Protein thermostability prediction within homologous families using temperature-dependent statistical potentials

Role of phosphate-coordinating arginine residues in the thermal stability of uridine phosphorylase from Shewanella oneidensis MR-1

The role of phosphate-coordinating arginine residues in the thermal stability of uridine phosphorylase from Shewanella oneidensis MR-1 was investigated by mutation analysis. Uridine phosphorylase mutant genes were constructed by site-directed mutagenesis. The enzyme mutants were prepared and isolated, and their kinetic parameters were determined. It was shown that all these arginine residues play an important role both in the catalysis and thermal stability. The arginine residues 176 were demonstrated to form a kind of a phosphate pore in the hexameric structure of uridine phosphorylase, and they not only contribute to thermal stabilization of the enzyme but also have a regulatory function. The replacement of arginine 176 with an alanine residue resulted in a significant decrease in the kinetic stability of the enzyme but led to a twofold increase in its specific activity.

PEGylation versus glycosylation: effect on the thermodynamics and thermostability of crisantaspase

Thermostability is an important and desired feature of therapeutic proteins and is critical for the success or failure of protein drugs development. It can be increased by PEGylation-binding of poly(ethylene glycol) moieties-or glycosylation-post-translational modification to add glycans. Here, the thermostability and thermodynamic parameters of native, PEGylated, and glycosylated versions of the antileukemic enzyme crisantaspase were investigated. First-order kinetics was found to describe the irreversible deactivation process. Activation energy of the enzyme-catalyzed reaction (E*) was estimated for native, PEGylated, and glycosylated enzyme (10.2, 14.8, and 18.8 kJ mol-1 respectively). Half-life decreased progressively with increasing temperature, and longer half-life was observed for PEG-crisantaspase (87.74 min) at 50 °C compared to the native form (9.79 min). The activation energy of denaturation of PEG-crisantaspase (307.1 kJ mol-1) was higher than for crisantaspase (218.1 kJ mol-1) and Glyco-crisantaspase (120.0 kJ mol-1), which means that more energy is required to overcome the energy barrier of the unfolding process. According to our results, PEG-crisantaspase is more thermostable than its native form, while Glyco-crisantaspase is more thermosensitive.

DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences

Thermally stable proteins find extensive applications in industrial production, pharmaceutical development, and serve as a highly evolved starting point in protein engineering. The thermal stability of proteins is commonly characterized by their melting temperature (Tm). However, due to the limited availability of experimentally determined Tm data and the insufficient accuracy of existing computational methods in predicting Tm, there is an urgent need for a computational approach to accurately forecast the Tm values of thermophilic proteins. Here, we present a deep learning-based model, called DeepTM, which exclusively utilizes protein sequences as input and accurately predicts the Tm values of target thermophilic proteins on a dataset consisting of 7790 thermophilic protein entries. On a test set of 1550 samples, DeepTM demonstrates excellent performance with a coefficient of determination (R2) of 0.75, Pearson correlation coefficient (P) of 0.87, and root mean square error (RMSE) of 6.24 ℃. We further analyzed the sequence features that determine the thermal stability of thermophilic proteins and found that dipeptide frequency, optimal growth temperature (OGT) of the host organisms, and the evolutionary information of the protein significantly affect its melting temperature. We compared the performance of DeepTM with recently reported methods, ProTstab2 and DeepSTABp, in predicting the Tm values on two blind test datasets. One dataset comprised 22 PET plastic-degrading enzymes, while the other included 29 thermally stable proteins of broader classification. In the PET plastic-degrading enzyme dataset, DeepTM achieved RMSE of 8.25 ℃. Compared to ProTstab2 (20.05 ℃) and DeepSTABp (20.97 ℃), DeepTM demonstrated a reduction in RMSE of 58.85% and 60.66%, respectively. In the dataset of thermally stable proteins, DeepTM (RMSE=7.66 ℃) demonstrated a 51.73% reduction in RMSE compared to ProTstab2 (RMSE=15.87 ℃). DeepTM, with the sole requirement of protein sequence information, accurately predicts the melting temperature and achieves a fully end-to-end prediction process, thus providing enhanced convenience and expediency for further protein engineering.

