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Castillo-Mendieta K, Agüero-Chapin G, Marquez EA, Perez-Castillo Y, Barigye SJ, Vispo NS, García-Jacas CR, Marrero-Ponce Y. Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks. NPJ Syst Biol Appl 2024; 10:115. [PMID: 39367008 PMCID: PMC11452708 DOI: 10.1038/s41540-024-00429-2] [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: 06/29/2023] [Accepted: 08/22/2024] [Indexed: 10/06/2024] Open
Abstract
Peptides are promising drug development frameworks that have been hindered by intrinsic undesired properties including hemolytic activity. We aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and data mining. Metadata networks (METNs) were useful to characterize and find general patterns associated with hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), represented the hemolytic peptide space. The best candidate HSPNs were used to extract various subsets of hemolytic peptides (scaffolds) considering network centrality and peptide similarity. These scaffolds have been proved to be useful in developing robust similarity-based model classifiers. Finally, using an alignment-free approach, we reported 47 putative hemolytic motifs, which can be used as toxic signatures when developing novel peptide-based drugs. We provided evidence that the number of hemolytic motifs in a sequence might be related to the likelihood of being hemolytic.
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Affiliation(s)
| | - Guillermin Agüero-Chapin
- CIIMAR-Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Porto, Portugal.
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, Portugal.
| | - Edgar A Marquez
- Grupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Universidad del Norte, Barranquilla, Colombia
| | - Yunierkis Perez-Castillo
- Bio-Chemoinformatics Research Group and Escuela de Ciencias Físicas y Matemáticas. Universidad de Las Américas, Quito, Ecuador
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | | | - Cesar R García-Jacas
- Investigador por México, Consejo Nacional de Humanidades, Ciencias y Tecnologías (Conahcyt), 03940, Ciudad de Mexico, Mexico
| | - Yovani Marrero-Ponce
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920, Ciudad de México, CDMX, México.
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas; and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito, Pichincha, Ecuador.
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2
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Sultan MF, Shaon MSH, Karim T, Ali MM, Hasan MZ, Ahmed K, Bui FM, Chen L, Dhasarathan V, Moni MA. MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool. Heliyon 2024; 10:e37820. [PMID: 39323787 PMCID: PMC11422610 DOI: 10.1016/j.heliyon.2024.e37820] [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: 06/01/2024] [Revised: 08/23/2024] [Accepted: 09/10/2024] [Indexed: 09/27/2024] Open
Abstract
Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.
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Affiliation(s)
- Md. Fahim Sultan
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Md. Shazzad Hossain Shaon
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Tasmin Karim
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Md. Mamun Ali
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Md. Zahid Hasan
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Group of Bio-photomatiχ, Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Francis M. Bui
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
| | - Li Chen
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
| | - Vigneswaran Dhasarathan
- Department of ECE, Centre for IoT and AI (CITI), KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Mohammad Ali Moni
- AI & Digital Health Technology, Artifcial Intelligence & Cyber Future Institute, Charles Stuart University, Bathurst, NSW, 2795, Australia
- AI & Digital Health Technology, Rural Health Research Institute, Charles Stuart University, Orange, NSW 2800, Australia
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3
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Pritam M, Dutta S, Medicherla KM, Kumar R, Singh SP. Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics. J Biomol Struct Dyn 2024; 42:7321-7339. [PMID: 37498146 DOI: 10.1080/07391102.2023.2239932] [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: 06/08/2022] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Manisha Pritam
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Somenath Dutta
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Department of Bioinformatics, Pondicherry Central University, Puducherry, India
| | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, Missouri, USA
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4
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Yue J, Xu J, Li T, Li Y, Chen Z, Liang S, Liu Z, Wang Y. Discovery of potential antidiabetic peptides using deep learning. Comput Biol Med 2024; 180:109013. [PMID: 39137670 DOI: 10.1016/j.compbiomed.2024.109013] [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: 04/10/2024] [Revised: 07/01/2024] [Accepted: 08/08/2024] [Indexed: 08/15/2024]
Abstract
Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.
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Affiliation(s)
- Jianda Yue
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Jiawei Xu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Tingting Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Yaqi Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Zihui Chen
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Songping Liang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Zhonghua Liu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China.
| | - Ying Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China.
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5
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Manoni M, Altomare A, Nonnis S, Ferrario G, Mazzoleni S, Tretola M, Bee G, Tedeschi G, Aldini G, Pinotti L. Preliminary investigation on the impact of salty and sugary former foods on pig liver and plasma profiles using OMICS approaches. Sci Rep 2024; 14:19386. [PMID: 39169123 PMCID: PMC11339069 DOI: 10.1038/s41598-024-70310-z] [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: 02/21/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024] Open
Abstract
Replacing cereals with food leftovers could reduce feed-food competition and keep nutrients and energy in the food chain. Former food products (FFPs) are industrial food leftovers no more intended for human but still suitable as alternative and sustainable feedstuffs for monogastric. In this study, omics approaches were applied to evaluate the impact of dietary FFPs on pig liver proteome and plasma peptidome. Thirty-six Swiss Large White male castrated pigs were randomly assigned to three dietary treatments [control (CTR), 30% CTR replaced with salty FFP (SA), 30% CTR replaced with sugary FFP (SU)] from the start of the growing phase (22.4 ± 1.7 kg) until slaughtering (110 ± 3 kg). The low number of differentially regulated proteins in each comparison matrix (SA/SU vs. CTR) and the lack of metabolic interaction indicated a marginal impact on hepatic lipid metabolism. The plasma peptidomics investigation showed low variability between the peptidome of the three dietary groups and identified three possible bioactive peptides in the SA group associated with anti-hypertension and vascular homeostasis regulation. To conclude, the limited modulation of liver proteome and plasma peptidome by the SA and SU diets strenghtened the idea of reusing FFPs as feed ingredients to make pig production more sustainable.
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Affiliation(s)
- Michele Manoni
- Department of Veterinary Medicine and Animal Science (DIVAS), University of Milan, Via dell'Università 6, 26900, Lodi, Italy.
