A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative thermostability of protein mutants

<p>Abstract</p> <p>Background</p> <p>The ability to design thermostable proteins is theoretically important and practically useful. Robust and accurate algorithms, however, remain elusive. One critical problem is the lack of reliable methods to estimate the relative thermostability of possible mutants.</p> <p>Results</p> <p>We report a novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting the relative thermostability of protein mutants. The scoring function was developed based on an elaborate analysis of a set of features calculated or predicted from 540 pairs of hyperthermophilic and mesophilic protein ortholog sequences. It was constructed by a linear combination of ten important features identified by a feature ranking procedure based on the random forest classification algorithm. The weights of these features in the scoring function were fitted by a hill-climbing algorithm. This scoring function has shown an excellent ability to discriminate hyperthermophilic from mesophilic sequences. The prediction accuracies reached 98.9% and 97.3% in discriminating orthologous pairs in training and the holdout testing datasets, respectively. Moreover, the scoring function can distinguish non-homologous sequences with an accuracy of 88.4%. Additional blind tests using two datasets of experimentally investigated mutations demonstrated that the scoring function can be used to predict the relative thermostability of proteins and their mutants at very high accuracies (92.9% and 94.4%). We also developed an amino acid substitution preference matrix between mesophilic and hyperthermophilic proteins, which may be useful in designing more thermostable proteins.</p> <p>Conclusions</p> <p>We have presented a novel scoring function which can distinguish not only HP/MP ortholog pairs, but also non-homologous pairs at high accuracies. Most importantly, it can be used to accurately predict the relative stability of proteins and their mutants, as demonstrated in two blind tests. In addition, the residue substitution preference matrix assembled in this study may reflect the thermal adaptation induced substitution biases. A web server implementing the scoring function and the dataset used in this study are freely available at <url>http://www.abl.ku.edu/thermorank/</url>.</p

In Silico Classification of Proteins from Acidic and Neutral Cytoplasms

Protein acidostability is a common problem in biopharmaceutical and other industries. However, it remains a great challenge to engineer proteins for enhanced acidostability because our knowledge of protein acidostabilization is still very limited. In this paper, we present a comparative study of proteins from bacteria with acidic (AP) and neutral cytoplasms (NP) using an integrated statistical and machine learning approach. We construct a set of 393 non-redundant AP-NP ortholog pairs and calculate a total of 889 sequence based features for these proteins. The pairwise alignments of these ortholog pairs are used to build a residue substitution propensity matrix between APs and NPs. We use Gini importance provided by the Random Forest algorithm to rank the relative importance of these features. A scoring function using the 10 most significant features is developed and optimized using a hill climbing algorithm. The accuracy of the score function is 86.01% in predicting AP-NP ortholog pairs and is 76.65% in predicting non-ortholog AP-NP pairs, suggesting that there are significant differences between APs and NPs which can be used to predict relative acidostability of proteins. The overall trends uncovered in the study can be used as general guidelines for designing acidostable proteins. To best of our knowledge, this work represents the first systematic comparative study of the acidostable proteins and their non-acidostable orthologs

Identification of Properties Important to Protein Aggregation Using Feature Selection

Background: Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process. Results: We use two feature selection algorithms to identify 16 features, out of a total of 560 physicochemical properties, presumably important to protein aggregation. Two predictors (ProA-SVM and ProA-RF) using selected features are built for predicting peptide aggregation propensity and identifying aggregation prone regions in proteins. Both methods are compared favourably to other state-of-the-art algorithms in cross validation. The identified important properties are fairly consistent with previous studies and bring some new insights into protein and peptide aggregation. One interesting new finding is that aggregation prone peptide sequences have similar properties to signal peptide and signal anchor sequences. Conclusions: Both predictors are implemented in a freely available web application (http://www.abl.ku.edu/ProA/ webcite). We suggest that the quaternary structure of protein aggregates, especially soluble oligomers, may allow the formation of new molecular recognition signals that guide aggregate targeting to specific cellular sites

Influence of the Valine Zipper Region on the Structure and Aggregation of the Basic Leucine Zipper (bZIP) Domain of Activating Transcription Factor 5 (ATF5)

