Changes In Protein Stability Research Articles

In Vivo Reporters for Protein Half-Life.

Determination of the general capacity of proteolytic activity of a certain cell or tissue type can be crucial for an assessment of various features of an organism's growth and development and also for the optimization of biotechnological applications. Here, we describe the use of chimeric protein stability reporters that can be detected by standard laboratory techniques such as histological staining, selection using selective media or fluorescence microscopy. Dependent on the expression of the reporters due to the promoters applied, cell- and tissue-specific questions can be addressed. Here, we concentrate on methods which can be used for large-scale screening for protein stability changes rather than for detailed protein stability studies.

Studying Catabolism of Protein ADP-Ribosylation.

Protein ADP-ribosylation is a conserved posttranslational modification that regulates many major cellular functions, such as DNA repair, transcription, translation, signal transduction, stress response, cell division, aging, and cell death. Protein ADP-ribosyl transferases catalyze the transfer of an ADP-ribose (ADPr) group from the β-nicotinamide adenine dinucleotide (β-NAD+) cofactor onto a specific target protein with the subsequent release of nicotinamide. ADP-ribosylation leads to changes in protein structure, function, stability, and localization, thus defining the appropriate cellular response. Signaling processes that are mediated by modifications need to be finely tuned and eventually silenced and one of the ways to achieve this is through the action of enzymes that remove (reverse) protein ADP-ribosylation in a timely fashion such as PARG, TARG1, MACROD1, and MACROD2. Here, we describe several basic methods used to study the enzymatic activity of de-ADP-ribosylating enzymes.

Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations

The stability of folded proteins is critical to their biological function and for the efficacy of protein therapeutics. Predicting the energetic effects of protein mutations can improve our fundamental understanding of structural biology, the molecular basis of diseases, and possible routes to addressing those diseases with biological drugs. Identifying the effect of single amino acid point mutations on the thermodynamic equilibrium between the folded and unfolded states of a protein can pinpoint residues of critical importance that should be avoided in the process of improving other properties (affinity, solubility, viscosity, etc.) and suggest changes at other positions for increasing stability in protein engineering. Multiple computational tools have been developed for in silico predictions of protein stability in recent years, ranging from sequence-based empirical approaches to rigorous physics-based free energy methods. In this work, we show that FEP+, which is a free energy perturbation method based on all-atom molecular dynamics simulations, can provide accurate thermal stability predictions for a wide range of biologically relevant systems. Significantly, the FEP+ approach, while originally developed for relative binding free energies of small molecules to proteins and not specifically fitted for protein stability calculations, performs well compared to other methods that were fitted specifically to predict protein stability. Here, we present the broadest validation of a rigorous free energy-based approach applied to protein stability reported to date: 700+ single-point mutations spanning 10 different protein targets. Across the entire data set, we correctly classify the mutations as stabilizing or destabilizing in 84% of the cases, and obtain statistically significant predictions as compared with experiment [average error of ~1.6kcal/mol and coefficient of determination (R2) of 0.40]. This study demonstrates, for the first time in a large-scale validation, that rigorous free energy calculations can be used to predict changes in protein stability from point mutations without parameterization or system-specific customization, although further improvements should be possible with additional sampling and a better representation of the unfolded state of the protein. Here, we describe the FEP+ method as applied to protein stability calculations, summarize the large-scale retrospective validation results, and discuss limitations of the method, along with future directions for further improvements.

Computational Protein Design Through Grafting and Stabilization.

Computational grafting of target residues onto existing protein scaffolds is a powerful method for the design of proteins with novel function. In the grafting method side chain mutations are introduced into a preexisting protein scaffold to recreate a target functional motif. The success of this approach relies on two primary criteria: (1) the availability of compatible structural scaffolds, and (2) the introduction of mutations that do not affect the protein structure or stability. To identify compatible structural motifs we use the Erebus webserver, to search the protein data bank (PDB) for user-defined structural scaffolds. To identify potential design mutations we use the Eris webserver, which accurately predicts changes in protein stability resulting from mutations. Mutations that increase the protein stability are more likely to maintain the protein structure and therefore produce the desired function. Together these tools provide effective methods for identifying existing templates and guiding further design experiments. The software tools for scaffold searching and design are available at http://dokhlab.org .

