Next Article in Journal / Special Issue
Molecular Dynamics Simulations Capture the Misfolding of the Bovine Prion Protein at Acidic pH
Previous Article in Journal
Analysis of Guanine Oxidation Products in Double-Stranded DNA and Proposed Guanine Oxidation Pathways in Single-Stranded, Double-Stranded or Quadruplex DNA
Previous Article in Special Issue
A Firefly-Inspired Method for Protein Structure Prediction in Lattice Models
Biomolecules 2014, 4(1), 160-180; doi:10.3390/biom4010160

Reconstructing Protein Structures by Neural Network Pairwise Interaction Fields and Iterative Decoy Set Construction

1,2, 1,2 and 1,2,*
Received: 24 December 2013 / Revised: 22 January 2014 / Accepted: 30 January 2014 / Published: 10 February 2014
(This article belongs to the Special Issue Protein Folding and Misfolding)
View Full-Text   |   Download PDF [556 KB, uploaded 10 February 2014]   |   Browse Figures


Predicting the fold of a protein from its amino acid sequence is one of the grand problems in computational biology. While there has been progress towards a solution, especially when a protein can be modelled based on one or more known structures (templates), in the absence of templates, even the best predictions are generally much less reliable. In this paper, we present an approach for predicting the three-dimensional structure of a protein from the sequence alone, when templates of known structure are not available. This approach relies on a simple reconstruction procedure guided by a novel knowledge-based evaluation function implemented as a class of artificial neural networks that we have designed: Neural Network Pairwise Interaction Fields (NNPIF). This evaluation function takes into account the contextual information for each residue and is trained to identify native-like conformations from non-native-like ones by using large sets of decoys as a training set. The training set is generated and then iteratively expanded during successive folding simulations. As NNPIF are fast at evaluating conformations, thousands of models can be processed in a short amount of time, and clustering techniques can be adopted for model selection. Although the results we present here are very preliminary, we consider them to be promising, with predictions being generated at state-of-the-art levels in some of the cases.
Keywords: protein folding; protein structure prediction; artificial neural networks protein folding; protein structure prediction; artificial neural networks
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

Mirabello, C.; Adelfio, A.; Pollastri, G. Reconstructing Protein Structures by Neural Network Pairwise Interaction Fields and Iterative Decoy Set Construction. Biomolecules 2014, 4, 160-180.

View more citation formats

Related Articles

Article Metrics


Cited By

[Return to top]
Biomolecules EISSN 2218-273X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert