1
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Almotairi S, Badr E, Abdelbaky I, Elhakeem M, Abdul Salam M. Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential. Sci Rep 2024; 14:14263. [PMID: 38902287 PMCID: PMC11190137 DOI: 10.1038/s41598-024-63446-5] [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: 04/02/2024] [Accepted: 05/29/2024] [Indexed: 06/22/2024] Open
Abstract
Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.
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Affiliation(s)
- Sultan Almotairi
- Department of Computer Science, Faculty of College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, 42351, Medinah, Saudi Arabia
| | - Elsayed Badr
- Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
- The Egyptian School of Data Science (ESDS), Benha, Egypt.
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Mohamed Elhakeem
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
| | - Mustafa Abdul Salam
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
- Department of Computer Science, College of Arts and Science, Wadi Addawasir, Prince Sattam Bin Abdulaziz University, 16273, Al-Kharj, Saudi Arabia
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2
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Orsi M, Reymond JL. Can large language models predict antimicrobial peptide activity and toxicity? RSC Med Chem 2024; 15:2030-2036. [PMID: 38911166 PMCID: PMC11187562 DOI: 10.1039/d4md00159a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/19/2024] [Indexed: 06/25/2024] Open
Abstract
Antimicrobial peptides (AMPs) are naturally occurring or designed peptides up to a few tens of amino acids which may help address the antimicrobial resistance crisis. However, their clinical development is limited by toxicity to human cells, a parameter which is very difficult to control. Given the similarity between peptide sequences and words, large language models (LLMs) might be able to predict AMP activity and toxicity. To test this hypothesis, we fine-tuned LLMs using data from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). GPT-3 performed well but not reproducibly for activity prediction and hemolysis, taken as a proxy for toxicity. The later GPT-3.5 performed more poorly and was surpassed by recurrent neural networks (RNN) trained on sequence-activity data or support vector machines (SVM) trained on MAP4C molecular fingerprint-activity data. These simpler models are therefore recommended, although the rapid evolution of LLMs warrants future re-evaluation of their prediction abilities.
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Affiliation(s)
- Markus Orsi
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern Freiestrasse 3 3012 Bern Switzerland
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3
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Singh V, Singh SK, Sharma R. A novel framework based on explainable AI and genetic algorithms for designing neurological medicines. Sci Rep 2024; 14:12807. [PMID: 38834718 DOI: 10.1038/s41598-024-63561-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024] Open
Abstract
The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India.
| | - Ritesh Sharma
- Department of ICT, Manipal Institute of Technology, Manipal, 576104, Uttar Pradesh, India
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4
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Aslan A, Ari Yuka S. Therapeutic peptides for coronary artery diseases: in silico methods and current perspectives. Amino Acids 2024; 56:37. [PMID: 38822212 PMCID: PMC11143054 DOI: 10.1007/s00726-024-03397-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/06/2024] [Indexed: 06/02/2024]
Abstract
Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies.
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Affiliation(s)
- Ayca Aslan
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey
- Health Biotechnology Joint Research and Application Center of Excellence, Esenler, Istanbul, Turkey
| | - Selcen Ari Yuka
- Department of Bioengineering, Faculty of Chemical and Metallurgical Engineering, Yildiz Technical University, Esenler, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Esenler, Istanbul, Turkey.
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5
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Aguilera-Puga MDC, Plisson F. Structure-aware machine learning strategies for antimicrobial peptide discovery. Sci Rep 2024; 14:11995. [PMID: 38796582 PMCID: PMC11127937 DOI: 10.1038/s41598-024-62419-y] [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: 02/08/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico
| | - Fabien Plisson
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico.
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6
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Castillo-Mendieta K, Agüero-Chapin G, Marquez E, Perez-Castillo Y, Barigye SJ, Pérez-Cárdenas M, Peréz-Giménez F, Marrero-Ponce Y. Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence. Chem Res Toxicol 2024; 37:580-589. [PMID: 38501392 DOI: 10.1021/acs.chemrestox.3c00408] [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: 03/20/2024]
Abstract
The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.
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Affiliation(s)
- Kevin Castillo-Mendieta
- School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, Terminal de Cruzeiros do Porto de Leixões, University of Porto, Av. General Norton de Matos s/n, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
| | - Edgar Marquez
- Grupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla 081007, Colombia
| | - Yunierkis Perez-Castillo
- Bio-Chemoinformatics Research Group and Escuela de Ciencias Físicas y Matemáticas. Universidad de Las Américas, Quito 170504, Ecuador
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049 Madrid, Spain
| | - Mariela Pérez-Cárdenas
- School of Biological Sciences and Engineering, Yachay Tech University, Hda. San José s/n y Proyecto Yachay, Urcuquí 100119, Ecuador
| | - Facundo Peréz-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia 46100, Spain
| | - Yovani Marrero-Ponce
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia 46100, Spain
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, CDMX, Mexico 03920, Mexico
- 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, Universidad San Francisco de Quito (USFQ), Quito, Pichincha 170157, Ecuador
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7
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McCorkindale W, Filep M, London N, Lee AA, King-Smith E. Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning. RSC Med Chem 2024; 15:1015-1021. [PMID: 38516605 PMCID: PMC10953487 DOI: 10.1039/d3md00719g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen to produce compounds of similar potency as a D2B assay.
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Affiliation(s)
| | - Mihajlo Filep
- Department of Chemical and Structural Biology, The Weizmann Institute of Science Israel
| | - Nir London
- Department of Chemical and Structural Biology, The Weizmann Institute of Science Israel
| | - Alpha A Lee
- Cavendish Laboratory, University of Cambridge UK
| | - Emma King-Smith
- Yusuf Hamied Department of Chemistry, University of Cambridge UK
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8
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Eweje F, Walsh ML, Ahmad K, Ibrahim V, Alrefai A, Chen J, Chaikof EL. Protein-based nanoparticles for therapeutic nucleic acid delivery. Biomaterials 2024; 305:122464. [PMID: 38181574 PMCID: PMC10872380 DOI: 10.1016/j.biomaterials.2023.122464] [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: 09/23/2023] [Revised: 12/25/2023] [Accepted: 12/31/2023] [Indexed: 01/07/2024]
Abstract
To realize the full potential of emerging nucleic acid therapies, there is a need for effective delivery agents to transport cargo to cells of interest. Protein materials exhibit several unique properties, including biodegradability, biocompatibility, ease of functionalization via recombinant and chemical modifications, among other features, which establish a promising basis for therapeutic nucleic acid delivery systems. In this review, we highlight progress made in the use of non-viral protein-based nanoparticles for nucleic acid delivery in vitro and in vivo, while elaborating on key physicochemical properties that have enabled the use of these materials for nanoparticle formulation and drug delivery. To conclude, we comment on the prospects and unresolved challenges associated with the translation of protein-based nucleic acid delivery systems for therapeutic applications.
