Abstract
BackgroundPrediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been applied to solve this problem and public domain prediction services exist. These methods are however still potentially subject to significant improvements both in terms of prediction accuracy and overall architectural complexity.ResultsWe introduce new methods for predicting disulfide bridges from protein sequences. The methods take advantage of two new decomposition kernels for measuring the similarity between protein sequences according to the amino acid environments around cysteines. Disulfide connectivity is predicted in two passes. First, a binary classifier is trained to predict whether a given protein chain has at least one intra-chain disulfide bridge. Second, a multiclass classifier (plemented by 1-nearest neighbor) is trained to predict connectivity patterns. The two passes can be easily cascaded to obtain connectivity prediction from sequence alone. We report an extensive experimental comparison on several data sets that have been previously employed in the literature to assess the accuracy of cysteine bonding state and disulfide connectivity predictors.ConclusionWe reach state-of-the-art results on bonding state prediction with a simple method that classifies chains rather than individual residues. The prediction accuracy reached by our connectivity prediction method compares favorably with respect to all but the most complex other approaches. On the other hand, our method does not need any model selection or hyperparameter tuning, a property that makes it less prone to overfitting and prediction accuracy overestimation.
Highlights
Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins
The prediction accuracy reached by our connectivity prediction method compares favorably with respect to all but the most complex other approaches
Since most of the proteins inferred from genomic sequencing lack this structural information, the ab-initio prediction of disulfide bridges from protein sequences can be very useful in several molecular biology studies
Summary
Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Since most of the proteins inferred from genomic sequencing lack this structural information, the ab-initio prediction of disulfide bridges from protein sequences can be very useful in several molecular biology studies. This computational problem has received significant attention during the last few years and a number of prediction servers have been recently developed [1,2,3,4,5]. Given known bonding state, disulfide bridges are assigned by predicting which pairs of half-cystines are linked The latter sub-problem is considerably more difficult from a machine learning perspective as it requires methods capable of predicting structured outputs. The main novelty in that method is the use of a recursive neural network that can predict the bonding probability between any pair of cysteines, so that bridges can be predicted directly from sequence (without previous knowledge of cysteine bonding state)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