Protein aggregation is a major problem for biopharmaceuticals. While the control of aggregation is critically important for the future of protein pharmaceuticals, mechanisms of aggregate assembly, particularly the role that structure plays, are still poorly understood. Increasing evidence indicates that partially folded intermediates critically influence the aggregation pathway. We have previously reported the use of the basic leucine zipper (bZIP) domain of Activating Transcription Factor 5 (ATF5) as a partially folded model system to investigate protein aggregation. This domain contains three regions with differing structural propensity: a N-terminal polybasic region, a central helical leucine zipper region, and a C-terminal extended valine zipper region. Additionally, a centrally positioned cysteine residue readily forms an intermolecular disulfide bond that reduces aggregation. Computational analysis of ATF5 predicts that the valine zipper region facilitates self-association. Here we test this hypothesis using a truncated mutant lacking the C-terminal valine zipper region. We compare the structure and aggregation of this mutant to the wild-type (WT) form under both reducing and non-reducing conditions. Our data indicate that removal of this region results in a loss of alpha-helical structure in the leucine zipper and a change in the mechanism of self-association. The mutant form displays increased association at low temperature but improved resistance to thermally induced aggregation

Spa47 is an oligomerization-activated type three secretion system (T3SS) ATPase from \u3cem\u3eShigella flexneri\u3c/em\u3e

Gram-negative pathogens often use conserved type three secretion systems (T3SS) for virulence. The Shigella type three secretion apparatus (T3SA) penetrates the host cell membrane and provides a unidirectional conduit for injection of effectors into host cells. The protein Spa47 localizes to the base of the apparatus and is speculated to be an ATPase that provides the energy for T3SA formation and secretion. Here, we developed an expression and purification protocol, producing active Spa47 and providing the first direct evidence that Spa47 is a bona fide ATPase. Additionally, size exclusion chromatography and analytical ultracentrifugation identified multiple oligomeric species of Spa47 with the largest greater than 8 fold more active for ATP hydrolysis than the monomer. An ATPase inactive Spa47 point mutant was then engineered by targeting a conserved Lysine within the predicted Walker A motif of Spa47. Interestingly, the mutant maintained a similar oligomerization pattern as active Spa47, but was unable to restore invasion phenotype when used to complement a spa47 null S. flexneri strain. Together, these results identify Spa47 as a Shigella T3SS ATPase and suggest that its activity is linked to oligomerization, perhaps as a regulatory mechanism as seen in some related pathogens. Additionally, Spa47 catalyzed ATP hydrolysis appears to be essential for host cell invasion, providing a strong platform for additional studies dissecting its role in virulence and providing an attractive target for anti-infective agents

Permeation of the three aromatic dipeptides through lipid bilayers: Experimental and computational study

Publisher's note added August 2016: "This article was originally published online on 27 June 2016 with a sentence missing in the Acknowledgments. After the funding acknowledgments, it should read, “G.S.J. would like to thank Wilson R. Veras Tavarez and Elizabeth De Leon Olmeda of UCC for helpful comments.” AIP Publishing apologizes for this error. All online versions of the article were corrected on 28 June 2016; the article is correct as it appears in the printed version of the journal."The time-resolved parallel artificial membrane permeability assay with fluorescence detection and comprehensive computer simulations are used to study the passive permeation of three aromatic dipeptides—N-acetyl-phenylalanineamide (NAFA), N-acetyltyrosineamide (NAYA), and N-acetyltryptophanamide (NATA) through a 1,2-dioleoyl-sn-glycero-3-phospocholine (DOPC) lipid bilayer. Measured permeation times and permeability coefficients show fastest translocation for NAFA, slowest for NAYA, and intermediate for NATA under physiological temperature and pH. Computationally, we perform umbrella sampling simulations to model the structure, dynamics, and interactions of the peptides as a function of z, the distance from lipid bilayer. The calculated profiles of the potential of mean force show two strong effects—preferential binding of each of the three peptides to the lipid interface and large free energy barriers in the membrane center. We use several approaches to calculate the position-dependent translational diffusion coefficients D(z), including one based on numerical solution the Smoluchowski equation. Surprisingly, computed D(z) values change very little with reaction coordinate and are also quite similar for the three peptides studied. In contrast, calculated values of sidechain rotational correlation times τrot(z) show extremely large changes with peptide membrane insertion—values become 100 times larger in the headgroup region and 10 times larger at interface and in membrane center, relative to solution. The peptides’ conformational freedom becomes systematically more restricted as they enter the membrane, sampling α and β and C7eq basins in solution, α and C7eq at the interface, and C7eq only in the center. Residual waters of solvation remain around the peptides even in the membrane center. Overall, our study provides an improved microscopic understanding of passive peptide permeation through membranes, especially on the sensitivity of rotational diffusion to position relative to the bilayer. Published by AIP Publishing. [http://dx.doi.org/10.1063/1.4954241

