Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hem...

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Published inToxicological sciences
Main Authors Castillo-Mendieta, Kevin, Agüero-Chapin, Guillermin, Mora, José R, Pérez, Noel, Contreras-Torres, Ernesto, Valdes-Martini, José R, Martinez-Rios, Felix, Marrero-Ponce, Yovani
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LanguageEnglish
Published United States 10.09.2024
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Abstract Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.
AbstractList Abstract Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2,004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, whereas aspartic acid, methionine, histidine, asparagine, and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models, which outperformed cutting-edge machine learning-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, characterize the chemical space, and discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.
Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.
Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.
Author Mora, José R
Contreras-Torres, Ernesto
Castillo-Mendieta, Kevin
Agüero-Chapin, Guillermin
Marrero-Ponce, Yovani
Valdes-Martini, José R
Martinez-Rios, Felix
Pérez, Noel
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  organization: Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Pichincha, Quito, 170157, Ecuador
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  fullname: Marrero-Ponce, Yovani
  organization: Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México, 03920, México
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Cites_doi 10.1109/JBHI.2023.3264941
10.1002/spe.4380211102
10.1007/978-1-4939-6737-7_31
10.1016/j.bbamem.2004.05.007
10.1093/nar/gkad976
10.1016/0022-2836(81)90087-5
10.3390/ijms20102383
10.1016/0022-2836(70)90057-4
10.1088/1742-5468/2008/10/P10008
10.4155/fmc-2016-0188
10.1142/S0219720021500219
10.1093/bioinformatics/btv180
10.1039/D1SC01713F
10.20944/preprints202303.0322.v1
10.1609/icwsm.v3i1.13937
10.20944/preprints202303.0193.v1
10.1093/bioinformatics/btz260
10.1038/s41598-020-69995-9
10.1186/s12859-022-04952-z
10.1186/s12864-019-6413-7
10.1093/nar/gkh340
10.1111/voxs.12340
10.1038/s41392-022-00904-4
10.1016/S0006-3495(82)84681-X
10.1111/bjh.13183
10.1021/acs.chemrestox.3c00408
10.1093/bioinformatics/btaa160
10.1021/acsomega.2c03398
10.1074/jbc.275.6.4230
10.3389/fphar.2020.00054
10.1038/s41581-019-0181-0
10.1093/nar/gkf436
10.1038/s41598-020-75029-1
10.1038/s41598-020-73644-6
10.1101/2021.08.23.457422
10.3390/ijms21197047
10.1186/s13321-016-0127-5
10.3390/antibiotics11030401
10.1186/s13040-021-00244-z
10.1128/AAC.49.1.388-397.2005
10.1038/s41598-020-67701-3
10.1038/srep22843
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Keywords Similarity searching model
Multiple sequence alignment
Hemolytic peptides
Motif discovery
Chemical space complex networks
Drug discovery
StarPep toolbox
Language English
License The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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References Smith (2024092008203892800_kfae115-B35) 2017; 12
Aguilera-Mendoza (2024092008203892800_kfae115-B2) 2019; 35
L’Acqua (2024092008203892800_kfae115-B26) 2015; 168
Fruchterman (2024092008203892800_kfae115-B20) 1991; 21
Needleman (2024092008203892800_kfae115-B29) 1970; 48
Aguilera-Mendoza (2024092008203892800_kfae115-B3) 2020; 10
Castillo-Mendieta (2024092008203892800_kfae115-B12) 2024; 37
Greco (2024092008203892800_kfae115-B21) 2020; 10
Ayala-Ruano (2024092008203892800_kfae115-B5) 2022; 7
Yaseen (2024092008203892800_kfae115-B41) 2021; 19
Chicco (2024092008203892800_kfae115-B15) 2020; 21
Browne (2024092008203892800_kfae115-B10) 2020; 21
Castillo-Mendieta (2024092008203892800_kfae115-B13) 2023
Hasan (2024092008203892800_kfae115-B22) 2020; 36
Sharma (2024092008203892800_kfae115-B34) 2023; 28
DeGrado (2024092008203892800_kfae115-B17) 1982; 37
Romero (2024092008203892800_kfae115-B32) 2022; 11
Zahoránszky-Kőhalmi (2024092008203892800_kfae115-B42) 2016; 8
Katoh (2024092008203892800_kfae115-B23) 2002; 30
Plisson (2024092008203892800_kfae115-B31) 2020; 10
Timmons (2024092008203892800_kfae115-B37) 2020; 10
Kumar (2024092008203892800_kfae115-B25) 2020; 11