Stability of trastuzumab during nanomedicine formulation using SEC-HPLC coupled with polyacrylamide gel electrophoresis

The anti-HER2 antibody trastuzumab has been proven to be an effective targeting ligand for drug delivery. This study investigates the structural integrity of trastuzumab under different stress factors in formulation development and its long-term stability. A validated size exclusion high performance liquid chromatographic (SEC-HPLC) method was first developed. The stability of trastuzumab (0.21-21 mg/ml) under stress conditions (mechanical, freeze-and-thaw, pH and temperature) and long-term storage in the presence of formulation excipients were monitored for up to 12 months, using both the SEC-HPLC method and sodium-dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). The anti-proliferation activity of the reconstituted antibody stored at 4 °C against HER2⁺ BT-474 breast cells was also monitored over 12 months. The developed SEC-HPLC method was sensitive and accurate. Solutions of trastuzumab were resistant to mechanical stress and repeated freeze-and-thaw; but were unstable under acidic (pH 2.0 and 4.0) and alkaline (pH 10.0 and 12.0) environments. The samples degraded over 5 days at 60 °C, and within 24 h at 75 °C. Low temperature (-80 °C or 4 °C) and low concentration (0.21 mg/ml) favoured the long-term stability. The anti-proliferation activity was conserved at 4 °C for at least 12 months. This study provided valuable stability information in developing trastuzumab involved nano-formulation as well as in clinical settings.

The Thermostability of Nucleoside Phosphorylases from Prokaryotes. I. The Role of the Primary Structure of the N-terminal fragment of the Protein in the Thermostability of Uridine Phosphorylases

Mutant uridine phosphorylase genes from Shewanella oneidensis MR-1 (S. oneidensis) were constructed by site-directed mutagenesis and strains-producers of the corresponding recombinant (F5I and F5G) proteins were obtained on the basis of Escherichia coli cells. The mutant proteins were purified and their physicochemical and enzymatic properties were studied. It was shown that the N-terminal fragment of uridine phosphorylase plays an important role in the thermal stabilization of the enzyme as a whole. The role of the aminoacid (a.a.) residue phenylalanine (F5) in the formation of thermotolerance of uridine phosphorylases from gamma-proteobacteria was revealed.

Crowding revisited: Open questions and future perspectives

Although biophysical studies have traditionally been performed in diluted solutions, it was pointed out in the late 1990s that the cellular milieu contains several other macromolecules, creating a condition of molecular crowding. How crowding affects protein stability is an important question heatedly discussed over the past 20 years. Theoretical estimations have suggested a 5-20°C effect of fold stabilisation. This estimate, however, is at variance with what has been verified experimentally that proposes only a limited increase of stability, opening the question whether some of the assumptions taken for granted should be reconsidered. The present review critically analyses the causes of this discrepancy and discusses the limitations and implications of the current concept of crowding.

ProTstab2 for Prediction of Protein Thermal Stabilities

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.

Thermostability engineering of an inulin fructotransferase for the biosynthesis of difructose anhydride I

The thermostability of enzymes is an essential factor that performs a vital role during practical applications. Inulin fructotransferases can efficiently convert inulin into bio-functional difructose anhydrides (DFAs). The present study aimed to improve the thermostability of a previously reported inulin fructotransferase, SpIFTase, and apply it to the biosynthesis of DFA I. In silico rational design was used to predict mutation sites, based on sequential and structural information. Two triple-site mutants, Q69L/Q234L/K310G and E201I/Q234L/K310G, were characterized and exhibited enhanced thermostability with approximately 5 °C higher in melting temperature (T_m), respectively, and a 45-fold longer half-life (t_1/2) at 70 °C, compared to that of SpIFTase. Molecular dynamic simulations and elaborate structural analysis suggested that the combinations of hydrophobic interaction, electrostatic potential distribution, and decreased flexibility via stabilization of loops and α-helix improved the thermostability of SpIFTase. Additionally, the promising mutants exhibited great potential to the industrial production of DFA I.