| | - Alessandra Altomare
- Department of Pharmaceutical Sciences (DISFARM), University of Milan, Via Mangiagalli 25, 20133, Milan, Italy
| | - Simona Nonnis
- Department of Veterinary Medicine and Animal Science (DIVAS), University of Milan, Via dell'Università 6, 26900, Lodi, Italy
- CRC I-WE, Coordinating Research Centre: Innovation for Well-Being and Environment, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Giulio Ferrario
- Department of Pharmaceutical Sciences (DISFARM), University of Milan, Via Mangiagalli 25, 20133, Milan, Italy
| | - Sharon Mazzoleni
- Department of Veterinary Medicine and Animal Science (DIVAS), University of Milan, Via dell'Università 6, 26900, Lodi, Italy
| | - Marco Tretola
- Agroscope, Institute for Livestock Sciences, Rte de la Tioleyre 4, 1725, Posieux, Switzerland
| | - Giuseppe Bee
- Agroscope, Institute for Livestock Sciences, Rte de la Tioleyre 4, 1725, Posieux, Switzerland
| | - Gabriella Tedeschi
- Department of Veterinary Medicine and Animal Science (DIVAS), University of Milan, Via dell'Università 6, 26900, Lodi, Italy
- CRC I-WE, Coordinating Research Centre: Innovation for Well-Being and Environment, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Giancarlo Aldini
- Department of Pharmaceutical Sciences (DISFARM), University of Milan, Via Mangiagalli 25, 20133, Milan, Italy
| | - Luciano Pinotti
- Department of Veterinary Medicine and Animal Science (DIVAS), University of Milan, Via dell'Università 6, 26900, Lodi, Italy
- CRC I-WE, Coordinating Research Centre: Innovation for Well-Being and Environment, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
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6
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Zhang F, Li J, Wen Z, Fang C. FusPB-ESM2: Fusion model of ProtBERT and ESM-2 for cell-penetrating peptide prediction. Comput Biol Chem 2024; 111:108098. [PMID: 38820799 DOI: 10.1016/j.compbiolchem.2024.108098] [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: 02/02/2024] [Revised: 04/18/2024] [Accepted: 05/16/2024] [Indexed: 06/02/2024]
Abstract
Cell-penetrating peptides have attracted much attention for their ability to break through cell membrane barriers, which can improve drug bioavailability, reduce side effects, and promote the development of gene therapy. Traditional wet-lab prediction methods are time-consuming and costly, and computational methods provide a short-time and low-cost alternative. Still, the accuracy and reliability need to be further improved. To solve this problem, this study proposes a feature fusion-based prediction model, where the protein pre-trained language models ProtBERT and ESM-2 are used as feature extractors, and the extracted features from both are fused to obtain a more comprehensive and effective feature representation, which is then predicted by linear mapping. Validated by many experiments on public datasets, the method has an AUC value as high as 0.983 and shows high accuracy and reliability in cell-penetrating peptide prediction.
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Affiliation(s)
- Fan Zhang
- Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Jinfeng Li
- Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhenguo Wen
- Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Chun Fang
- Beijing Institute of Petrochemical Technology, Beijing 102617, China.
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7
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Arshad F, Ahmed S, Amjad A, Kabir M. An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides. Anal Biochem 2024; 691:115546. [PMID: 38670418 DOI: 10.1016/j.ab.2024.115546] [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: 01/13/2024] [Revised: 04/20/2024] [Accepted: 04/24/2024] [Indexed: 04/28/2024]
Abstract
Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.
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Affiliation(s)
- Farwa Arshad
- School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan.
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan.
| | - Aqsa Amjad
- School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan.
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan.
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8
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Zhong G, Liu H, Deng L. Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification. Interdiscip Sci 2024:10.1007/s12539-024-00640-z. [PMID: 38972032 DOI: 10.1007/s12539-024-00640-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 07/08/2024]
Abstract
The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .
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Affiliation(s)
- Guolun Zhong
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Hui Liu
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China.
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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9
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Patnaik SK, Ayyamperumal S, Jade D, Palathoti N, Akey KS, Jupudi S, Harrison MA, Ponnambalam S, Mj N, Mjn C. Virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential inhibitors for cancer chemotherapy. J Biomol Struct Dyn 2024; 42:5551-5574. [PMID: 37387589 DOI: 10.1080/07391102.2023.2226744] [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: 10/13/2022] [Accepted: 06/13/2023] [Indexed: 07/01/2023]
Abstract
Human epidermal growth factor receptors (EGFR), namely ErbB1/HER1, ErbB2/HER2/neu, ErbB3/HER3, and ErbB4/HER4, the trans-membrane family of tyrosine kinase receptors, are overexpressed in many types of cancers. These receptors play an important role in cell proliferation, differentiation, invasion, metastasis and angiogenesis including unregulated activation of cancer cells. Overexpression of ErbB1 and ErbB2 that occurs in several types of cancers is associated with poor prognosis leading to resistance to ErbB1-directed therapies. In this connection, promising strategy to overcome the disadvantages of the existing chemotherapeutic drugs is the use of short peptides as anticancer agents. In the present study, we have performed virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential dual inhibitors and identified five inhibitors based on their binding affinities, ADMET analysis, MD simulation studies and calculation of free energy of binding. These natural peptides could be further exploited for developing drugs for treating cancer.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sunil Kumar Patnaik
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Selvaraj Ayyamperumal
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Dhananjay Jade
- School of Biomedical Sciences, University of Leeds, Leeds, UK
| | - Nagarjuna Palathoti
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Krishna Swaroop Akey
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Srikanth Jupudi
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | | | | | - Nanjan Mj
- JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
| | - Chandrasekar Mjn
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamil Nadu, India
- School of Life Sciences, JSS Academy of Higher Education & Research(Ooty Campus), Ooty, Tamil Nadu, India
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10
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Lanzoni D, Grassi Scalvini F, Petrosillo E, Nonnis S, Tedeschi G, Savoini G, Buccioni A, Invernizzi G, Baldi A, Giromini C. Antioxidant capacity and peptidomic analysis of in vitro digested Camelina sativa L. Crantz and Cynara cardunculus co-products. Sci Rep 2024; 14:14456. [PMID: 38914602 PMCID: PMC11196266 DOI: 10.1038/s41598-024-64989-3] [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: 03/20/2024] [Accepted: 06/14/2024] [Indexed: 06/26/2024] Open
Abstract
In recent decades, the food system has been faced with the significant problem of increasing food waste. Therefore, the feed industry, supported by scientific research, is attempting to valorise the use of discarded biomass as co-products for the livestock sector, in line with EU objectives. In parallel, the search for functional products that can ensure animal health and performances is a common fundamental goal for both animal husbandry and feeding. In this context, camelina cake (CAMC), cardoon cake (CC) and cardoon meal (CM), due valuable nutritional profile, represent prospective alternatives. Therefore, the aim of this work was to investigate the antioxidant activity of CAMC, CC and CM following in vitro digestion using 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), Ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAC) assays. Total phenolic content (TPC) and angiotensin converting enzyme (ACE) inhibitory activity, actively involved in modulating antioxidant properties, were also studied. Further, a peptidomic analysis was adopted to substantiate the presence of bioactive peptides after in vitro digestion. The results obtained confirmed an interesting nutritional profile of CAMC, CC and CM and relevant antioxidant and ACE inhibitory activities. In particular, considering antioxidant profile, CM and CC revealed a significantly higher (10969.80 ± 18.93 mg TE/100 g and 10451.40 ± 149.17 mg TE/100 g, respectively; p < 0.05) ABTS value than CAMC (9511.18 ± 315.29 mg TE/100 g); a trend also confirmed with the FRAP assay (306.74 ± 5.68 mg FeSO4/100 g; 272.84 ± 11.02 mg FeSO4/100 g; 103.84 ± 3.27 mg FeSO4/100 g, for CC, CM and CAMC, respectively). Similar results were obtained for TPC, demonstrating the involvement of phenols in modulating antioxidant activity. Finally, CAMC was found to have a higher ACE inhibitory activity (40.34 ± 10.11%) than the other matrices. Furthermore, potentially bioactive peptides associated with ACE inhibitory, anti-hypertensive, anti-cancer, antimicrobial, antiviral, antithrombotic, DPP-IV inhibitory and PEP-inhibitory activities were identified in CAMC. This profile was broader than that of CC and CM. The presence of such peptides corroborates the antioxidant and ACE profile of the sample. Although the data obtained report the important antioxidant profile of CAMC, CC, and CM and support their possible use, future investigations, particularly in vivo trials will be critical to evaluate and further investigate their effects on the health and performance of farm animals.
