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Parallel Chemoselective Profiling for Mapping Protein Structure.

ABSTRACT: Solution-based structural techniques complement high-resolution structural data by providing insight into the oft-missed links between protein structure and dynamics. Here, we present Parallel Chemoselective Profiling, a solution-based structural method for characterizing protein structure and dynamics. Our method utilizes deep mutational scanning saturation mutagenesis data to install amino acid residues with specific chemistries at defined positions on the solvent-exposed surface of a protein. Differences in the extent of labeling of installed mutant residues are quantified using targeted mass spectrometry, reporting on each residue's local environment and structural dynamics. Using our method, we studied how conformation-selective, ATP-competitive inhibitors affect the local and global structure and dynamics of full-length Src kinase. Our results highlight how parallel chemoselective profiling can be used to study a dynamic multi-domain protein, and suggest that our method will be a useful addition to the relatively small toolkit of existing protein footprinting techniques.

SUBMITTER: Potter ZE

PROVIDER: S-EPMC7484201 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Massively parallel sequencing offers an enormous potential for expression profiling, in particular for interspecific comparisons. Currently, different platforms for massively parallel sequencing are available, which differ in read length and sequencing costs. The 454-technology offers the highest read length. The other sequencing technologies are more cost effective, on the expense of shorter reads. Reliable expression profiling by massively parallel sequencing depends crucially on the accuracy to which the reads could be mapped to the corresponding genes. METHODOLOGY/PRINCIPAL FINDINGS:We performed an in silico analysis to evaluate whether incorrect mapping of the sequence reads results in a biased expression pattern. A comparison of six available mapping software tools indicated a considerable heterogeneity in mapping speed and accuracy. Independently of the software used to map the reads, we found that for compact genomes both short (35 bp, 50 bp) and long sequence reads (100 bp) result in an almost unbiased expression pattern. In contrast, for species with a larger genome containing more gene families and repetitive DNA, shorter reads (35-50 bp) produced a considerable bias in gene expression. In humans, about 10% of the genes had fewer than 50% of the sequence reads correctly mapped. Sequence polymorphism up to 9% had almost no effect on the mapping accuracy of 100 bp reads. For 35 bp reads up to 3% sequence divergence did not affect the mapping accuracy strongly. The effect of indels on the mapping efficiency strongly depends on the mapping software. CONCLUSIONS/SIGNIFICANCE:In complex genomes, expression profiling by massively parallel sequencing could introduce a considerable bias due to incorrectly mapped sequence reads if the read length is short. Nevertheless, this bias could be accounted for if the genomic sequence is known. Furthermore, sequence polymorphisms and indels also affect the mapping accuracy and may cause a biased gene expression measurement. The choice of the mapping software is highly critical and the reliability depends on the presence/absence of indels and the divergence between reads and the reference genome. Overall, we found SSAHA2 and CLC to produce the most reliable mapping results.

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Parallel Chemoselective Profiling for Mapping Protein Structure.

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