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Affiliation(s)
- Feyisayo Eweje
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Harvard and MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Harvard/MIT MD-PhD Program, Boston, MA, USA, 02115; Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
| | - Michelle L Walsh
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Harvard and MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Harvard/MIT MD-PhD Program, Boston, MA, USA, 02115
| | - Kiran Ahmad
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Vanessa Ibrahim
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Assma Alrefai
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Jiaxuan Chen
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
| | - Elliot L Chaikof
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
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9
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Saraswat S, Chugh A. Engraulisin: A novel marine derived cell penetrating peptide with activity against drug resistant bacteria. BIOCHIMICA ET BIOPHYSICA ACTA. BIOMEMBRANES 2024; 1866:184255. [PMID: 37995845 DOI: 10.1016/j.bbamem.2023.184255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/26/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023]
Abstract
Cell penetrating peptides (CPP) with their intrinsic ability to penetrate plasma membranes facilitate intracellular uptake of various macromolecules. Although a substantial number of CPPs have been reported over the last three decades, the number is still inadequate when compared to the theoretically feasible peptides with similar physicochemical composition. Marine organisms, due to their hostile environment, are an immense source of several high-valued therapeutically relevant peptides. Various marine derived antibacterial, antimycotic and anticancer peptides have demonstrated improved activity in comparison to peptides of terrestrial origin. While a significant number of marine bioactive peptides exist, cell penetrating peptides from marine organisms remain unravelled. In this study, we report Engraulisin from Engraulis japonicus, a computationally derived novel cell penetrating peptide of marine origin. Engraulisin manifest successful uptake in mammalian cells at 5 μM concentration with negligible cytotoxicity observed through MTT assay. Analysis of its cellular uptake mechanism revealed significant inhibition at 4 °C suggesting endocytosis as the major route of cellular entry. Interestingly, the novel peptide also demonstrated selective antimicrobial activity against Methicillin-resistant Staphylococcus aureus (MRSA). Additionally, molecular dynamics simulation with POPC and POPG bilayer system unveiled significance of positively charged residues in forming a stable membrane interaction. Engraulisin represents a novel marine-derived cell penetrating peptide which can be explored for cellular delivery of pharmaceutically relevant molecules.
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Affiliation(s)
- Saurabh Saraswat
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi 110016, India.
| | - Archana Chugh
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi 110016, India.
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10
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Olcay B, Ozdemir GD, Ozdemir MA, Ercan UK, Guren O, Karaman O. Prediction of the synergistic effect of antimicrobial peptides and antimicrobial agents via supervised machine learning. BMC Biomed Eng 2024; 6:1. [PMID: 38233957 DOI: 10.1186/s42490-024-00075-z] [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: 03/24/2023] [Accepted: 01/09/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Infectious diseases not only cause severe health problems but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. Both conventional approaches, such as antimicrobial agents, and novel approaches, like antimicrobial peptides (AMPs), are used to treat infections. However, due to the drawbacks of current approaches, new solutions are still being investigated. One recent approach is the use of AMPs and antimicrobial agents in combination, but determining synergism is with a huge variety of AMPs time-consuming and requires multiple experimental studies. Machine learning (ML) algorithms are widely used to predict biological outcomes, particularly in the field of AMPs, but no previous research reported on predicting the synergistic effects of AMPs and antimicrobial agents. RESULTS Several supervised ML models were implemented to accurately predict the synergistic effect of AMPs and antimicrobial agents. The results demonstrated that the hyperparameter-optimized Light Gradient Boosted Machine Classifier (oLGBMC) yielded the best test accuracy of 76.92% for predicting the synergistic effect. Besides, the feature importance analysis reveals that the target microbial species, the minimum inhibitory concentrations (MICs) of the AMP and the antimicrobial agents, and the used antimicrobial agent were the most important features for the prediction of synergistic effect, which aligns with recent experimental studies in the literature. CONCLUSION This study reveals that ML algorithms can predict the synergistic activity of two different antimicrobial agents without the need for complex and time-consuming experimental procedures. The implications support that the ML models may not only reduce the experimental cost but also provide validation of experimental procedures.
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Affiliation(s)
- Basak Olcay
- Department of Biomedical Engineering, Graduate School of Natural and Applied Sciences, Izmir Katip Celebi University, Izmir, Turkey
| | - Gizem D Ozdemir
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Mehmet A Ozdemir
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey.
| | - Utku K Ercan
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Onan Guren
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
| | - Ozan Karaman
- Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey
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11
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Yue L, Song L, Zhu S, Fu X, Li X, He C, Li J. Machine learning assisted rational design of antimicrobial peptides based on human endogenous proteins and their applications for cosmetic preservative system optimization. Sci Rep 2024; 14:947. [PMID: 38200054 PMCID: PMC10781772 DOI: 10.1038/s41598-023-50832-8] [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: 07/25/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.
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Affiliation(s)
- Lizhi Yue
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
- School of Chemistry and Chemical Engineering, Qilu Normal University, Shandong, China
| | - Liya Song
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China
| | - Siyu Zhu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Xiaolei Fu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Xuhui Li
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
- Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China
| | - Congfen He
- Key Laboratory of Cosmetic of China National Light Industry, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing, China.
| | - Junxiang Li
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China.
- Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China.
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12
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Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [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: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - 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.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
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13
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Wu Z, Wu Y, Zhu C, Wu X, Zhai S, Wang X, Su Z, Duan H. Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides. J Chem Inf Model 2023; 63:7655-7668. [PMID: 38049371 DOI: 10.1021/acs.jcim.3c01385] [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: 12/06/2023]
Abstract
The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.
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Affiliation(s)
- Zhipeng Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yejian Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Cheng Zhu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinyi Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Silong Zhai
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinqiao Wang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zhihao Su
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
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14
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Gómez-Pérez D, Schmid M, Chaudhry V, Hu Y, Velic A, Maček B, Ruhe J, Kemen A, Kemen E. Proteins released into the plant apoplast by the obligate parasitic protist Albugo selectively repress phyllosphere-associated bacteria. THE NEW PHYTOLOGIST 2023; 239:2320-2334. [PMID: 37222268 DOI: 10.1111/nph.18995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 04/11/2023] [Indexed: 05/25/2023]
Abstract
Biotic and abiotic interactions shape natural microbial communities. The mechanisms behind microbe-microbe interactions, particularly those protein based, are not well understood. We hypothesize that released proteins with antimicrobial activity are a powerful and highly specific toolset to shape and defend plant niches. We have studied Albugo candida, an obligate plant parasite from the protist Oomycota phylum, for its potential to modulate the growth of bacteria through release of antimicrobial proteins into the apoplast. Amplicon sequencing and network analysis of Albugo-infected and uninfected wild Arabidopsis thaliana samples revealed an abundance of negative correlations between Albugo and other phyllosphere microbes. Analysis of the apoplastic proteome of Albugo-colonized leaves combined with machine learning predictors enabled the selection of antimicrobial candidates for heterologous expression and study of their inhibitory function. We found for three candidate proteins selective antimicrobial activity against Gram-positive bacteria isolated from A. thaliana and demonstrate that these inhibited bacteria are precisely important for the stability of the community structure. We could ascribe the antibacterial activity of the candidates to intrinsically disordered regions and positively correlate it with their net charge. This is the first report of protist proteins with antimicrobial activity under apoplastic conditions that therefore are potential biocontrol tools for targeted manipulation of the microbiome.
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Affiliation(s)
- Daniel Gómez-Pérez
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Monja Schmid
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Vasvi Chaudhry
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Yiheng Hu
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Ana Velic
- Department of Biology, Quantitative Proteomics Group, Interfaculty Institute of Cell Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Boris Maček
- Department of Biology, Quantitative Proteomics Group, Interfaculty Institute of Cell Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Jonas Ruhe
- Max Planck Institute for Plant Breeding Research, 50829, Cologne, Germany
| | - Ariane Kemen
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
| | - Eric Kemen
- Microbial Interactions in Plant Ecosystems, Center for Plant Molecular Biology, University of Tübingen, 72076, Tübingen, Germany
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15
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Codina JR, Mascini M, Dikici E, Deo SK, Daunert S. Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning. Int J Mol Sci 2023; 24:12144. [PMID: 37569520 PMCID: PMC10419121 DOI: 10.3390/ijms241512144] [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: 06/13/2023] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
This research introduces a novel pipeline that couples machine learning (ML), and molecular docking for accelerating the process of small peptide ligand screening through the prediction of peptide-protein docking. Eight ML algorithms were analyzed for their potential. Notably, Light Gradient Boosting Machine (LightGBM), despite having comparable F1-score and accuracy to its counterparts, showcased superior computational efficiency. LightGBM was used to classify peptide-protein docking performance of the entire tetrapeptide library of 160,000 peptide ligands against four viral envelope proteins. The library was classified into two groups, 'better performers' and 'worse performers'. By training the LightGBM algorithm on just 1% of the tetrapeptide library, we successfully classified the remaining 99%with an accuracy range of 0.81-0.85 and an F1-score between 0.58-0.67. Three different molecular docking software were used to prove that the process is not software dependent. With an adjustable probability threshold (from 0.5 to 0.95), the process could be accelerated by a factor of at least 10-fold and still get 90-95% concurrence with the method without ML. This study validates the efficiency of machine learning coupled to molecular docking in rapidly identifying top peptides without relying on high-performance computing power, making it an effective tool for screening potential bioactive compounds.