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Affiliation(s)
- Davide Lanzoni
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy.
| | - Francesca Grassi Scalvini
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
| | - Elena Petrosillo
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
| | - Simona Nonnis
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
- CRC, Innovation for Well-Being and Environment, Università degli Studi di Milano, 20122, Milano, Italy
| | - Gabriella Tedeschi
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
- CRC, Innovation for Well-Being and Environment, Università degli Studi di Milano, 20122, Milano, Italy
| | - Giovanni Savoini
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
| | - Arianna Buccioni
- Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, University of Florence, Piazzale delle Cascine 18, 50144, Firenze, Italy
- Centro Interdipartimentale di Ricerca e Valorizzazione Degli Alimenti, University of Florence, viale Pieraccini 6, 50139, Firenze, Italy
| | - Guido Invernizzi
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
| | - Antonella Baldi
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
| | - Carlotta Giromini
- Department of Veterinary Medicine and Animal Sciences (DIVAS), Università degli Studi di Milano, Via Dell'Università 6, 29600, Lodi, Italy
- Institute for Food, Nutrition and Health, University of Reading, Reading, RG6 5EU, UK
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11
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Kao HJ, Weng TH, Chen CH, Chen YC, Chi YH, Huang KY, Weng SL. Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells. Int J Mol Sci 2024; 25:6848. [PMID: 38999958 PMCID: PMC11240926 DOI: 10.3390/ijms25136848] [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: 05/15/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024] Open
Abstract
Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.
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Affiliation(s)
- Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
| | - Tzu-Han Weng
- Department of Dermatology, MacKay Memorial Hospital, Taipei City 104, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
| | - Yu-Chi Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
| | - Yu-Hsiang Chi
- National Center for High-Performance Computing, Hsinchu City 300, Taiwan
| | - Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Medical Research, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
- Institute of Biomedical Sciences, MacKay Medical College, New Taipei City 252, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City 300, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu Municipal MacKay Children's Hospital, Hsinchu City 300, Taiwan
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12
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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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13
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Chen Z, Wang R, Guo J, Wang X. The role and future prospects of artificial intelligence algorithms in peptide drug development. Biomed Pharmacother 2024; 175:116709. [PMID: 38713945 DOI: 10.1016/j.biopha.2024.116709] [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: 03/10/2024] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/09/2024] Open
Abstract
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.
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Affiliation(s)
- Zhiheng Chen
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Ruoxi Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Junqi Guo
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Xiaogang Wang
- Guangdong Provincial Key Laboratory of Bone and Joint Degenerative Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China.
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14
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Cordoves-Delgado G, García-Jacas CR. Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning. J Chem Inf Model 2024; 64:4310-4321. [PMID: 38739853 DOI: 10.1021/acs.jcim.3c02061] [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: 05/16/2024]
Abstract
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.
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Affiliation(s)
- Greneter Cordoves-Delgado
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - César R García-Jacas
- Cátedras CONAHCYT - Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
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15
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Sun X, Liu Y, Ma T, Zhu N, Lao X, Zheng H. DCTPep, the data of cancer therapy peptides. Sci Data 2024; 11:541. [PMID: 38796630 PMCID: PMC11128002 DOI: 10.1038/s41597-024-03388-9] [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: 07/19/2023] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.
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Affiliation(s)
- Xin Sun
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Yanchao Liu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Tianyue Ma
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Ning Zhu
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China
| | - Xingzhen Lao
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, P. R. China.
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16
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Goles M, Daza A, Cabas-Mora G, Sarmiento-Varón L, Sepúlveda-Yañez J, Anvari-Kazemabad H, Davari MD, Uribe-Paredes R, Olivera-Nappa Á, Navarrete MA, Medina-Ortiz D. Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides. Brief Bioinform 2024; 25:bbae275. [PMID: 38856172 PMCID: PMC11163380 DOI: 10.1093/bib/bbae275] [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] [Revised: 04/23/2024] [Accepted: 06/04/2024] [Indexed: 06/11/2024] Open
Abstract
With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.
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Affiliation(s)
- Montserrat Goles
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Anamaría Daza
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Gabriel Cabas-Mora
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Lindybeth Sarmiento-Varón
- Centro Asistencial de Docencia e Investigación, CADI, Universidad de Magallanes, Av. Los Flamencos 01364, 6210005, Punta Arenas, Chile
| | - Julieta Sepúlveda-Yañez
- Facultad de Ciencias de la Salud, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Hoda Anvari-Kazemabad
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Mehdi D Davari
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120, Halle, Germany
| | - Roberto Uribe-Paredes
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
| | - Marcelo A Navarrete
- Centro Asistencial de Docencia e Investigación, CADI, Universidad de Magallanes, Av. Los Flamencos 01364, 6210005, Punta Arenas, Chile
- Escuela de Medicina, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
| | - David Medina-Ortiz
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Av. Pdte. Manuel Bulnes 01855, 6210427, Punta Arenas, Chile
- Centre for Biotechnology and Bioengineering, CeBiB, Universidad de Chile, Beauchef 851, 8370456, Santiago, Chile
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17
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Chen N, Yu J, Zhe L, Wang F, Li X, Wong KC. TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation. Brief Bioinform 2024; 25:bbae308. [PMID: 38920345 PMCID: PMC11200197 DOI: 10.1093/bib/bbae308] [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: 04/08/2024] [Revised: 05/28/2024] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.
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Affiliation(s)
- Nanjun Chen
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Liu Zhe
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Chang Chun, Ji Lin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, Guang Dong, China
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18
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Kao HJ, Weng TH, Chen CH, Chen YC, Huang KY, Weng SL. iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides. Proteomics 2024; 24:e2300257. [PMID: 38263811 DOI: 10.1002/pmic.202300257] [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: 06/21/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
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Affiliation(s)
- Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Tzu-Hsiang Weng
- Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei City, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Yu-Chi Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
| | - Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Institute of Biomedical Sciences, MacKay Medical College, New Taipei City, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, Taipei City, Taiwan
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19
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Gu ZF, Hao YD, Wang TY, Cai PL, Zhang Y, Deng KJ, Lin H, Lv H. Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur. BMC Biol 2024; 22:86. [PMID: 38637801 PMCID: PMC11027412 DOI: 10.1186/s12915-024-01883-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 04/05/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.
