Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

After Skin Wounding, Noncoding dsRNA Coordinates Prostaglandins and Wnts to Promote Regeneration.

The Journal of investigative dermatology·2017

Same author

Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer.

Oncotarget·2017

Same author

Identification of a six-lncRNA signature associated with recurrence of ovarian cancer.

Scientific reports·2017

Same author

Ropivacaine versus levobupivacaine in peripheral nerve block: A PRISMA-compliant meta-analysis of randomized controlled trials.

Medicine·2017

Same author

Fabrication of fluorescent composite hydrogel using in situ synthesis of upconversion nanoparticles.

Nanotechnology·2017

Same author

Ammonium assimilation: An important accessory during aerobic denitrification of Pseudomonas stutzeri T13.

Bioresource technology·2017

Search research articles

Related Experiment Video

Updated: Nov 6, 2025

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

Published on: December 9, 2017

A Transfer Learning-Based Approach for Lysine Propionylation Prediction.

Ang Li¹, Yingwei Deng¹, Yan Tan¹

¹School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China.

Frontiers in Physiology

|May 10, 2021

Summary

We developed a novel deep learning method to predict lysine propionylation sites, a crucial posttranslational modification. This approach significantly advances large-scale propionylation detection, aiding cellular process research.

Keywords:

deep learning long short term memory malonylation propionylation recurrent neural network support machine vector transfer learning

More Related Videos

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Published on: November 29, 2014

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

Related Experiment Videos

Last Updated: Nov 6, 2025

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

Published on: December 9, 2017

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Published on: November 29, 2014

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

Published on: April 4, 2018

Area of Science:

Biochemistry
Computational Biology
Molecular Biology

Background:

Lysine propionylation is an emerging posttranslational modification (PTM) vital for cellular functions.
Current proteomics methods face challenges in large-scale propionylation site detection.

Purpose of the Study:

To develop an accurate computational method for predicting lysine propionylation sites.
To overcome limitations in large-scale propionylation detection using existing techniques.

Main Methods:

A transfer learning approach using a recurrent neural network (RNN) and support vector machine (SVM).
The RNN model was pre-trained on malonylation data and fine-tuned for propionylation.
Protein sequences were converted into numerical vectors for classification.

Main Results:

The model achieved a Matthews Correlation Coefficient (MCC) of 0.6615 (10-fold cross-validation) and 0.3174 (independent test).
Performance surpassed existing state-of-the-art methods for propionylation site prediction.
Enrichment analysis linked propionylation to metabolic processes and specific Gene Ontology (GO) terms.

Conclusions:

The developed transfer learning method provides an effective tool for predicting propionylation sites.
This advancement facilitates large-scale analysis of propionylation's role in cellular processes.
A user-friendly online prediction tool is now available.