In Silico and Functional Characterization of TBL1XR1 as a Tumor Suppressor in Large B-Cell Lymphomas

Large B-cell lymphomas (LBCL) exhibit multiple genetic alterations, often in specific combinations. However, little is known about the biologic consequences of these concurrent genetic features. We recently characterized the comprehensive molecular signatures of two LBCL subtypes that exclusively involve extranodal disease sites, primary central nervous system lymphoma (PCNSL) and primary testicular lymphoma (PTL). In contrast to systemic diffuse large B-cell lymphomas (DLBCLs) or most activated B-cell (ABC)-type DLBCLs, the majority of PCNSLs and PTLs exhibit oncogenic Toll-like receptor (TLR) signaling (MYD88L265P activating mutations), often with concurrent B-cell receptor (BCR) activation (CD79BY196mut) and/or additional mutations or inactivating rearrangements of TBL1XR1 (Chapuy and Roemer et al, Blood 2016; 127:869-81).TBL1XR1 is a component of the NCoR/SMRT co-repressor complex that modulates TLR/MYD88 signaling by increasing the clearance of NCoR/SMRT transcriptional co-repressors from certain TLR/MYD88 target genes. However, the functional consequences of somatic mutations in TBL1XR1 (TBL1XR1mut) or reduced TBL1XR1wt expression in LBCLs remain to be defined. For these reasons, we performed in silico modeling of the identified TBL1XR1mut and functionally characterized TBL1XR1mut and decreased TBL1XR1wt expression in model LBCL systems.First, we evaluated the frequency and types of TBL1XR1mut in our local series of PCNSLs and 151 primary DLBCLs (102 local and 49 previously published) for which we had whole exome sequenceing data. Thirty-six percent of PCNSLs and 6% of DLBCLs in this series exhibit TBL1XR1mut, over half of which occur in the context of MYD88 and/or CD79B mutations. In these PCNSLs and DLBCLs, we identifed 13 different non-synonymous TBL1XR1 mutations. All identified non-overlapping TBL1XR1 mutations were in the WD40 propellar domains, which are critical for protein-protein interactions. To gain insights into the structural consequences of TBL1XR1mut, we overlayed these alterations onto the TBL1XR1 protein crystal structure, modeled the corresponding mutations in silico and assessed protein stability changes. Eighty percent of the TBL1XR1mut resulted in significant destabilization of the mutant protein (ΔΔGibbsFreeEnergy median: 9.78 kcal/mol, range: 1.91 - 28.31). We found that mutations often perturbed the 3D protein structure by abrogating critical polar interactions (representative example TBL1XR1wt vs. TBL1XR1H390R in Figure 1). These in silico modeling data suggest that genetic perturbations of TBL1XR1mut disrupt the function of the encoded protein.For that reason, we modeled reduced TBL1XR1wt expression in informative DLBCL cell lines with or without concurrent CD79B/MYD88 mutations (TMD8 and OCI-Ly1, respectively) by genetically depleting endogenous TBL1XR1 alleles with CRISPR/Cas9. All of the resulting LBCL cell lines with loss of either one or two TBL1XR1 alleles exhibited significantly increased proliferation in comparison to the parental controls. We next assessed the role of TBL1XR1mut by generating HA-tagged versions of each identified TBL1XR1 mutation with site-directed mutagenesis. Viral particles of TBL1XR1mut constructs and the TBL1XR1wt control were used to reconstitute mutant and wild type protein in the TBL1XR1-/- Ly1 cell line. Reconstitution with each TBL1XR1mut significantly enhanced LBCL proliferation, in comparison to TBL1XR1wt.Taken together, these in silico and experimental data suggest that TBL1XR1mut in PCNSL and DLBCL are inactivating events and establish TBL1XR1 as a tumor suppressor in these diseases. Additional analysis of potential interactions between perturbed TBL1XR1 and MYD88/CD79B are underway. [Display omitted] DisclosuresRodig:Bristol-Myers Squibb: Honoraria, Research Funding; Perkin Elmer: Membership on an entity's Board of Directors or advisory committees.

IN SILICO PREDICTION OF DELETERIOUS AND NON-DELETERIOUS nsSNPs IN CFTR GENE VARIANTS

<p><strong>Objective: </strong>The major objective of the study was to carry out comparative bioinformatics analyses to identify different nsSNPs that were predicted to be deleterious or damaging to the structure and functions of CFTR protein causing cystic fibrosis.</p><p><strong>Methods: </strong>The CFTR gene variants (nsSNPs) and their related protein sequences from <em>Homo sapiens </em>were subjected to computational analyses using the following bioinformatics tools (a) SIFT: a sequence-homology based prediction tool that can be used to distinguish between the intolerant from tolerant SNP changes. (b) PolyPhen2: a structure and sequence-based physical and comparison tool to study the impact of amino acid substitution on the structure and function of human proteins and (c) I-Mutant2: to predict the protein stability changes arising due to single point mutations.</p><p><strong>Results: </strong>SIFT, PolyPhen2, and I-Mutant2 analyses indicated that 21 out of 108 nsSNPs were identified to be common that were strongly predicted to be deleterious and damaging for CTFR protein in cystic fibrosis conditions. Most of the substitutions in the CFTR protein contained the amino acids valine followed by cysteine and proline respectively. Homology modeling carried out to determine if any of these nsSNPs had a role in changing the conformation of CFTR protein drastically. Homology modeling of selected nsSNP variants indicated that these substitutions,however did not change the overall CFTR protein structure but predicted to cause severe damaging changes to the phenotypes of CFTR protein. Results indicated that multiple bioinformatics tools are needed to predictthe effect of substitutions and these prediction tools need to be analyzed more into detail and common determination factors are required to predict a nsSNP to be deleterious or damaging to the overall functioning of the CFTR protein.</p><p><strong>Conclusion: </strong>Multiple bioinformatics tools are in fact the need of the hour to establish if a strong relationship between nsSNPs that could alter the protein stability and cause a deleterious or damaging phenotypic change to the individual with cystic fibrosis involving the CFTR protein.</p>

MutLBSgeneDB: mutated ligand binding site gene DataBase.

Mutations at the ligand binding sites (LBSs) can influence protein structure stability, binding affinity with small molecules, and drug resistance in cancer patients. Our recent analysis revealed that ligand binding residues had a significantly higher mutation rate than other parts of the protein. Here, we built mutLBSgeneDB (mutated Ligand Binding Site gene DataBase) available at http://zhaobioinfo.org/mutLBSgeneDB. We collected and curated over 2300 genes (mutLBSgenes) having ∼12 000 somatic mutations at ∼10 000 LBSs across 16 cancer types and selected 744 drug targetable genes (targetable_mutLBSgenes) by incorporating kinases, transcription factors, pharmacological genes, and cancer driver genes. We analyzed LBS mutation information, differential gene expression network, drug response correlation with gene expression, and protein stability changes for all mutLBSgenes using integrated genetic, genomic, transcriptomic, proteomic, network and functional information. We calculated and compared the binding affinities of 20 carefully selected genes with their drugs in wild type and mutant forms. mutLBSgeneDB provides a user-friendly web interface for searching and browsing through seven categories of annotations: Gene summary, Mutated information, Protein structure related information, Differential gene expression and gene-gene network, Phenotype information, Pharmacological information, and Conservation information. mutLBSgeneDB provides a useful resource for functional genomics, protein structure, drug and disease research communities.