Van Avondt (2024092008203892800_kfae115-B38) 2019; 15
Bailey (2024092008203892800_kfae115-B6) 2021
Edgar (2024092008203892800_kfae115-B18) 2004; 32
Bastian (2024092008203892800_kfae115-B7) 2009; 3
Win (2024092008203892800_kfae115-B40) 2017; 9
Oddo (2024092008203892800_kfae115-B30) 2017
Aguilera-Mendoza (2024092008203892800_kfae115-B4) 2015; 31
Chicco (2024092008203892800_kfae115-B16) 2021; 14
Blondel (2024092008203892800_kfae115-B9) 2008; 2008
Knox (2024092008203892800_kfae115-B24) 2024; 52
Li (2024092008203892800_kfae115-B28) 2005; 49
Salem (2024092008203892800_kfae115-B33) 2022; 23
Wang (2024092008203892800_kfae115-B39) 2022; 7
Chaudhary (2024092008203892800_kfae115-B14) 2016; 6
Capecchi (2024092008203892800_kfae115-B11) 2021; 12
Belokoneva (2024092008203892800_kfae115-B8) 2004; 1664
Smith (2024092008203892800_kfae115-B36) 1981; 147
Agüero-Chapin (2024092008203892800_kfae115-B1) 2023; 12
Feder (2024092008203892800_kfae115-B19) 2000; 275
Lee (2024092008203892800_kfae115-B27) 2019; 20
References_xml – volume: 28
  start-page: 1896
  year: 2023
  ident: 2024092008203892800_kfae115-B34
  article-title: EnDL-HemoLyt: Ensemble deep learning-based tool for identifying therapeutic peptides with low hemolytic activity
  publication-title: IEEE J Biomed Health Inform
  doi: 10.1109/JBHI.2023.3264941
  contributor:
    fullname: Sharma
– volume: 21
  start-page: 1129
  year: 1991
  ident: 2024092008203892800_kfae115-B20
  article-title: Graph drawing by force-directed placement
  publication-title: Softw Pract Exp
  doi: 10.1002/spe.4380211102
  contributor:
    fullname: Fruchterman
– start-page: 427
  volume-title: Antimicrobial peptides: methods and protocols
  year: 2017
  ident: 2024092008203892800_kfae115-B30
  doi: 10.1007/978-1-4939-6737-7_31
  contributor:
    fullname: Oddo
– volume: 1664
  start-page: 182
  year: 2004
  ident: 2024092008203892800_kfae115-B8
  article-title: Pore formation of phospholipid membranes by the action of two hemolytic arachnid peptides of different size
  publication-title: Biochim Biophys Acta
  doi: 10.1016/j.bbamem.2004.05.007
  contributor:
    fullname: Belokoneva
– volume: 52
  start-page: D1265
  year: 2024
  ident: 2024092008203892800_kfae115-B24
  article-title: DrugBank 6.0: the DrugBank knowledgebase for 2024
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkad976
  contributor:
    fullname: Knox
– volume: 147
  start-page: 195
  year: 1981
  ident: 2024092008203892800_kfae115-B36
  article-title: Identification of common molecular subsequences
  publication-title: J Mol Biol
  doi: 10.1016/0022-2836(81)90087-5
  contributor:
    fullname: Smith
– volume: 20
  start-page: 2383
  year: 2019
  ident: 2024092008203892800_kfae115-B27
  article-title: A comprehensive review on current advances in peptide drug development and design
  publication-title: Int J Mol Sci
  doi: 10.3390/ijms20102383
  contributor:
    fullname: Lee
– volume: 48
  start-page: 443
  year: 1970
  ident: 2024092008203892800_kfae115-B29
  article-title: A general method applicable to the search for similarities in the amino acid sequence of two proteins
  publication-title: J Mol Biol
  doi: 10.1016/0022-2836(70)90057-4
  contributor:
    fullname: Needleman
– volume: 2008
  start-page: P10008
  year: 2008
  ident: 2024092008203892800_kfae115-B9
  article-title: Fast unfolding of communities in large networks
  publication-title: J Stat Mech
  doi: 10.1088/1742-5468/2008/10/P10008
  contributor:
    fullname: Blondel
– volume: 9
  start-page: 275
  year: 2017
  ident: 2024092008203892800_kfae115-B40
  article-title: HemoPred: a web server for predicting the hemolytic activity of peptides
  publication-title: Fut Med Chem
  doi: 10.4155/fmc-2016-0188
  contributor:
    fullname: Win
– volume: 19
  start-page: 2150021
  year: 2021
  ident: 2024092008203892800_kfae115-B41
  article-title: HemoNet: predicting hemolytic activity of peptides with integrated feature learning
  publication-title: J Bioinform Comput Biol
  doi: 10.1142/S0219720021500219
  contributor:
    fullname: Yaseen
– volume: 31
  start-page: 2553
  year: 2015
  ident: 2024092008203892800_kfae115-B4
  article-title: Overlap and diversity in antimicrobial peptide databases: compiling a non-redundant set of sequences
