ProTherm: Thermodynamic Database for Proteins and Mutants

A fast and accurate method for predicting pKa of residues in proteins

Predicting the pH-activities of residues in proteins is an important problem in enzyme engineering and protein design. A novel predictor called 'Pred-pK(a)' was developed based on the physicochemical properties of amino acids and protein 3D structure. The Pred-pK(a) approach considers the influence of all other residues of the protein to predict the pK(a) value of an ionizable residue. An empirical equation was formulated, in which the pK(a) value was a distance-dependent function of physicochemical parameters of 20 amino acid types, describing their electrostatic and van der Waals interaction, as well as the effects of hydrogen bonds and solvation. Two sets of coefficients, {a(alpha)} and {b(l)}, were used in the predictor: {a(alpha)} is the weight factors of 20 amino acid types and {b(l)} is the weight factors of physicochemical properties of amino acids. An iterative double least square procedure was proposed to solve the two sets of weight factors alternately and iteratively in a training set. The two coefficient sets {a(alpha)} and {b(l)} thus obtained were used to predict the pK(a) values of residues in a protein. The average predictive error is +/-0.6 pH in less than a minute in common personal computer.

Thermodynamic database for proteins: features and applications

We have developed a thermodynamic database for proteins and mutants, ProTherm, which is a collection of a large number of thermodynamic data on protein stability along with the sequence and structure information, experimental methods and conditions, and literature information. This is a valuable resource for understanding/predicting the stability of proteins, and it can be accessible at http://www.gibk26.bse.kyutech.ac.jp/jouhou/Protherm/protherm.html . ProTherm has several features including various search, display, and sorting options and visualization tools. We have analyzed the data in ProTherm to examine the relationship among thermodynamics, structure, and function of proteins. We describe the progress on the development of methods for understanding/predicting protein stability, such as (i) relationship between the stability of protein mutants and amino acid properties, (ii) average assignment method, (iii) empirical energy functions, (iv) torsion, distance, and contact potentials, and (v) machine learning techniques. The list of online resources for predicting protein stability has also been provided.

Physico-chemical studies of molecular interactions between non-ionic surfactants and bovine serum albumin

Surfactants, particularly non-ionic types, are often added to prevent and/or minimize protein aggregation during fermentation, purification, freeze-drying, shipping, and/or storage. In this work we have investigated the interactions between two non-ionic surfactants (Tween 20 and Tween 80) and bovine serum albumin (BSA), as model protein, using surface tension, fluorescence measurements and computational analysis. The results showed that, in both cases, the surface tension profile of the surfactants curve is modified upon addition of the protein, and the CMC values of Tween 20 and Tween 80 in the presence of protein are higher than the CMC values of the pure surfactants. The results indicate that although Tween 20 and Tween 80 do not greatly differ in their chemical structures, their interactions with BSA are of different nature, with distinct binding sites. Measurements at different protein concentrations showed that the interactions are also dependent on the protein aggregation state in solution. It was found from fluorescence studies that changes observed in both the intensity and wavelength of the tryptophan emission are probably caused by modifications of tryptophan environment due to surfactant binding, rather than by direct interaction. Based on a computational analysis of a BSA three-dimensional model, we hypothesize about the binding mechanism of non-ionic surfactant to globular protein, which allowed us to explain surface tension profiles and fluorescence results.

Reliable prediction of protein thermostability change upon double mutation from amino acid sequence

SUMMARY

The accurate prediction of protein stability change upon mutation is one of the important issues for protein design. In this work, we have focused on the stability change of double mutations and systematically analyzed the wild-type and mutant residues, patterns in amino acid sequence and locations of mutants. Based on the sequence information of wild-type, mutant and three neighboring residues, we have presented a weighted decision table method (WET) for predicting the stability changes of 180 double mutants obtained from thermal (DeltaDeltaG) denaturation. Using 10-fold cross-validation test, our method showed a correlation of 0.75 between experimental and predicted values of stability changes, and an accuracy of 82.2% for discriminating the stabilizing and destabilizing mutants.