Effects of the Temperature and Salt Concentration on the Structural Characteristics of the Protein (PDB Code 1BBL)

The effect of the temperature and salt solution on the structural characteristics of the protein 1BBL was investigated by molecular dynamics simulations. The paper presents simulation results regarding the non-bonded energy and the structural stability of the protein immersed in salt solutions with different concentrations and temperatures. Our work demonstrates that the electrostatic potential energy and van der Waals energy of the system show the opposite changes with the influence of the external environment. Since the electrostatic potential energy changes more obviously, it is dominated in the non-bonding interactions. The structural parameters, such as the root mean square deviation and the radius of gyration, increased initially and decreased afterward with the increase of the salt concentration. The protein presented the loose structure with a relative low stability when it was immersed in a monovalent solution with a salt concentration of 0.8 mol/L. The salt concentration corresponding to the maximum value of structural parameters in the monovalent salt solution was double that in the divalent salt solution. It was also concluded that the protein presented a compact and stable structure when immersed in salt solutions with a high concentration of 2.3 mol/L. The analysis of the root mean square deviation and root mean square fluctuation of the protein sample also exhibited that the structural stability and chain flexibility are strongly guided by the effect of the temperature. These conclusions help us to understand the structural characteristics of the protein immersed in the salt solutions with different concentrations and temperatures.

Recipes for Inducing Cold Denaturation in an Otherwise Stable Protein

Although cold denaturation is a fundamental phenomenon common to all proteins, it can only be observed in a handful of cases where it occurs at temperatures above the freezing point of water. Understanding the mechanisms that determine cold denaturation and the rules that permit its observation is an important challenge. A way to approach them is to be able to induce cold denaturation in an otherwise stable protein by means of mutations. Here, we studied CyaY, a relatively stable bacterial protein with no detectable cold denaturation and a high melting temperature of 54 °C. We have characterized for years the yeast orthologue of CyaY, Yfh1, a protein that undergoes cold and heat denaturation at 5 and 35 °C, respectively. We demonstrate that, by transferring to CyaY the lessons learnt from Yfh1, we can induce cold denaturation by introducing a restricted number of carefully designed mutations aimed at destabilizing the overall fold and inducing electrostatic frustration. We used molecular dynamics simulations to rationalize our findings and demonstrate the individual effects observed experimentally with the various mutants. Our results constitute the first example of rationally designed cold denaturation and demonstrate the importance of electrostatic frustration on the mechanism of cold denaturation.

BRANEart: Identify Stability Strength and Weakness Regions in Membrane Proteins

Understanding the role of stability strengths and weaknesses in proteins is a key objective for rationalizing their dynamical and functional properties such as conformational changes, catalytic activity, and protein-protein and protein-ligand interactions. We present BRANEart, a new, fast and accurate method to evaluate the per-residue contributions to the overall stability of membrane proteins. It is based on an extended set of recently introduced statistical potentials derived from membrane protein structures, which better describe the stability properties of this class of proteins than standard potentials derived from globular proteins. We defined a per-residue membrane propensity index from combinations of these potentials, which can be used to identify residues which strongly contribute to the stability of the transmembrane region or which would, on the contrary, be more stable in extramembrane regions, or vice versa. Large-scale application to membrane and globular proteins sets and application to tests cases show excellent agreement with experimental data. BRANEart thus appears as a useful instrument to analyze in detail the overall stability properties of a target membrane protein, to position it relative to the lipid bilayer, and to rationally modify its biophysical characteristics and function. BRANEart can be freely accessed from http://babylone.3bio.ulb.ac.be/BRANEart.