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Affiliation(s)
- Josep-Ramon Codina
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (J.-R.C.); (E.D.); (S.K.D.)
| | - Marcello Mascini
- Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, 64100 Teramo, Italy
| | - Emre Dikici
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (J.-R.C.); (E.D.); (S.K.D.)
- Dr. John T. Macdonald Foundation Biomedical Nanotechnology Institute (BioNIUM), University of Miami, Miami, FL 33136, USA
| | - Sapna K. Deo
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (J.-R.C.); (E.D.); (S.K.D.)
- Dr. John T. Macdonald Foundation Biomedical Nanotechnology Institute (BioNIUM), University of Miami, Miami, FL 33136, USA
| | - Sylvia Daunert
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA; (J.-R.C.); (E.D.); (S.K.D.)
- Dr. John T. Macdonald Foundation Biomedical Nanotechnology Institute (BioNIUM), University of Miami, Miami, FL 33136, USA
- Clinical and Translational Science Institute (CTSI), University of Miami, Miami, FL 33136, USA
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16
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Sowers A, Wang G, Xing M, Li B. Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology. Microorganisms 2023; 11:1129. [PMID: 37317103 PMCID: PMC10223199 DOI: 10.3390/microorganisms11051129] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/13/2023] [Accepted: 04/19/2023] [Indexed: 06/16/2023] Open
Abstract
Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.
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Affiliation(s)
- Alexa Sowers
- Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV 26506, USA
- School of Pharmacy, West Virginia University, Morgantown, WV 26506, USA
| | - Guangshun Wang
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, NE 68198, USA
| | - Malcolm Xing
- Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Bingyun Li
- Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV 26506, USA
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17
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Ligorio C, Mata A. Synthetic extracellular matrices with function-encoding peptides. NATURE REVIEWS BIOENGINEERING 2023; 1:1-19. [PMID: 37359773 PMCID: PMC10127181 DOI: 10.1038/s44222-023-00055-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/16/2023] [Indexed: 06/28/2023]
Abstract
The communication of cells with their surroundings is mostly encoded in the epitopes of structural and signalling proteins present in the extracellular matrix (ECM). These peptide epitopes can be incorporated in biomaterials to serve as function-encoding molecules to modulate cell-cell and cell-ECM interactions. In this Review, we discuss natural and synthetic peptide epitopes as molecular tools to bioengineer bioactive hydrogel materials. We present a library of functional peptide sequences that selectively communicate with cells and the ECM to coordinate biological processes, including epitopes that directly signal to cells, that bind ECM components that subsequently signal to cells, and that regulate ECM turnover. We highlight how these epitopes can be incorporated in different biomaterials as individual or multiple signals, working synergistically or additively. This molecular toolbox can be applied in the design of biomaterials aimed at regulating or controlling cellular and tissue function, repair and regeneration.
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Affiliation(s)
- Cosimo Ligorio
- Biodiscovery Institute, University of Nottingham, Nottingham, UK
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham, UK
| | - Alvaro Mata
- Biodiscovery Institute, University of Nottingham, Nottingham, UK
- Department of Chemical and Environmental Engineering, University of Nottingham, Nottingham, UK
- School of Pharmacy, University of Nottingham, Nottingham, UK
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18
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Behling AH, Wilson BC, Ho D, Virta M, O'Sullivan JM, Vatanen T. Addressing antibiotic resistance: computational answers to a biological problem? Curr Opin Microbiol 2023; 74:102305. [PMID: 37031568 DOI: 10.1016/j.mib.2023.102305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 04/11/2023]
Abstract
The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.
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Affiliation(s)
- Anna H Behling
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Brooke C Wilson
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Daniel Ho
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Marko Virta
- Department of Microbiology, University of Helsinki, Helsinki, Finland
| | - Justin M O'Sullivan
- Liggins Institute, University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Private Bag 92019, Auckland, New Zealand; Australian Parkinsons Mission, Garvan Institute of Medical Research, Sydney, New South Wales, 384 Victoria Street, Darlinghurst, NSW 2010, Australia; MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton SO16 6YD, United Kingdom; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore.
| | - Tommi Vatanen
- Liggins Institute, University of Auckland, Auckland, New Zealand; Department of Microbiology, University of Helsinki, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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19
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Szymczak P, Możejko M, Grzegorzek T, Jurczak R, Bauer M, Neubauer D, Sikora K, Michalski M, Sroka J, Setny P, Kamysz W, Szczurek E. Discovering highly potent antimicrobial peptides with deep generative model HydrAMP. Nat Commun 2023; 14:1453. [PMID: 36922490 PMCID: PMC10017685 DOI: 10.1038/s41467-023-36994-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.
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Affiliation(s)
- Paulina Szymczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marcin Możejko
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Tomasz Grzegorzek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA, 95051, USA
| | - Radosław Jurczak
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Marta Bauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Damian Neubauer
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Karol Sikora
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Michał Michalski
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Jacek Sroka
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland
| | - Piotr Setny
- The Centre of New Technologies, University of Warsaw, Stefana Banacha 2c, 02-097, Warsaw, Poland
| | - Wojciech Kamysz
- Department of Inorganic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Al. Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Ewa Szczurek
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Stefana Banacha 2, 02-097, Warsaw, Poland.
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20
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Zhang K, Teng D, Mao R, Yang N, Hao Y, Wang J. Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening. Int J Mol Sci 2023; 24:ijms24043134. [PMID: 36834553 PMCID: PMC9960615 DOI: 10.3390/ijms24043134] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/08/2023] Open
Abstract
With the accelerating growth of antimicrobial resistance (AMR), there is an urgent need for new antimicrobial agents with low or no AMR. Antimicrobial peptides (AMPs) have been extensively studied as alternatives to antibiotics (ATAs). Coupled with the new generation of high-throughput technology for AMP mining, the number of derivatives has increased dramatically, but manual running is time-consuming and laborious. Therefore, it is necessary to establish databases that combine computer algorithms to summarize, analyze, and design new AMPs. A number of AMP databases have already been established, such as the Antimicrobial Peptides Database (APD), the Collection of Antimicrobial Peptides (CAMP), the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and the Database of Antimicrobial Peptides (dbAMPs). These four AMP databases are comprehensive and are widely used. This review aims to cover the construction, evolution, characteristic function, prediction, and design of these four AMP databases. It also offers ideas for the improvement and application of these databases based on merging the various advantages of these four peptide libraries. This review promotes research and development into new AMPs and lays their foundation in the fields of druggability and clinical precision treatment.
