Abstract
BackgroundNumerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology.ResultsWe developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86 %, 94.11 %, 84.31 %, 94.30 % and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB.ConclusionPredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0633-x) contains supplementary material, which is available to authorized users.
Highlights
Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation
Evaluation of feature sets for machine learning outcomes The training data set of 144 sequential tri-disulfide peptide (STP) and 393 non-STP chains was evaluated using randomized sampling over
A large number of these peptides include a sequentially paired disulfide bonding pattern (C1–C4, C2–C5, C3–C6), confirming a compact array of this cystine trio which we refer to here as Sequential Tri-disulfide Peptides (STP). This array includes the well-defined knottin and cyclotide groups that have knotted tertiary structures. They include a large number of stable toxins that contain the STP bonding pattern but lack the knotted motif typically created by C3–C6 in knottins and cyclotides
Summary
Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation Their effective interstitial and macro-environmental use requires energetic and structural stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. Cystine stabilized toxins which do not contain the exact STP bonding array may offer stability and toxicity [25,26,27,28] and can be denoted as nonsequential tri-disulfide peptides (NTPs) (Fig. 1). While STP toxins imply a compact tri-disulfide tertiary confirmation, NTPs toxins may contain both compact or non-compact tri-disulfide folds (Fig. 2)
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