Computational Analysis of Thermal Adaptation in Extremophilic Chitinases: The Achilles' Heel in Protein Structure and Industrial Utilization

Understanding protein stability is critical for the application of enzymes in biotechnological processes. The structural basis for the stability of thermally adapted chitinases has not yet been examined. In this study, the amino acid sequences and X-ray structures of psychrophilic, mesophilic, and hyperthermophilic chitinases were analyzed using computational and molecular dynamics (MD) simulation methods. From the findings, the key features associated with higher stability in mesophilic and thermophilic chitinases were fewer and/or shorter loops, oligomerization, and less flexible surface regions. No consistent trends were observed between stability and amino acid composition, structural features, or electrostatic interactions. Instead, unique elements affecting stability were identified in different chitinases. Notably, hyperthermostable chitinase had a much shorter surface loop compared to psychrophilic and mesophilic homologs, implying that the extended floppy surface region in cold-adapted and mesophilic chitinases may have acted as a “weak link” from where unfolding was initiated. MD simulations confirmed that the prevalence and flexibility of the loops adjacent to the active site were greater in low-temperature-adapted chitinases and may have led to the occlusion of the active site at higher temperatures compared to their thermostable homologs. Following this, loop “hot spots” for stabilizing and destabilizing mutations were also identified. This information is not only useful for the elucidation of the structure–stability relationship, but will be crucial for designing and engineering chitinases to have enhanced thermoactivity and to withstand harsh industrial processing conditions

SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

MOTIVATION

Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes.

RESULTS

We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale analysis of the different types of strengths and weaknesses demonstrated their complementarity and the enhancement of the information they provide. Moreover, a good average correlation was observed between predicted and experimental strengths and weaknesses obtained from native hydrogen exchange data. SWOTein application to three test cases further showed its suitability to predict and interpret strong and weak residues in the context of folding, conformational changes and protein-protein binding. In summary, SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes.

AVAILABILITY

The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein/.

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study

Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross-validations as well as holdout tests, and it is freely accessible as a web-service at arimees.com.

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

As the availability of high-throughput metagenomic data is increasing, agile and accurate tools are required to analyze and exploit this valuable and plentiful resource. Cellulose-degrading enzymes have various applications, and finding appropriate cellulases for different purposes is becoming increasingly challenging. An in silico screening method for high-throughput data can be of great assistance when combined with the characterization of thermal and pH dependence. By this means, various metagenomic sources with high cellulolytic potentials can be explored. Using a sequence similarity-based annotation and an ensemble of supervised learning algorithms, this study aims to identify and characterize cellulolytic enzymes from a given high-throughput metagenomic data based on optimum temperature and pH. The prediction performance of MCIC (metagenome cellulase identification and characterization) was evaluated through multiple iterations of sixfold cross-validation tests. This tool was also implemented for a comparative analysis of four metagenomic sources to estimate their cellulolytic profile and capabilities. For experimental validation of MCIC’s screening and prediction abilities, two identified enzymes from cattle rumen were subjected to cloning, expression, and characterization. To the best of our knowledge, this is the first time that a sequence-similarity based method is used alongside an ensemble machine learning model to identify and characterize cellulase enzymes from extensive metagenomic data. This study highlights the strength of machine learning techniques to predict enzymatic properties solely based on their sequence. MCIC is freely available as a python package and standalone toolkit for Windows and Linux-based operating systems with several functions to facilitate the screening and thermal and pH dependence prediction of cellulases.

SOLart: a structure-based method to predict protein solubility and aggregation

MOTIVATION

The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.

RESULTS

We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists.

AVAILABILITY AND IMPLEMENTATION

The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Why small proteins tend to have high denaturation temperatures

Data indicate that small globular proteins (consisting of less than about 70 residues) tend to have high denaturation temperatures. This finding is analysed by comparing experimental denaturation enthalpy and entropy changes of a selected set of small proteins with values calculated on the basis of average and common properties of globular proteins. The conclusion is that the denaturation entropy change is smaller than expected, leading to an increase in denaturation temperature. The proposed molecular rationalization considers the existence of long-wavelength, low-frequency vibrational modes in the native state of small proteins due to their large surface-to-interior ratio. The effect of decreasing the conformational entropy gain associated with denaturation on thermal stability is directly verified by means of an already devised theoretical model [G. Graziano, Phys. Chem. Chem. Phys. 2010, 12, 14245-14252; 2014, 16, 21755-21767].

