Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure
Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/13/5/789 |
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| author | Raina Rhoades Brianna Henry Dominique Prichett Yayin Fang Shaolei Teng |
| author_facet | Raina Rhoades Brianna Henry Dominique Prichett Yayin Fang Shaolei Teng |
| author_sort | Raina Rhoades |
| collection | DOAJ |
| description | Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental <i>Bos taurus</i> structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures. |
| first_indexed | 2024-03-10T03:50:12Z |
| format | Article |
| id | doaj.art-512f148e226a4ef7b6846cca630c40e0 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-10T03:50:12Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-512f148e226a4ef7b6846cca630c40e02023-11-23T11:09:30ZengMDPI AGGenes2073-44252022-04-0113578910.3390/genes13050789Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold StructureRaina Rhoades0Brianna Henry1Dominique Prichett2Yayin Fang3Shaolei Teng4Department of Biology, Howard University, Washington, DC 20059, USADepartment of Biology, Howard University, Washington, DC 20059, USADepartment of Biology, Howard University, Washington, DC 20059, USADepartment of Biochemistry and Molecular Biology, Howard University, Washington, DC 20059, USADepartment of Biology, Howard University, Washington, DC 20059, USANeurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental <i>Bos taurus</i> structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.https://www.mdpi.com/2073-4425/13/5/789neurexin-1AlphaFoldmissense mutationprotein stabilitycomputational saturation mutagenesis |
| spellingShingle | Raina Rhoades Brianna Henry Dominique Prichett Yayin Fang Shaolei Teng Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure Genes neurexin-1 AlphaFold missense mutation protein stability computational saturation mutagenesis |
| title | Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure |
| title_full | Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure |
| title_fullStr | Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure |
| title_full_unstemmed | Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure |
| title_short | Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure |
| title_sort | computational saturation mutagenesis to investigate the effects of neurexin 1 mutations on alphafold structure |
| topic | neurexin-1 AlphaFold missense mutation protein stability computational saturation mutagenesis |
| url | https://www.mdpi.com/2073-4425/13/5/789 |
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