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Monroe LK, Truong DP, Miner JC, Adikari SH, Sasiene ZJ, Fenimore PW, Alexandrov B, Williams RF, Nguyen HB. Conotoxin Prediction: New Features to Increase Prediction Accuracy. Toxins (Basel) 2023; 15:641. [PMID: 37999504 PMCID: PMC10675404 DOI: 10.3390/toxins15110641] [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: 09/11/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/25/2023] Open
Abstract
Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.
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Affiliation(s)
- Lyman K. Monroe
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Duc P. Truong
- Theoretical Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Jacob C. Miner
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Samantha H. Adikari
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Zachary J. Sasiene
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Paul W. Fenimore
- Theoretical Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Boian Alexandrov
- Theoretical Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Robert F. Williams
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Hau B. Nguyen
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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Sun Z, Huang S, Zheng L, Liang P, Yang W, Zuo Y. ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors. Comput Biol Chem 2020; 89:107371. [PMID: 32950852 DOI: 10.1016/j.compbiolchem.2020.107371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 12/27/2022]
Abstract
Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.
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Affiliation(s)
- Zijie Sun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China; School of Mathematical Sciences, Inner Mongolia University, Hohhot, 010021, China
| | - Shenghui Huang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Wuritu Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
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Jin AH, Muttenthaler M, Dutertre S, Himaya SWA, Kaas Q, Craik DJ, Lewis RJ, Alewood PF. Conotoxins: Chemistry and Biology. Chem Rev 2019; 119:11510-11549. [PMID: 31633928 DOI: 10.1021/acs.chemrev.9b00207] [Citation(s) in RCA: 161] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The venom of the marine predatory cone snails (genus Conus) has evolved for prey capture and defense, providing the basis for survival and rapid diversification of the now estimated 750+ species. A typical Conus venom contains hundreds to thousands of bioactive peptides known as conotoxins. These mostly disulfide-rich and well-structured peptides act on a wide range of targets such as ion channels, G protein-coupled receptors, transporters, and enzymes. Conotoxins are of interest to neuroscientists as well as drug developers due to their exquisite potency and selectivity, not just against prey but also mammalian targets, thereby providing a rich source of molecular probes and therapeutic leads. The rise of integrated venomics has accelerated conotoxin discovery with now well over 10,000 conotoxin sequences published. However, their structural and pharmacological characterization lags considerably behind. In this review, we highlight the diversity of new conotoxins uncovered since 2014, their three-dimensional structures and folds, novel chemical approaches to their syntheses, and their value as pharmacological tools to unravel complex biology. Additionally, we discuss challenges and future directions for the field.
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Affiliation(s)
- Ai-Hua Jin
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia
| | - Markus Muttenthaler
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia.,Institute of Biological Chemistry, Faculty of Chemistry , University of Vienna , 1090 Vienna , Austria
| | - Sebastien Dutertre
- Département des Acides Amines, Peptides et Protéines, Unité Mixte de Recherche 5247, Université Montpellier 2-Centre Nationale de la Recherche Scientifique , Institut des Biomolécules Max Mousseron , Place Eugène Bataillon , 34095 Montpellier Cedex 5 , France
| | - S W A Himaya
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia
| | - Quentin Kaas
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia
| | - David J Craik
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia
| | - Richard J Lewis
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia
| | - Paul F Alewood
- Institute for Molecular Bioscience , The University of Queensland , Brisbane Queensland 4072 , Australia
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Taju SW, Ou Y. DeepIon: Deep learning approach for classifying ion transporters and ion channels from membrane proteins. J Comput Chem 2019; 40:1521-1529. [DOI: 10.1002/jcc.25805] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 01/19/2019] [Accepted: 01/30/2019] [Indexed: 01/20/2023]
Affiliation(s)
- Semmy Wellem Taju
- Department of Computer Science and EngineeringYuan Ze University Chung‐Li 32003 Taiwan
| | - Yu‐Yen Ou
- Department of Computer Science and EngineeringYuan Ze University Chung‐Li 32003 Taiwan
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Mansbach RA, Travers T, McMahon BH, Fair JM, Gnanakaran S. Snails In Silico: A Review of Computational Studies on the Conopeptides. Mar Drugs 2019; 17:E145. [PMID: 30832207 PMCID: PMC6471681 DOI: 10.3390/md17030145] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 12/26/2022] Open
Abstract
Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.
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Affiliation(s)
- Rachael A Mansbach
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Timothy Travers
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Benjamin H McMahon
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Jeanne M Fair
- Biosecurity and Public Health Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - S Gnanakaran
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
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Classes, Databases, and Prediction Methods of Pharmaceutically and Commercially Important Cystine-Stabilized Peptides. Toxins (Basel) 2018; 10:toxins10060251. [PMID: 29921767 PMCID: PMC6024828 DOI: 10.3390/toxins10060251] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 06/12/2018] [Accepted: 06/14/2018] [Indexed: 12/13/2022] Open
Abstract
Cystine-stabilized peptides represent a large family of peptides characterized by high structural stability and bactericidal, fungicidal, or insecticidal properties. Found throughout a wide range of taxa, this broad and functionally important family can be subclassified into distinct groups dependent upon their number and type of cystine bonding patters, tertiary structures, and/or their species of origin. Furthermore, the annotation of proteins related to the cystine-stabilized family are under-represented in the literature due to their difficulty of isolation and identification. As a result, there are several recent attempts to collate them into data resources and build analytic tools for their dynamic prediction. Ultimately, the identification and delivery of new members of this family will lead to their growing inclusion into the repertoire of commercial viable alternatives to antibiotics and environmentally safe insecticides. This review of the literature and current state of cystine-stabilized peptide biology is aimed to better describe peptide subfamilies, identify databases and analytics resources associated with specific cystine-stabilized peptides, and highlight their current commercial success.
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Dao FY, Yang H, Su ZD, Yang W, Wu Y, Hui D, Chen W, Tang H, Lin H. Recent Advances in Conotoxin Classification by Using Machine Learning Methods. Molecules 2017; 22:molecules22071057. [PMID: 28672838 PMCID: PMC6152242 DOI: 10.3390/molecules22071057] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 06/12/2017] [Accepted: 06/19/2017] [Indexed: 11/16/2022] Open
Abstract
Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.
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Affiliation(s)
- Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Hui Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zhen-Dong Su
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Wuritu Yang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Development and Planning Department, Inner Mongolia University, Hohhot 010021, China.
| | - Yun Wu
- College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
| | - Ding Hui
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Wei Chen
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China.
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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