Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior.
Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior.
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DOI：
10.1186/1471-2105-10-394
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发表时间：
2009-11-30
影响因子：
3
通讯作者：
Peters B
中科院分区：
文献类型：
--
作者：
Kim Y;Sidney J;Pinilla C;Sette A;Peters B
肽：MHC结合研究的专家通常能够在实验环境中基于对氨基酸相似性的启发式理解来估计单个残基取代的影响。我们的目标是量化这种相似性的措施，以改善肽：MHC结合预测方法。这应该有助于补偿现有肽结合数据集的序列空间覆盖中的漏洞和偏差。在这里，一种新的氨基酸相似性矩阵（PMBEC）是直接从组合肽混合物的结合亲和力数据。像BL0SUM62一样，该基质捕获氨基酸残基的众所周知的物理化学性质。然而，PMBEC显着不同，从现有的矩阵中的情况下，残基取代涉及静电电荷的逆转。为了证明它的有用性，我们已经开发了一种新的肽：MHC I类结合预测方法，使用矩阵作为贝叶斯先验。我们表明，新的方法可以补偿训练数据中特定残基的缺失信息。我们还进行了一个大规模的基准测试，其结果表明，新方法的预测性能是最好的基于神经网络的方法肽：MHC I类结合。一种新的氨基酸相似性矩阵已被推导出肽：MHC结合相互作用。矩阵的一个突出特征是它不赞成具有相反电荷的残基的取代。考虑到基质来源于实验确定的肽：MHC结合亲和力测量，该特征可能为所有肽：蛋白质相互作用所共有。此外，我们已经证明了有用的矩阵作为贝叶斯先验在一个改进的评分矩阵为基础的肽：MHC I类预测方法。该方法的软件实现可在http://www.mhc-pathway.net/smmpmbec获得。
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. 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. 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: http://www.mhc-pathway.net/smmpmbec.
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DOI：
10.1186/1745-7580-4-2
发表时间：
2008-01-25
期刊：
Immunome research
影响因子：
--
作者：
Sidney J;Assarsson E;Moore C;Ngo S;Pinilla C;Sette A;Peters B
通讯作者：
Peters B
DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
DOI：
10.1093/protein/7.11.1323
发表时间：
1994-11-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
BENNER, SA;COHEN, MA;GONNET, GH
通讯作者：
GONNET, GH
影响因子：
5.8
作者：
Lundegaard, Claus;Lund, Ole;Nielsen, Morten
通讯作者：
Nielsen, Morten
影响因子：
5.6
作者：
JOHNSON, MS;OVERINGTON, JP
通讯作者：
OVERINGTON, JP