SIRT3 substrate specificity determined by peptide arrays and machine learning

Abstract

Accumulating evidence suggests that reversible protein acetylation may be a major regulatory mechanism that rivals phosphorylation. With the recent cataloging of thousands of acetylation sites on hundreds of proteins comes the challenge of identifying the acetyltransferases and deacetylases that regulate acetylation levels. Sirtuins are a conserved family of NAD(+)-dependent protein deacetylases that are implicated in genome maintenance, metabolism, cell survival, and lifespan. SIRT3 is the dominant protein deacetylase in mitochondria, and emerging evidence suggests that SIRT3 may control major pathways by deacetylation of central metabolic enzymes. Here, to identify potential SIRT3 substrates, we have developed an unbiased screening strategy that involves a novel acetyl-lysine analogue (thiotrifluoroacetyl-lysine), SPOT-peptide libraries, machine learning, and kinetic validation. SPOT peptide libraries based on known and potential mitochondrial acetyl-lysine sites were screened for SIRT3 binding and then analyzed using machine learning to establish binding trends. These trends were then applied to the mitochondrial proteome as a whole to predict binding affinity of all lysine sites within human mitochondria. Machine learning prediction of SIRT3 binding correlated with steady-state kinetic k(cat)/K(m) values for 24 acetyl-lysine peptides that possessed a broad range of predicted binding. Thus, SPOT peptide-binding screens and machine learning prediction provides an accurate and efficient method to evaluate sirtuin substrate specificity from a relatively small learning set. These analyses suggest potential SIRT3 substrates involved in several metabolic pathways such as the urea cycle, ATP synthesis, and fatty acid oxidation.

Figure 1

Schematic of SIRT3 binding assay to the SPOT library cellulose membranes. Each individual spot within the SPOT libraries is a 9-mer with four randomized residues proximal to a central thiotrifluoroacetyl-lysine residue and C-terminal covalent attachment to amine-modified cellulose membranes.

Figure 2

Binding of SIRT3 to a SPOT library of 300 lysine-centered 9-mer peptides randomly selected from the mitochondrial proteome. A representative SPOT library is shown with SIRT3. Performing the same procedure without SIRT3 gave no signal above background (data not shown). SPOT library screens were performed as described under Experimental Procedures.

Figure 3

(A) Sequence logos illustrating learned linear regression weights for the position-specific residue features. The height of each residue is proportional to the coefficient used by the model in predicting SIRT3 binding specificity. Positively-correlated residue positions are shown above the baseline, while negatively-correlated resides are below. (B) Global feature model parameters. A bar chart illustrating learned linear regression weights for the global peptide scale features. Positive values suggest that the corresponding chemical property (e.g. hydrophobicity) or secondary structure (e.g. antiparallel strand) is positively correlated with SIRT3 binding specificity. Negative values likewise suggest a negatively correlated relationship.

Figure 3

(A) Sequence logos illustrating learned linear regression weights for the position-specific residue features. The height of each residue is proportional to the coefficient used by the model in predicting SIRT3 binding specificity. Positively-correlated residue positions are shown above the baseline, while negatively-correlated resides are below. (B) Global feature model parameters. A bar chart illustrating learned linear regression weights for the global peptide scale features. Positive values suggest that the corresponding chemical property (e.g. hydrophobicity) or secondary structure (e.g. antiparallel strand) is positively correlated with SIRT3 binding specificity. Negative values likewise suggest a negatively correlated relationship.

Figure 4

Steady state kinetic validation of acetyl-lysine peptides selected from SPOT-library screens of SIRT3. Predicted percentile refers to the relative binding affinity predicted from the learned model trained on the binding intensities from SPOT library screens (Figure 2). Blue circles are sequences tested on the SPOT membrane, and red squares represent sequences that were not tested.

Figure 5

Models of acetyl-lysine peptides bound in the SIRT3 active site. Stick models of (a) acetyl-CoA synthetase 2 (ACS2), (b) K88 of ornithine transcarbamoylase (OTC), (c) K230 of the alpha subunit of ATP synthase, and (d) K498 of the flavoprotein subunit of succinate dehydrogenase docked into the SIRT3 active site were based on the structure of a thioalkylamidate bound to SIRT3 resulting from the reaction of a thioacetyl-lysine acetyl-CoA synthetase 2 (ACS2) peptide and NAD+ (PDB code 3GLT) (63). The enzyme surface shown is prior to energy minimization and colored based on electrostatic potential with red, blue, and white surface representing negative, positive, and hydrophobic residues, respectively. Modeling was performed by mutating an acetyl-lysine ACS2 peptide bound to SIRT3 (PDB code 3GLR; (63)) using Pymol (75), and the structure was energy-minimized using CHARMMing (76) using the CHARMM defaults for Shake. Images were generated using Pymol (75).