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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

The JCSG MR pipeline: optimized alignments, multiple models and parallel searches.

Robert Schwarzenbacher¹, Adam Godzik, Lukasz Jaroszewski

¹University of Salzburg, Structural Biology, Billrothstrasse 11, 5020 Salzburg, Austria.

Acta Crystallographica. Section D, Biological Crystallography

|December 21, 2007

Summary

Molecular replacement (MR) success improves for low-sequence identity proteins using fold-recognition methods and advanced pipelines. This enhances structure determination for at least 50% of recognizable homologues below 35% identity.

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Area of Science:

Structural biology
Computational biology
Biophysics

Background:

Molecular replacement (MR) struggles with low sequence identity (<35%) between search models and templates.
Conventional alignment methods (FASTA, BLAST) are less effective for building MR models in these challenging cases.

Purpose of the Study:

To improve the success rate of molecular replacement (MR) for protein structures with low sequence identity to known templates.
To present updated results from the JCSG MR pipeline demonstrating enhanced capabilities.

Main Methods:

Utilizing fold-recognition algorithms to identify homologous structures and build more accurate search models.
Implementing exhaustive, parallelized MR searches with integrated phasing and automated refinement.
Employing strategies for model trimming and exploring alternative models for difficult cases.

Main Results:

The JCSG MR pipeline has successfully solved 33 structures with <35% sequence identity.
Fold-recognition based models improve MR phasing success, even when sequence similarity is only detectable by these advanced algorithms.
The enhanced pipeline is estimated to achieve >50% success for recognizable homologues below 35% sequence identity.

Conclusions:

Advanced computational methods, particularly fold-recognition, significantly boost MR success rates for low-identity targets.
Optimized MR pipelines integrating model building, parallel searches, and refinement are crucial for structure determination.
Approximately one-third of bacterial proteome proteins become potential MR targets with these improved methods.