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Affiliation(s)
- Zhi-Feng Gu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu-Duo Hao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Tian-Yu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Pei-Ling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, 610106, PR China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, PR China
| | - Ke-Jun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Hao Lin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Hao Lv
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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20
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Fukunaga I, Matsukiyo Y, Kaitoh K, Yamanishi Y. Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization. Mol Inform 2024; 43:e202300148. [PMID: 38182544 DOI: 10.1002/minf.202300148] [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: 06/20/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 01/07/2024]
Abstract
Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.
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Affiliation(s)
- Itsuki Fukunaga
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yuki Matsukiyo
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Kazuma Kaitoh
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
- Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, 464-8601, Japan
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21
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Kanwal S, Arif R, Ahmed S, Kabir M. A novel stacking-based predictor for accurate prediction of antimicrobial peptides. J Biomol Struct Dyn 2024:1-12. [PMID: 38500243 DOI: 10.1080/07391102.2024.2329298] [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: 09/21/2023] [Accepted: 03/06/2024] [Indexed: 03/20/2024]
Abstract
Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sameera Kanwal
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Roha Arif
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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22
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Wu X, Lin H, Bai R, Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur J Med Chem 2024; 268:116262. [PMID: 38387334 DOI: 10.1016/j.ejmech.2024.116262] [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: 01/04/2024] [Revised: 02/06/2024] [Accepted: 02/17/2024] [Indexed: 02/24/2024]
Abstract
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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Affiliation(s)
- Xinyi Wu
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Huitian Lin
- College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, 310014, PR China
| | - Renren Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, PR China.
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, PR China.
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23
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Bian J, Liu X, Dong G, Hou C, Huang S, Zhang D. ACP-ML: A sequence-based method for anticancer peptide prediction. Comput Biol Med 2024; 170:108063. [PMID: 38301519 DOI: 10.1016/j.compbiomed.2024.108063] [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: 11/14/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Cancer is a serious malignant tumor and is difficult to cure. Chemotherapy, as a primary treatment for cancer, causes significant harm to normal cells in the body and is often accompanied by serious side effects. Recently, anti-cancer peptides (ACPs) as a type of protein for treating cancers dominated research into the development of new anti-tumor drugs because of their ability to specifically target and destroy cancer cells. The screening of proteins with cancer-inhibiting properties from a large pool of proteins is key to the development of anti-tumor drugs. However, it is expensive and inefficient to accurately identify protein functions only through biological experiments due to their complex structure. Therefore, we propose a new prediction model ACP-ML to effectively predict ACPs. In terms of feature extraction, DPC, PseAAC, CTDC, CTDT and CS-Pse-PSSM features were used and the most optimal feature set was selected by comparing combinations of these features. Then, a two-step feature selection process using MRMD and RFE algorithms was performed to determine the most crucial features from the most optimal feature set for identifying ACPs. Furthermore, we assessed the classification accuracy of single learning models and different strategies-based ensemble models through ten-fold cross-validation. Ultimately, a voting-based ensemble learning method is developed to predict ACPs. To validate its effectiveness, two independent test sets were used to perform tests, achieving accuracy of 90.891 % and 92.578 % respectively. Compared with existing anticancer peptide prediction algorithms, the proposed feature processing method is more effective, and the proposed ensemble model ACP-ML exhibits stronger generalization capability and higher accuracy.
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Affiliation(s)
- Jilong Bian
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China.
| | - Xuan Liu
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Guanghui Dong
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Chang Hou
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China.
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
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24
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Iwaniak A, Minkiewicz P, Darewicz M. Bioinformatics and bioactive peptides from foods: Do they work together? ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 108:35-111. [PMID: 38461003 DOI: 10.1016/bs.afnr.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2024]
Abstract
We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.
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Affiliation(s)
- Anna Iwaniak
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland.
| | - Piotr Minkiewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
| | - Małgorzata Darewicz
- Chair of Food Biochemistry, Faculty of Food Science, University of Warmia and Mazury in Olsztyn, Olsztyn-Kortowo, Poland
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25
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Yu H, Wang R, Qiao J, Wei L. Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design. J Chem Inf Model 2024; 64:316-326. [PMID: 38135439 DOI: 10.1021/acs.jcim.3c01881] [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: 12/24/2023]
Abstract
Antimicrobial peptides are peptides that are effective against bacteria and viruses, and the discovery of new antimicrobial peptides is of great importance to human life and health. Although the design of antimicrobial peptides using machine learning methods has achieved good results in recent years, it remains a challenge to learn and design novel antimicrobial peptides with multiple properties of interest from peptide data with certain property labels. To this end, we propose Multi-CGAN, a deep generative model-based architecture that can learn from single-attribute peptide data and generate antimicrobial peptide sequences with multiple attributes that we need, which may have a potentially wide range of uses in drug discovery. In particular, we verified that our Multi-CGAN generated peptides with the desired properties have good performance in terms of generation rate. Moreover, a comprehensive statistical analysis demonstrated that our generated peptides are diverse and have a low probability of being homologous to the training data. Interestingly, we found that the performance of many popular deep learning methods on the antimicrobial peptide prediction task can be improved by using Multi-CGAN to expand the data on the training set of the original task, indicating the high quality of our generated peptides and the robust ability of our method. In addition, we also investigated whether it is possible to directionally generate peptide sequences with specified properties by controlling the input noise sampling for our model.
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Affiliation(s)
- Haoqing Yu
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Jianbo Qiao
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
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26
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Xing W, Zhang J, Li C, Huo Y, Dong G. iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. Brief Bioinform 2023; 25:bbad443. [PMID: 38055840 PMCID: PMC10699745 DOI: 10.1093/bib/bbad443] [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: 09/08/2023] [Revised: 10/31/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role in maintaining the health of organisms and fortifying defenses against diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation of human resources and material inputs. Alternatively, computing approaches can assist researchers effectively and promptly predict AMPs. In this study, we present a novel AMP predictor called iAMP-Attenpred. As far as we know, this is the first work that not only employs the popular BERT model in the field of natural language processing (NLP) for AMPs feature encoding, but also utilizes the idea of combining multiple models to discover AMPs. Firstly, we treat each amino acid from preprocessed AMPs and non-AMP sequences as a word, and then input it into BERT pre-training model for feature extraction. Moreover, the features obtained from BERT method are fed to a composite model composed of one-dimensional CNN, BiLSTM and attention mechanism for better discriminating features. Finally, a flatten layer and various fully connected layers are utilized for the final classification of AMPs. Experimental results reveal that, compared with the existing predictors, our iAMP-Attenpred predictor achieves better performance indicators, such as accuracy, precision and so on. This further demonstrates that using the BERT approach to capture effective feature information of peptide sequences and combining multiple deep learning models are effective and meaningful for predicting AMPs.
