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Cavaco M, Fraga P, Valle J, Silva RDM, Gano L, Correia JDG, Andreu D, Castanho MARB, Neves V. Molecular determinants for brain targeting by peptides: a meta-analysis approach with experimental validation. Fluids Barriers CNS 2024; 21:45. [PMID: 38802930 PMCID: PMC11131246 DOI: 10.1186/s12987-024-00545-5] [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: 01/18/2024] [Accepted: 05/03/2024] [Indexed: 05/29/2024] Open
Abstract
Blood-brain barrier (BBB) peptide-shuttles (BBBpS) are able to translocate the BBB and reach the brain. Despite the importance of brain targeting in pharmacology, BBBpS are poorly characterized. Currently, their development relies on the empiric assumption that cell-penetrating peptides (CPPs), with proven ability to traverse lipid membranes, will likewise behave as a BBBpS. The relationship between CPPs/BBBpS remains elusive and, to the best of our knowledge, has not hitherto been subject to thorough experimental scrutiny. In this work, we have identified/quantified the main physicochemical properties of BBBpS and then searched for CPPs with these properties, hence potential BBBpS. The specific features found for BBBpS are: (i) small size, (ii) none or few aromatic residues, (iii) hydrophobic, and (iv) slight cationic nature. Then, we selected the 10 scoring best in an ordinary least squares analysis, and tested them in vitro and in vivo. Overall, we identified the molecular determinants for brain targeting by peptides, devised a methodology that can be used to assist in the design of peptides with potential brain penetration from amino acid residue sequences, and found four new BBBpS within the CPP library.
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Affiliation(s)
- Marco Cavaco
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal
- Proteomics and Protein Chemistry Unit, Department of Medicine and Life Sciences, Pompeu Fabra University, Dr. Aiguader 88, Barcelona Biomedical Research Park, 08003, Barcelona, Spain
| | - Patrícia Fraga
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal
| | - Javier Valle
- Proteomics and Protein Chemistry Unit, Department of Medicine and Life Sciences, Pompeu Fabra University, Dr. Aiguader 88, Barcelona Biomedical Research Park, 08003, Barcelona, Spain
| | - Ruben D M Silva
- Centro de Ciências E Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, Estrada Nacional 10 (Km 139,7), 2695-066, Bobadela LRS, Portugal
| | - Lurdes Gano
- Centro de Ciências E Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, Estrada Nacional 10 (Km 139,7), 2695-066, Bobadela LRS, Portugal
- Departamento de Engenharia E Ciências Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, Estrada Nacional 10 (Km 139,7), 2695-066, Bobadela LRS, Portugal
| | - João D G Correia
- Centro de Ciências E Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, Estrada Nacional 10 (Km 139,7), 2695-066, Bobadela LRS, Portugal
- Departamento de Engenharia E Ciências Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, Estrada Nacional 10 (Km 139,7), 2695-066, Bobadela LRS, Portugal
| | - David Andreu
- Proteomics and Protein Chemistry Unit, Department of Medicine and Life Sciences, Pompeu Fabra University, Dr. Aiguader 88, Barcelona Biomedical Research Park, 08003, Barcelona, Spain.
| | - Miguel A R B Castanho
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.
| | - Vera Neves
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.
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Gu ZF, Hao YD, Wang TY, Cai PL, Zhang Y, Deng KJ, Lin H, Lv H. Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur. BMC Biol 2024; 22:86. [PMID: 38637801 PMCID: PMC11027412 DOI: 10.1186/s12915-024-01883-4] [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: 01/03/2024] [Accepted: 04/05/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.