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Affiliation(s)
- Kun Zhang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Da Teng
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Ruoyu Mao
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Na Yang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Ya Hao
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Jianhua Wang
- Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Innovative Team of Antimicrobial Peptides and Alternatives to Antibiotics, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- Key Laboratory of Feed Biotechnology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
- Correspondence: ; Tel.: +86-10-82106081 or +86-10-82106079; Fax: +86-10-82106079
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21
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Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database. Comput Biol Chem 2023; 102:107800. [PMID: 36516617 DOI: 10.1016/j.compbiolchem.2022.107800] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 12/07/2022]
Abstract
Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack of new commercially available antibiotics have made in silico discovery of AMPs increasingly important. Machine learning (ML) has improved the speed and efficiency of AMP discovery while reducing the cost of experimental approaches. Despite various ML platforms developed, there is still a lack of integrative use of ML platforms for AMP discovery from publicly available protein databases. Therefore, our study aims to screen potential AMPs with antibiofilm properties from databases using ML platforms, followed by protein-peptide molecular docking analysis and molecular dynamics (MD) simulations. A total of 5850 peptides classified as non-AMP were screened from UniProtKB and analyzed using various online ML platforms (e.g., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). Eight potential AMP peptides against Klebsiella pneumoniae with antibiofilm, non-toxic and non-hemolytic properties were then docked to MrkH, a transcriptional regulator of type 3 fimbriae involved in biofilm formation. Five of eight peptides bound more strongly than the native MrkH ligand when analyzed using HADDOCK and HPEPDOCK. Following the docking studies, our MD simulated that a Neuropeptide B (Peptide 3) bind strongly to the MrkH active sites. The discovery of putative AMPs that exceed the binding energies of the native ligand underscores the utility of the combined ML and molecular simulation strategies for discovering novel AMPs with antibiofilm properties.
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22
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Ebrahimi Tarki F, Zarrabi M, Abdiali A, Sharbatdar M. Integration of Machine Learning and Structural Analysis for Predicting Peptide Antibiofilm Effects: Advancements in Drug Discovery for Biofilm-Related Infections. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH : IJPR 2023; 22:e138704. [PMID: 38450220 PMCID: PMC10916117 DOI: 10.5812/ijpr-138704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 03/08/2024]
Abstract
Background The rise of antibiotic resistance has become a major concern, signaling the end of the golden age of antibiotics. Bacterial biofilms, which exhibit high resistance to antibiotics, significantly contribute to the emergence of antibiotic resistance. Therefore, there is an urgent need to discover new therapeutic agents with specific characteristics to effectively combat biofilm-related infections. Studies have shown the promising potential of peptides as antimicrobial agents. Objectives This study aimed to establish a cost-effective and streamlined computational method for predicting the antibiofilm effects of peptides. This method can assist in addressing the intricate challenge of designing peptides with strong antibiofilm properties, a task that can be both challenging and costly. Methods A positive library, consisting of peptide sequences with antibiofilm activity exceeding 50%, was assembled, along with a negative library containing quorum-sensing peptides. For each peptide sequence, feature vectors were calculated, while considering the primary structure, the order of amino acids, their physicochemical properties, and their distributions. Multiple supervised learning algorithms were used to classify peptides with significant antibiofilm effects for subsequent experimental evaluations. Results The computational approach exhibited high accuracy in predicting the antibiofilm effects of peptides, with accuracy, precision, Matthew's correlation coefficient (MCC), and F1 score of 99%, 99%, 0.97, and 0.99, respectively. The performance level of this computational approach was comparable to that of previous methods. This study introduced a novel approach by combining the feature space with high antibiofilm activity. Conclusions In this study, a reliable and cost-effective method was developed for predicting the antibiofilm effects of peptides using a computational approach. This approach allows for the identification of peptide sequences with substantial antibiofilm activities for further experimental investigations. Accessible source codes and raw data of this study can be found online (hiABF), providing easy access and enabling future updates.
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Affiliation(s)
- Fatemeh Ebrahimi Tarki
- Department of Biotechnology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran
| | - Mahboobeh Zarrabi
- Department of Biotechnology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran
| | - Ahya Abdiali
- Department of Microbiology, Faculty of Biological Sciences, Alzahra University, Tehran, Iran
| | - Mahkame Sharbatdar
- Department of Mechanical Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran
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23
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Perveen G, Alturise F, Alkhalifah T, Daanial Khan Y. Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features. Digit Health 2023; 9:20552076231180739. [PMID: 37434723 PMCID: PMC10331097 DOI: 10.1177/20552076231180739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 05/22/2023] [Indexed: 07/13/2023] Open
Abstract
Objective The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information. Methods Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/. Results XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately. Conclusions The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.
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Affiliation(s)
- Gulnaz Perveen
- Department of Computer Science, School
of Systems and Technology, University of Management and Technology, Lahore, Punjab,
Pakistan
| | - Fahad Alturise
- Department of Computer, College of
Science and Arts in Ar Rass Qassim University, Buraidah, Qassim, Saudi Arabia
| | - Tamim Alkhalifah
- Department of Computer, College of
Science and Arts in Ar Rass Qassim University, Buraidah, Qassim, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, School
of Systems and Technology, University of Management and Technology, Lahore, Punjab,
Pakistan
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24
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Chamoli T, Khera A, Sharma A, Gupta A, Garg S, Mamgain K, Bansal A, Verma S, Gupta A, Alajangi HK, Singh G, Barnwal RP. Peptide Utility (PU) search server: A new tool for peptide sequence search from multiple databases. Heliyon 2022; 8:e12283. [PMID: 36590540 PMCID: PMC9800339 DOI: 10.1016/j.heliyon.2022.e12283] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/21/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Proteins are essential building blocks in humans that have garnered huge attention from researchers worldwide due to their numerous therapeutic applications. To date, different computational tools have been developed to extract pre-existing information on these biological molecules, but most of these tools suffer from limitations such as non-user friendly interface, redundancy of data, etc. To overcome these limitations, a user-friendly interface, the Peptide Utility (PU) webserver (https://chain-searching.herokuapp.com/) has been developed for searching and analyzing homologous and identical protein/peptide sequences that can be searched from approximately 0.4 million sequences (structural and sequence information) in both online and offline modes. The PU web server can also be used to study different types of interactions in PDBSum, identifying the most dominating interface residues, the most prevalent interactions, and the interaction preferences of different residues. The webserver would also pave way for the design of novel therapeutic peptides and folds by identifying conserved residues in the three-dimensional structure space of proteins.
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Affiliation(s)
- Tanishq Chamoli
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Alisha Khera
- Department of Biophysics, Panjab University, Chandigarh 160014, India,National Centre for Cell Science, NCCS Complex, S. P. Pune University Campus, Ganeshkhind, Pune, Maharashtra 411007, India
| | - Akanksha Sharma
- Department of Biophysics, Panjab University, Chandigarh 160014, India,University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India
| | - Anshul Gupta
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Sonam Garg
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Kanishk Mamgain
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Aayushi Bansal
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Shriya Verma
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Ankit Gupta
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India
| | - Hema K. Alajangi
- Department of Biophysics, Panjab University, Chandigarh 160014, India,University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India,Corresponding author.
| | - Gurpal Singh
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India,Corresponding author.
| | - Ravi P. Barnwal
- Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, India,Corresponding author.
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25
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Bajorath J, Chávez-Hernández AL, Duran-Frigola M, Fernández-de Gortari E, Gasteiger J, López-López E, Maggiora GM, Medina-Franco JL, Méndez-Lucio O, Mestres J, Miranda-Quintana RA, Oprea TI, Plisson F, Prieto-Martínez FD, Rodríguez-Pérez R, Rondón-Villarreal P, Saldívar-Gonzalez FI, Sánchez-Cruz N, Valli M. Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J Cheminform 2022; 14:82. [PMID: 36461094 PMCID: PMC9716667 DOI: 10.1186/s13321-022-00661-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/25/2022] [Indexed: 12/03/2022] Open
Abstract
We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .
