A Study on Protein Residue Contacts Prediction by Recurrent Neural Network

• 出 处: Journal of Bionics Engineering
• 发表日期: 2005年03期
• 关键词: Residue Prediction contacts hidden residue residues Recognition folding capture

A new method was described for using a recurrent neural network with bias units to predict contact maps in proteins. The main inputs to the neural network include residues pairwise, residue classification according to hydrophobicity, polar, acidic, basic and secondary structure information and residue separation between two residues. In our work, a dataset was used which was composed of 53 globulin proteins of known 3D structure. An average predictive accuracy of 0^29 was obtained. Our results demonstrate the viability of the approach for predicting contact maps.

A new method was described for using recurrent neural network with bias units to predict contact maps in proteins. The main inputs to the neural network include residues pairwise, residue classification according to hydrophobicity, polar, acidic, basic and secondary structure information and residue separation In our work, a dataset was used which was composed of 53 globulin proteins of known 3D structure. An average predictive accuracy of 0 ^ 29 was obtained. Our results demonstrate the viability of the approach for predicting contact maps.