Were Ancestral Proteins Less Specific?

This repository contains the files and scripts necessary to reproduce the analyses and generate the graphs shown in the manuscript by Wheeler & Harms entitled "Were Ancestral Proteins Less Specific?" https://doi.org/10.1101/2020.05.27.120261.

I. Repository structure

Contents

• download-and-count: scripts that will allow reproduction of the counts files in fig_2cd-s3-s4-s5 from Illumina fastq files uploaded to the NCBI SRA database.
• fig_2a notebooks to reproduce weblogo in Fig 2A
• fig_2de-s3-s4-s5 notebooks and scripts to reproduce peptide enrichment calculations. Reproduces Fig 2D & E, S3, S4, and S5.
• fig_3-s6-s7-s8-s9 notebooks, scripts, and raw data to reproduce the peptide binding experimental analyses. Reproduces Fig 3, S6, S7, S8, and S9.
• fig_4-5 jupyter notebook and files to analyze set overlap (Venn diagrams and related). Reproduces Fig 4 and 5.
• fig_s2 jupyter notebook and files to reproduce figure S2 (identifying minimum read count cutoff)

Naming conventions

Throughout this repository, samples are labeled by the following convention PROTEIN_TREATMENT_REPLICATE.

PROTEIN is one of:

• 'hA5' (human S100A5)
• 'hA6' (human S100A6)
• 'aA5A6' (ancA5/A6)
• 'alt' (alternate reconstruction of ancA5/A6).

TREATMENT is one of:

• 'conv' (conventional, no peptide competitor)
• 'comp' (competitor peptide added)
• 'all' (pooled reads from conventional and competitor runs)

REPLICATE is one of:

• '1' (replicate one)
• '2' (replicate two)
• 'pooled' (combined replicates)

Computing environment

• This analysis assumes a modern scientific python computing environment (python 3.x, jupyter, numpy, scipy, matplotlib, and pandas). It will also install a few other dependences (emcee and corner). We have tested this pipeline in linux (Ubuntu 16.4 and 18.04) and macOS (10.15 Catalina). In principle it should work in windows, but we have not tested it.
• Install the hops_enrich package. (Linked v0.1.1 release is the software used in the publication.)
• Install the venninator package. (Linked v0.1.1 release is the software used in the publication.)
• If you intend to run our scripts to download our raw sequencing reads from scratch, install and configure the SRA toolkit.

II. Determine Enrichment of Peptides

Experimental Design:

We panned a commercial library of randomized 12-mer peptides expressed as fusions with the M13 phage coat protein. The S100 peptide-binding interface is only exposed upon Ca2+-binding; therefore, we performed phage panning experiments in the presence of Ca2+ and then eluted the bound phage using EDTA. The population of enriched phage will be a mixture of phage that bind at the site of interest and phage that bind adventitiously (blue and purple phage, panel A). Peptides in this latter category enrich in Ca2+-dependent manner through avidity or binding at an alternate site. To separate these populations, we repeated the panning experiment in the presence of a saturating concentration of competitor peptide known to bind at the site of interest (panel B). This should lower enrichment of peptides that bind at the site of interest, while allowing any adventitious interactions to remain. By comparing the competitor and conventional, non-competitor pools, we can distinguish between actual and adventitious binders.

Pipeline summary:

0. Obtain the fastq files (for example, hA5_conv_1.fastq.gz and hA5_comp_1.fastq.gz)

1. Count the number of times each peptide is seen in the fastq file (hA5_conv_1.counts and hA5_comp_1.counts for example)

2. Create clusters of peptides seen in the counts files (hA5_1.cluster) and calculate enrichments for each peptide by comparing counts in conventional and competitor experiments (hA5_1.enrich).

To run 0-2:

To download the fastq files from the NCBI and generate peptide counts, run:

cd download-and-count
bash download-and-count.sh sra-files.txt

In 2020, this script took about 6 hours to run on a 100 Mbit residential connection with a 2018 macbook pro. It will create about 10 Gb of fastq.gz files.

To calculate enrichments from the counts files, run the fig_2cd-s3-s4-s5/fig_2cd-s3-s4-s5.ipynb jupyter notebook.

Detailed breakdown of steps 0 and 1:

0. Obtain the fastq files:

The raw reads associated with this analysis are available as BioProject PRJNA646756. The samples are:

Accession	Sample
SRR12244639	hA6_conv_1
SRR12244813	hA6_comp_1
SRR12244638	hA6_conv_2
SRR12244812	hA6_comp_2
SRR12244629	hA5_conv_1
SRR12244637	hA5_comp_1
SRR12244628	hA5_conv_2
SRR12244636	hA5_comp_2
SRR12244543	aA5A6_conv_1
SRR12244560	aA5A6_comp_1
SRR12244542	aA5A6_conv_2
SRR12244559	aA5A6_comp_2
SRR12244562	alt_conv_1
SRR12244584	alt_comp_1
SRR12244561	alt_conv_2
SRR12244583	alt_comp_2

1. Count the number of times each sequence is seen in the fastq files

Calculate the the number of time each peptide is seen in the relavent .fastq.gz file using hops_count. This script applies some quality control:

1. Is the sequence translatable in-frame, without stops or nonsensical codons?
2. Is the average PHRED score above a cutoff (15 as we ran the analysis)?
3. Is the flanking phage region correct to within one base across the whole sequence?

hops_count hA5.fastq.gz -o hA5.counts

III. Figures

Each figure directory contains data files, jupyter notebooks, and scripts necessary to reproduce the indicated figures.