Dataset Information

Predicting the number of sulfur atoms in peptides and small proteins based on the observed aggregated isotope distribution.

ABSTRACT:

Rationale

Identification of peptides and proteins is a challenging task in mass spectrometry-based proteomics. Knowledge of the number of sulfur atoms can improve the identification of peptides and proteins.

Methods

In this article, we propose a method for the prediction of S-atoms based on the aggregated isotope distribution. The Mahalanobis distance is used as dissimilarity measure to compare mass- and intensity-based features from the observed and theoretical isotope distributions.

Results

The relative abundance of the second and the third aggregated isotopic variants (as compared to the monoisotopic one) and the mass difference between the second and third aggregated isotopic variants are the most important features to predict the number of S-atoms.

Conclusions

The mass and intensity accuracies of the observed aggregated isotopic variants are insufficient to accurately predict the number of atoms. However, using a limited set of predictions for a peptide, rather than predicting a single number of S-atoms, has a reasonably high prediction accuracy.

SUBMITTER: Claesen J

PROVIDER: S-EPMC8459233 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Predicting the number of sulfur atoms in peptides and small proteins based on the observed aggregated isotope distribution.

Claesen Jürgen J Valkenborg Dirk D Burzykowski Tomasz T

Rapid communications in mass spectrometry : RCM 20211001 19

<h4>Rationale</h4>Identification of peptides and proteins is a challenging task in mass spectrometry-based proteomics. Knowledge of the number of sulfur atoms can improve the identification of peptides and proteins.<h4>Methods</h4>In this article, we propose a method for the prediction of S-atoms based on the aggregated isotope distribution. The Mahalanobis distance is used as dissimilarity measure to compare mass- and intensity-based features from the observed and theoretical isotope distributi ...[more]

PMID: 34240492

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Project description:BackgroundOver the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.DescriptionWe obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.ConclusionToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

Project description:The precise interpretation of environmental sulfur isotope records requires a quantitative understanding of the biochemical controls on sulfur isotope fractionation by the principle isotope-fractionating process within the S cycle, microbial sulfate reduction (MSR). Here we provide the only direct observation of the major ((34)S/(32)S) and minor ((33)S/(32)S, (36)S/(32)S) sulfur isotope fractionations imparted by a central enzyme in the energy metabolism of sulfate reducers, dissimilatory sulfite reductase (DsrAB). Results from in vitro sulfite reduction experiments allow us to calculate the in vitro DsrAB isotope effect in (34)S/(32)S (hereafter, [Formula: see text]) to be 15.3 ± 2‰, 2σ. The accompanying minor isotope effect in (33)S, described as [Formula: see text], is calculated to be 0.5150 ± 0.0012, 2σ. These observations facilitate a rigorous evaluation of the isotopic fractionation associated with the dissimilatory MSR pathway, as well as of the environmental variables that govern the overall magnitude of fractionation by natural communities of sulfate reducers. The isotope effect induced by DsrAB upon sulfite reduction is a factor of 0.3-0.6 times prior indirect estimates, which have ranged from 25 to 53‰ in (34)εDsrAB. The minor isotope fractionation observed from DsrAB is consistent with a kinetic or equilibrium effect. Our in vitro constraints on the magnitude of [Formula: see text] is similar to the median value of experimental observations compiled from all known published work, where (34)ε r-p = 16.1‰ (r-p indicates reactant vs. product, n = 648). This value closely matches those of MSR operating at high sulfate reduction rates in both laboratory chemostat experiments ([Formula: see text] 17.3 ± 1.5‰, 2σ) and in modern marine sediments ([Formula: see text] 17.3 ± 3.8‰). Targeting the direct isotopic consequences of a specific enzymatic processes is a fundamental step toward a biochemical foundation for reinterpreting the biogeochemical and geobiological sulfur isotope records in modern and ancient environments.

Dataset Information

Predicting the number of sulfur atoms in peptides and small proteins based on the observed aggregated isotope distribution.

Rationale

Methods

Results

Conclusions

Publications

Predicting the number of sulfur atoms in peptides and small proteins based on the observed aggregated isotope distribution.

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