TMFunction: database for functional residues in membrane proteins

We have developed the database TMFunction, which is a collection of more than 2900 experimentally observed functional residues in membrane proteins. Each entry includes the numerical values for the parameters IC50 (measure of the effectiveness of a compound in inhibiting biological function), V_max (maximal velocity of transport), relative activity of mutants with respect to wild-type protein, binding affinity, dissociation constant, etc., which are important for understanding the sequence–structure–function relationship of membrane proteins. In addition, we have provided information about name and source of the protein, Uniprot and Protein Data Bank codes, mutational and literature information. Furthermore, TMFunction is linked to related databases and other resources. We have set up a web interface with different search and display options so that users have the ability to get the data in several ways. TMFunction is freely available at http://tmbeta-genome.cbrc.jp/TMFunction/.

Prediction of protein stability upon point mutations

Prediction of protein stability upon amino acid substitution is a challenging problem and it will be helpful for designing stable mutants. We have developed a thermodynamic database for proteins and mutants (ProTherm), which has more than 20000 thermodynamic data along with sequence and structure information, experimental conditions and literature information. It is freely accessible at http://gibk26.bse.kyutech.ac.jp/jouhou/protherm/protherm.html. Utilizing the database, we have analysed the relationship between amino acid properties and protein stability and developed different methods, such as average assignment method, distance and torsion potentials and decision tree models to discriminate the stabilizing and destabilizing mutants, and to predict the stability change upon mutation. Our method could distinguish the stabilizing and destabilizing mutants with an accuracy of 82 and 85% respectively from amino acid sequence and protein three-dimensional structure. We obtained the correlation of 0.70 and 0.87, between the experimental and predicted stability changes upon mutations, from sequence and structure respectively. Furthermore, we have developed different web servers for discrimination and prediction and they are freely accessible at http://bioinformatics.myweb.hinet.net/iptree.htm and http://cupsat.tu-bs.de/.

Protein databases on the internet

Protein databases have become a crucial part of modern biology. Huge amounts of data for protein structures, functions, and particularly sequences are being generated. Searching databases is often the first step in the study of a new protein. Comparison between proteins and between protein families in databases provides information about the relationship between proteins within a genome or across different species, and hence offers much more information than can be obtained by studying only an isolated protein. In addition, secondary databases derived from experimental databases are also widely available. These databases reorganize and annotate the data or provide predictions. The use of multiple databases often helps researchers understand the structure and function of proteins. Although some protein databases are widely known, they are far from being fully utilized in the protein science community. This unit provides a starting point for readers to explore the potential of protein databases on the Internet.

Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development

Background

Understanding and predicting protein stability upon point mutations has wide-spread importance in molecular biology. Several prediction models have been developed in the past with various algorithms. Statistical potentials are one of the widely used algorithms for the prediction of changes in stability upon point mutations. Although the methods provide flexibility and the capability to develop an accurate and reliable prediction model, it can be achieved only by the right selection of the structural factors and optimization of their parameters for the statistical potentials. In this work, we have selected five atom classification systems and compared their efficiency for the development of amino acid atom potentials. Additionally, torsion angle potentials have been optimized to include the orientation of amino acids in such a way that altered backbone conformation in different secondary structural regions can be included for the prediction model. This study also elaborates the importance of classifying the mutations according to their solvent accessibility and secondary structure specificity. The prediction efficiency has been calculated individually for the mutations in different secondary structural regions and compared.

Results

Results show that, in addition to using an advanced atom description, stepwise regression and selection of atoms are necessary to avoid the redundancy in atom distribution and improve the reliability of the prediction model validation. Comparing to other atom classification models, Melo-Feytmans model shows better prediction efficiency by giving a high correlation of 0.85 between experimental and theoretical ΔΔG with 84.06% of the mutations correctly predicted out of 1538 mutations. The theoretical ΔΔG values for the mutations in partially buried β-strands generated by the structural training dataset from PISCES gave a correlation of 0.84 without performing the Gaussian apodization of the torsion angle distribution. After the Gaussian apodization, the correlation increased to 0.92 and prediction accuracy increased from 80% to 88.89% respectively.

Conclusion

These findings were useful for the optimization of the Melo-Feytmans atom classification system and implementing them to develop the statistical potentials. It was also significant that the prediction efficiency of mutations in the partially buried β-strands improves with the help of Gaussian apodization of the torsion angle distribution. All these comparisons and optimization techniques demonstrate their advantages as well as the restrictions for the development of the prediction model. These findings will be quite helpful not only for the protein stability prediction, but also for various structure solutions in future.

