Machine Learning-Driven Methods for Nanobody Affinity Prediction
Because of their high affinity, specificity, and environmental stability, nanobodies (Nbs) have continuously received attention from the field of biological research. However, it is tough work to obtain high-affinity Nbs using experimental methods. In the current study, 12 machine learning algorithm...
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2024
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| _version_ | 1852025113734545408 |
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| author | Hua Feng (234718) |
| author2 | Xuefeng Sun (408801) Ning Li (45258) Qian Xu (119888) Qin Li (36669) Shenli Zhang (3236712) Guangxu Xing (336317) Gaiping Zhang (106747) Fangyu Wang (4279168) |
| author2_role | author author author author author author author author |
| author_facet | Hua Feng (234718) Xuefeng Sun (408801) Ning Li (45258) Qian Xu (119888) Qin Li (36669) Shenli Zhang (3236712) Guangxu Xing (336317) Gaiping Zhang (106747) Fangyu Wang (4279168) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hua Feng (234718) Xuefeng Sun (408801) Ning Li (45258) Qian Xu (119888) Qin Li (36669) Shenli Zhang (3236712) Guangxu Xing (336317) Gaiping Zhang (106747) Fangyu Wang (4279168) |
| dc.date.none.fl_str_mv | 2024-11-19T17:07:08Z |
| dc.identifier.none.fl_str_mv | 10.1021/acsomega.4c09718.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Machine_Learning-Driven_Methods_for_Nanobody_Affinity_Prediction/27855283 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biochemistry Molecular Biology Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified continuously received attention eight noncovalent interactions true affinitive nbs showed higher sensitivity predicting nonaffinitive nbs mcc values ). good model robustness future nonaffinitive nbs two stacked models key noncovalent interactions nanobody affinity prediction effectively predict whether current study provides four optimized models effectively predict current study optimized models models showed associated interactions model comparison key factors 5 ). tough work potential patterns obtain high nine uncorrelated nb drugs machine learning intended ligands hydrogen bonding high affinity first time f1 score environmental stability driven methods design process could improve correlation coefficient biological research |
| dc.title.none.fl_str_mv | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Because of their high affinity, specificity, and environmental stability, nanobodies (Nbs) have continuously received attention from the field of biological research. However, it is tough work to obtain high-affinity Nbs using experimental methods. In the current study, 12 machine learning algorithms were compared in parallel to explore the potential patterns between Nb–ligand affinity and eight noncovalent interactions. After model comparison and optimization, four optimized models (SVMrB, RotFB, RFB, and C50B) and two stacked models (StackKNN and StackRF) based on nine uncorrelated (correlation coefficient <0.65) optimized models were selected. All the models showed an accuracy of around 0.70 and high specificity. Compared to the other models, RotFB and RFB were not capable of predicting nonaffinitive Nbs with lower precision (<0.44) but showed higher sensitivity at 0.6761 and 0.3521 and good model robustness (F1 score and MCC values). On the contrary, SVMrB, C50B, and StackKNN were able to effectively predict the future nonaffinitive Nbs (specificity >0.92) and reduce the number of true affinitive Nbs (precision >0.5). On the other hand, StackRF showed intermediate model performance. Furthermore, an in-depth feature analysis indicated that hydrogen bonding and aromatic-associated interactions were the key noncovalent interactions in determining Nb–ligand binding affinity. In summary, the current study provides, for the first time, a tool that can effectively predict whether there is an affinity between nanobodies and their intended ligands and explores the key factors that influence their affinity, which could improve the screening and design process of Nbs and accelerate the development of Nb drugs and applications. |
| eu_rights_str_mv | openAccess |
| id | Manara_eaee6caa1cdb2db930a1465451fa72b5 |