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Affiliation(s)
- Jürgen Bajorath
- grid.10388.320000 0001 2240 3300Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53113 Bonn, Germany
| | - Ana L. Chávez-Hernández
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Miquel Duran-Frigola
- Ersilia Open Source Initiative, Cambridge, UK ,grid.7722.00000 0001 1811 6966Joint IRB-BSC-CRG Programme in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia Spain
| | - Eli Fernández-de Gortari
- grid.420330.60000 0004 0521 6935Nanosafety Laboratory, International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal
| | - Johann Gasteiger
- grid.5330.50000 0001 2107 3311Computer-Chemie-Centrum, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Edgar López-López
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico ,grid.512574.0Department of Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV), 07360 Mexico City, Mexico
| | - Gerald M. Maggiora
- grid.134563.60000 0001 2168 186XBIO5 Institute, University of Arizona, Tucson, AZ 85721 USA
| | - José L. Medina-Franco
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | | | - Jordi Mestres
- grid.5841.80000 0004 1937 0247Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), 08028 Barcelona, Catalonia Spain ,grid.20522.370000 0004 1767 9005Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Parc de Recerca Biomedica (PRBB), 08003 Barcelona, Catalonia Spain
| | | | - Tudor I. Oprea
- grid.266832.b0000 0001 2188 8502Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131 USA ,grid.8761.80000 0000 9919 9582Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at Gothenburg University, 40530 Gothenburg, Sweden ,grid.5254.60000 0001 0674 042XNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark ,Present Address: Roivant Discovery Sciences, Inc., 451 D Street, Boston, MA 02210 USA
| | - Fabien Plisson
- grid.512574.0Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824 Irapuato, Gto Mexico
| | - Fernando D. Prieto-Martínez
- grid.9486.30000 0001 2159 0001Chemistry Institute, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Raquel Rodríguez-Pérez
- grid.419481.10000 0001 1515 9979Novartis Institutes for Biomedical Research, 4002 Basel, Switzerland
| | - Paola Rondón-Villarreal
- grid.442204.40000 0004 0486 1035Universidad de Santander, Facultad de Ciencias Médicas y de la Salud, Instituto de Investigación Masira, Calle 70 No. 55-210, 680003 Santander, Bucaramanga Colombia
| | - Fernanda I. Saldívar-Gonzalez
- grid.9486.30000 0001 2159 0001DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, 04510 Mexico City, Mexico
| | - Norberto Sánchez-Cruz
- grid.5841.80000 0004 1937 0247Chemotargets SL, Baldiri Reixac 4, Parc Cientific de Barcelona (PCB), 08028 Barcelona, Catalonia Spain ,grid.9486.30000 0001 2159 0001Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Carretera Mérida-Tetiz Km. 4.5, Yucatán, 97357 Ucú, Mexico
| | - Marilia Valli
- grid.410543.70000 0001 2188 478XNuclei of Bioassays, Biosynthesis and Ecophysiology of Natural Products (NuBBE), Department of Organic Chemistry, Institute of Chemistry, São Paulo State University-UNESP, Araraquara, Brazil
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26
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Kato M, Yanai T. Pulled fly balls are harder to catch: a game analysis with a machine learning approach. SPORTS ENGINEERING 2022. [DOI: 10.1007/s12283-022-00373-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
AbstractTwo hypotheses were tested: (1) the deflecting motion of fly balls caused by aerodynamic effects varies between the pull side and opposite side of the fair territory, and (2) the probability of flyout is lower on the pull side than the opposite side in Japan’s professional baseball games. From all radar-tracking outputs of official games in 2018–2019, fly balls that resulted in outs or base hits were selected for analysis (N = 25,413), and indices representing horizontal and vertical deflecting motions of fly balls were computed and compared between the pull side and opposite side. A machine learning algorithm was used to construct a model to predict the probability of flyout from the kinematic characteristics of fly balls. Flyout zones where the probability of flyout was > 0.6 were computed for a systematically constructed set of fly balls having identical distribution between the pull side and opposite side. The results showed that: (1) most fly balls landing on the opposite side deflected in the same direction whereas the pulled fly balls deflected to either direction, (2) the pulled low fly balls had greater variability in the deflecting motions than the opposite side counterpart, (3) overall probability of flyout of the low fly balls was lower in the pull side (0.41) than the opposite side (0.49), and (4) the flyout zone of an outfielder in the pull side (mean = 698 m2) for low fly balls was smaller than that of the others (≥ 779 m2). The hypotheses were supported. The pulled low fly balls had substantial variations in the direction and magnitude of deflections, which might have reduced the flyout zone on the pull side.
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27
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Cheng Q, Zeng P, Chi Chan EW, Chen S. Development of Peptide-based Metallo-β-lactamase Inhibitors as a New Strategy to Combat Antimicrobial Resistance: A Mini-review. Curr Pharm Des 2022; 28:3538-3545. [PMID: 36177630 DOI: 10.2174/1381612828666220929154255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/10/2022] [Accepted: 08/22/2022] [Indexed: 01/28/2023]
Abstract
Global dissemination of antimicrobial resistance (AMR) not only poses a significant threat to human health, food security, and social development but also results in millions of deaths each year. In Gram-negative bacteria, the primary mechanism of resistance to β-lactam antibiotics is the production of β-lactamases, one of which is carbapenem-hydrolyzing β-lactamases known as carbapenemases. As a general scheme, these enzymes are divided into Ambler class A, B, C, and D based on their protein sequence homology. Class B β-lactamases are also known as metallo-β-lactamases (MBLs). The incidence of recovery of bacteria expressing metallo-β- lactamases (MBLs) has increased dramatically in recent years, almost reaching a pandemic proportion. MBLs can be further divided into three subclasses (B1, B2, and B3) based on the homology of protein sequences as well as the differences in zinc coordination. The development of inhibitors is one effective strategy to suppress the activities of MBLs and restore the activity of β-lactam antibiotics. Although thousands of MBL inhibitors have been reported, none have been approved for clinical use. This review describes the clinical application potential of peptide-based drugs that exhibit inhibitory activity against MBLs identified in past decades. In this report, peptide-based inhibitors of MBLs are divided into several groups based on the mode of action, highlighting compounds of promising properties that are suitable for further advancement. We discuss how traditional computational tools, such as in silico screening and molecular docking, along with new methods, such as deep learning and machine learning, enable a more accurate and efficient design of peptide-based inhibitors of MBLs.
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Affiliation(s)
- Qipeng Cheng
- Anhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Diseases and Key Laboratory of Biomedicine in Gene Diseases and Health of Anhui Higher Education Institutes, College of Life Sciences, Anhui Normal University, Wuhu, Anhui, China
| | - Ping Zeng
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Edward Wai Chi Chan
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Sheng Chen
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong
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28
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Function Prediction of Peptide Toxins with Sequence-Based Multi-Tasking PU Learning Method. Toxins (Basel) 2022; 14:toxins14110811. [PMID: 36422985 PMCID: PMC9696491 DOI: 10.3390/toxins14110811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 10/28/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022] Open
Abstract
Peptide toxins generally have extreme pharmacological activities and provide a rich source for the discovery of drug leads. However, determining the optimal activity of a new peptide can be a long and expensive process. In this study, peptide toxins were retrieved from Uniprot; three positive-unlabeled (PU) learning schemes, adaptive basis classifier, two-step method, and PU bagging were adopted to develop models for predicting the biological function of new peptide toxins. All three schemes were embedded with 14 machine learning classifiers. The prediction results of the adaptive base classifier and the two-step method were highly consistent. The models with top comprehensive performances were further optimized by feature selection and hyperparameter tuning, and the models were validated by making predictions for 61 three-finger toxins or the external HemoPI dataset. Biological functions that can be identified by these models include cardiotoxicity, vasoactivity, lipid binding, hemolysis, neurotoxicity, postsynaptic neurotoxicity, hypotension, and cytolysis, with relatively weak predictions for hemostasis and presynaptic neurotoxicity. These models are discovery-prediction tools for active peptide toxins and are expected to accelerate the development of peptide toxins as drugs.