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Affiliation(s)
- Wenxuan Xing
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Jie Zhang
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Chen Li
- School of Computer Science and Engineering, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang 110170, China
| | - Yujia Huo
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
| | - Gaifang Dong
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, No.29 Erdos East Street, Saihan District, Hohhot 010011, China
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27
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Lv H, Yan K, Liu B. TPpred-LE: therapeutic peptide function prediction based on label embedding. BMC Biol 2023; 21:238. [PMID: 37904157 PMCID: PMC10617231 DOI: 10.1186/s12915-023-01740-w] [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: 02/17/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data. RESULTS In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data. CONCLUSIONS In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.
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Affiliation(s)
- Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Haidian District, Beijing, 100081, China.
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28
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Yang Y, Wu H, Gao Y, Tong W, Li K. MFPPDB: a comprehensive multi-functional plant peptide database. FRONTIERS IN PLANT SCIENCE 2023; 14:1224394. [PMID: 37908832 PMCID: PMC10613858 DOI: 10.3389/fpls.2023.1224394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/29/2023] [Indexed: 11/02/2023]
Abstract
Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.
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Affiliation(s)
- Yaozu Yang
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, Anhui, China
| | - Hongwei Wu
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Yu Gao
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Wei Tong
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, Anhui, China
| | - Ke Li
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, China
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29
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Ma C, Wolfinger R. A prediction model for blood-brain barrier penetrating peptides based on masked peptide transformers with dynamic routing. Brief Bioinform 2023; 24:bbad399. [PMID: 37985456 DOI: 10.1093/bib/bbad399] [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: 07/20/2023] [Revised: 09/26/2023] [Accepted: 10/17/2023] [Indexed: 11/22/2023] Open
Abstract
Blood-brain barrier penetrating peptides (BBBPs) are short peptide sequences that possess the ability to traverse the selective blood-brain interface, making them valuable drug candidates or carriers for various payloads. However, the in vivo or in vitro validation of BBBPs is resource-intensive and time-consuming, driving the need for accurate in silico prediction methods. Unfortunately, the scarcity of experimentally validated BBBPs hinders the efficacy of current machine-learning approaches in generating reliable predictions. In this paper, we present DeepB3P3, a novel framework for BBBPs prediction. Our contribution encompasses four key aspects. Firstly, we propose a novel deep learning model consisting of a transformer encoder layer, a convolutional network backbone, and a capsule network classification head. This integrated architecture effectively learns representative features from peptide sequences. Secondly, we introduce masked peptides as a powerful data augmentation technique to compensate for small training set sizes in BBBP prediction. Thirdly, we develop a novel threshold-tuning method to handle imbalanced data by approximating the optimal decision threshold using the training set. Lastly, DeepB3P3 provides an accurate estimation of the uncertainty level associated with each prediction. Through extensive experiments, we demonstrate that DeepB3P3 achieves state-of-the-art accuracy of up to 98.31% on a benchmarking dataset, solidifying its potential as a promising computational tool for the prediction and discovery of BBBPs.
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Affiliation(s)
- Chunwei Ma
- JMP Statistical Discovery, LLC, Cary, 27513, NC, USA
- Department of Computer Science and Engineering, University at Buffalo, Buffalo, 14260, NY, USA
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30
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Kaur D, Arora A, Patiyal S, Raghava GPS. Hmrbase2: a comprehensive database of hormones and their receptors. Hormones (Athens) 2023; 22:359-366. [PMID: 37291365 DOI: 10.1007/s42000-023-00455-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/19/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE Hormones play a critical role in regulating various physiological processes and any hormonal imbalances can lead to major endocrine disorders. Thus, studying hormones is essential for both the therapeutics and the diagnostics of hormonal diseases. To facilitate this need, we have developed Hmrbase2, a comprehensive platform that provides extensive information on hormones. METHODS Hmrbase2 is a web-based database which is an update of a previously published database, Hmrbase ( http://crdd.osdd.net/raghava/hmrbase/ ). We collected a large amount of information on peptide and non-peptide hormones and hormone receptors, this information being sourced from Hmrbase, HMDB, UniProt, HORDB, ENDONET, PubChem, and the medical literature. RESULTS Hmrbase2 contains a total of 12,056 entries, which is more than twice the number of entries contained in the previous version Hmrbase. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones, and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties such as melting point and water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been supplied. Additionally, a similarity search module has been incorporated enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. CONCLUSIONS To make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Hmrbase2 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2 .
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Affiliation(s)
- Dashleen Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Akanksha Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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31
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Singh V, Singh SK. A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines. Sci Rep 2023; 13:13722. [PMID: 37608092 PMCID: PMC10444765 DOI: 10.1038/s41598-023-40922-y] [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: 04/09/2023] [Accepted: 08/18/2023] [Indexed: 08/24/2023] Open
Abstract
An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app , is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student's t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, 221005, India.
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Ngo NK, Hy TS, Kondor R. Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures. J Chem Phys 2023; 159:034109. [PMID: 37466225 DOI: 10.1063/5.0152833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/29/2023] [Indexed: 07/20/2023] Open
Abstract
Contemporary graph learning algorithms are not well-suited for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determining the molecular properties of macromolecules. In this work, we propose Multiresolution Graph Transformers (MGT), the first graph transformer architecture that can learn to represent large molecules at multiple scales. MGT can learn to produce representations for the atoms and group them into meaningful functional groups or repeating units. We also introduce Wavelet Positional Encoding (WavePE), a new positional encoding method that can guarantee localization in both spectral and spatial domains. Our proposed model achieves competitive results on three macromolecule datasets consisting of polymers, peptides, and protein-ligand complexes, along with one drug-like molecule dataset. Significantly, our model outperforms other state-of-the-art methods and achieves chemical accuracy in estimating molecular properties (e.g., highest occupied molecular orbital, lowest unoccupied molecular orbital, and their gap) calculated by Density Functional Theory in the polymers dataset. Furthermore, the visualizations, including clustering results on macromolecules and low-dimensional spaces of their representations, demonstrate the capability of our methodology in learning to represent long-range and hierarchical structures. Our PyTorch implementation is publicly available at https://github.com/HySonLab/Multires-Graph-Transformer.
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Affiliation(s)
| | - Truong Son Hy
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California 92093, USA
| | - Risi Kondor
- Department of Computer Science, University of Chicago, Chicago, Illinois 60637, USA
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Fernandes FC, Cardoso MH, Gil-Ley A, Luchi LV, da Silva MGL, Macedo MLR, de la Fuente-Nunez C, Franco OL. Geometric deep learning as a potential tool for antimicrobial peptide prediction. FRONTIERS IN BIOINFORMATICS 2023; 3:1216362. [PMID: 37521317 PMCID: PMC10374423 DOI: 10.3389/fbinf.2023.1216362] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/13/2023] [Indexed: 08/01/2023] Open
Abstract
Antimicrobial peptides (AMPs) are components of natural immunity against invading pathogens. They are polymers that fold into a variety of three-dimensional structures, enabling their function, with an underlying sequence that is best represented in a non-flat space. The structural data of AMPs exhibits non-Euclidean characteristics, which means that certain properties, e.g., differential manifolds, common system of coordinates, vector space structure, or translation-equivariance, along with basic operations like convolution, in non-Euclidean space are not distinctly established. Geometric deep learning (GDL) refers to a category of machine learning methods that utilize deep neural models to process and analyze data in non-Euclidean settings, such as graphs and manifolds. This emerging field seeks to expand the use of structured models to these domains. This review provides a detailed summary of the latest developments in designing and predicting AMPs utilizing GDL techniques and also discusses both current research gaps and future directions in the field.
