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Bebal FF, Bagwe AD, D’Souza RC, Sharma BB. Identifying toxic effects and metabolic perturbations of Duttaphrynus melanostictus skin extracts in human erythrocytes. Toxicol Res (Camb) 2024; 13:tfae086. [PMID: 38835821 PMCID: PMC11145735 DOI: 10.1093/toxres/tfae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/13/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024] Open
Abstract
Background Skin secretions of toads are widely used in medicine all over the world for their antiviral, anti-infective, and cardiotonic properties. Because these secretions are mostly employed to combat blood parasite infection, it is important to understand their potential toxic effects on human erythrocytes. Therefore, the objective of the current investigation was to elucidate the effects of Duttaphrynus melanostictus (Schneider) skin extracts on the physiology of human erythrocytes. Methods Toads captured from their natural habitat were separated into three groups according to their body size. Hydroalcoholic extracts of toad skin were prepared by reflux heating. These extracts were then evaluated for their hemolytic and hemoglobin denaturation potential. The effects of the extracts on cytosolic and membrane-bound enzymes of human erythrocytes were assessed. Results The hemolysis and hemoglobin denaturation caused by these extracts correlated positively with the respective toad sizes. Extracts from medium and large toads led to increased osmotic fragility even at near iso-osmotic concentrations. Biochemical analysis of hemolysate showed that the treatment induced a shift of metabolic flux toward the glutathione pathway. Analysis of membrane-bound enzymes revealed a significant decrease in the activity of Na+/K+ ATPase and acetylcholinesterase. SDS-PAGE analysis of the erythrocyte membrane did not show the band of tropomodulin for the cells treated with 1000 𝜇g/ml extract from large toads. Conclusions In conclusion, the present study demonstrates that the toxicity of toad skin secretions aggravates with the size of the animal and interferes with the physiology of human erythrocytes, leading to their membrane disruption and rapid lysis.
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Affiliation(s)
- Fatima F Bebal
- Department of Zoology, KET’s V. G. Vaze College of Arts, Science & Commerce (Autonomous), Mithagar Road, Mumbai - 400 081, Maharashtra, India
| | - Akshay D Bagwe
- Department of Zoology, KET’s V. G. Vaze College of Arts, Science & Commerce (Autonomous), Mithagar Road, Mumbai - 400 081, Maharashtra, India
- Department of Zoology, Sophia College for Women (Autonomous), Bhulabhai Desai Road, Mumbai - 400 026, Maharashtra, India
| | - Roshan C D’Souza
- Department of Zoology, Sophia College for Women (Autonomous), Bhulabhai Desai Road, Mumbai - 400 026, Maharashtra, India
| | - Bharatbhushan B Sharma
- Department of Zoology, KET’s V. G. Vaze College of Arts, Science & Commerce (Autonomous), Mithagar Road, Mumbai - 400 081, Maharashtra, India
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2
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Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD, Ciriaco F, Amoroso N, Nicolotti O. TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity. Chem Res Toxicol 2024; 37:323-339. [PMID: 38200616 DOI: 10.1021/acs.chemrestox.3c00310] [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: 01/12/2024]
Abstract
Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.
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Affiliation(s)
- Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Viviana Giannuzzi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Fedele Bonifazi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Antonella Liantonio
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Annamaria De Luca
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
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3
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Fralish Z, Chen A, Skaluba P, Reker D. DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning. J Cheminform 2023; 15:101. [PMID: 37885017 PMCID: PMC10605784 DOI: 10.1186/s13321-023-00769-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules, limiting their ability to learn from smaller datasets and to directly compare the anticipated properties of two molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization, especially for tasks with limited available data. Here, we develop DeepDelta, a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules from small datasets. On 10 ADMET benchmark tasks, our DeepDelta approach significantly outperforms two established molecular machine learning algorithms, the directed message passing neural network (D-MPNN) ChemProp and Random Forest using radial fingerprints, for 70% of benchmarks in terms of Pearson's r, 60% of benchmarks in terms of mean absolute error (MAE), and all external test sets for both Pearson's r and MAE. We further analyze our performance and find that DeepDelta is particularly outperforming established approaches at predicting large differences in molecular properties and can perform scaffold hopping. Furthermore, we derive mathematically fundamental computational tests of our models based on mathematical invariants and show that compliance to these tests correlates with overall model performance - providing an innovative, unsupervised, and easily computable measure of expected model performance and applicability. Taken together, DeepDelta provides an accurate approach to predict molecular property differences by directly training on molecular pairs and their property differences to further support fidelity and transparency in molecular optimization for drug development and the chemical sciences.