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29
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Machine Learning Models to Predict Protein-Protein Interaction Inhibitors. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27227986. [PMID: 36432086 PMCID: PMC9694076 DOI: 10.3390/molecules27227986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/09/2022] [Accepted: 11/16/2022] [Indexed: 11/19/2022]
Abstract
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.
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30
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Gao B, Huang Y, Peng C, Lin B, Liao Y, Bian C, Yang J, Shi Q. High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development. BIODESIGN RESEARCH 2022; 2022:9895270. [PMID: 37850131 PMCID: PMC10521759 DOI: 10.34133/2022/9895270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/23/2022] [Indexed: 10/19/2023] Open
Abstract
Cone snail venoms have been considered a valuable treasure for international scientists and businessmen, mainly due to their pharmacological applications in development of marine drugs for treatment of various human diseases. To date, around 800 Conus species are recorded, and each of them produces over 1,000 venom peptides (termed as conopeptides or conotoxins). This reflects the high diversity and complexity of cone snails, although most of their venoms are still uncharacterized. Advanced multiomics (such as genomics, transcriptomics, and proteomics) approaches have been recently developed to mine diverse Conus venom samples, with the main aim to predict and identify potentially interesting conopeptides in an efficient way. Some bioinformatics techniques have been applied to predict and design novel conopeptide sequences, related targets, and their binding modes. This review provides an overview of current knowledge on the high diversity of conopeptides and multiomics advances in high-throughput prediction of novel conopeptide sequences, as well as molecular modeling and design of potential drugs based on the predicted or validated interactions between these toxins and their molecular targets.
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Affiliation(s)
- Bingmiao Gao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Pharmacy, Hainan Medical University, Haikou, Hainan 570102, China
| | - Yu Huang
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
| | - Chao Peng
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
- BGI-Marine Research Institute for Biomedical Technology, Shenzhen Huahong Marine Biomedicine Co. Ltd., Shenzhen, Guangdong 518119, China
| | - Bo Lin
- Hainan Provincial Key Laboratory of Carcinogenesis and Intervention, Hainan Medical University, Haikou, Hainan 570102, China
| | - Yanling Liao
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Pharmacy, Hainan Medical University, Haikou, Hainan 570102, China
| | - Chao Bian
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
| | - Jiaan Yang
- Research and Development Department, Micro Pharmtech Ltd., Wuhan, Hubei 430075, China
| | - Qiong Shi
- Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
- BGI-Marine Research Institute for Biomedical Technology, Shenzhen Huahong Marine Biomedicine Co. Ltd., Shenzhen, Guangdong 518119, China
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Suri S, Dakshanamurthy S. IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method. Vaccines (Basel) 2022; 10:vaccines10101678. [PMID: 36298543 PMCID: PMC9611587 DOI: 10.3390/vaccines10101678] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/03/2022] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers’ clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac’s prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement.
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Affiliation(s)
- Sadhana Suri
- Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Sivanesan Dakshanamurthy
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA
- Correspondence:
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Salem M, Keshavarzi Arshadi A, Yuan JS. AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning. BMC Bioinformatics 2022; 23:389. [PMID: 36163001 PMCID: PMC9511757 DOI: 10.1186/s12859-022-04952-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
Abstract
Background Deep learning’s automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of Hemolytic Activity Prediction of Antimicrobial Peptides creates a challenge due to the small amount of available data. Results Three different datasets for hemolysis activity prediction of therapeutic and antimicrobial peptides are gathered and the AMPDeep pipeline is implemented for each. The result demonstrate that AMPDeep outperforms the previous works on all three datasets, including works that use physicochemical features to represent the peptides or those who solely rely on the sequence and use deep learning to learn representation for the peptides. Moreover, a combined dataset is introduced for hemolytic activity prediction to address the problem of sequence similarity in this domain. AMPDeep fine-tunes a large transformer based model on a small amount of peptides and successfully leverages the patterns learned from other protein and peptide databases to assist hemolysis activity prediction modeling. Conclusions In this work transfer learning is leveraged to overcome the challenge of small data and a deep learning based model is successfully adopted for hemolysis activity classification of antimicrobial peptides. This model is first initialized as a protein language model which is pre-trained on masked amino acid prediction on many unlabeled protein sequences in a self-supervised manner. Having done so, the model is fine-tuned on an aggregated dataset of labeled peptides in a supervised manner to predict secretion. Through transfer learning, hyper-parameter optimization and selective fine-tuning, AMPDeep is able to achieve state-of-the-art performance on three hemolysis datasets using only the sequence of the peptides. This work assists the adoption of large sequence-based models for peptide classification and modeling tasks in a practical manner.
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Affiliation(s)
- Milad Salem
- Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL, USA.
| | | | - Jiann Shiun Yuan
- Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL, USA
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33
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Herrera-Bravo J, Farías JG, Sandoval C, Herrera-Belén L, Quiñones J, Díaz R, Beltrán JF. nAChR-PEP-PRED: A Robust Tool for Predicting Peptide Inhibitors of Acetylcholine Receptors Using the Random Forest Classifier. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-022-10460-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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34
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Zakharova E, Orsi M, Capecchi A, Reymond JL. Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides. ChemMedChem 2022; 17:e202200291. [PMID: 35880810 PMCID: PMC9541320 DOI: 10.1002/cmdc.202200291] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/29/2022] [Indexed: 12/05/2022]
Abstract
Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α‐helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non‐hemolytic from hemolytic AMPs and ACPs to discover new non‐hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty‐three peptides resulted in eleven active ACPs, four of which were non‐hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non‐hemolytic ACPs.
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Affiliation(s)
- Elena Zakharova
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Markus Orsi
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Alice Capecchi
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Jean-Louis Reymond
- Universität Bern: Universitat Bern, Department of Chemistry and Biochemistry, Department of Chemistry and Biochemistry, Freiestrasse 3, 3012, Switzerland, 3012, Bern, SWITZERLAND
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35
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Guido-Patiño JC, Plisson F. Profiling hymenopteran venom toxins: Protein families, structural landscape, biological activities, and pharmacological benefits. Toxicon X 2022; 14:100119. [PMID: 35372826 PMCID: PMC8971319 DOI: 10.1016/j.toxcx.2022.100119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 12/24/2022] Open
Abstract
Hymenopterans are an untapped source of venom secretions. Their recent proteo-transcriptomic studies have revealed an extraordinary pool of toxins that participate in various biological processes, including pain, paralysis, allergic reactions, and antimicrobial activities. Comprehensive and clade-specific campaigns to collect hymenopteran venoms are therefore needed. We consider that data-driven bioprospecting may help prioritise sampling and alleviate associated costs. This work established the current protein landscape from hymenopteran venoms to evaluate possible sample bias by studying their origins, sequence diversity, known structures, and biological functions. We collected all 282 reported hymenopteran toxins (peptides and proteins) from the UniProt database that we clustered into 21 protein families from the three studied clades - wasps, bees, and ants. We identified 119 biological targets of hymenopteran toxins ranging from pathogen membranes to eukaryotic proteases, ion channels and protein receptors. Our systematic study further extended to hymenopteran toxins' therapeutic and biotechnological values, where we revealed promising applications in crop pests, human infections, autoimmune diseases, and neurodegenerative disorders. The hymenopteran toxin diversity includes 21 protein families from 81 species. Some toxins are shared across wasps, bees and ants, others are clade-specific. Their venoms contain membrane-active peptides, neurotoxins, allergens and enzymes. Hymenopteran toxins have been tested against a total of 119 biological targets. Hymenopteran toxins were predominantly evaluated as anti-infective agents.
