are highly needed that can predict potential pupylation sites using sequence features. In this research, a new prediction model, PUP-Fuse, has been developed for pupylation site prediction by integrating multiple sequence representations. Meanwhile, we explored the five types of feature encoding approaches and three machine learning (ML) algorithms. In the final model, we integrated the successive ML scores using a linear regression model. The PUP-Fuse achieved a Mathew correlation value of 0.768 by a 10-fold cross-validation test. It also outperformed existing predictors in an independent test. The web server of the PUP-Fuse with curated datasets is freely available.
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Affiliation(s)
- Firda Nurul Auliah
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
| | - Andi Nur Nilamyani
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;
| | - 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;
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
- Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; (F.N.A.); (A.N.N.); (M.M.H.)
- Correspondence:
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20
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Hasan MM, Alam MA, Shoombuatong W, Kurata H. IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations. J Comput Aided Mol Des 2021; 35:315-323. [PMID: 33392948 DOI: 10.1007/s10822-020-00368-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 12/06/2020] [Indexed: 12/11/2022]
Abstract
Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs using the sequence information are urgently needed. Herein, an improved and robust computational predictor named IRC-Fuse was developed to identify the RSC by fusing of multiple feature representations. To enhance the performance of our model, we integrated the probability scores evaluated by the random forest models implementing different encoding schemes. Cross-validation results exhibited that the IRC-Fuse achieved accuracy and AUC of 0.741 and 0.807, respectively. The IRC-Fuse outperformed exiting methods with improvement of 10% and 13% on accuracy and MCC, respectively, over independent test data. Comparative analysis suggested that the IRC-Fuse was more effective and promising than the existing predictors. For the convenience of experimental scientists, the IRC-Fuse online web server was implemented and publicly accessible at http://kurata14.bio.kyutech.ac.jp/IRC-Fuse/ .
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Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo, 102-0083, Japan.
| | - Md Ashad Alam
- Tulane Center of 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
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
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