Abstract
In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteriori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous probabilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility.
Highlights
Over the past two decades, the notion of a structural alphabet (SA) has attracted much attention
The optimal structural alphabet model was selected by comparing structural alphabets of different number of structural letters (SL) using the Bayesian Information Criterion, which balances the log-likelihood of the model and a penalty term related to the number of parameters of the model and the sample size
Conceiving protein structures has moved beyond static representations to include dynamic aspects of quaternary structures, like conformational changes upon binding and structural fluctuations occurring within fully assembled complexes [38]
Summary
Over the past two decades, the notion of a structural alphabet (SA) has attracted much attention. Many studies have developed SAs, based on mixture models [7], classification methods such as AutoANN [8], SOM [9] and K-Nearest Neighbor [10]: Structural Building Blocks [11], Protein Blocks [4], SABD [12] and USA [13], M32K25 [14]; and hidden Markov model (HMM): HMM-SA [2, 3, 15] The choice between these methods and models plays a major part in the construction of an accurate SA. The SA approach appears to be promising to characterize structural variability [24, 25], to explore the local backbone deformation involved in protein-protein interactions [26, 27] and to predict local protein flexibility [28, 29]
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