Insights on protein thermal stability: a graph representation of molecular interactions

Motivation

Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity.

Results

Here, we present a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. In this approach we describe proteins as energy-weighted graphs and compare them using ensembles of interaction networks. Investigating the position of specific interactions within the 3D native structure, we developed a parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and area under the receiver operating characteristic curve of 78%.

Availability and implementation

Code is available upon request to edoardo.milanetti@uniroma1.it

Supplementary information

Supplementary data are available at Bioinformatics online.

Protein Thermal Stability Engineering Using HoTMuSiC

The rational design of enzymes is a challenging research field, which plays an important role in the optimization of a wide series of biotechnological processes. Computational approaches allow screening all possible amino acid substitutions in a target protein and to identify a subset likely to have the desired properties. They can thus be used to guide and restrict the huge, time-consuming search in sequence space to reach protein optimality. Here we present HoTMuSiC, a tool that predicts the impact of point mutations on the protein melting temperature, which uses the experimental or modeled protein structure as sole input and is available at the dezyme.com website. Its main advantages include accuracy and speed, which makes it a perfect instrument for thermal stability engineering projects aiming at designing new proteins that feature increased heat resistance or remain active and stable in nonphysiological conditions. We set up a HoTMuSiC-based pipeline, which uses additional information to avoid mutations of functionally important residues, identified as being too well conserved among homologous proteins or too close to annotated functional sites. The efficiency of this pipeline is successfully demonstrated on Rhizomucor miehei lipase.

ProTstab - predictor for cellular protein stability

BACKGROUND

Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples.

RESULTS

We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities.

CONCLUSIONS

The Pearson's correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.

A comprehensive computational study of amino acid interactions in membrane proteins

Transmembrane proteins play a fundamental role in a wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because their embedding in lipid membranes hampers their experimental characterization. In this paper, we improved our understanding of their structural stability through the development of new knowledge-based energy functions describing amino acid pair interactions that prevail in the transmembrane and extramembrane regions of membrane proteins. The comparison of these potentials and those derived from globular proteins yields an objective view of the relative strength of amino acid interactions in the different protein environments, and their role in protein stabilization. Separate potentials were also derived from α-helical and β-barrel transmembrane regions to investigate possible dissimilarities. We found that, in extramembrane regions, hydrophobic residues are less frequent but interactions between aromatic and aliphatic amino acids as well as aromatic-sulfur interactions contribute more to stability. In transmembrane regions, polar residues are less abundant but interactions between residues of equal or opposite charges or non-charged polar residues as well as anion-π interactions appear stronger. This shows indirectly the preference of the water and lipid molecules to interact with polar and hydrophobic residues, respectively. We applied these new energy functions to predict whether a residue is located in the trans- or extramembrane region, and obtained an AUC score of 83% in cross validation, which demonstrates their accuracy. As their application is, moreover, extremely fast, they are optimal instruments for membrane protein design and large-scale investigations of membrane protein stability.

Thermophilic Adaptation in Prokaryotes Is Constrained by Metabolic Costs of Proteostasis