Protein stability changes of the novel p.Arg180Cys mutant A glycosyltransferase resulted in a weak A phenotype.

A novel A subgroup allele (c.538C>T p.Arg180Cys) showing weak A phenotype was found in a 30-year-old Korean woman with ABO discrepancy. Using 3D structural analysis, protein stability prediction and flow cytometric analysis of ABO antigen expression on HeLa cells transfected with plasmids containing the p.Arg180Cys mutant, we found that the Arg180 residue in the loop region of the A glycosyltransferases (GTA) structure plays significant role in stabilizing its closed conformation, which is required for substrate binding and catalysis study.

MCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance.

The ability to predict how a mutation affects ligand binding is an essential step in understanding, anticipating and improving the design of new treatments for drug resistance, and in understanding genetic diseases. Here we present mCSM-lig, a structure-guided computational approach for quantifying the effects of single-point missense mutations on affinities of small molecules for proteins. mCSM-lig uses graph-based signatures to represent the wild-type environment of mutations, and small-molecule chemical features and changes in protein stability as evidence to train a predictive model using a representative set of protein-ligand complexes from the Platinum database. We show our method provides a very good correlation with experimental data (up to ρ = 0.67) and is effective in predicting a range of chemotherapeutic, antiviral and antibiotic resistance mutations, providing useful insights for genotypic screening and to guide drug development. mCSM-lig also provides insights into understanding Mendelian disease mutations and as a tool for guiding protein design. mCSM-lig is freely available as a web server at http://structure.bioc.cam.ac.uk/mcsm_lig.

STRUM: structure-based prediction of protein stability changes upon single-point mutation

Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. http://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.edu Supplementary data are available at Bioinformatics online.

High-quality Thermodynamic Data on the Stability Changes of Proteins Upon Single-site Mutations

We have set up and manually curated a dataset containing experimental information on the impact of amino acid substitutions in a protein on its thermal stability. It consists of a repository of experimentally measured melting temperatures (Tm) and their changes upon point mutations (ΔTm) for proteins having a well-resolved x-ray structure. This high-quality dataset is designed for being used for the training or benchmarking of in silico thermal stability prediction methods. It also reports other experimentally measured thermodynamic quantities when available, i.e., the folding enthalpy (ΔH) and heat capacity (ΔCP) of the wild type proteins and their changes upon mutations (ΔΔH and ΔΔCP), as well as the change in folding free energy (ΔΔG) at a reference temperature. These data are analyzed in view of improving our insights into the correlation between thermal and thermodynamic stabilities, the asymmetry between the number of stabilizing and destabilizing mutations, and the difference in stabilization potential of thermostable versus mesostable proteins.

INPS-MD: a web server to predict stability of protein variants from sequence and structure.

Protein function depends on its structural stability. The effects of single point variations on protein stability can elucidate the molecular mechanisms of human diseases and help in developing new drugs. Recently, we introduced INPS, a method suited to predict the effect of variations on protein stability from protein sequence and whose performance is competitive with the available state-of-the-art tools. In this article, we describe INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension), a web server for the prediction of protein stability changes upon single point variation from protein sequence and/or structure. Here, we complement INPS with a new predictor (INPS3D) that exploits features derived from protein 3D structure. INPS3D scores with Pearson's correlation to experimental ΔΔG values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based INPS scores slightly lower than the structure-based INPS3D and both on the same blind test sets well compare with the state-of-the-art methods. INPS and INPS3D are available at the same web server: http://inpsmd.biocomp.unibo.it Supplementary data are available at Bioinformatics online. gigi@biocomp.unibo.it.

