Prediction of inhibitory peptides against E.coli with desired MIC value
Abstract In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we developed predictive models for MIC values of antibacterial pe...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-86638-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862279575175168 |
|---|---|
| author | Nisha Bajiya Nishant Kumar Gajendra P. S. Raghava |
| author_facet | Nisha Bajiya Nishant Kumar Gajendra P. S. Raghava |
| author_sort | Nisha Bajiya |
| collection | DOAJ |
| description | Abstract In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we developed predictive models for MIC values of antibacterial peptides against Escherichia coli (E. coli), comprised of 3143 peptides for training and 786 peptides for validation, with experimentally determined MIC values. We found that the Composition Enhanced Transition and Distribution (CeTD) attributes significantly correlate with MIC values. Initially, we attempted to estimate MIC using BLAST similarity searches but found them inadequate. Subsequently, we employed machine learning regression models that integrated various features, including peptide composition, binary profiles and embeddings from large language models. Feature selection techniques, particularly mRMR, were utilized to refine our model inputs. Our Random Forest regressor built using default parameters achieved a correlation coefficient (R) of 0.78, R2 of 0.59, and RMSE of 0.53 on the validation set. Our best model outperformed existing methods when benchmarked on an independent dataset of 498 anti-E. coli peptides. Additionally, we screened anti-E. coli proteins in the proteomes of three probiotic bacterial strains and created a web-based platform, “EIPpred”, enabling users to design peptides with desired MIC values ( https://webs.iiitd.edu.in/raghava/eippred ). |
| format | Article |
| id | doaj-art-13ea838e58144061a09d345ad970868f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-13ea838e58144061a09d345ad970868f2025-02-09T12:33:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-86638-zPrediction of inhibitory peptides against E.coli with desired MIC valueNisha Bajiya0Nishant Kumar1Gajendra P. S. Raghava2Department of Computational Biology, Indraprastha Institute of Information Technology, DelhiDepartment of Computational Biology, Indraprastha Institute of Information Technology, DelhiDepartment of Computational Biology, Indraprastha Institute of Information Technology, DelhiAbstract In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we developed predictive models for MIC values of antibacterial peptides against Escherichia coli (E. coli), comprised of 3143 peptides for training and 786 peptides for validation, with experimentally determined MIC values. We found that the Composition Enhanced Transition and Distribution (CeTD) attributes significantly correlate with MIC values. Initially, we attempted to estimate MIC using BLAST similarity searches but found them inadequate. Subsequently, we employed machine learning regression models that integrated various features, including peptide composition, binary profiles and embeddings from large language models. Feature selection techniques, particularly mRMR, were utilized to refine our model inputs. Our Random Forest regressor built using default parameters achieved a correlation coefficient (R) of 0.78, R2 of 0.59, and RMSE of 0.53 on the validation set. Our best model outperformed existing methods when benchmarked on an independent dataset of 498 anti-E. coli peptides. Additionally, we screened anti-E. coli proteins in the proteomes of three probiotic bacterial strains and created a web-based platform, “EIPpred”, enabling users to design peptides with desired MIC values ( https://webs.iiitd.edu.in/raghava/eippred ).https://doi.org/10.1038/s41598-025-86638-zInhibitory peptidesMinimum Inhibitory concentrationMachine learningEscherichia coliPeptide designRegression models |
| spellingShingle | Nisha Bajiya Nishant Kumar Gajendra P. S. Raghava Prediction of inhibitory peptides against E.coli with desired MIC value Scientific Reports Inhibitory peptides Minimum Inhibitory concentration Machine learning Escherichia coli Peptide design Regression models |
| title | Prediction of inhibitory peptides against E.coli with desired MIC value |
| title_full | Prediction of inhibitory peptides against E.coli with desired MIC value |
| title_fullStr | Prediction of inhibitory peptides against E.coli with desired MIC value |
| title_full_unstemmed | Prediction of inhibitory peptides against E.coli with desired MIC value |
| title_short | Prediction of inhibitory peptides against E.coli with desired MIC value |
| title_sort | prediction of inhibitory peptides against e coli with desired mic value |
| topic | Inhibitory peptides Minimum Inhibitory concentration Machine learning Escherichia coli Peptide design Regression models |
| url | https://doi.org/10.1038/s41598-025-86638-z |
| work_keys_str_mv | AT nishabajiya predictionofinhibitorypeptidesagainstecoliwithdesiredmicvalue AT nishantkumar predictionofinhibitorypeptidesagainstecoliwithdesiredmicvalue AT gajendrapsraghava predictionofinhibitorypeptidesagainstecoliwithdesiredmicvalue |