Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy

Abstract

BackgroundDisulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins.MethodsIn this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction.ResultsThe 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%.ConclusionsOur computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve.

Identification and expression analysis of two interleukin-23α (p19) isoforms, in rainbow trout Oncorhynchus mykiss and Atlantic salmon Salmo salar

“…The signal peptide and the four helices (A-D) forming the four helical bundles are indicated above the alignment. Conserved cysteine residues that are connected by a line are potentially involved in the formation of intra-chain disulfide bonds, as predicted by Dinosolve program (Yaseen and Li, 2013). The cysteine residues conserved across all sequences or are mammalian/fish-specific are indicated by black and grey triangle heads, respectively.…”

“…4). A fish-specific pair of cysteine residues also exists, one at the start of helix C and the other in the predicted helix D, which may form an additional disulphide bond in fish p19 as predicted by the Dinosolve program (Yaseen and Li, 2013). The cysteine residue (i.e.…”

“…Protein secondary structure was predicted using the Jpred3 (Cole et al, 2008) and PSIPRED programmes (Buchan et al, 2010). Potential intrachain disulphide bonds were predicted using the Dinosolve program (http://hpcr.cs.odu.edu/dinosolve; Yaseen and Li, 2013). Multiple sequence alignments were generated using CLUSTALW (version 1.82) (Chenna et al, 2003) and shaded using BOXSHADE (version 3.21, http://www.ch.embnet.org/software/BOX form.html).…”

Large scale ab initio modeling of structurally uncharacterized antimicrobial peptides reveals known and novel folds

“…Disulfide bond predictions were made using Disulfind, Dianna, and Dinosolve . Disulfind and Dianna were run at their respective servers while Dinosolve was run locally (and used the nr database from 17 March 2015 and PSI‐BLAST v.2.2.26 58 ).…”

Expanding the structural atlas of peptide toxins

“…198 In the absence of a satisfying experimental methodology to reliably determine Cys connectivity, several attempts at developing computational approaches using machine learning algorithms have been reported. 103,[199][200][201][202][203] These methods, recently reviewed by Márquez-Charmorro et al, (2015), have a range of limitations. 204 The majority of connectivity prediction methods extract cross linking information from homologous proteins.…”