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Affiliation(s)
- Fabiano C. Fernandes
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- Departamento de Ciência da Computação, Instituto Federal de Brasília, Brasília, Brazil
| | - Marlon H. Cardoso
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Campo Grande, Mato Grosso do Sul, Brazil
| | - Abel Gil-Ley
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Lívia V. Luchi
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Maria G. L. da Silva
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
| | - Maria L. R. Macedo
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Universidade Federal de Mato Grosso do Sul, Cidade Universitária, Campo Grande, Mato Grosso do Sul, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Perelman School of Medicine, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, United States
| | - Octavio L. Franco
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
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Yadav S, Singh P. Advancement and application of novel cell-penetrating peptide in cancer management. 3 Biotech 2023; 13:234. [PMID: 37323859 PMCID: PMC10264343 DOI: 10.1007/s13205-023-03649-1] [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: 09/20/2022] [Accepted: 05/26/2023] [Indexed: 06/17/2023] Open
Abstract
Cell-penetrating peptides (CPPs) are small amino acid sequences with the potential to enter cell membranes. Along with nucleic acids, large proteins, and other chemical compounds, they can deliver several bioactive cargos inside cells. Numerous CPPs have been extracted from natural or synthetic materials since the discovery of the first CPP. In the past few decades, a significant variety of studies have shown the potential of CPPs to cure different diseases. The low toxicity in peptide compared to other drug delivery carriers is a significant benefit of CPP-based therapy, in addition to the high efficacy brought about by swift and effective delivery. A significant tendency for intracellular DNA delivery may also be observed when nanoparticles and the cell penetration peptide are combined. CPPs are frequently used to increase intracellular absorption of nucleic acid, and other therapeutic agents inside the cell. Due to long-term side effects and possible toxicity, its implementation is restricted. The use of cell-permeating peptides is a commonly used technique to increase their intracellular absorption. Additionally, CPPs have lately been sought for application in vivo, following their success in cellular studies. This review will go through the numerous CPPs, the chemical modifications that improve their cellular uptake, the various means for getting them across cell membranes, and the biological activity they acquire after being conjugate with specific chemicals.
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Affiliation(s)
- Shikha Yadav
- Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Plot No. 2, Sector 17-A, Yamuna Expressway, Gautam Budh Nagar, Greater Noida, Uttar Pradesh 201310 India
| | - Pratichi Singh
- Department of Biosciences, School of Basic and Applied Sciences, Galgotias University, Greater Noida, Uttar Pradesh India
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Bhandu P, Verma H, Raju B, Narendra G, Choudhary S, Singh M, Singh PK, Silakari O. Identification of natural peptides from "PlantPepDB" database as anti-SARS-CoV-2 agents: A protein-protein docking approach. PHYTOMEDICINE PLUS : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2023; 3:100446. [PMID: 37033295 PMCID: PMC10065049 DOI: 10.1016/j.phyplu.2023.100446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
BACKGROUND A global pandemic owing to COVID-19 infection has created havoc in the entire world. The etiological agent responsible for this viral outbreak is classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Still, there's no specific drug or preventive medication to treat SARS-CoV-2. This study was designed to demonstrate the efficacy of some anti-viral peptides obtained from a plant database i.e., PlantPepDB as potential ACE-2-Spike (S) protein complex neutralizers using a structure-based drug designing approach. METHOD A total of 83 anti-viral plant peptides were screened from a peptide database i.e. PlantPepDB based on their reported anti-viral activities against various viral strains. In order to screen peptides that may potentially interfere with ACE-2 and S complex formation, molecular docking studies were conducted using the flare module of Cresset software and subsequently, analysed the crucial interactions between the peptides and S complexes and ACE-2/S complex. Herein, the interactions and docking scores obtained for ACE-2/S complex were considered as references. The S-peptides complexes which displayed superior interactions and docking scores than reference complex i.e., ACE2-S were considered as final hits. The Molecular dynamics studies were conducted for a period of 30 ns for each of the final hit/S complex to understand the interaction stability and binding mechanism of designed peptides. RESULTS The molecular docking results revealed that five peptides including Cycloviolacin Y3, Cycloviolacin Y1, White cloud bean defensin, Putative defensin 3.1, and Defensin D1 showed superior docking scores (i.e. -1372.5 kJ/mol to -1232.6 kJ/mol) when docked at the ACE2 binding site of S-protein than score obtained for the complex of ACE-2 and S protein i.e. -1183.4 kJ/mol. Moreover, these top five peptides manifested key interactions required to prevent the binding of S protein with ACE2. The molecular dynamics simulation study revealed that two of these five peptides i.e. Cycloviolacin Y3 and Cycloviolacin Y1 displayed minimal RMSD fluctuations. CONCLUSIONS The current structure-based drug-designing approach shows the possible role of anti-viral plant peptides as potential molecules to be explored at the initial stage of viral pathogenesis.
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Affiliation(s)
- Priyanka Bhandu
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Himanshu Verma
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Baddipadige Raju
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Gera Narendra
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Shalki Choudhary
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Manmeet Singh
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
| | - Pankaj Kumar Singh
- Faculty of Medicine, Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, FI-20014, Finland
| | - Om Silakari
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Punjab, 147002, India
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Agüero-Chapin G, Antunes A, Mora JR, Pérez N, Contreras-Torres E, Valdes-Martini JR, Martinez-Rios F, Zambrano CH, Marrero-Ponce Y. Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery. Antibiotics (Basel) 2023; 12:antibiotics12040747. [PMID: 37107109 PMCID: PMC10135022 DOI: 10.3390/antibiotics12040747] [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: 03/10/2023] [Revised: 04/04/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Agostinho Antunes
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Porto, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - José R Mora
- Universidad San Francisco de Quito (USFQ), Colegio de Ciencias e Ingenierías "El Politécnico", Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Noel Pérez
- Universidad San Francisco de Quito (USFQ), Colegio de Ciencias e Ingenierías "El Politécnico", Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Ernesto Contreras-Torres
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | | | - Felix Martinez-Rios
- Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México 03920, Mexico
| | - Cesar H Zambrano
- Universidad San Francisco de Quito (USFQ), Colegio de Ciencias e Ingenierías "El Politécnico", Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada 22860, Baja California, Mexico
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Yan K, Lv H, Guo Y, Peng W, Liu B. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics 2023; 39:btac715. [PMID: 36342186 PMCID: PMC9805557 DOI: 10.1093/bioinformatics/btac715] [Citation(s) in RCA: 53] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/24/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
MOTIVATION Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yichen Guo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Wei Peng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
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Ayala-Ruano S, Marrero-Ponce Y, Aguilera-Mendoza L, Pérez N, Agüero-Chapin G, Antunes A, Aguilar AC. Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides. ACS OMEGA 2022; 7:46012-46036. [PMID: 36570318 PMCID: PMC9773354 DOI: 10.1021/acsomega.2c03398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/21/2022] [Indexed: 05/13/2023]
Abstract
Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.
