Neural network pairwise interaction fields for protein model quality assessment

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationLearning and Intelligent Optimization - Third International Conference, LION 3, Selected Papers
Pages235-248
Number of pages14
DOIs
StatePublished - 2009
Externally publishedYes
Event3rd International Conference on Learning and Intelligent Optimization, LION 3 - Trento, Italy
Duration: 20092009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5851 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Learning and Intelligent Optimization, LION 3
Country/TerritoryItaly
CityTrento
Period14/01/0918/01/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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