Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks. / Helles, Glennie; Fonseca, Rasmus.
In: BMC Bioinformatics, Vol. 10, No. 338, 16.10.2009.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks
AU - Helles, Glennie
AU - Fonseca, Rasmus
N1 - Paper id:: doi:10.1186/1471-2105-10-338
PY - 2009/10/16
Y1 - 2009/10/16
N2 - Predicting the three-dimensional structure of a protein from its amino acid sequence is currently one of the most challenging problems in bioinformatics. The internal structure of helices and sheets is highly recurrent and help reduce the search space significantly. However, random coil segments make up nearly 40\% of proteins, and they do not have any apparent recurrent patterns which complicates overall prediction accuracy of protein structure prediction methods. Luckily, previous work has indicated that coil segments are in fact not completely random in structure and flanking residues do seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles. Results: In this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30°×30° area of the dihedral angle space) for all amino acids in the data set compared to first order statistics. An accuracy comparable to that of secondary structure prediction (˜80%) is achieved by observing the 20 bins with highest output values. Conclusions: Many different protein structure prediction methods exist and each uses different tools and auxiliary predictions to help determine the native structure. In this work the sequence is used to predict local context dependent dihedral angle propensities in coil-regions. This predicted distribution can potentially improve tertiary structure prediction methods that are based on sampling the backbone dihedral angles of individual amino acids. The predicted distribution may also help predict local structure fragments used in fragment assembly methods.
AB - Predicting the three-dimensional structure of a protein from its amino acid sequence is currently one of the most challenging problems in bioinformatics. The internal structure of helices and sheets is highly recurrent and help reduce the search space significantly. However, random coil segments make up nearly 40\% of proteins, and they do not have any apparent recurrent patterns which complicates overall prediction accuracy of protein structure prediction methods. Luckily, previous work has indicated that coil segments are in fact not completely random in structure and flanking residues do seem to have a significant influence on the dihedral angles adopted by the individual amino acids in coil segments. In this work we attempt to predict a probability distribution of these dihedral angles based on the flanking residues. While attempts to predict dihedral angles of coil segments have been done previously, none have, to our knowledge, presented comparable results for the probability distribution of dihedral angles. Results: In this paper we develop an artificial neural network that uses an input-window of amino acids to predict a dihedral angle probability distribution for the middle residue in the input-window. The trained neural network shows a significant improvement (4-68%) in predicting the most probable bin (covering a 30°×30° area of the dihedral angle space) for all amino acids in the data set compared to first order statistics. An accuracy comparable to that of secondary structure prediction (˜80%) is achieved by observing the 20 bins with highest output values. Conclusions: Many different protein structure prediction methods exist and each uses different tools and auxiliary predictions to help determine the native structure. In this work the sequence is used to predict local context dependent dihedral angle propensities in coil-regions. This predicted distribution can potentially improve tertiary structure prediction methods that are based on sampling the backbone dihedral angles of individual amino acids. The predicted distribution may also help predict local structure fragments used in fragment assembly methods.
KW - Faculty of Science
KW - Bioinformatik
KW - Protein Struktur Forudsigelse
KW - Neurale netværk
KW - Bioinformatics
KW - Protein Structure Prediction
KW - Neural networks
U2 - 10.1186/1471-2105-10-338
DO - 10.1186/1471-2105-10-338
M3 - Journal article
C2 - 19835576
VL - 10
JO - B M C Bioinformatics
JF - B M C Bioinformatics
SN - 1471-2105
IS - 338
ER -
ID: 14880912