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Affiliation(s)
- Juan Carlos Guido-Patiño
- Centro de Investigación y de Estudios Avanzados Del IPN (CINVESTAV), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para La Biodiversidad (Langebio), Irapuato, Guanajuato, 36824, Mexico
| | - Fabien Plisson
- CONACYT, Centro de Investigación y de Estudios Avanzados Del IPN (CINVESTAV), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para La Biodiversidad (Langebio), Irapuato, Guanajuato, 36824, Mexico
- Corresponding author.
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Ripperda T, Yu Y, Verma A, Klug E, Thurman M, Reid SP, Wang G. Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses. Pharmaceuticals (Basel) 2022; 15:ph15050521. [PMID: 35631348 PMCID: PMC9143221 DOI: 10.3390/ph15050521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/16/2022] [Accepted: 04/22/2022] [Indexed: 02/07/2023] Open
Abstract
The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants.
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Affiliation(s)
| | | | | | | | | | - St Patrick Reid
- Correspondence: (S.P.R.); (G.W.); Tel.: +1-(402)-559-3644 (S.P.R.); +1-(402)-559-4176 (G.W.)
| | - Guangshun Wang
- Correspondence: (S.P.R.); (G.W.); Tel.: +1-(402)-559-3644 (S.P.R.); +1-(402)-559-4176 (G.W.)
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37
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Gupta S, Azadvari N, Hosseinzadeh P. Design of Protein Segments and Peptides for Binding to Protein Targets. BIODESIGN RESEARCH 2022; 2022:9783197. [PMID: 37850124 PMCID: PMC10521657 DOI: 10.34133/2022/9783197] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 03/16/2022] [Indexed: 10/19/2023] Open
Abstract
Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.
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Affiliation(s)
- Suchetana Gupta
- Knight Campus Center for Accelerating Scientific Impact, University of Oregon, Eugene OR 97403, USA
| | - Noora Azadvari
- Knight Campus Center for Accelerating Scientific Impact, University of Oregon, Eugene OR 97403, USA
| | - Parisa Hosseinzadeh
- Knight Campus Center for Accelerating Scientific Impact, University of Oregon, Eugene OR 97403, USA
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Chen CH, Bepler T, Pepper K, Fu D, Lu TK. Synthetic molecular evolution of antimicrobial peptides. Curr Opin Biotechnol 2022; 75:102718. [PMID: 35395425 DOI: 10.1016/j.copbio.2022.102718] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/14/2022] [Accepted: 03/01/2022] [Indexed: 01/18/2023]
Abstract
As we learn more about how peptide structure and activity are related, we anticipate that antimicrobial peptides will be engineered to have strong potency and distinct functions and that synthetic peptides will have new biomedical applications, such as treatments for emerging infectious diseases. As a result of the enormous number of possible amino acid sequences and the low-throughput nature of antimicrobial peptide assays, computational tools for peptide design and optimization are needed for direct experimentation toward obtaining functional sequences. Recent developments in computational tools have improved peptide design, saving labor, reagents, costs, and time. At the same time, improvements in peptide synthesis and experimental platforms continue to reduce the cost and increase the throughput of peptide-drug screening. In this review, we discuss the current methods of peptide design and engineering, including in silico methods and peptide synthesis and screening, and highlight areas of potential improvement.
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Affiliation(s)
- Charles H Chen
- Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Tristan Bepler
- Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Simons Machine Learning Center, New York Structural Biology Center, New York, NY 10027, USA
| | - Karen Pepper
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Debbie Fu
- Department of Biology, Tufts University, Medford, MA 02155, USA
| | - Timothy K Lu
- Synthetic Biology Center, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02142, USA; Senti Biosciences, South San Francisco, CA 94080, USA.
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Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073631] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.
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Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. Pharmaceuticals (Basel) 2022; 15:ph15030323. [PMID: 35337121 PMCID: PMC8953747 DOI: 10.3390/ph15030323] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/27/2022] Open
Abstract
Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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Lessons from a Single Amino Acid Substitution: Anticancer and Antibacterial Properties of Two Phospholipase A2-Derived Peptides. Curr Issues Mol Biol 2021; 44:46-62. [PMID: 35723383 PMCID: PMC8929095 DOI: 10.3390/cimb44010004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/13/2021] [Accepted: 12/15/2021] [Indexed: 12/15/2022] Open
Abstract
The membrane-active nature of phospholipase A2-derived peptides makes them potential candidates for antineoplastic and antibacterial therapies. Two short 13-mer C-terminal fragments taken from snake venom Lys49-PLA2 toxins (p-AppK and p-Acl), differing by a leucine/phenylalanine substitution, were synthesized and their bioactivity was evaluated. Their capacity to interfere with the survival of Gram-positive and Gram-negative bacteria as well as with solid and liquid tumors was assessed in vitro. Toxicity to red blood cells was investigated via in silico and in vitro techniques. The mode of action was mainly studied by molecular dynamics simulations and membrane permeabilization assays. Briefly, both peptides have dual activity, i.e., they act against both bacteria, including multidrug-resistant strains and tumor cells. All tested bacteria were susceptible to both peptides, Pseudomonas aeruginosa being the most affected. RAMOS, K562, NB4, and CEM cells were the main leukemic targets of the peptides. In general, p-Acl showed more significant activity, suggesting that phenylalanine confers advantages to the antibacterial and antitumor mechanism, particularly for osteosarcoma lines (HOS and MG63). Peptide-based treatment increased the uptake of a DNA-intercalating dye by bacteria, suggesting membrane damage. Indeed, p-AppK and p-Acl did not disrupt erythrocyte membranes, in agreement with in silico predictions. The latter revealed that the peptides deform the membrane and increase its permeability by facilitating solvent penetration. This phenomenon is expected to catalyze the permeation of solutes that otherwise could not cross the hydrophobic membrane core. In conclusion, the present study highlights the role of a single amino acid substitution present in natural sequences towards the development of dual-action agents. In other words, dissecting and fine-tuning biomembrane remodeling proteins, such as snake venom phospholipase A2 isoforms, is again demonstrated as a valuable source of therapeutic peptides.
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[Antibacterial mechanism of Brevinin-2GHk, an antimicrobial peptide from Fejervarya limnocharis skin]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1657-1663. [PMID: 34916191 PMCID: PMC8685708 DOI: 10.12122/j.issn.1673-4254.2021.11.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To investigate the secondary structure, physicochemical properties and antibacterial activity of Brevinin- 2GHk (BR2GK), an antimicrobial peptide from frog skin, and explore its antibacterial mechanism. METHODS BR2GK was synthesized, purified with high performance liquid chromatography (HPLC) and identified using mass spectrometry. Circular dichroism was used to determine the secondary structure and physicochemical properties of BR2GK. Two-fold dilution method was used to determine the antibacterial activity of BR2GK, and its antibacterial mechanism was explored using laser scanning confocal microscopy (LSCM) and scanning electron microscopy (SEM). The hemolytic activity of BR2GK was analyzed in red blood cells. Isothermal titration calorimetry (ITC) and surface plasmon resonance imaging (SPRi) were employed to detect the binding of BR2GK to lipopolysaccharide (LPS), and the antioxidant activity of BR2GK was evaluated using biochemical kits. RESULTS The synthesized BR2GK, with a purity exceeding 95% after purification, had the basic characteristics of cationic antimicrobial peptides. BR2GK consisted mainly of α-helical structure in SDS solution and exhibited a broad-spectrum antibacterial activity. Antibacterial activity assay showed that BR2GK had inhibitory and killing activity against a variety of strains with a minimum inhibitory concentration (MIC) of 2.76 μmol/L against Staphylococcus aureus. Observation with LSCM and SEM showed that BR2GK at an active concentration caused bacterial cell membrane damage, cell swelling, contraction, deformation, and massive exudation of intracellular contents without causing hemolysis. ITC showed that the binding affinity KD of BR2GK to LPS was 18.2±0.8 μmol/L. The antioxidant test showed that BR2GK was capable of effectively scavenging NO, ABTS and DPPH. CONCLUSION BR2GK, as a broad-spectrum antibacterial peptide with also a strong antioxidant capacity, does not cause hemolysis and is capable of binding to LPS. BR2GK has an important value for future design and synthesis of antimicrobial peptides with stronger antibacterial activity and lower cytotoxicity.