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btv180
  contributor:
    fullname: Aguilera-Mendoza
– volume: 12
  start-page: 9221
  year: 2021
  ident: 2024092008203892800_kfae115-B11
  article-title: Machine learning designs non-hemolytic antimicrobial peptides
  publication-title: Chem Sci
  doi: 10.1039/D1SC01713F
  contributor:
    fullname: Capecchi
– year: 2023
  ident: 2024092008203892800_kfae115-B13
  doi: 10.20944/preprints202303.0322.v1
  contributor:
    fullname: Castillo-Mendieta
– volume: 3
  start-page: 361
  year: 2009
  ident: 2024092008203892800_kfae115-B7
  article-title: Gephi: an open source software for exploring and manipulating networks
  publication-title: Proc Int AAAI Conf Web Soc Media
  doi: 10.1609/icwsm.v3i1.13937
  contributor:
    fullname: Bastian
– volume: 12
  start-page: 747
  year: 2023
  ident: 2024092008203892800_kfae115-B1
  article-title: Complex networks analyses of antibiofilm peptides: an emerging tool for next generation antimicrobials discovery
  publication-title: Antibiotics
  doi: 10.20944/preprints202303.0193.v1
  contributor:
    fullname: Agüero-Chapin
– volume: 35
  start-page: 4739
  year: 2019
  ident: 2024092008203892800_kfae115-B2
  article-title: Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btz260
  contributor:
    fullname: Aguilera-Mendoza
– volume: 10
  start-page: 13206
  year: 2020
  ident: 2024092008203892800_kfae115-B21
  article-title: Correlation between hemolytic activity, cytotoxicity and systemic in vivo toxicity of synthetic antimicrobial peptides
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-69995-9
  contributor:
    fullname: Greco
– volume: 23
  start-page: 389
  year: 2022
  ident: 2024092008203892800_kfae115-B33
  article-title: AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning
  publication-title: BMC Bioinformatics
  doi: 10.1186/s12859-022-04952-z
  contributor:
    fullname: Salem
– volume: 21
  start-page: 6
  year: 2020
  ident: 2024092008203892800_kfae115-B15
  article-title: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
  publication-title: BMC Genomics
  doi: 10.1186/s12864-019-6413-7
  contributor:
    fullname: Chicco
– volume: 32
  start-page: 1792
  year: 2004
  ident: 2024092008203892800_kfae115-B18
  article-title: MUSCLE: multiple sequence alignment with high accuracy and high throughput
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkh340
  contributor:
    fullname: Edgar
– volume: 12
  start-page: 119
  year: 2017
  ident: 2024092008203892800_kfae115-B35
  article-title: Mechanisms of haem toxicity in haemolysis and protection by the haem-binding protein, haemopexin
  publication-title: ISBT Sci Ser
  doi: 10.1111/voxs.12340
  contributor:
    fullname: Smith
– volume: 7
  start-page: 48
  year: 2022
  ident: 2024092008203892800_kfae115-B39
  article-title: Therapeutic peptides: current applications and future directions
  publication-title: Signal Transduct Target Ther
  doi: 10.1038/s41392-022-00904-4
  contributor:
    fullname: Wang
– volume: 37
  start-page: 329
  year: 1982
  ident: 2024092008203892800_kfae115-B17
  article-title: Kinetics and mechanism of hemolysis induced by melittin and by a synthetic melittin analogue
  publication-title: Biophys J
  doi: 10.1016/S0006-3495(82)84681-X
  contributor:
    fullname: DeGrado
– volume: 168
  start-page: 175
  year: 2015
  ident: 2024092008203892800_kfae115-B26
  article-title: New perspectives on the thrombotic complications of haemolysis
  publication-title: Br J Haematol
  doi: 10.1111/bjh.13183
  contributor:
    fullname: L’Acqua
– volume: 37
  start-page: 580
  year: 2024
  ident: 2024092008203892800_kfae115-B12
  article-title: Multiquery similarity searching models: an alternative approach for predicting hemolytic activity from peptide sequence
  publication-title: Chem Res Toxicol
  doi: 10.1021/acs.chemrestox.3c00408
  contributor:
    fullname: Castillo-Mendieta
– volume: 36
  start-page: 3350
  year: 2020
  ident: 2024092008203892800_kfae115-B22
  article-title: HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btaa160
  contributor:
    fullname: Hasan
– volume: 7
  start-page: 46012
  year: 2022
  ident: 2024092008203892800_kfae115-B5
  article-title: Network science and group fusion similarity-based searching to explore the chemical space of antiparasitic peptides