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Affiliation(s)
- Zachary Fralish
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Ashley Chen
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | - Paul Skaluba
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA.
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Shirokii N, Din Y, Petrov I, Seregin Y, Sirotenko S, Razlivina J, Serov N, Vinogradov V. Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2207106. [PMID: 36772908 DOI: 10.1002/smll.202207106] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/09/2023] [Indexed: 05/11/2023]
Abstract
Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q2 = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.
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Affiliation(s)
- Nikolai Shirokii
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Yevgeniya Din
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Ilya Petrov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Yurii Seregin
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Sofia Sirotenko
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Julia Razlivina
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
| | - Nikita Serov
- Advanced Engineering School, Almetyevsk State Oil Institute, Almetyevsk, Russia
| | - Vladimir Vinogradov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, 191002, Saint-Petersburg, Russian Federation
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Togo MV, Mastrolorito F, Ciriaco F, Trisciuzzi D, Tondo AR, Gambacorta N, Bellantuono L, Monaco A, Leonetti F, Bellotti R, Altomare CD, Amoroso N, Nicolotti O. TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity. J Chem Inf Model 2023; 63:56-66. [PMID: 36520016 DOI: 10.1021/acs.jcim.2c01126] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure-Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it.
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Affiliation(s)
- Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Anna Rita Tondo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy.,Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy.,Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
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6
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Venkatraman V. FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminform 2021; 13:75. [PMID: 34583740 PMCID: PMC8479898 DOI: 10.1186/s13321-021-00557-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 09/20/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs plays a key role in determining which among the potential candidates are to be prioritized. In silico approaches based on machine learning methods are becoming increasing popular, but are nonetheless limited by the availability of data. With a view to making both data and models available to the scientific community, we have developed FPADMET which is a repository of molecular fingerprint-based predictive models for ADMET properties. In this article, we have examined the efficacy of fingerprint-based machine learning models for a large number of ADMET-related properties. The predictive ability of a set of 20 different binary fingerprints (based on substructure keys, atom pairs, local path environments, as well as custom fingerprints such as all-shortest paths) for over 50 ADMET and ADMET-related endpoints have been evaluated as part of the study. We find that for a majority of the properties, fingerprint-based random forest models yield comparable or better performance compared with traditional 2D/3D molecular descriptors. AVAILABILITY The models are made available as part of open access software that can be downloaded from https://gitlab.com/vishsoft/fpadmet .
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Affiliation(s)
- Vishwesh Venkatraman
- Norwegian University of Science and Technology, Realfagbygget, Gløshaugen, Høgskoleringen, 7491, Trondheim, Norway.
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7
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Melo MCR, Maasch JRMA, de la Fuente-Nunez C. Accelerating antibiotic discovery through artificial intelligence. Commun Biol 2021; 4:1050. [PMID: 34504303 PMCID: PMC8429579 DOI: 10.1038/s42003-021-02586-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023] Open
Abstract
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
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Affiliation(s)
- Marcelo C R Melo
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline R M A Maasch
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
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8
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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9
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Machine learning-guided discovery and design of non-hemolytic peptides. Sci Rep 2020; 10:16581. [PMID: 33024236 PMCID: PMC7538962 DOI: 10.1038/s41598-020-73644-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 09/18/2020] [Indexed: 12/13/2022] Open
Abstract
Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.
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