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Liang JY, Li Q, Feng LB, Hu SX, Zhang SQ, Li CX, Zhang XB. Injectable antimicrobial hydrogels with antimicrobial peptide and sanguinarine controlled release ability for preventing bacterial infections. Am J Transl Res 2021; 13:12614-12625. [PMID: 34956477 PMCID: PMC8661223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 08/16/2021] [Indexed: 06/14/2023]
Abstract
The emergence of antibiotic resistant bacteria represents a significant and common clinical problem worldwide as infections are becoming increasingly common. It is urgent to broaden the sources of biomaterials that can prevent both bacterial infection and antibiotic resistance. In this work, oxidized sodium alginate/aminated hyaluronic acid (OSA/AHA) hydrogel with various proportions was developed based on Schiff base reaction. Herein, polydopamine (PDA)-Bmkn2 nanoparticle and sanguinarine were incorporated into hydrogels to enhance antibacterial properties. The prepared PDA-Bmkn2 nanoparticles, with uniform particle size and good dispersion, could serve as a delivery system for Bmkn2. The prepared hydrogels showed appropriate swelling ratio, extremely good mechanical strengths and improved biodegradability. Meanwhile, the Bmkn2 and sanguinarine were released from the hydrogels in a sustainable manner. Furthermore, OSA/AHA/sanguinarine/PDA-Bmkn2 hydrogel (less than 10 μg/mL BmKn2 and 0.2 μg/mL sanguinarine) had excellent biocompatibility. Antibacterial experiments confirmed that OSA/AHA/sanguinarine/PDA-Bmkn2 hydrogel had effective antimicrobial activity on Escherichia coli and Staphylococcus aureus. Therefore, the prepared injectable hydrogels with good biocompatibility and excellent synergistic antibacterial activity promise great potential for preventing localized bacterial infections.
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Affiliation(s)
- Jing-Yao Liang
- Institute of Dermatology, Guangzhou Medical UniversityGuangzhou 510095, Guangdong, China
- Department of Dermatology, Guangzhou Institute of DermatologyGuangzhou 510095, Guangdong, China
| | - Qian Li
- Institute of Dermatology, Guangzhou Medical UniversityGuangzhou 510095, Guangdong, China
- Department of Dermatology, Guangzhou Institute of DermatologyGuangzhou 510095, Guangdong, China
| | - Long-Bao Feng
- Beogene Biotech (Guangzhou) Co., LTDGuangzhou 510663, Guangdong, China
| | - Sheng-Xue Hu
- Beogene Biotech (Guangzhou) Co., LTDGuangzhou 510663, Guangdong, China
| | - San-Quan Zhang
- Institute of Dermatology, Guangzhou Medical UniversityGuangzhou 510095, Guangdong, China
- Department of Dermatology, Guangzhou Institute of DermatologyGuangzhou 510095, Guangdong, China
| | - Chang-Xing Li
- Department of Dermatology, Nanfang Hospital, Southern Medical UniversityGuangzhou 510515, Guangdong, China
| | - Xi-Bao Zhang
- Institute of Dermatology, Guangzhou Medical UniversityGuangzhou 510095, Guangdong, China
- Department of Dermatology, Guangzhou Institute of DermatologyGuangzhou 510095, Guangdong, China
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45
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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: 59] [Impact Index Per Article: 19.7] [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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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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Zakaryan H, Chilingaryan G, Arabyan E, Serobian A, Wang G. Natural antimicrobial peptides as a source of new antiviral agents. J Gen Virol 2021; 102. [PMID: 34554085 PMCID: PMC10026734 DOI: 10.1099/jgv.0.001661] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Current antiviral drugs are limited because of their adverse side effects and increased rate of resistance. In recent decades, much scientific effort has been invested in the discovery of new synthetic and natural compounds with promising antiviral properties. Among this new generation of compounds, antimicrobial peptides with antiviral activity have been described and are attracting attention due to their mechanism of action and biological properties. To understand the potential of antiviral peptides (AVPs), we analyse the antiviral activity of well-known AVP families isolated from different natural sources, discuss their physical-chemical properties, and demonstrate how AVP databases can guide us to design synthetic AVPs with better therapeutic properties. All considerations in this sphere of antiviral therapy clearly demonstrate the remarkable contribution that AVPs may make in conquering old as well as newly emerging viruses that plague humanity.
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Affiliation(s)
- Hovakim Zakaryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, 0014, Yerevan, Armenia
- Denovo Sciences CJSC, 0033, Yerevan, Armenia
| | - Garri Chilingaryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, 0014, Yerevan, Armenia
| | - Erik Arabyan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, 0014, Yerevan, Armenia
| | | | - Guangshun Wang
- Department of Pathology and Microbiology, College of Medicine, University of Nebraska Medical Center, 985900 Nebraska Medical Center, Omaha, NE 68198-5900, USA
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Schistocins: Novel antimicrobial peptides encrypted in the Schistosoma mansoni Kunitz Inhibitor SmKI-1. Biochim Biophys Acta Gen Subj 2021; 1865:129989. [PMID: 34389467 DOI: 10.1016/j.bbagen.2021.129989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Here we describe a new class of cryptides (peptides encrypted within a larger protein) with antimicrobial properties, named schistocins, derived from SmKI-1, a key protein in Shistosoma mansoni survival. This is a multi-functional protein with biotechnological potential usage as a therapeutic molecule in inflammatory diseases and to control schistosomiasis. METHODS We used our algorithm enCrypted, to perform an in silico proteolysis of SmKI-1 and a screening for potential antimicrobial activity. The selected peptides were chemically synthesized, tested in vitro and evaluated by both structural (CD, NMR) and biophysical (ITC) studies to access their structure-function relationship. RESULTS EnCrypted was capable of predicting AMPs in SmKI-1. Our biophysical analyses described a membrane-induced conformational change from random coil-to-α-helix and a peptide-membrane equilibrium for all schistocins. Our structural data allowed us to suggest a well-known mode of peptide-membrane interaction in which electrostatic attraction between the cationic peptides and anionic membranes results in the bilayer disordering. Moreover, the NMR exchange H/D data with the higher entropic contribution observed for the peptide-membrane interaction showed that shistocins have different orientations upon the membrane. CONCLUSIONS This work demonstrate the robustness for using the physicochemical features of predicted peptides in the identification of new bioactive cryptides besides the relevance of combining these analyses with biophysical methods to understand the peptide-membrane affinity and improve further algorithms. GENERAL SIGNIFICANCE Bioprospecting cryptides can be conducted through data mining of protein databases demonstrating the success of our strategy. The peptides-based agents derived from SmKI-1 might have high impact for system-biology and biotechnology.
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Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021; 18:527-556. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text]. EXPERT OPINION Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).
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Affiliation(s)
- Luís Perpetuo
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Université Toulouse III, Toulouse, France
| | - Rita Ferreira
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Francisco Amado
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Adelino Leite-Moreira
- UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
| | - Artur M S Silva
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Rui Vitorino
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro.,LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro.,UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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