AI Article Synopsis
- Direct-coupling analysis is a method for predicting protein contacts using only sequence data, relying on a statistical approach known as the inverse Potts model.
- Using local fields and couplings from multiple sequence alignments, predictions usually focus on two-body couplings' l_{2} norm, which might overlook valuable data.
- The study highlights that incorporating all available field and coupling information can enhance the accuracy of contact predictions in proteins.
Article Abstract
Direct-coupling analysis is a statistical learning method for protein contact prediction based on sequence information alone. The maximum entropy principle leads to an effective inverse Potts model. Predictions on contacts are based on fitted local fields and couplings from an empirical multiple sequence alignment. Typically, the l_{2} norm of the resulting two-body couplings is used for contact prediction. However, this procedure discards important information. In this paper we show that the usage of the full fields and coupling information improves prediction accuracy.
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| http://dx.doi.org/10.1103/PhysRevE.103.042418 | DOI Listing |
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