Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

Abstract

Proteins evolve through two primary mechanisms: substitution, where mutations alter a protein’s amino-acid sequence, and insertions and deletions (indels), where amino acids are either added to or removed from the sequence. Protein structure has been shown to influence the rate at which substitutions accumulate across sites in proteins, but whether structure similarly constrains the occurrence of indels has not been rigorously studied. Here, we investigate the extent to which structural properties known to covary with protein evolutionary rates might also predict protein tolerance to indels. Specifically, we analyze a publicly available dataset of single—amino-acid deletion mutations in enhanced green fluorescent protein (eGFP) to assess how well the functional effect of deletions can be predicted from protein structure. We find that weighted contact number (WCN), which measures how densely packed a residue is within the protein’s three-dimensional structure, provides the best single predictor for whether eGFP will tolerate a given deletion. We additionally find that using protein design to explicitly model deletions results in improved predictions of functional status when combined with other structural predictors. Our work suggests that structure plays fundamental role in constraining deletions at sites in proteins, and further that similar biophysical constraints influence both substitutions and deletions. This study therefore provides a solid foundation for future work to examine how protein structure influences tolerance of more complex indel events, such as insertions or large deletions.

Analyzing Change in Protein Stability Associated with Single Point Deletions in a Newly Defined Protein Structure Database

“…An inductive logic programming based machine learning approach was proposed to predict disease-causing nonframeshift (NFS) InDels . The foldability encoding of 72 instances of single point deletions (SPDs) in amino acids of the enhanced green fluorescent protein (eGFP) corroborated well with the experimental findings . The effect of loop length modification on the thermodynamic stability of human muscle acylphosphatase protein was explored using both computational and experimental approaches .…”

Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation

“…We calculated ΔΔGs for missense variants with Rosetta [ 12 , 73 , 74 ] ( Fig 2B and 2C ). For in-frame deletions, we modified a previously described protocol [ 75 ]. Briefly, this consists of creating the sequence of the deletion variant of interest, creating a homology model using the original crystal structure (PDB: 3V42) as the template, relaxing this model with Rosetta, and calculating the energy difference (ΔΔG) to the original structure (see Methods ).…”

“…based on Jackson et al [ 75 ]. The median of the resulting 50 scores was calculated to estimate the ΔG (in Rosetta Energy Units) of the respective deletion variant.…”

“…/path/to/rosetta/source/bin/relax.linuxgccrelease / -database /path/to/rosetta/database \ -l list_w_models.lst -ignore_unrecognized_res -use_input_sc -constrain_relax_to_start_coords -flip_HNQ -no_optH false -relax:fast -nstruct 2 based on Jackson et al [75]. The median of the resulting 50 scores was calculated to estimate the ΔG (in Rosetta Energy Units) of the respective deletion variant.…”

Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation

“…2BC). For in-frame deletions, we modified a previously described protocol(Jackson et al, 2017). Briefly, this consists of creating the sequence of the deletion variant of interest, creating a homology model using the original crystal structure (PDB: 3V42) as the template, relaxing this model with Rosetta, and calculating the energy difference (ΔΔG) to the original structure (see Methods)…”