Human SIRT1 Multispecificity Is Modulated by Active-Site Vicinity Substitutions during Natural Evolution

The evolution of post-translational modifications

Post-translational modifications (PTMs) are chemical modifications that can regulate the activity and function of proteins. From an evolutionary perspective, they also represent a fast mechanism for the generation of phenotypic diversity and divergence. Advances in mass spectrometry have now enabled the identification of over 600 distinct PTM classes collectively spanning an order of 10⁶ unique sites. However, the chemical detection of PTMs has lagged far behind their functional characterisation, and relatively little is still known about the selective constraints that govern PTM evolution. In particular, the true fraction of PTM sites that are functional - and thus subject to selection - remains an open question. Here, I review advances made in the past two years towards understanding the evolution of PTMs and their associated enzymes.

The Elephant evolved p53 isoforms that escape mdm2-mediated repression and cancer

The p53 tumor suppressor is a transcription factor with roles in cell development, apoptosis, oncogenesis, aging, and homeostasis in response to stresses and infections. p53 is tightly regulated by the MDM2 E3 ubiquitin ligase. The p53–MDM2 pathway has coevolved, with MDM2 remaining largely conserved, whereas the TP53 gene morphed into various isoforms. Studies on prevertebrate ancestral homologs revealed the transition from an environmentally induced mechanism activating p53 to a tightly regulated system involving cell signaling. The evolution of this mechanism depends on structural changes in the interacting protein motifs. Elephants such as Loxodonta africana constitute ideal models to investigate this coevolution as they are large and long-living as well as having 20 copies of TP53 isoformic sequences expressing a variety of BOX-I MDM2-binding motifs. Collectively, these isoforms would enhance sensitivity to cellular stresses, such as DNA damage, presumably accounting for strong cancer defenses and other adaptations favoring healthy aging. Here we investigate the molecular evolution of the p53–MDM2 system by combining in silico modeling and in vitro assays to explore structural and functional aspects of p53 isoforms retaining the MDM2 interaction, whereas forming distinct pools of cell signaling. The methodology used demonstrates, for the first time that in silico docking simulations can be used to explore functional aspects of elephant p53 isoforms. Our observations elucidate structural and mechanistic aspects of p53 regulation, facilitate understanding of complex cell signaling, and suggest testable hypotheses of p53 evolution referencing Peto’s Paradox.

Quantitative structure-activity relationship modeling reveals the minimal sequence requirement and amino acid preference of sirtuin-1's deacetylation substrates in diabetes mellitus

Sirtuin 1 (SIRT1) is a nicotinamide adenine dinucleotide (NAD[Formula: see text]-dependent deacetylase involved in multiple glucose metabolism pathways and plays an important role in the pathogenesis of diabetes mellitus (DM). The enzyme specifically recognizes its deacetylation substrates' peptide segments containing a central acetyl-lysine residue as well as a number of amino acids flanking the central residue. In this study, we attempted to ascertain the minimal sequence requirement (MSR) around the central acetyl-lysine residue of SIRT1 substrate-recognition sites as well as the amino acid preference (AAP) at different residues of the MSR window through quantitative structure-activity relationship (QSAR) strategy, which would benefit our understanding of SIRT1 substrate specificity at the molecular level and is also helpful to rationally design substrate-mimicking peptidic agents against DM by competitively targeting SIRT1 active site. In this procedure, a large-scale dataset containing 6801 13-mer acetyl-lysine peptides (and their SIRT1-catalyized deacetylation activities) were compiled to train 10 QSAR regression models developed by systematic combination of machine learning methods (PLS and SVM) and five amino acids descriptors (DPPS, T-scale, MolSurf, [Formula: see text]-score, and FASGAI). The two best QSAR models (PLS+FASGAI and SVM+DPPS) were then employed to statistically examine the contribution of residue positions to the deacetylation activity of acetyl-lysine peptide substrates, revealing that the MSR can be represented by 5-mer acetyl-lysine peptides that meet a consensus motif X[Formula: see text]X[Formula: see text]X[Formula: see text](AcK)₀X[Formula: see text]. Structural analysis found that the X[Formula: see text] and (AcK)₀ residues are tightly packed against the enzyme active site and confer both stability and specificity for the enzyme-substrate complex, whereas the X[Formula: see text], X[Formula: see text] and X[Formula: see text] residues are partially exposed to solvent but can also effectively stabilize the complex system. Subsequently, a systematic deacetylation activity change profile (SDACP) was created based on QSAR modeling, from which the AAP for each residue position of MSR was depicted. With the profile, we were able to rationally design an SDACP combinatorial library with promising deacetylation activity, from which nine MSR acetyl-lysine peptides as well as two known SIRT1 acetyl-lysine peptide substrates were tested by using SIRT1 deacetylation assay. It is revealed that the designed peptides exhibit a comparable or even higher activity than the controls, although the former is considerably shorter than the latter.

Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics

Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.