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Affiliation(s)
- Sebastián Ayala-Ruano
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina,
Colegio de Ciencias de la Salud (COCSA), Universidad San Francisco de Quito, Av. Interoceánica Km 12 1/2 y Av. Florencia, Quito 17-1200-841, Ecuador
- Colegio
de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ), Quito 170901, Ecuador
| | - Yovani Marrero-Ponce
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina,
Colegio de Ciencias de la Salud (COCSA), Universidad San Francisco de Quito, Av. Interoceánica Km 12 1/2 y Av. Florencia, Quito 17-1200-841, Ecuador
- Computer-Aided
Molecular “Biosilico” Discovery and Bioinformatics Research
International Network (CAMD-BIR IN), Cumbayá, Quito 170901, Ecuador
- Universidad
San Francisco de Quito (USFQ), Instituto
de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
- Departamento
de Ciencias de la Computación, Centro
de Investigación Científica y de Educación Superior
de Ensenada (CICESE), Baja California 22860, Mexico
| | - Longendri Aguilera-Mendoza
- Departamento
de Ciencias de la Computación, Centro
de Investigación Científica y de Educación Superior
de Ensenada (CICESE), Baja California 22860, Mexico
| | - Noel Pérez
- Colegio
de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ), Quito 170901, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR/CIMAR,
Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, 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
| | - Agostinho Antunes
- CIIMAR/CIMAR,
Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, 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
| | - Ana Cristina Aguilar
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina,
Colegio de Ciencias de la Salud (COCSA), Universidad San Francisco de Quito, Av. Interoceánica Km 12 1/2 y Av. Florencia, Quito 17-1200-841, Ecuador
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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: 4] [Impact Index Per Article: 2.0] [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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40
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The potential of antifungal peptide Sesquin as natural food preservative. Biochimie 2022; 203:51-64. [PMID: 35395327 DOI: 10.1016/j.biochi.2022.03.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 12/16/2022]
Abstract
Sesquin is a wide spectrum antimicrobial peptide displaying a remarkable activity on fungi. Contrarily to most antimicrobial peptides, it presents an overall negative charge. In the present study, we elucidate the molecular basis of its mode of action towards biomimetic membranes by NMR and MD experiments. While a specific recognition of phosphatidylethanolamine (PE) might explain its activity in a variety of different organisms (including bacteria), a further interaction with ergosterol accounts for its strong antifungal activity. NMR data reveal a charge gradient along its amide protons allowing the peptide to reach the membrane phosphate groups despite its negative charge. Subsequently, the peptide gets structured inside the bilayer, reducing its order. MD simulations predict that its activity is retained in conditions commonly used for food preservation: low temperatures, high pressure, or the presence of electric field pulses, making Sesquin a good candidate as food preservative.
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41
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Molecular dynamics simulations to study the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides. J Mol Graph Model 2022; 117:108282. [PMID: 35961218 DOI: 10.1016/j.jmgm.2022.108282] [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: 02/09/2022] [Revised: 07/14/2022] [Accepted: 07/24/2022] [Indexed: 01/14/2023]
Abstract
The hydrophobic amino acid biphenylalanine (B) plays a key role in the antibacterial activity of ultrashort peptides. In this study, the interactions of tetrapeptide BRBR-NH2 (BRBR) and pentapeptide BRBRB-NH2 (BRBRB) with dioleoylphosphatidylcholine/dioleoylphosphatidylglycerol (DOPC/DOPG) mixed model membrane were studied by molecular dynamics simulation to assess the role of biphenylalanine in promoting the antibacterial activity of ultrashort peptides. At low peptide concentrations, both peptides presented amphiphilic conformations; residues B of the pentapeptide approached the membrane faster than those of the tetrapeptide and made more contacts with the membrane; BRBRB exhibited stronger membrane affinity than BRBR. However, due to the low peptide concentrations, the effects of these two peptides on the membrane were not significantly different. At high peptide concentrations, the strong affinity of BRBRB made it have more interaction with membrane than BRBR and most residues B of BRBRB inserted into the membrane; BRBRB was more prone to aggregation and caused the membrane more disordered and thinner than BRBR. Hydrophobic residues often act as anchors in the antibacterial activity of ultrashort antimicrobial peptides. Adding a hydrophobic residue B to the C-terminal of BRBR could improve the ability of the peptide to "grasp" the membrane. At high peptide concentrations, the addition of residue B might enhance the antibacterial activity of the peptide. Thus, our results will be helpful in designing efficient antibacterial drugs.
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42
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Huang KY, Kao HJ, Weng TH, Chen CH, Weng SL. iDVIP: identification and characterization of viral integrase inhibitory peptides. Brief Bioinform 2022; 23:6754756. [PMID: 36215051 DOI: 10.1093/bib/bbac406] [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: 05/26/2022] [Revised: 08/12/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022] Open
Abstract
Antiretroviral peptides are a kind of bioactive peptides that present inhibitory activity against retroviruses through various mechanisms. Among them, viral integrase inhibitory peptides (VINIPs) are a class of antiretroviral peptides that have the ability to block the action of integrase proteins, which is essential for retroviral replication. As the number of experimentally verified bioactive peptides has increased significantly, the lack of in silico machine learning approaches can effectively predict the peptides with the integrase inhibitory activity. Here, we have developed the first prediction model for identifying the novel VINIPs using the sequence characteristics, and the hybrid feature set was considered to improve the predictive ability. The performance was evaluated by 5-fold cross-validation based on the training dataset, and the result indicates the proposed model is capable of predicting the VINIPs, with a sensitivity of 85.82%, a specificity of 88.81%, an accuracy of 88.37%, a balanced accuracy of 87.32% and a Matthews correlation coefficient value of 0.64. Most importantly, the model also consistently provides effective performance in independent testing. To sum up, we propose the first computational approach for identifying and characterizing the VINIPs, which can be considered novel antiretroviral therapy agents. Ultimately, to facilitate further research and development, iDVIP, an automatic computational tool that predicts the VINIPs has been developed, which is now freely available at http://mer.hc.mmh.org.tw/iDVIP/.