Prokaryotes evolved to thrive in an extremely diverse set of habitats, and their proteomes bear signatures of environmental conditions. Although correlations between amino acid usage and environmental temperature are well-documented, understanding of the mechanisms of thermal adaptation remains incomplete. Here, we couple the energetic costs of protein folding and protein homeostasis to build a microscopic model explaining both the overall amino acid composition and its temperature trends. Low biosynthesis costs lead to low diversity of physical interactions between amino acid residues, which in turn makes proteins less stable and drives up chaperone activity to maintain appropriate levels of folded, functional proteins. Assuming that the cost of chaperone activity is proportional to the fraction of unfolded client proteins, we simulated thermal adaptation of model proteins subject to minimization of the total cost of amino acid synthesis and chaperone activity. For the first time, we predicted both the proteome-average amino acid abundances and their temperature trends simultaneously, and found strong correlations between model predictions and 402 genomes of bacteria and archaea. The energetic constraint on protein evolution is more apparent in highly expressed proteins, selected by codon adaptation index. We found that in bacteria, highly expressed proteins are similar in composition to thermophilic ones, whereas in archaea no correlation between predicted expression level and thermostability was observed. At the same time, thermal adaptations of highly expressed proteins in bacteria and archaea are nearly identical, suggesting that universal energetic constraints prevail over the phylogenetic differences between these domains of life.

RankProt: A multi criteria-ranking platform to attain protein thermostabilizing mutations and its in vitro applications - Attribute based prediction method on the principles of Analytical Hierarchical Process

Attaining recombinant thermostable proteins is still a challenge for protein engineering. The complexity is the length of time and enormous efforts required to achieve the desired results. Present work proposes a novel and economic strategy of attaining protein thermostability by predicting site-specific mutations at the shortest possible time. The success of the approach can be attributed to Analytical Hierarchical Process and the outcome was a rationalized thermostable mutation(s) prediction tool- RankProt. Briefly the method involved ranking of 17 biophysical protein features as class predictors, derived from 127 pairs of thermostable and mesostable proteins. Among the 17 predictors, ionic interactions and main-chain to main-chain hydrogen bonds were the highest ranked features with eigen value of 0.091. The success of the tool was judged by multi-fold in silico validation tests and it achieved the prediction accuracy of 91% with AUC 0.927. Further, in vitro validation was carried out by predicting thermostabilizing mutations for mesostable Bacillus subtilis lipase and performing the predicted mutations by multi-site directed mutagenesis. The rationalized method was successful to render the lipase thermostable with optimum temperature stability and T_m increase by 20°C and 7°C respectively. Conclusively it can be said that it was the minimum number of mutations in comparison to the number of mutations incorporated to render Bacillus subtilis lipase thermostable, by directed evolution techniques. The present work shows that protein stabilizing mutations can be rationally designed by balancing the biophysical pleiotropy of proteins, in accordance to the selection pressure.

Computational analysis of the amino acid interactions that promote or decrease protein solubility

The solubility of globular proteins is a basic biophysical property that is usually a prerequisite for their functioning. In this study, we probed the solubility of globular proteins with the help of the statistical potential formalism, in view of objectifying the connection of solubility with structural and energetic properties and of the solubility-dependence of specific amino acid interactions. We started by setting up two independent datasets containing either soluble or aggregation-prone proteins with known structures. From these two datasets, we computed solubility-dependent distance potentials that are by construction biased towards the solubility of the proteins from which they are derived. Their analysis showed the clear preference of amino acid interactions such as Lys-containing salt bridges and aliphatic interactions to promote protein solubility, whereas others such as aromatic, His-π, cation-π, amino-π and anion-π interactions rather tend to reduce it. These results indicate that interactions involving delocalized π-electrons favor aggregation, unlike those involving no (or few) dispersion forces. Furthermore, using our potentials derived from either highly or weakly soluble proteins to compute protein folding free energies, we found that the difference between these two energies correlates better with solubility than other properties analyzed before such as protein length, isoelectric point and aliphatic index. This is, to the best of our knowledge, the first comprehensive in silico study of the impact of residue-residue interactions on protein solubility properties.The results of this analysis provide new insights that will facilitate future rational protein design applications aimed at modulating the solubility of targeted proteins.

An optimized strategy to measure protein stability highlights differences between cold and hot unfolded states

Macromolecular crowding ought to stabilize folded forms of proteins, through an excluded volume effect. This explanation has been questioned and observed effects attributed to weak interactions with other cell components. Here we show conclusively that protein stability is affected by volume exclusion and that the effect is more pronounced when the crowder's size is closer to that of the protein under study. Accurate evaluation of the volume exclusion effect is made possible by the choice of yeast frataxin, a protein that undergoes cold denaturation above zero degrees, because the unfolded form at low temperature is more expanded than the corresponding one at high temperature. To achieve optimum sensitivity to changes in stability we introduce an empirical parameter derived from the stability curve. The large effect of PEG 20 on cold denaturation can be explained by a change in water activity, according to Privalov's interpretation of cold denaturation.

