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Thermodynamic propensities of amino acids in the native state ensemble: implications for fold recognition.

ABSTRACT: An amino acid sequence, in the context of the solvent environment, contains all of the thermodynamic information necessary to encode a three-dimensional protein structure. To investigate the relationship between an amino acid sequence and its corresponding protein fold, a database of thermodynamic stability information was assembled that spanned 2951 residues from 44 nonhomologous proteins. This information was obtained using the COREX algorithm, which computes an ensemble-based description of the native state of a protein. It was observed that amino acid types partitioned unequally into high, medium, and low thermodynamic stability environments. Furthermore, these distributions were reproducible and were significantly different than those expected from random partitioning. To assess the structural importance of the distributions, simple fold-recognition experiments were performed based on a 3D-1D scoring matrix containing only COREX residue stability information. This procedure was able to recover amino acid sequences corresponding to correct target structures more effectively than scoring matrices derived from randomized data. High-scoring sequences were often aligned correctly with their corresponding target profiles, suggesting that calculated thermodynamic stability profiles have the potential to encode sequence information. As a control, identical fold-recognition experiments were performed on the same database of proteins using DSSP secondary structure information in the scoring matrix, instead of COREX residue stability information. The comparable performance of both approaches suggested that COREX residue stability information and secondary structure information could be of equivalent utility in more sophisticated fold-recognition techniques. The results of this work are a consequence of the idea that amino acid sequences fold not into single, rigidly stable structures but rather into thermodynamic ensembles best represented by a time-averaged structure.

SUBMITTER: Wrabl JO

PROVIDER: S-EPMC2374190 | biostudies-literature | 2001 May

REPOSITORIES: biostudies-literature

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Thermodynamic propensities of amino acids in the native state ensemble: implications for fold recognition.

Wrabl J O JO Larson S A SA Hilser V J VJ

Protein science : a publication of the Protein Society 20010501 5

An amino acid sequence, in the context of the solvent environment, contains all of the thermodynamic information necessary to encode a three-dimensional protein structure. To investigate the relationship between an amino acid sequence and its corresponding protein fold, a database of thermodynamic stability information was assembled that spanned 2951 residues from 44 nonhomologous proteins. This information was obtained using the COREX algorithm, which computes an ensemble-based description of t ...[more]

PMID: 11316884

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Project description:Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

Project description:BackgroundA large number of studies have been carried out to obtain amino acid propensities for α-helices and β-sheets. The obtained propensities for α-helices are consistent with each other, and the pair-wise correlation coefficient is frequently high. On the other hand, the β-sheet propensities obtained by several studies differed significantly, indicating that the context significantly affects β-sheet propensity.ResultsWe calculated amino acid propensities for α-helices and β-sheets for 39 and 24 protein folds, respectively, and addressed whether they correlate with the fold. The propensities were also calculated for exposed and buried sites, respectively. Results showed that α-helix propensities do not differ significantly by fold, but β-sheet propensities are diverse and depend on the fold. The propensities calculated for exposed sites and buried sites are similar for α-helix, but such is not the case for the β-sheet propensities. We also found some fold dependence on amino acid frequency in β-strands. Folds with a high Ser, Thr and Asn content at exposed sites in β-strands tend to have a low Leu, Ile, Glu, Lys and Arg content (correlation coefficient = -0.90) and to have flat β-sheets. At buried sites in β-strands, the content of Tyr, Trp, Gln and Ser correlates negatively with the content of Val, Ile and Leu (correlation coefficient = -0.93). "All-β" proteins tend to have a higher content of Tyr, Trp, Gln and Ser, whereas "α/β" proteins tend to have a higher content of Val, Ile and Leu.ConclusionsThe α-helix propensities are similar for all folds and for exposed and buried residues. However, β-sheet propensities calculated for exposed residues differ from those for buried residues, indicating that the exposed-residue fraction is one of the major factors governing amino acid composition in β-strands. Furthermore, the correlations we detected suggest that amino acid composition is related to folding properties such as the twist of a β-strand or association between two β sheets.

Dataset Information

Thermodynamic propensities of amino acids in the native state ensemble: implications for fold recognition.

Publications

Thermodynamic propensities of amino acids in the native state ensemble: implications for fold recognition.

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