Improving the Stability of the EC1 Domain of E-cadherin by Thiol Alkylation of the Cysteine Residue

The objective of this work was to improve chemical and physical stability of the EC1 protein derived from the extracellular domain of E-cadherin. In solution, the EC1 protein has been shown to form a covalent dimer via a disulfide bond formation followed by physical aggregation and precipitation. To improve solution stability of the EC1 protein, the thiol group of the Cys13 residue in EC1 was alkylated with iodoacetate, iodoacetamide, and maleimide-PEG-5000 to produce thioether derivatives called EC1-IA, EC1-IN, and EC1-PEG. The physical and chemical stabilities of the EC1 derivatives and the parent EC1 were evaluated at various pHs (3.0, 7.0, and 9.0) and temperatures (0, 3, 70 °C). The structural characteristics of each molecule were analyzed by circular dichroism (CD) and fluorescence spectroscopy and the derivatives have similar secondary structure as the parent EC1 protein at pH 7.0. Both EC1-IN and EC1-PEG derivatives showed better chemical and physical stability profiles than did the parent EC1 at pH 7.0. EC1-PEG had the best stability profile compared to EC1-IN and EC1 in solution under various conditions

Multidimensional Methods for the Formulation of Bipharmaceuticals and Vaccines

Determining and preserving the higher order structural integrity and conformational stability of proteins, plasmid DNA and macromolecular complexes such as viruses, virus-like particles and adjuvanted antigens is often a significant barrier to the successful stabilization and formulation of biopharmaceutical drugs and vaccines. These properties typically must be investigated with multiple lower resolution experimental methods, since each technique monitors only a narrow aspect of the overall conformational state of a macromolecular system. This review describes the use of empirical phase diagrams (EPDs) to combine large amounts of data from multiple high-throughput instruments and construct a map of a target macromolecule's physical state as a function of temperature, solvent conditions, and other stress variables. We present a tutorial on the mathematical methodology, an overview of some of the experimental methods typically used, and examples of some of the previous major formulation applications. We also explore novel applications of EPDs including potential new mathematical approaches as well as possible new biopharmaceutical applications such as analytical comparability, chemical stability, and protein dynamics

Application of radar chart array analysis to visualize effects of formulation variables on IgG1 particle formation as measured by multiple analytical techniques

This study presents a novel method to visualize protein aggregate and particle formation data to rapidly evaluate the effect of solution and stress conditions on the physical stability of an IgG1 monoclonal antibody (mAb). Radar chart arrays were designed so that hundreds of Microflow Digital Imaging (MFI) solution measurements, evaluating different mAb formulations under varying stresses, could be presented in a single figure with minimal loss of data resolution. These MFI radar charts show measured changes in subvisible particle number, size and morphology distribution as a change in the shape of polygons. Radar charts were also created to visualize mAb aggregate and particle formation across a wide size range by combining data sets from size exclusion chromatography (SEC), Archimedes resonant mass measurements, and MFI. We found that the environmental/mechanical stress condition (e.g., heat vs. agitation) was the most important factor in influencing the particle size and morphology distribution with this IgG1 mAb. Additionally, the presence of NaCl exhibited a pH and stress dependent behavior resulting in promotion or inhibition mAb particle formation. This data visualization technique provides a comprehensive analysis of the aggregation tendencies of this IgG1 mAb in different formulations with varying stresses as measured by different analytical techniques