[Prediction of protein thermostability from their primary structure: the current state and development factors]

The construction of proteins and peptides with desired properties, including resistance to high temperatures, as well as optimization of their amino acid composition, is an important and complex task, which attracts much attention in various branches of the basic sciences, and also in biomedicine and biotechnology. This raises the question: what method is more relevant for the at the pilot stage of research in order to estimate the influence of the planned amino acid substitutions on the thermostability of the resultant protein construct? In this brief review we have classified existing basic practical and theoretical approaches used in studies and predicting the thermal stability of native and recombinant polypeptides. Particular attention has been paid to the predictive potential of statistical methods for studying the thermodynamic parameters of the primary protein structure and prospects of their use.

Improved insights into protein thermal stability: from the molecular to the structurome scale

Despite the intense efforts of the last decades to understand the thermal stability of proteins, the mechanisms responsible for its modulation still remain debated. In this investigation, we tackle this issue by showing how a multiscale perspective can yield new insights. With the help of temperature-dependent statistical potentials, we analysed some amino acid interactions at the molecular level, which are suggested to be relevant for the enhancement of thermal resistance. We then investigated the thermal stability at the protein level by quantifying its modification upon amino acid substitutions. Finally, a large scale analysis of protein stability-at the structurome level-contributed to the clarification of the relation between stability and natural evolution, thereby showing that the mutational profile of proteins differs according to their thermal properties. Some considerations on how the multiscale approach could help in unravelling the protein stability mechanisms are briefly discussed.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC

The accurate prediction of the impact of an amino acid substitution on the thermal stability of a protein is a central issue in protein science, and is of key relevance for the rational optimization of various bioprocesses that use enzymes in unusual conditions. Here we present one of the first computational tools to predict the change in melting temperature ΔT_m upon point mutations, given the protein structure and, when available, the melting temperature T_m of the wild-type protein. The key ingredients of our model structure are standard and temperature-dependent statistical potentials, which are combined with the help of an artificial neural network. The model structure was chosen on the basis of a detailed thermodynamic analysis of the system. The parameters of the model were identified on a set of more than 1,600 mutations with experimentally measured ΔT_m. The performance of our method was tested using a strict 5-fold cross-validation procedure, and was found to be significantly superior to that of competing methods. We obtained a root mean square deviation between predicted and experimental ΔT_m values of 4.2 °C that reduces to 2.9 °C when ten percent outliers are removed. A webserver-based tool is freely available for non-commercial use at soft.dezyme.com.

Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

Cold denaturation as a tool to measure protein stability

Protein stability is an important issue for the interpretation of a wide variety of biological problems but its assessment is at times difficult. The most common parameter employed to describe protein stability is the temperature of melting, at which the populations of folded and unfolded species are identical. This parameter may yield ambiguous results. It would always be preferable to measure the whole stability curve. The calculation of this curve is greatly facilitated whenever it is possible to observe cold denaturation. Using Yfh1, one of the few proteins whose cold denaturation occurs at neutral pH and low ionic strength, we could measure the variation of its full stability curve under several environmental conditions. Here we show the advantages of gauging stability as a function of external variables using stability curves.

