Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior
<p>Abstract</p> <p>Background</p> <p>Experts in peptide:MHC binding studies are often able to estimate the impact of a single residue substitution based on a heuristic understanding of amino acid similarity in an experimental context. Our aim is to quantify this measure...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-11-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/394 |
| _version_ | 1811275502473707520 |
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| author | Sette Alessandro Pinilla Clemencia Sidney John Kim Yohan Peters Bjoern |
| author_facet | Sette Alessandro Pinilla Clemencia Sidney John Kim Yohan Peters Bjoern |
| author_sort | Sette Alessandro |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Experts in peptide:MHC binding studies are often able to estimate the impact of a single residue substitution based on a heuristic understanding of amino acid similarity in an experimental context. Our aim is to quantify this measure of similarity to improve peptide:MHC binding prediction methods. This should help compensate for holes and bias in the sequence space coverage of existing peptide binding datasets.</p> <p>Results</p> <p>Here, a novel amino acid similarity matrix (PMBEC) is directly derived from the binding affinity data of combinatorial peptide mixtures. Like BLOSUM62, this matrix captures well-known physicochemical properties of amino acid residues. However, PMBEC differs markedly from existing matrices in cases where residue substitution involves a reversal of electrostatic charge. To demonstrate its usefulness, we have developed a new peptide:MHC class I binding prediction method, using the matrix as a Bayesian prior. We show that the new method can compensate for missing information on specific residues in the training data. We also carried out a large-scale benchmark, and its results indicate that prediction performance of the new method is comparable to that of the best neural network based approaches for peptide:MHC class I binding.</p> <p>Conclusion</p> <p>A novel amino acid similarity matrix has been derived for peptide:MHC binding interactions. One prominent feature of the matrix is that it disfavors substitution of residues with opposite charges. Given that the matrix was derived from experimentally determined peptide:MHC binding affinity measurements, this feature is likely shared by all peptide:protein interactions. In addition, we have demonstrated the usefulness of the matrix as a Bayesian prior in an improved scoring-matrix based peptide:MHC class I prediction method. A software implementation of the method is available at: <url>http://www.mhc-pathway.net/smmpmbec</url>.</p> |
| first_indexed | 2024-04-12T23:38:55Z |
| format | Article |
| id | doaj.art-125b4323c61f45dda337138e55fbcb46 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T23:38:55Z |
| publishDate | 2009-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-125b4323c61f45dda337138e55fbcb462022-12-22T03:12:02ZengBMCBMC Bioinformatics1471-21052009-11-0110139410.1186/1471-2105-10-394Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian priorSette AlessandroPinilla ClemenciaSidney JohnKim YohanPeters Bjoern<p>Abstract</p> <p>Background</p> <p>Experts in peptide:MHC binding studies are often able to estimate the impact of a single residue substitution based on a heuristic understanding of amino acid similarity in an experimental context. Our aim is to quantify this measure of similarity to improve peptide:MHC binding prediction methods. This should help compensate for holes and bias in the sequence space coverage of existing peptide binding datasets.</p> <p>Results</p> <p>Here, a novel amino acid similarity matrix (PMBEC) is directly derived from the binding affinity data of combinatorial peptide mixtures. Like BLOSUM62, this matrix captures well-known physicochemical properties of amino acid residues. However, PMBEC differs markedly from existing matrices in cases where residue substitution involves a reversal of electrostatic charge. To demonstrate its usefulness, we have developed a new peptide:MHC class I binding prediction method, using the matrix as a Bayesian prior. We show that the new method can compensate for missing information on specific residues in the training data. We also carried out a large-scale benchmark, and its results indicate that prediction performance of the new method is comparable to that of the best neural network based approaches for peptide:MHC class I binding.</p> <p>Conclusion</p> <p>A novel amino acid similarity matrix has been derived for peptide:MHC binding interactions. One prominent feature of the matrix is that it disfavors substitution of residues with opposite charges. Given that the matrix was derived from experimentally determined peptide:MHC binding affinity measurements, this feature is likely shared by all peptide:protein interactions. In addition, we have demonstrated the usefulness of the matrix as a Bayesian prior in an improved scoring-matrix based peptide:MHC class I prediction method. A software implementation of the method is available at: <url>http://www.mhc-pathway.net/smmpmbec</url>.</p>http://www.biomedcentral.com/1471-2105/10/394 |
| spellingShingle | Sette Alessandro Pinilla Clemencia Sidney John Kim Yohan Peters Bjoern Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior BMC Bioinformatics |
| title | Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior |
| title_full | Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior |
| title_fullStr | Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior |
| title_full_unstemmed | Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior |
| title_short | Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior |
| title_sort | derivation of an amino acid similarity matrix for peptide mhc binding and its application as a bayesian prior |
| url | http://www.biomedcentral.com/1471-2105/10/394 |
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