  publication-title: ACS Omega
  doi: 10.1021/acsomega.2c03398
  contributor:
    fullname: Ayala-Ruano
– volume: 275
  start-page: 4230
  year: 2000
  ident: 2024092008203892800_kfae115-B19
  article-title: Structure-activity relationship study of antimicrobial dermaseptin S4 showing the consequences of peptide oligomerization on selective cytotoxicity
  publication-title: J Biol Chem
  doi: 10.1074/jbc.275.6.4230
  contributor:
    fullname: Feder
– volume: 11
  start-page: 54
  year: 2020
  ident: 2024092008203892800_kfae115-B25
  article-title: A method for predicting hemolytic potency of chemically modified peptides from its structure
  publication-title: Front Pharmacol
  doi: 10.3389/fphar.2020.00054
  contributor:
    fullname: Kumar
– volume: 15
  start-page: 671
  year: 2019
  ident: 2024092008203892800_kfae115-B38
  article-title: Mechanisms of haemolysis-induced kidney injury
  publication-title: Nat Rev Nephrol
  doi: 10.1038/s41581-019-0181-0
  contributor:
    fullname: Van Avondt
– volume: 30
  start-page: 3059
  year: 2002
  ident: 2024092008203892800_kfae115-B23
  article-title: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform
  publication-title: Nucleic Acids Res
  doi: 10.1093/nar/gkf436
  contributor:
    fullname: Katoh
– volume: 10
  start-page: 18074
  year: 2020
  ident: 2024092008203892800_kfae115-B3
  article-title: Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-75029-1
  contributor:
    fullname: Aguilera-Mendoza
– volume: 10
  start-page: 16581
  year: 2020
  ident: 2024092008203892800_kfae115-B31
  article-title: Machine learning-guided discovery and design of non-hemolytic peptides
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-73644-6
  contributor:
    fullname: Plisson
– year: 2021
  ident: 2024092008203892800_kfae115-B6
  doi: 10.1101/2021.08.23.457422
  contributor:
    fullname: Bailey
– volume: 21
  start-page: 7047
  year: 2020
  ident: 2024092008203892800_kfae115-B10
  article-title: A new era of antibiotics: the clinical potential of antimicrobial peptides
  publication-title: Int J Mol Sci
  doi: 10.3390/ijms21197047
  contributor:
    fullname: Browne
– volume: 8
  start-page: 16
  year: 2016
  ident: 2024092008203892800_kfae115-B42
  article-title: Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes
  publication-title: J Cheminform
  doi: 10.1186/s13321-016-0127-5
  contributor:
    fullname: Zahoránszky-Kőhalmi
– volume: 11
  start-page: 401
  year: 2022
  ident: 2024092008203892800_kfae115-B32
  article-title: A novel network science and similarity-searching-based approach for discovering potential tumor-homing peptides from antimicrobials
  publication-title: Antibiotics (Basel)
  doi: 10.3390/antibiotics11030401
  contributor:
    fullname: Romero
– volume: 14
  start-page: 13
  year: 2021
  ident: 2024092008203892800_kfae115-B16
  article-title: The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
  publication-title: BioData Min
  doi: 10.1186/s13040-021-00244-z
  contributor:
    fullname: Chicco
– volume: 49
  start-page: 388
  year: 2005
  ident: 2024092008203892800_kfae115-B28
  article-title: Hemolysis of erythrocytes by granulysin-derived peptides but not by granulysin
  publication-title: Antimicrob Agents Chemother
  doi: 10.1128/AAC.49.1.388-397.2005
  contributor:
    fullname: Li
– volume: 10
  start-page: 10869
  year: 2020
  ident: 2024092008203892800_kfae115-B37
  article-title: HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks
  publication-title: Sci Rep
  doi: 10.1038/s41598-020-67701-3
  contributor:
    fullname: Timmons
– volume: 6
  start-page: 22843
  year: 2016
  ident: 2024092008203892800_kfae115-B14
  article-title: A web server and mobile app for computing hemolytic potency of peptides
  publication-title: Sci Rep
  doi: 10.1038/srep22843
  contributor:
    fullname: Chaudhary
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Snippet Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is...
Abstract Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of...
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Title Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks
URI https://www.ncbi.nlm.nih.gov/pubmed/39254655
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