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model

Understanding the mechanism of the protein stability change is one of the most challenging tasks. Recently, the prediction of protein stability change affected by single point mutations has become an interesting topic in molecular biology. However, it is desirable to further acquire knowledge from large databases to provide new insights into the nature of them. This paper presents an interpretable prediction tree method (named iPTREE-2) that can accurately predict changes of protein stability upon mutations from sequence based information and analyze sequence characteristics from the viewpoint of composition and order. Therefore, iPTREE-2 based on a regression tree algorithm exhibits the ability of finding important factors and developing rules for the purpose of data mining. On a dataset of 1859 different single point mutations from thermodynamic database, ProTherm, iPTREE-2 yields a correlation coefficient of 0.70 between predicted and experimental values. In the task of data mining, detailed analysis of sequences reveals the possibility of the compositional specificity of residues in different ranges of stability change and implies the existence of certain patterns. As building rules, we found that the mutation residues in wild type and in mutant protein play an important role. The present study demonstrates that iPTREE-2 can serve the purpose of predicting protein stability change, especially when one requires more understandable knowledge.

iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations

We have developed a web server, iPTREE-STAB for discriminating the stability of proteins (stabilizing or destabilizing) and predicting their stability changes (delta deltaG) upon single amino acid substitutions from amino acid sequence. The discrimination and prediction are mainly based on decision tree coupled with adaptive boosting algorithm, and classification and regression tree, respectively, using three neighboring residues of the mutant site along N- and C-terminals. Our method showed an accuracy of 82% for discriminating the stabilizing and destabilizing mutants, and a correlation of 0.70 for predicting protein stability changes upon mutations.

AVAILABILITY

http://bioinformatics.myweb.hinet.net/iptree.htm.

SUPPLEMENTARY INFORMATION

Dataset and other details are given.

Databases and QSAR for cancer research

In this review, we take a survey of bioinformatics databases and quantitative structure-activity relationship studies reported in published literature. Databases from the most general to special cancer-related ones have been included. Most commonly used methods of structure-based analysis of molecules have been reviewed, along with some case studies where they have been used in cancer research. This article is expected to be of use for general bioinformatics researchers interested in cancer and will also provide an update to those who have been actively pursuing this field of research.

Prediction of protein mutant stability using classification and regression tool

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and the solving of which would help for designing stable mutants. In this work, we have analyzed the stability of protein mutants using two different datasets of 1396 and 2204 mutants obtained from ProTherm database, respectively for free energy change due to thermal (DeltaDeltaG) and denaturant denaturations (DeltaDeltaG(H(2)O)). We have used a set of 48 physical, chemical energetic and conformational properties of amino acid residues and computed the difference of amino acid properties for each mutant in both sets of data. These differences in amino acid properties have been related to protein stability (DeltaDeltaG and DeltaDeltaG(H(2)O)) and are used to train with classification and regression tool for predicting the stability of protein mutants. Further, we have tested the method with 4 fold, 5 fold and 10 fold cross validation procedures. We found that the physical properties, shape and flexibility are important determinants of protein stability. The classification of mutants based on secondary structure (helix, strand, turn and coil) and solvent accessibility (buried, partially buried, partially exposed and exposed) distinguished the stabilizing/destabilizing mutants at an average accuracy of 81% and 80%, respectively for DeltaDeltaG and DeltaDeltaG(H(2)O). The correlation between the experimental and predicted stability change is 0.61 for DeltaDeltaG and 0.44 for DeltaDeltaG(H(2)O). Further, the free energy change due to the replacement of amino acid residue has been predicted within an average error of 1.08 kcal/mol and 1.37 kcal/mol for thermal and chemical denaturation, respectively. The relative importance of secondary structure and solvent accessibility, and the influence of the dataset on prediction of protein mutant stability have been discussed.

