ABIN1 dysfunction as a genetic basis for lupus nephritis

The genetic factors underlying the pathogenesis of lupus nephritis associated with systemic lupus erythematosus are largely unknown, although animal studies indicate that nuclear factor (NF)-?B is involved. We reported previously that aknockin mouse expressinganin active form of ABIN1 (ABIN1[D485N]) develops lupus-like autoimmune disease and demonstrates enhanced activation of NF-?B and mitogen-activated protein kinases in immune cells after toll-like receptor stimulation. In the current study, we show that ABIN1[D485N] mice develop progressive GN similar to class III and IV lupus nephritis in humans. To investigate the clinical relevance of ABIN1 dysfunction, we genotyped five single-nucleotide polymorphisms in the gene encoding ABIN1, TNIP1, in samples from European-American, African American, Asian, Gullah, and Hispanic participants in the Large Lupus Association Study 2. Comparing cases of systemic lupus erythematosus with nephritis and cases ofsystemic lupus erythematosus without nephritis revealed strong associations with lupus nephritis at rs7708392 in European Americans and rs4958881 in African Americans. Comparing cases of systemic lupus erythematosus with nephritis and healthy controls revealed a stronger association at rs7708392 in European Americans but not at rs4958881 in African Americans. Our data suggest that variants in the TNIP1 gene are associated with the risk for lupus nephritis and could be mechanistically involved in disease development via aberrant regulation of NF-?B and mitogen-activated protein kinase activity. Copyright © 2013 by the American Society of Nephrology

Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset

Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. The development of new experimental datasets and computational tools to guide these engineering efforts remains an active area of research. Thus, to complement the previously reported measures of T 50 and kinetic constants, we are reporting an expansion of our previously published dataset of mutants for β-glucosidase to include both measures of T M and ΔΔG. For a set of 51 mutants, we found that T 50 and T M are moderately correlated, with a Pearson correlation coefficient and Spearman's rank coefficient of 0.58 and 0.47, respectively, indicating that the two methods capture different physical features. The performance of predicted stability using nine computational tools was also evaluated on the dataset of 51 mutants, none of which are found to be strong predictors of the observed changes in T 50, T M, or ΔΔG. Furthermore, the ability of the nine algorithms to predict the production of isolatable soluble protein was examined, which revealed that Rosetta ΔΔG, FoldX, DeepDDG, PoPMuSiC, and SDM were capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of mutants that maintain fold and soluble production properties

Thermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed.

Accurate modeling of enzyme activity and stability is an important goal of the protein engineering community. However, studies seeking to evaluate current progress are limited by small data sets of quantitative kinetic constants and thermal stability measurements. Here, we report quantitative measurements of soluble protein expression in E. coli, thermal stability, and Michaelis-Menten constants (kcat, KM, and kcat/KM) for 129 designed mutants of a glycoside hydrolase. Statistical analyses reveal that functional Tm is independent of kcat, KM, and kcat/KM in this system, illustrating that an individual mutation can modulate these functional parameters independently. In addition, this data set is used to evaluate computational predictions of protein stability using the established Rosetta and FoldX algorithms. Predictions for both are found to correlate only weakly with experimental measurements, suggesting improvements are needed in the underlying algorithms

Thermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed

<div><p>Accurate modeling of enzyme activity and stability is an important goal of the protein engineering community. However, studies seeking to evaluate current progress are limited by small data sets of quantitative kinetic constants and thermal stability measurements. Here, we report quantitative measurements of soluble protein expression in <i>E</i>. <i>coli</i>, thermal stability, and Michaelis-Menten constants (<i>k</i>_cat, K_M, and <i>k</i>_cat/K_M) for 129 designed mutants of a glycoside hydrolase. Statistical analyses reveal that functional T_m is independent of <i>k</i>_cat, K_M, and <i>k</i>_cat/K_M in this system, illustrating that an individual mutation can modulate these functional parameters independently. In addition, this data set is used to evaluate computational predictions of protein stability using the established Rosetta and FoldX algorithms. Predictions for both are found to correlate only weakly with experimental measurements, suggesting improvements are needed in the underlying algorithms.</p></div

Relationship between protein melting temperature (T_m) and kinetic constants <i>k</i>_cat, K_M, and <i>k</i>_cat/K_M in the BglB system.

