Automatic Epitope Recognition in Proteins Oriented to the System for Macromolecular Interaction Assessment MIAX
スポンサーリンク
概要
- 論文の詳細を見る
In the present work we evaluate the performance of an algorithm for the automatic recognition of binding sites in proteins as well as in other macromolecules whose interactions are involved in many cellular and physiological processes. The algorithm is a combination of an unsupervised learning algorithm-based on Kohonen self organizing maps-to characterize the properties of patches of protein solvent accessible surfaces and a filtering algorithm to establish both the physical boundaries of the patches as well as the level of contribution of different and distant atoms involved in the interaction. We have found that the algorithm performs extremely well in a set of randomly selected protein complexes for which the interaction interfaces are extracted and compared with the results of the algorithm. A statistical evaluation of the algorithm is additionally performed by analysis of the degree of hydrophobicity and hydrophilicity of the output patches and comparison with that of the observed interface constituent amino acids.
- 日本バイオインフォマティクス学会の論文
日本バイオインフォマティクス学会 | 論文
- Performance Improvement in Protein N-Myristoyl Classification by BONSAI with Insignificant Indexing Symbol
- A combined pathway to simulate CDK-dependent phosphorylation and ARF-dependent stabilization for p53 transcriptional activity
- A versatile petri net based architecture for modeling and simulation of complex biological processes
- XML documentation of biopathways and their simulations in Genomic Object Net
- Prediction of debacle points for robustness of biological pathways by using recurrent neural networks