Structural analysis and prediction of protein mutant stability using distance and torsion potentials: Role of secondary structure and solvent accessibility

Analyzing the factors behind protein stability is a key research topic in molecular biology, and has direct implications on protein structure prediction and protein-protein interactions. We have analyzed protein stability upon point mutations using a distance-dependant pair potential representing mainly through-space interactions, and torsion angle potential representing mainly neighboring effects as a basic statistical mechanical setup for the analysis. The synergetic effect of accessible surface area and secondary structure preferences was used as a classifier for the potentials. In addition, short-, medium-, and long-range interactions of the protein environment were also analyzed. Two datasets of point mutations were taken for the comparison of theoretically predicted stabilizing energy values with experimental DeltaDeltaG and DeltaDeltaGH(2)O from thermal and chemical denaturation experiments. These include 1538 and 1603 mutations, respectively, and contain 101 proteins that share a wide range of sequence identity. The resulting force fields were carefully evaluated with different statistical tests. Results show a maximum correlation of 0.87 with a standard error of 0.71 kcal/mol between predicted and measured DeltaDeltaG values and a prediction accuracy of 85.3% (stabilizing or destabilizing) for all mutations together. A correlation of 0.77 (more than 80% prediction accuracy with a standard error of 0.95 kcal/mol) each for the test dataset of split-sample validation and fivefold crossvalidation was obtained and a correlation of 0.70 (77.4% prediction accuracy with a standard error of 1.17 kcal/mol) was shown by the jackknife test. The same model was implemented, and the results were analyzed for mutations with DeltaDeltaGH(2)O. A correlation of 0.78 (standard error 0.96 kcal/mol) was observed with a prediction efficiency of 84.65%. This model can be used for the future prediction of protein structural stability together with various experimental techniques.

CUPSAT: prediction of protein stability upon point mutations

CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict ΔΔG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link .

Average assignment method for predicting the stability of protein mutants

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work, we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (DeltaTm), free energy change due to thermal (DeltaDeltaG), and denaturant denaturations (DeltaDeltaGH2O), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing mutants to an accuracy of 70-80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix, strand, and coil distinguished the stabilizing/destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84-89% for the three data sets. Further, the present method is able to predict the free energy change (DeltaDeltaG) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants.

ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions

ProTherm and ProNIT are two thermodynamic databases that contain experimentally determined thermodynamic parameters of protein stability and protein–nucleic acid interactions, respectively. The current versions of both the databases have considerably increased the total number of entries and enhanced search interface with added new fields, improved search, display and sorting options. As on September 2005, ProTherm release 5.0 contains 17 113 entries from 771 proteins, retrieved from 1497 scientific articles (∼20% increase in data from the previous version). ProNIT release 2.0 contains 4900 entries from 273 research articles, representing 158 proteins. Both databases can be queried using WWW interfaces. Both quick search and advanced search are provided on this web page to facilitate easy retrieval and display of the data from these databases. ProTherm is freely available online at and ProNIT at .

Electronic data sources for kinetic models of cell signaling

Functional understanding of signaling pathways requires detailed information about the constituent molecules and their interactions. Simulations of signaling pathways therefore build upon a great deal of data from various sources. We first survey electronic data resources for cell signaling modeling and then based on the type of data representation the data sources are broadly classified into five groups. None of the data sources surveyed provide all required data in a ready-to-be-modeled fashion. We then put forward a "wish list" for the desired attributes for an ideal modeling centric database. Finally, we close with perspectives on how electronic data sources for cell signaling modeling have developed. We suggest that future directions in such data sources are largely model-driven and are hinged on interoperability of data sources.

Energy Functions for Protein Design: Adjustment with Protein–Protein Complex Affinities, Models for the Unfolded State, and Negative Design of Solubility and Specificity

The development of the EGAD program and energy function for protein design is described. In contrast to most protein design methods, which require several empirical parameters or heuristics such as patterning of residues or rotamers, EGAD has a minimalist philosophy; it uses very few empirical factors to account for inaccuracies resulting from the use of fixed backbones and discrete rotamers in protein design calculations, and describes the unfolded state, aggregates, and alternative conformers explicitly with physical models instead of fitted parameters. This approach unveils important issues in protein design that are often camouflaged by heuristic-emphasizing methods. Inter-atom energies are modeled with the OPLS-AA all-atom forcefield, electrostatics with the generalized Born continuum model, and the hydrophobic effect with a solvent-accessible surface area-dependent term. Experimental characterization of proteins designed with an unmodified version of the energy function revealed problems with under-packing, stability, aggregation, and structural specificity. Under-packing was addressed by modifying the van der Waals function. By optimizing only three parameters, the effects of >400 mutations on protein-protein complex formation were predicted to within 1.0 kcal mol(-1). As an independent test, this modified energy function was used to predict the stabilities of >1500 mutants to within 1.0 kcal mol(-1); this required a physical model of the unfolded state that includes more interactions than traditional tripeptide-based models. Solubility and structural specificity were addressed with simple physical approximations of aggregation and conformational equilibria. The complete energy function can design protein sequences that have high levels of identity with their natural counterparts, and have predicted structural properties more consistent with soluble and uniquely folded proteins than the initial designs.