Towards an accurate prediction of the thermal stability of homologous proteins

Protein thermostability has been the focus of growing research interests in the last decades since its understanding and control play important roles in the optimization of a wide series of bioprocesses of academic and industrial importance. The complexity of this issue is rooted in the fact that the mechanisms ensuring thermal resistance are not unique and specific, but rather family- or even protein-dependent. Therefore, and despite the amount of research already accomplished, obtaining fast and precise thermal stability predictions is still a challenge, especially on a large scale. This article deepens the study of protein thermal stability and is focused on the prediction of its best descriptor, the melting temperature Tm. The relations between Tm and a series of factors that are expected to influence the protein stability are analyzed and discussed. Different Tm-prediction methods that utilize these factors, sometimes with additional information about homologous proteins, are introduced, and their individual performances are evaluated. The best methods are based on temperature-dependent statistical potentials, on the environmental temperature of the host organism, on the fraction of charged residues, and on the number of residues. They are combined to build an improved prediction method with significantly increased score. The root mean square deviation between the computed and experimental Tm-values for 45 proteins of known structure from 11 families is about 7°C in cross-validation and decreases to 5°C when 10% outliers are removed. The associated linear correlation coefficients are equal to .91 and .95, respectively.

The fundamental tradeoff in genomes and proteomes of prokaryotes established by the genetic code, codon entropy, and physics of nucleic acids and proteins

Background

Mutations in nucleotide sequences provide a foundation for genetic variability, and selection is the driving force of the evolution and molecular adaptation. Despite considerable progress in the understanding of selective forces and their compositional determinants, the very nature of underlying mutational biases remains unclear.

Results

We explore here a fundamental tradeoff, which analytically describes mutual adjustment of the nucleotide and amino acid compositions and its possible effect on the mutational biases. The tradeoff is determined by the interplay between the genetic code, optimization of the codon entropy, and demands on the structure and stability of nucleic acids and proteins.

Conclusion

The tradeoff is the unifying property of all prokaryotes regardless of the differences in their phylogenies, life styles, and extreme environments. It underlies mutational biases characteristic for genomes with different nucleotide and amino acid compositions, providing foundation for evolution and adaptation.

Reviewers

This article was reviewed by Eugene Koonin, Michael Gromiha, and Alexander Schleiffer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-014-0029-2) contains supplementary material, which is available to authorized users.

Stability curve prediction of homologous proteins using temperature-dependent statistical potentials

The unraveling and control of protein stability at different temperatures is a fundamental problem in biophysics that is substantially far from being quantitatively and accurately solved, as it requires a precise knowledge of the temperature dependence of amino acid interactions. In this paper we attempt to gain insight into the thermal stability of proteins by designing a tool to predict the full stability curve as a function of the temperature for a set of 45 proteins belonging to 11 homologous families, given their sequence and structure, as well as the melting temperature () and the change in heat capacity () of proteins belonging to the same family. Stability curves constitute a fundamental instrument to analyze in detail the thermal stability and its relation to the thermodynamic stability, and to estimate the enthalpic and entropic contributions to the folding free energy. In summary, our approach for predicting the protein stability curves relies on temperature-dependent statistical potentials derived from three datasets of protein structures with targeted thermal stability properties. Using these potentials, the folding free energies () at three different temperatures were computed for each protein. The Gibbs-Helmholtz equation was then used to predict the protein's stability curve as the curve that best fits these three points. The results are quite encouraging: the standard deviations between the experimental and predicted 's, 's and folding free energies at room temperature () are equal to 13 , 1.3 ) and 4.1 , respectively, in cross-validation. The main sources of error and some further improvements and perspectives are briefly discussed.

The prediction of protein stability remains one of the key goals of protein science. Despite the significant efforts of the last decades, faster and more accurate stability predictors on the proteomic-wide scale are currently demanded. The determination and control of protein stability are indeed fundamental steps on the path towards de novo design. In this paper we develop a method for predicting the stability curve of proteins. This curve encodes the temperature dependence of the folding free energy (). Its knowledge is important in the study of protein stability since all the thermodynamic parameters characterizing the folding transition can be extracted from it. Our prediction method is based on temperature-dependent mean force potentials and uses the tertiary structure of the target protein as well as the melting temperature () and the heat capacity change () of some other proteins belonging to the same family. From the predicted stability curves, the , the and the at room temperature can be inferred. The predictions obtained are compared with experimental data and show reasonable performances.