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Related Concept Videos

Preparation and Reactions of Sulfides02:26

Preparation and Reactions of Sulfides

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Sulfides are the sulfur analog of ethers, just as thiols are the sulfur analog of alcohol. Like ethers, sulfides also consist of two hydrocarbon groups bonded to the central sulfur atom. Depending upon the type of groups present, sulfides can be symmetrical or asymmetrical. Symmetrical sulfides can be prepared via an SN2 reaction between 2 equivalents of an alkyl halide and one equivalent of sodium sulfide.
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Preparation and Reactions of Thiols02:33

Preparation and Reactions of Thiols

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Thiols are prepared using the hydrosulfide anion as a nucleophile in a nucleophilic substitution reaction with alkyl halides. For instance, bromobutane reacts with sodium hydrosulfide to give butanethiol.
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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Predicting Products: SN1 vs. SN202:27

Predicting Products: SN1 vs. SN2

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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...
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Structure and Nomenclature of Thiols and Sulfides02:17

Structure and Nomenclature of Thiols and Sulfides

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Thiols and sulfides are sulfur analogs of alcohols and ethers, respectively, where the sulfur atom takes the place of the oxygen atom. Thus, thiols are generally represented as RSH, where R is an alkyl substituent and —SH is the functional group. On the other hand, in sulfides, the central sulfur atom is bonded to two hydrocarbon groups on either side. Depending upon the type of group, sulfides can be either symmetrical or asymmetrical. Both thiols and sulfides display a bent geometry,...
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Electrophilic Aromatic Substitution: Sulfonation of Benzene01:22

Electrophilic Aromatic Substitution: Sulfonation of Benzene

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Sulfonation of benzene is a reaction wherein benzene is treated with fuming sulfuric acid at room temperature to produce benzenesulfonic acid. Fuming sulfuric acid is a mixture of sulfur trioxide and concentrated sulfuric acid.
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Related Experiment Video

Updated: Nov 18, 2025

Combining Non-reducing SDS-PAGE Analysis and Chemical Crosslinking to Detect Multimeric Complexes Stabilized by Disulfide Linkages in Mammalian Cells in Culture
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diSBPred: A machine learning based approach for disulfide bond prediction.

Avdesh Mishra1, Md Wasi Ul Kabir2, Md Tamjidul Hoque2

  • 1Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville, TX, USA.

Computational Biology and Chemistry
|February 7, 2021
PubMed
Summary
This summary is machine-generated.

A new machine learning method, diSBPred, accurately predicts protein disulfide bonds using sequence and structure features. This computational approach enhances protein structure prediction and aids experimental studies by identifying cysteine bonding residues.

Keywords:
Disulfide bond predictionMachine learningProtein sequenceProtein structure

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Protein disulfide bonds are crucial covalent links formed by cysteine oxidation during post-translational modification.
  • These bonds stabilize protein 3D structure, influence folding, and are vital for ab initio protein structure prediction (aiPSP) by constraining conformational searches.
  • Experimental determination of disulfide bonds is costly and time-consuming, necessitating computational prediction methods.

Purpose of the Study:

  • To develop an accurate, sequence-based computational method for predicting protein disulfide bonds.
  • To improve the efficiency and accuracy of ab initio protein structure prediction (aiPSP) through better disulfide bond identification.
  • To provide a tool for annotating cysteine bonding residues in proteins with unknown structures.

Main Methods:

  • A stacking-based machine learning approach, diSBPred, was developed for disulfide bond prediction.
  • Features extracted include conservation profiles, solvent accessibility, torsion angle flexibility, disorder probability, and sequential cysteine distance.
  • A two-stage prediction process was employed: individual cysteine bonding prediction followed by cysteine-pair bonding prediction.

Main Results:

  • diSBPred demonstrated improved feature relevance compared to existing methods, yielding a 7.39% increase in balanced accuracy via jackknife validation.
  • The method achieved 82.29% balanced accuracy for individual cysteine prediction and 94.20% for cysteine-pair prediction using 10-fold cross-validation.
  • Overall, diSBPred showed a 43.25% improvement in balanced accuracy over the nearest neighbor algorithm (NNA) based approach.

Conclusions:

  • diSBPred offers a highly accurate and efficient computational tool for predicting protein disulfide bonds.
  • The method can significantly aid in annotating cysteine bonding residues for proteins with unknown structures.
  • Improved disulfide bond prediction accuracy using diSBPred is expected to enhance ab initio protein structure prediction and support experimental structure determination efforts.