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Affiliation(s)
- Zhi-Feng Gu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu-Duo Hao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Tian-Yu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Pei-Ling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, 610106, PR China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, PR China
| | - Ke-Jun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Hao Lin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Hao Lv
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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de Oliveira ECL, Hirmz H, Wynendaele E, Seixas Feio JA, Moreira IMS, da Costa KS, Lima AH, De Spiegeleer B, de Sales Júnior CDS. BrainPepPass: A Framework Based on Supervised Dimensionality Reduction for Predicting Blood-Brain Barrier-Penetrating Peptides. J Chem Inf Model 2024; 64:2368-2382. [PMID: 38054399 DOI: 10.1021/acs.jcim.3c00951] [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: 12/07/2023]
Abstract
Peptides that pass through the blood-brain barrier (BBB) not only are implicated in brain-related pathologies but also are promising therapeutic tools for treating brain diseases, e.g., as shuttles carrying active medicines across the BBB. Computational prediction of BBB-penetrating peptides (B3PPs) has emerged as an interesting approach because of its ability to screen large peptide libraries in a cost-effective manner. In this study, we present BrainPepPass, a machine learning (ML) framework that utilizes supervised manifold dimensionality reduction and extreme gradient boosting (XGB) algorithms to predict natural and chemically modified B3PPs. The results indicate that the proposed tool outperforms other classifiers, with average accuracies exceeding 94% and 98% in 10-fold cross-validation and leave-one-out cross-validation (LOOCV), respectively. In addition, accuracy values ranging from 45% to 97.05% were achieved in the independent tests. The BrainPepPass tool is available in a public repository for academic use (https://github.com/ewerton-cristhian/BrainPepPass).
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Affiliation(s)
- Ewerton Cristhian Lima de Oliveira
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campos Belém, Instituto de Tecnologia, Universidade Federal do Pará, 66075-110 Belém, Pará, Brasil
- Instituto Tecnológico Vale, 66055-090 Belém, Pará, Brasil
| | - Hannah Hirmz
- Drug Quality and Registration (DruQuaR) Group, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - Evelien Wynendaele
- Drug Quality and Registration (DruQuaR) Group, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - Juliana Auzier Seixas Feio
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campos Belém, Instituto de Tecnologia, Universidade Federal do Pará, 66075-110 Belém, Pará, Brasil
| | - Igor Matheus Souza Moreira
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campos Belém, Instituto de Tecnologia, Universidade Federal do Pará, 66075-110 Belém, Pará, Brasil
| | - Kauê Santana da Costa
- Laboratório de Simulação Computacional, Campos Marechal Rondon, Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, 68040-255 Santarém, Pará, Brasil
| | - Anderson H Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, 66075-110 Belém, Pará, Brasil
| | - Bart De Spiegeleer
- Drug Quality and Registration (DruQuaR) Group, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, B-9000 Ghent, Belgium
| | - Claudomiro de Souza de Sales Júnior
- Laboratório de Inteligência Computacional e Pesquisa Operacional, Campos Belém, Instituto de Tecnologia, Universidade Federal do Pará, 66075-110 Belém, Pará, Brasil
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Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. J Comput Aided Mol Des 2022; 36:781-796. [DOI: 10.1007/s10822-022-00476-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/15/2022] [Indexed: 11/27/2022]
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de Oliveira ECL, da Costa KS, Taube PS, Lima AH, Junior CDSDS. Biological Membrane-Penetrating Peptides: Computational Prediction and Applications. Front Cell Infect Microbiol 2022; 12:838259. [PMID: 35402305 PMCID: PMC8992797 DOI: 10.3389/fcimb.2022.838259] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/21/2022] [Indexed: 12/14/2022] Open
Abstract
Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.
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Affiliation(s)
- Ewerton Cristhian Lima de Oliveira
- Institute of Technology, Federal University of Pará, Belém, Brazil
- *Correspondence: Kauê Santana da Costa, ; Ewerton Cristhian Lima de Oliveira,
| | - Kauê Santana da Costa
- Laboratory of Computational Simulation, Institute of Biodiversity, Federal University of Western Pará, Santarém, Brazil
- *Correspondence: Kauê Santana da Costa, ; Ewerton Cristhian Lima de Oliveira,
| | - Paulo Sérgio Taube
- Laboratory of Computational Simulation, Institute of Biodiversity, Federal University of Western Pará, Santarém, Brazil
| | - Anderson H. Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
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