title: Predicting the effect of missense mutations on protein function: analysis with Bayesian networks.
abstract: BACKGROUND
  A number of methods that use both protein structural and evolutionary information are available to predict the functional consequences of missense mutations. However, many of these methods break down if either one of the two types of data are missing. Furthermore, there is a lack of rigorous assessment of how important the different factors are to prediction.
  
  
  RESULTS
  Here we use Bayesian networks to predict whether or not a missense mutation will affect the function of the protein. Bayesian networks provide a concise representation for inferring models from data, and are known to generalise well to new data. More importantly, they can handle the noisy, incomplete and uncertain nature of biological data. Our Bayesian network achieved comparable performance with previous machine learning methods. The predictive performance of learned model structures was no better than a naïve Bayes classifier. However, analysis of the posterior distribution of model structures allows biologically meaningful interpretation of relationships between the input variables.
  
  
  CONCLUSIONS
  The ability of the Bayesian network to make predictions when only structural or evolutionary data was observed allowed us to conclude that structural information is a significantly better predictor of the functional consequences of a missense mutation than evolutionary information, for the dataset used. Analysis of the posterior distribution of model structures revealed that the top three strongest connections with the class node all involved structural nodes. With this in mind, we derived a simplified Bayesian network that used just these three structural descriptors, with comparable performance to that of an all node network.
authors.affiliation: School of Computing, University of Leeds, Leeds, LS2 9JT, UK. chrisn@comp.leeds.ac.uk
authors.forename: Chris J
authors.initials: CJ
authors.lastname: Needham
authors.forename: James R
authors.initials: JR
authors.lastname: Bradford
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Bulpitt
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Care
authors.forename: David R
authors.initials: DR
authors.lastname: Westhead
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry / Q000235:genetics; D001499:Bayes Theorem; D030541:Databases, Genetic; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009113:Muramidase / Q000737:chemistry / Q000235:genetics; D020125:Mutation, Missense; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D012372:ROC Curve; D012097:Repressor Proteins / Q000737:chemistry / Q000235:genetics; D013329:Structure-Activity Relationship
chemical_list: D000596:Amino Acids; D011506:Proteins; D012097:Repressor Proteins; D009113:Muramidase