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Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment

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Learning and Intelligent Optimization (LION 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5851))

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Abstract

We present a new knowledge-based Model Quality Assessment Program (MQAP) at the residue level which evaluates single protein structure models. We use a tree representation of the C α trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. We also attempt to extract local quality from global quality. The model allows fast evaluation of multiple different structure models for a single sequence. In our tests on a large set of structures, our model outperforms most other methods based on different and more complex protein structure representations in both local and global quality prediction. The method is available upon request from the authors. Method-specific rankers may also built by the authors upon request.

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Authors and Affiliations

  1. School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin, Ireland

    Alberto J. M. Martin, Alessandro Vullo & Gianluca Pollastri

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  1. Alberto J. M. Martin
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  2. Alessandro Vullo
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  3. Gianluca Pollastri
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Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Thomas Stützle

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Martin, A.J.M., Vullo, A., Pollastri, G. (2009). Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_17

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  • DOI: https://doi.org/10.1007/978-3-642-11169-3_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11168-6

  • Online ISBN: 978-3-642-11169-3

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