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Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization.

ABSTRACT: Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (??Gbind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ??Gbind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ??Gbind data points on purified proteins to generate ??Gbind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ??Gbind for this interaction could be quantified with high accuracy over the range of 12?kcal?mol-1 displayed by various BPTI single mutants.

SUBMITTER: Heyne M

PROVIDER: S-EPMC6962383 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization.

Heyne Michael M Papo Niv N Shifman Julia M JM

Nature communications 20200115 1

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔG<sub>bind</sub>) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔG<sub>bind</sub> for thousands of protein mutants in one e ...[more]

PMID: 31941882

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Project description:UnlabelledPopulations of RNA viruses can spontaneously produce variants that differ in genome size, sequence, and biological activity. Defective variants that lack essential genes can nevertheless reproduce by coinfecting cells with viable virus, a process that interferes with virus growth. How such defective interfering particles (DIPs) change in abundance and biological activity within a virus population is not known. Here, a prototype RNA virus, vesicular stomatitis virus (VSV), was cultured for three passages on BHK host cells, and passages were subjected to Illumina sequencing. Reads from the initial population, when aligned to the full-length viral sequence (11,161 nucleotides [nt]), distributed uniformly across the genome. However, during passages two plateaus in read counts appeared toward the 5' end of the negative-sense viral genome. Analysis by normalization and a simple sliding-window approach revealed plateau boundaries that suggested the emergence and enrichment of at least two truncated species having medium (∼5,900 nt) and short (∼4,000 nt) genomes. Relative measures of full-length and truncated species based on read counts were validated by quantitative reverse transcription-PCR (qRT-PCR). Limit-of-detection analysis suggests that deep sequencing can be more sensitive than complementary measures for detecting and quantifying defective particles in a population. Further, particle counts from transmission electron microscopy, coupled with infectivity assays, linked the rise in smaller genomes with an increase in truncated particles and interference activity. In summary, variation in deep sequencing coverage simultaneously shows the size, location, and relative level of truncated-genome variants, revealing a level of population heterogeneity that is masked by other measures of viral genomes and particles.ImportanceWe show how deep sequencing can be used to characterize the emergence, diversity, and relative abundance of truncated virus variants in virus populations. Adaptation of this approach to natural isolates may elucidate factors that influence the stability and persistence of virus populations in nature.

Dataset Information

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization.

Publications

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization.

Similar Datasets

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