DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations
Abstract Background Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2019-07-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2923-1 |
| _version_ | 1818317903265529856 |
|---|---|
| author | Ludovica Montanucci Emidio Capriotti Yotam Frank Nir Ben-Tal Piero Fariselli |
| author_facet | Ludovica Montanucci Emidio Capriotti Yotam Frank Nir Ben-Tal Piero Fariselli |
| author_sort | Ludovica Montanucci |
| collection | DOAJ |
| description | Abstract Background Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ∆∆G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ∆∆G(A → B) = −∆∆G(B → A), where A and B are amino acids. Results Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ∆∆G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ∆∆G values of ~ 0.5 and ~ 0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ∆∆G of a reciprocal variation as almost equal (depending on the sequence profile) to -∆∆G of the direct variation. This is a valuable property that is missing in the majority of the methods. Conclusions Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ∆∆G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods. |
| first_indexed | 2024-12-13T09:44:43Z |
| format | Article |
| id | doaj.art-034989d72abe4bc9a9797911b04d7791 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T09:44:43Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-034989d72abe4bc9a9797911b04d77912022-12-21T23:52:06ZengBMCBMC Bioinformatics1471-21052019-07-0120S1411010.1186/s12859-019-2923-1DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variationsLudovica Montanucci0Emidio Capriotti1Yotam Frank2Nir Ben-Tal3Piero Fariselli4Department of Comparative Biomedicine and Food Science (BCA), University of PadovaBioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of BolognaDepartment of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv UniversityDepartment of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv UniversityDepartment of Comparative Biomedicine and Food Science (BCA), University of PadovaAbstract Background Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ∆∆G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ∆∆G(A → B) = −∆∆G(B → A), where A and B are amino acids. Results Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ∆∆G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ∆∆G values of ~ 0.5 and ~ 0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ∆∆G of a reciprocal variation as almost equal (depending on the sequence profile) to -∆∆G of the direct variation. This is a valuable property that is missing in the majority of the methods. Conclusions Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ∆∆G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods.http://link.springer.com/article/10.1186/s12859-019-2923-1Unfolding free energy changeMultiple site variationProtein stabilityProtein variant |
| spellingShingle | Ludovica Montanucci Emidio Capriotti Yotam Frank Nir Ben-Tal Piero Fariselli DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations BMC Bioinformatics Unfolding free energy change Multiple site variation Protein stability Protein variant |
| title | DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations |
| title_full | DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations |
| title_fullStr | DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations |
| title_full_unstemmed | DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations |
| title_short | DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations |
| title_sort | ddgun an untrained method for the prediction of protein stability changes upon single and multiple point variations |
| topic | Unfolding free energy change Multiple site variation Protein stability Protein variant |
| url | http://link.springer.com/article/10.1186/s12859-019-2923-1 |
| work_keys_str_mv | AT ludovicamontanucci ddgunanuntrainedmethodforthepredictionofproteinstabilitychangesuponsingleandmultiplepointvariations AT emidiocapriotti ddgunanuntrainedmethodforthepredictionofproteinstabilitychangesuponsingleandmultiplepointvariations AT yotamfrank ddgunanuntrainedmethodforthepredictionofproteinstabilitychangesuponsingleandmultiplepointvariations AT nirbental ddgunanuntrainedmethodforthepredictionofproteinstabilitychangesuponsingleandmultiplepointvariations AT pierofariselli ddgunanuntrainedmethodforthepredictionofproteinstabilitychangesuponsingleandmultiplepointvariations |