Article reading briefings, Nov 3rd-6th 2017

Hi there! I’m here to share some articles I read recently. They mainly focus on protein sequence-based functional annotations and protein interaction analysis or prediction. Although not all of them are the latest, the articles are no older than one year. I wish I could study from them and apply their methods in my future work.

Protein interaction perturbation profiling at amid-acid resolution

doi:10.1038/nmeth.4464

The research presented a sensitive yeast two-hybrid-based pipeline for positive selection of interaction-disruptive variants by developing Int-Seq, a scalable method that can dissect binary PPI at individual amino-acid resolution.

BBSome interaction Network was drawn.

It seems that this method can identify protein interaction change when an amino acid altered.

I think the most interesting part of this job is that it can characterize residues involved in maintaining interaction.

Didn’t find the figures valuable.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

doi:10.3390/ijms17111946

This review introduced something I’m familiar with. Boring. But it is a nice collection. It’s worth reading when I want to search for useful tools and algorithms.

Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding

doi:10.1186/s12859-016-1035-4

A novel prediction model for PPI was developed by combining weighted sparse representation based classifier and global encoding representation of proteins. The figures are convincing but can be improved. It’s a standard research article presenting a method. Not exciting. This article included comparison with SVM and other methods, and pointed out the weakness of methods based on homology and structure.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

doi:10.1093/bioinformatics/btx624

I should have realized the exact name of this tool before starting reading. The article represented a novel method with deep learning to predict protein GO annotation based on sequence. The web server is available here.

Just put this article down, take a deep breath and switch to your own job.

That’s all about it.