<p>T_m values are on a linear scale in units of degrees Celsius and values for kinetic constants are on a log scale, with units of min^–1, mM, and M^–1min^–1, respectively. These parameters are not correlated in BglB (Pearson correlation < 0.25 for T_m versus each of the kinetic constants <i>k</i>_cat, K_M, and <i>k</i>_cat/K_M). The independence of these parameters suggests that they can be separately engineered in a rational manner.</p

Relative effects on enzyme functional parameters for 129 mutants of BglB.

<p>Each mutant gets a bar with six colored boxes, depicting 1) soluble protein expression, 2) T_m, 3) <i>k</i>_cat, 4) K_M, 5) <i>k</i>_cat/K_M, and 6) conservation within Pfam GH01. For expression (box 1), a black box indicates soluble expression > 0.10 mg/mL, and a white box indicates expression < 0.10 mg/mL in <i>E</i>. <i>coli</i> BLR (DE3). For T_m (box 2), a linear scale is used to depict change in T_m compared to wild type, with mutants with greater T_m in green, and those with lower T_m in purple. For <i>k</i>_cat, 1/K_M, and <i>k</i>_cat/K_M (boxes 3–5), blue indicates lower values, and orange indicates higher values relative to the wild type value, as indicated by the color legend (top). For conservation (box 6), the frequency of native BglB residue in an alignment of the BglB sequence to 1,554 sequences from Pfam GH01 is shown, on a linear scale from 0% to 100%. The quantitative measurements used to produce this illustration are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176255#pone.0176255.s001" target="_blank">S1 Table</a>.</p

Correlations between experimentally-determined T_m and structural features from molecular modeling algorithms.

<p>For each of the three computational protocols used for prediction of stability in this study, the two most-correlated (black) and two least-correlated (gray) features are plotted against experimentally-determined T_m. Pearson correlation between the two sets of values is provided above each plot. For descriptions of individual features for each of the three algorithms, see references for RosettaDesign [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176255#pone.0176255.ref011" target="_blank">11</a>], Rosetta ΔΔG [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176255#pone.0176255.ref012" target="_blank">12</a>], and FoldX [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176255#pone.0176255.ref009" target="_blank">9</a>].</p

Structural analysis of Rosetta models of designed point mutants of BglB with effects on thermal stability.

<p>Four mutant panels are shown, sorted from left to right by increasing T_m. In the top panel, experimentally-determined change in T_m and k_cat/K_M are given. For reference, the T_m for the wild type sequence is 39.9°C, and the <i>k</i>_cat/K_M is 174,000 M^–1min^–1. In the next panel down, sequence logos depict the local area of sequence conservation based on an alignment of 1,544 sequences from Pfam GH01. At bottom, depictions of the local area of the mutation in the BglB WT protein (top) and RosettaDesign model of mutation (bottom).</p

Overview of the modeled BglB-pNPG complex showing positions mutated in this study and reaction used to determine functional properties of individual mutants.

<p>PyMOL rendering [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176255#pone.0176255.ref032" target="_blank">32</a>] of modeled BglB in complex with pNPG showing the 68 sequence positions selected for mutation in this study (teal spheres) and the modeled transition-state structure (white ball and stick model). Below, reaction scheme of the hydrolysis of pNPG by BglB used to determine functional T_m and kinetic parameters <i>k</i>_cat, K_M, and <i>k</i>_cat/K_M.</p

Correlations between conservation within functional protein family and enzyme functional parameters protein melting temperature (T_m) and kinetic constants (<i>k</i>_cat, K_M, and <i>k</i>_cat/K_M) in the BglB system.

<p>Scatter plots showing conservation analysis from an alignment of 1,554 proteins in Pfam family 1 (glycoside hydrolases) versus measured values for T_m (linear scale, units of °C) and each of the kinetic constants <i>k</i>_cat, K_M, and <i>k</i>_cat/K_M (log scale) with units of min^–1, mM, and M^–1min^–1, respectively.</p