Milk protein composition and stability changes affected by iron in water sources

Water makes up more than 80% of the total weight of milk. However, the influence of water chemistry on the milk proteome has not been extensively studied. The objective was to evaluate interaction of water-sourced iron (low, medium, and high levels) on milk proteome and implications on milk oxidative state and mineral content. Protein composition, oxidative stability, and mineral composition of milk were investigated under conditions of iron ingestion through bovine drinking water (infused) as well as direct iron addition to commercial milk in 2 studies. Four ruminally cannulated cows each received aqueous infusions (based on water consumption of 100L) of 0, 2, 5, and 12.5mg/L Fe2+ as ferrous lactate, resulting in doses of 0, 200, 500 or 1,250mg of Fe/d, in a 4×4Latin square design for a 14-d period. For comparison, ferrous sulfate solution was directly added into commercial retail milk at the same concentrations: control (0mg of Fe/L), low (2mg of Fe/L), medium (5mg of Fe/L), and high (12.5mg of Fe/L). Two-dimensional electrophoresis coupled with matrix-assisted laser desorption/ionization–tandem time-of-flight (MALDI-TOF/TOF) high-resolution tandem mass spectrometry analysis was applied to characterize milk protein composition. Oxidative stability of milk was evaluated by the thiobarbituric acid reactive substances (TBARS) assay for malondialdehyde, and mineral content was measured by inductively coupled plasma mass spectrometry. For milk from both abomasal infusion of ferrous lactate and direct addition of ferrous sulfate, an iron concentration as low as 2mg of Fe/L was able to cause oxidative stress in dairy cattle and infused milk, respectively. Abomasal infusion affected both caseins and whey proteins in the milk, whereas direct addition mainly influenced caseins. Although abomasal iron infusion did not significantly affect oxidation state and mineral balance (except iron), it induced oxidized off-flavor and partial degradation of whey proteins. Direct iron addition to milk led to lipid oxidation during storage at 4°C. Oxidation level was positively associated with the concentration of added iron. Minerals (Mg, P, Na, K, Ca, Zn) in milk were not affected by the added iron in milk. This study indicated that a small amount of iron contamination in bovine drinking water at the farm or incidental iron addition from potable water sources causes oxidation, affects milk protein composition and stability, and affects final milk quality.

Novel Regulation of the Stability of Transcriptional Cofactors Ski and SnoN by GPCR Signals and Actin‐Cytoskeleton Dynamics

TGF‐β signaling controls several cellular processes from growth arrest to apoptosis in epithelial cells. The transcriptional cofactors Ski and SnoN are main regulators of TGF‐β signaling by sequestering SMAD proteins or recruiting transcriptional repressor complexes at TGF‐β/SMADs target genes. TGF‐β signaling regulates the stability of Ski and SnoN proteins through the ubiquitin‐proteasome system. Even though the important role that Ski and SnoN proteins play in early development and homeostasis, cell cycle was the unique signal besides TGF‐β pathway that regulated their stability. Nevertheless, the mechanisms that regulate Ski and SnoN protein stability in diverse cellular processes remain unknown. In this study, we found out that the actin cytoskeleton dynamics is a novel regulator of Ski and SnoN protein stability in hepatocytes. Inhibitors of actin cytoskeleton dynamics and cell confluence status regulate Ski and SnoN protein levels. Actin polymerization is a signal for Ski protein degradation via proteasome. We also demonstrated that G protein‐coupled receptors (GPCR) signaling control Ski and SnoN protein levels through promoting and decreasing its stability by cAMP or Rho signals, respectively. In conclusion, we identified a new regulation for Ski and SnoN protein stability by changes in actin cytoskeleton rearrangements. These findings suggest that changes in Ski and SnoN protein stability could be controlled by this novel regulation in physiological and pathological events as a result of changes in actin cytoskeleton dynamics induced by cell contact, extracellular matrix remodeling and soluble ligands; thus, it will be important to evaluate TGF‐β/SMADs signaling in all these cellular contexts.Support or Funding InformationThis work is supported by grants from DGAPA/PAPIIT/UNAM (IN208115) and CONACYT (No. 240224).

