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Preparation and Reactions of Sulfides

Preparation and Reactions of Sulfides

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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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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Related Experiment Video

Updated: Mar 30, 2026

Combining Non-reducing SDS-PAGE Analysis and Chemical Crosslinking to Detect Multimeric Complexes Stabilized by Disulfide Linkages in Mammalian Cells in Culture

Combining Non-reducing SDS-PAGE Analysis and Chemical Crosslinking to Detect Multimeric Complexes Stabilized by Disulfide Linkages in Mammalian Cells in Culture

Published on: May 2, 2019

Soft Computing Methods for Disulfide Connectivity Prediction.

Alfonso E Márquez-Chamorro¹, Jesús S Aguilar-Ruiz¹

¹School of Engineering, Pablo de Olavide University, Seville, Spain.

Evolutionary Bioinformatics Online

|November 3, 2015

Summary

This summary is machine-generated.

Predicting disulfide bonds in proteins aids 3D structure prediction. This paper reviews soft computing methods, including neural networks and support vector machines, for accurate disulfide connectivity prediction.

Keywords:

disulfide connectivity prediction neural networks protein structure prediction soft computing support vector machines

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Area of Science:

Structural bioinformatics
Computational biology
Biophysics

Background:

Protein structure prediction (PSP) is a significant challenge in structural bioinformatics.
Disulfide bond connectivity prediction is a crucial subproblem of PSP.
Accurate disulfide bond identification reduces the conformational search space for 3D PSP.

Purpose of the Study:

To summarize representative soft computing approaches for disulfide bond connectivity prediction.
To classify these methods based on their underlying algorithms and features.

Main Methods:

Review of soft computing methodologies, including artificial neural networks (ANNs) and support vector machines (SVMs).
Analysis of algorithm features for classification.
Focus on methods developed within the last decade.

Main Results:

Identification of key soft computing techniques for disulfide bond prediction.
Classification framework for evaluating different prediction algorithms.
Highlighting the importance of feature selection and algorithm choice.

Conclusions:

Soft computing approaches offer powerful tools for disulfide bond connectivity prediction.
Methodologies like ANNs and SVMs are prominent in this field.
Further research can refine these methods for improved 3D PSP accuracy.