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Affiliation(s)
- Kai-Yao Huang
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan.,Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
| | - Hui-Ju Kao
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan
| | - Tzu-Hsiang Weng
- Department of Obstetrics and Gynecology, MacKay Memorial Hospital, Taipei city 104, Taiwan
| | - Chia-Hung Chen
- Department of Medical Research, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan
| | - Shun-Long Weng
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan.,Department of Obstetrics and Gynecology, Hsinchu MacKay Memorial Hospital, Hsinchu city 300, Taiwan.,MacKay Junior College of Medicine, Nursing and Management, Taipei 112, Taiwan
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43
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Caira S, Picariello G, Renzone G, Arena S, Troise AD, De Pascale S, Ciaravolo V, Pinto G, Addeo F, Scaloni A. Recent developments in peptidomics for the quali-quantitative analysis of food-derived peptides in human body fluids and tissues. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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44
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Thi Phan L, Woo Park H, Pitti T, Madhavan T, Jeon YJ, Manavalan B. MLACP 2.0: An updated machine learning tool for anticancer peptide prediction. Comput Struct Biotechnol J 2022; 20:4473-4480. [PMID: 36051870 PMCID: PMC9421197 DOI: 10.1016/j.csbj.2022.07.043] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 12/24/2022] Open
Abstract
We present a novel meta-approach, MLACP 2.0, and implement it as a user-friendly webserver for the accurate identification of ACPs. MLACP 2.0 employed 11 different encoding schemes and eight different classifiers, including convolutional neural networks, to create a stable meta-model. Benchmarking study has demonstrated that MLACP 2.0 achieves superior performance in ACP prediction compared to publicly available state-of-the-art predictors.
Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.
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45
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Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence. MEMBRANES 2022; 12:membranes12070708. [PMID: 35877911 PMCID: PMC9320227 DOI: 10.3390/membranes12070708] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/16/2022]
Abstract
Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.
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46
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Chen X, Huang J, He B. AntiDMPpred: a web service for identifying anti-diabetic peptides. PeerJ 2022; 10:e13581. [PMID: 35722269 PMCID: PMC9205309 DOI: 10.7717/peerj.13581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/23/2022] [Indexed: 01/17/2023] Open
Abstract
Diabetes mellitus (DM) is a chronic metabolic disease that has been a major threat to human health globally, causing great economic and social adversities. The oral administration of anti-diabetic peptide drugs has become a novel route for diabetes therapy. Numerous bioactive peptides have demonstrated potential anti-diabetic properties and are promising as alternative treatment measures to prevent and manage diabetes. The computational prediction of anti-diabetic peptides can help promote peptide-based drug discovery in the process of searching newly effective therapeutic peptide agents for diabetes treatment. Here, we resorted to random forest to develop a computational model, named AntiDMPpred, for predicting anti-diabetic peptides. A benchmark dataset with 236 anti-diabetic and 236 non-anti-diabetic peptides was first constructed. Four types of sequence-derived descriptors were used to represent the peptide sequences. We then combined four machine learning methods and six feature scoring methods to select the non-redundant features, which were fed into diverse machine learning classifiers to train the models. Experimental results show that AntiDMPpred reached an accuracy of 77.12% and area under the receiver operating curve (AUCROC) of 0.8193 in the nested five-fold cross-validation, yielding a satisfactory performance and surpassing other classifiers implemented in the study. The web service is freely accessible at http://i.uestc.edu.cn/AntiDMPpred/cgi-bin/AntiDMPpred.pl. We hope AntiDMPpred could improve the discovery of anti-diabetic bioactive peptides.
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Affiliation(s)
- Xue Chen
- Medical College, Guizhou University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bifang He
- Medical College, Guizhou University, Guiyang, China
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47
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Li Y, Li X, Liu Y, Yao Y, Huang G. MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides. Pharmaceuticals (Basel) 2022; 15:707. [PMID: 35745625 PMCID: PMC9231127 DOI: 10.3390/ph15060707] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022] Open
Abstract
Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.
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Affiliation(s)
- You Li
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
| | - Xueyong Li
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
| | - Yuewu Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China;
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China;
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
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48
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Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics. Pharmaceutics 2022; 14:pharmaceutics14050997. [PMID: 35631583 PMCID: PMC9147327 DOI: 10.3390/pharmaceutics14050997] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/28/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.
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49
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Lv H, Yan K, Guo Y, Zou Q, Hesham AEL, Liu B. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning. Comput Biol Med 2022; 146:105577. [PMID: 35576825 DOI: 10.1016/j.compbiomed.2022.105577] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/22/2022] [Accepted: 04/28/2022] [Indexed: 11/15/2022]
Abstract
Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have been proposed. However, existing methods have difficulty in accurately predicting AMPs. In this paper, we propose a novel AMP prediction method called AMPpred-EL based on an ensemble learning strategy. AMPred-EL is constructed based on ensemble learning combined with LightGBM and logistic regression. Experimental results demonstrate that AMPpred-EL outperforms several state-of-the-art methods on the benchmark datasets and then improves the efficiency performance.
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Affiliation(s)
- Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yichen Guo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt.
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China; Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China.
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50
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Khater I, Nassar A. SARS-CoV-2 variant surge and vaccine breakthrough infection: A computational analysis. INFORMATICS IN MEDICINE UNLOCKED 2022; 29:100873. [PMID: 35136832 PMCID: PMC8813761 DOI: 10.1016/j.imu.2022.100873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 11/20/2022] Open
Abstract
Coronavirus Delta variant was first detected in India in October of 2020, and it led to a massive second wave of COVID-19 cases in the country. Since then, the highly infectious Delta strain has been spreading globally. The Delta variant and its sub-lineages showed an increased infection rate with a reduced effect of the potential antibody neutralization. The current work is a modeled computational analysis of the mutated receptor-binding domain (RBD) of the SARS-CoV-2 B.1.617 lineage binding with ACE2 and GRP78 to understand the increased strain transmissibility. The cell-surface Glucose Regulated Protein 78 (GRP78) attached to the mutated ACE2-SARS-CoV-2 Spike RBD complex is modeled. The results showed that GRP78 β-substrate-binding domain weakly binds to the wild-type RBD combined with angiotensin-converting enzyme 2 (ACE2) within the SARS-CoV-2 Spike RBD-ACE2 complex. Both GRP78 and ACE2 bind approximately in the same region on the wild-type SARS-CoV-2 Spike RBD surface. On the other hand, GRP78 strongly binds to the mutated SARS-CoV-2 Spike RBD in the RBD-ACE2 complex through the α-substrate-binding domain instead of β-substrate-binding domain in a different region from that of ACE2. The current findings suggest that blocking the main ACE2 pathway may not prevent the interactions between GRP78 and the mutated SARS-CoV-2 Spike RBD, which might introduce an additional avenue into the virus invasion for the host cell if the ACE2 pathway is blocked by the neutralized antibodies. Hence, the peptide satpdb10668 has been proposed as a potential inhibitor of SARS-CoV-2 attachment and virus invasion into the host cell.
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Affiliation(s)
- Ibrahim Khater
- Biophysics Department, Faculty of Science, Cairo University, Giza, Egypt
| | - Aaya Nassar
- Biophysics Department, Faculty of Science, Cairo University, Giza, Egypt
- Department of Clinical Research and Leadership, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
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