Osmolyte Effects on Monoclonal Antibody Stability and Concentration-Dependent Protein Interactions with Water and Common Osmolytes.

Preferential interactions of proteins with water and osmolytes play a major role in controlling the thermodynamics of protein solutions. While changes in protein stability and shifts in phase behavior are often reported with the addition of osmolytes, the underlying protein interactions with water and/or osmolytes are typically inferred rather than measured directly. In this work, Kirkwood-Buff integrals for protein-water interactions (G12) and protein-osmolyte interactions (G23) were determined as a function of osmolyte concentration from density measurements of antistreptavidin immunoglobulin gamma-1 (AS-IgG1) in ternary aqueous solutions for a set of common neutral osmolytes: sucrose, trehalose, sorbitol, and poly(ethylene glycol) (PEG). For sucrose and PEG solutions, both protein-water and protein-osmolyte interactions depend strongly on osmolyte concentrations (c3). Strikingly, both osmolytes change from being preferentially excluded to preferentially accumulated with increasing c3. In contrast, sorbitol and trehalose solutions do not show large enough preferential interactions to be detected by densimetry. G12 and G23 values are used to estimate the transfer free energy for native AS-IgG1 (Δμ2N) and compared with existing models. AS-IgG1 unfolding via calorimetry shows a linear increase in midpoint temperatures as a function of trehalose, sucrose, and sorbitol concentrations, but the opposite behavior for PEG. Together, the results highlight limitations of existing models and common assumptions regarding the mechanisms of protein stabilization by osmolytes. Finally, PEG preferential interactions destabilize the Fab regions of AS-IgG1 more so than the CH2 or CH3 domains, illustrating preferential interactions can be specific to different protein domains.

In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity.

Despite interest in associating polymorphisms with clinical or experimental phenotypes, functional interpretation of mutation data has lagged behind generation of data from modern high-throughput techniques and the accurate prediction of the molecular impact of a mutation remains a non-trivial task. We present here an integrated knowledge-driven computational workflow designed to evaluate the effects of experimental and disease missense mutations on protein structure and interactions. We exemplify its application with analyses of saturation mutagenesis of DBR1 and Gal4 and show that the experimental phenotypes for over 80% of the mutations correlate well with predicted effects of mutations on protein stability and RNA binding affinity. We also show that analysis of mutations in VHL using our workflow provides valuable insights into the effects of mutations, and their links to the risk of developing renal carcinoma. Taken together the analyses of the three examples demonstrate that structural bioinformatics tools, when applied in a systematic, integrated way, can rapidly analyse a given system to provide a powerful approach for predicting structural and functional effects of thousands of mutations in order to reveal molecular mechanisms leading to a phenotype. Missense or non-synonymous mutations are nucleotide substitutions that alter the amino acid sequence of a protein. Their effects can range from modifying transcription, translation, processing and splicing, localization, changing stability of the protein, altering its dynamics or interactions with other proteins, nucleic acids and ligands, including small molecules and metal ions. The advent of high-throughput techniques including sequencing and saturation mutagenesis has provided large amounts of phenotypic data linked to mutations. However, one of the hurdles has been understanding and quantifying the effects of a particular mutation, and how they translate into a given phenotype. One approach to overcome this is to use robust, accurate and scalable computational methods to understand and correlate structural effects of mutations with disease.

EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models

Protein engineering and characterisation of non-synonymous single nucleotide variants (SNVs) require accurate prediction of protein stability changes (ΔΔGu) induced by single amino acid substitutions. Here, we have developed a new prediction method called Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM), which comprises five specialised support vector machine (SVM) models and makes the final prediction from a consensus of two models selected based on the predicted secondary structure and accessible surface area of the mutated residue. The new method is applicable to single-domain monomeric proteins and can predict ΔΔGu with a protein sequence and mutation as the only inputs. EASE-MM yielded a Pearson correlation coefficient of 0.53–0.59 in 10-fold cross-validation and independent testing and was able to outperform other sequence-based methods. When compared to structure-based energy functions, EASE-MM achieved a comparable or better performance. The application to a large dataset of human germline non-synonymous SNVs showed that the disease-causing variants tend to be associated with larger magnitudes of ΔΔGu predicted with EASE-MM. The EASE-MM web-server is available at http://sparks-lab.org/server/ease.

Detection and Analysis of Cell Cycle-Associated APC/C-Mediated Cellular Ubiquitylation In Vitro and In Vivo.

The anaphase-promoting complex or cyclosome (APC/C) is one of the major orchestrators of the cell division cycle in mammalian cells. The APC/C acts as a ubiquitin ligase that triggers sequential ubiquitylation of a significant number of substrates which will be eventually degraded by proteasomes during major transitions of the cell cycle. In this chapter, we present accessible methodologies to assess both in in vitro conditions and in cellular systems ubiquitylation reactions mediated by the APC/C. In addition, we also describe techniques to evidence the changes in protein stability provoked by modulation of the activity of the APC/C. Finally, specific methods to analyze interactors or posttranslational modifications of particular APC/C subunits are also discussed. Given the crucial role played by the APC/C in the regulation of the cell cycle, this review only focuses on its action and effects in actively proliferating cells.

Salt effects on the picosecond dynamics of lysozyme hydration water investigated by terahertz time-domain spectroscopy and an insight into the Hofmeister series for protein stability and solubility.

The addition of salts into protein aqueous solutions causes changes in protein solubility and stability, whose ability is known to be ordered in the Hofmeister series. We investigated the effects of Hofmeister salts on the picosecond dynamics of water around a lysozyme molecule using terahertz time-domain spectroscopy. The change in the absorption coefficient for 200 mg mL(-1) lysozyme aqueous solution by the addition of salts was found to depend on the salts used, whereas that for pure water was almost independent of salts. From the difference in the salt concentration dependence for various salts, it has been found that chaotropic anions make the dynamics of water around the lysozyme molecule slower, whereas kosmotropic anions make the dynamics faster. The ability of an anion to slow down the water dynamics was found to have the following order: SCN(-) > Cl(-) > H2PO4(-) > NO3(-) ≈ SO4(2-). This result indicates that the effects of anions on the dynamics of water around the lysozyme molecule are the opposite of those for bulk water. This finding agrees with a prediction from a molecular model proposed by Collins [K. D. Collins, Methods, 2004, 34, 300]. The results presented here are compared with the results from preferential interaction studies and the results from sum frequency generation spectroscopy. These discussions have led to the conclusion that the picosecond dynamics of protein hydration water strongly contributes to protein stability, whereas electrostatic interactions between protein molecules contribute to protein solubility.

A conventional procedure to reduce Asn deamidation artifacts during trypsin peptide mapping.

Asn deamidation is a common post-translational modification of proteins with significant biological consequences. Asn deamidation can cause changes in structure, stability and function of proteins. LC-MS peptide mapping is the most widely used method to detect and quantify Asn deamidation. However, a significant amount of deamidation can occur during sample preparation for peptide mapping, making it challenging to accurately determine the original level of deamidation. Although several protocols to reduce procedure-induced deamidation have been reported, they either require special procedural steps or are not optimal for maintaining trypsin activity. In the current study, several commonly used buffers that are optimal for trypsin activity were evaluated. The results demonstrated that much lower levels of Asn deamidation artifacts were observed when Tris buffer was used, especially at lower concentrations. The addition of 10% acetonitrile further reduced the levels of Asn deamidation artifacts. The utility of the optimized procedure was demonstrated by the digestion of a recombinant monoclonal antibody. The proposed procedure can be readily applied to any laboratory settings as it does not require any special reagents or procedures.