| identifier_str_mv | 10.1021/acsomega.4c09718.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27855283 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Machine Learning-Driven Methods for Nanobody Affinity PredictionHua Feng (234718)Xuefeng Sun (408801)Ning Li (45258)Qian Xu (119888)Qin Li (36669)Shenli Zhang (3236712)Guangxu Xing (336317)Gaiping Zhang (106747)Fangyu Wang (4279168)BiophysicsBiochemistryMolecular BiologyEcologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedcontinuously received attentioneight noncovalent interactionstrue affinitive nbsshowed higher sensitivitypredicting nonaffinitive nbsmcc values ).good model robustnessfuture nonaffinitive nbstwo stacked modelskey noncovalent interactionsnanobody affinity predictioneffectively predict whethercurrent study providesfour optimized modelseffectively predictcurrent studyoptimized modelsmodels showedassociated interactionsmodel comparisonkey factors5 ).tough workpotential patternsobtain highnine uncorrelatednb drugsmachine learningintended ligandshydrogen bondinghigh affinityfirst timef1 scoreenvironmental stabilitydriven methodsdesign processcould improvecorrelation coefficientbiological researchBecause of their high affinity, specificity, and environmental stability, nanobodies (Nbs) have continuously received attention from the field of biological research. However, it is tough work to obtain high-affinity Nbs using experimental methods. In the current study, 12 machine learning algorithms were compared in parallel to explore the potential patterns between Nb–ligand affinity and eight noncovalent interactions. After model comparison and optimization, four optimized models (SVMrB, RotFB, RFB, and C50B) and two stacked models (StackKNN and StackRF) based on nine uncorrelated (correlation coefficient <0.65) optimized models were selected. All the models showed an accuracy of around 0.70 and high specificity. Compared to the other models, RotFB and RFB were not capable of predicting nonaffinitive Nbs with lower precision (<0.44) but showed higher sensitivity at 0.6761 and 0.3521 and good model robustness (F1 score and MCC values). On the contrary, SVMrB, C50B, and StackKNN were able to effectively predict the future nonaffinitive Nbs (specificity >0.92) and reduce the number of true affinitive Nbs (precision >0.5). On the other hand, StackRF showed intermediate model performance. Furthermore, an in-depth feature analysis indicated that hydrogen bonding and aromatic-associated interactions were the key noncovalent interactions in determining Nb–ligand binding affinity. In summary, the current study provides, for the first time, a tool that can effectively predict whether there is an affinity between nanobodies and their intended ligands and explores the key factors that influence their affinity, which could improve the screening and design process of Nbs and accelerate the development of Nb drugs and applications.2024-11-19T17:07:08ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acsomega.4c09718.s001https://figshare.com/articles/dataset/Machine_Learning-Driven_Methods_for_Nanobody_Affinity_Prediction/27855283CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/278552832024-11-19T17:07:08Z |
| spellingShingle | Machine Learning-Driven Methods for Nanobody Affinity Prediction Hua Feng (234718) Biophysics Biochemistry Molecular Biology Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified continuously received attention eight noncovalent interactions true affinitive nbs showed higher sensitivity predicting nonaffinitive nbs mcc values ). good model robustness future nonaffinitive nbs two stacked models key noncovalent interactions nanobody affinity prediction effectively predict whether current study provides four optimized models effectively predict current study optimized models models showed associated interactions model comparison key factors 5 ). tough work potential patterns obtain high nine uncorrelated nb drugs machine learning intended ligands hydrogen bonding high affinity first time f1 score environmental stability driven methods design process could improve correlation coefficient biological research |
| status_str | publishedVersion |
| title | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| title_full | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| title_fullStr | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| title_full_unstemmed | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| title_short | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| title_sort | Machine Learning-Driven Methods for Nanobody Affinity Prediction |
| topic | Biophysics Biochemistry Molecular Biology Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified continuously received attention eight noncovalent interactions true affinitive nbs showed higher sensitivity predicting nonaffinitive nbs mcc values ). good model robustness future nonaffinitive nbs two stacked models key noncovalent interactions nanobody affinity prediction effectively predict whether current study provides four optimized models effectively predict current study optimized models models showed associated interactions model comparison key factors 5 ). tough work potential patterns obtain high nine uncorrelated nb drugs machine learning intended ligands hydrogen bonding high affinity first time f1 score environmental stability driven methods design process could improve correlation coefficient biological research |