Relative importance of secondary structure and solvent accessibility to the stability of protein mutants

Understanding the factors influencing the stability of protein mutants is an important task in molecular and computational biology. In this work, we have approached this problem by examining the relative importance of secondary structure and solvent accessibility of the mutant residue for understanding/predicting the stability of protein mutants. We have used hydrophobic, electrostatic and hydrogen bond free energy terms and nine unique physicochemical, energetic and conformational properties of amino acids in the present study and these parameters have been related with changes in thermal stability (DeltaTm) of all the single mutants of lysozymes based on single and multiple correlation coefficients. As expected the properties reflecting hydrophobicity and hydrophobic free energy play a major role to distinguish stabilizing and destabilizing mutants. The hydrophobic free energy due to carbon and nitrogen atoms distinguish the stability of coil and strand mutations to the accuracy of 100 and 90%, respectively. In agreement with previous results, the subgroup classification based on secondary structure and the information about its location in the structure yielded good relationship with the experimental DeltaTm. We revealed that the secondary structure information is equally or more important than solvent accessibility for understanding the stability of protein mutants. The comparison of amino acid properties with free-energy terms indicate that the energetic contribution explains the mutant stability better in coil region whereas the amino acid properties do better in strand region. Further, the combination of free energies with amino acid properties increased the correlation significantly. The present study demonstrates the importance of classifying the mutants based on secondary structure to the stability of proteins upon mutations.

Elucidating protein thermodynamics from the three-dimensional structure of the native state using network rigidity

Given the three-dimensional structure of a protein, its thermodynamic properties are calculated using a recently introduced distance constraint model (DCM) within a mean-field treatment. The DCM is constructed from a free energy decomposition that partitions microscopic interactions into a variety of constraint types, i.e., covalent bonds, salt-bridges, hydrogen-bonds, and torsional-forces, each associated with an enthalpy and entropy contribution. A Gibbs ensemble of accessible microstates is defined by a set of topologically distinct mechanical frameworks generated by perturbing away from the native constraint topology. The total enthalpy of a given framework is calculated as a linear sum of enthalpy components over all constraints present. Total entropy is generally a nonadditive property of free energy decompositions. Here, we calculate total entropy as a linear sum of entropy components over a set of independent constraints determined by a graph algorithm that builds up a mechanical framework one constraint at a time, placing constraints with lower entropy before those with greater entropy. This procedure provides a natural mechanism for enthalpy-entropy compensation. A minimal DCM with five phenomenological parameters is found to capture the essential physics relating thermodynamic response to network rigidity. Moreover, two parameters are fixed by simultaneously fitting to heat capacity curves for histidine binding protein and ubiquitin at five different pH conditions. The three free parameter DCM provides a quantitative characterization of conformational flexibility consistent with thermodynamic stability. It is found that native hydrogen bond topology provides a key signature in governing molecular cooperativity and the folding-unfolding transition.

Inter-residue interactions in protein folding and stability

During the process of protein folding, the amino acid residues along the polypeptide chain interact with each other in a cooperative manner to form the stable native structure. The knowledge about inter-residue interactions in protein structures is very helpful to understand the mechanism of protein folding and stability. In this review, we introduce the classification of inter-residue interactions into short, medium and long range based on a simple geometric approach. The features of these interactions in different structural classes of globular and membrane proteins, and in various folds have been delineated. The development of contact potentials and the application of inter-residue contacts for predicting the structural class and secondary structures of globular proteins, solvent accessibility, fold recognition and ab initio tertiary structure prediction have been evaluated. Further, the relationship between inter-residue contacts and protein-folding rates has been highlighted. Moreover, the importance of inter-residue interactions in protein-folding kinetics and for understanding the stability of proteins has been discussed. In essence, the information gained from the studies on inter-residue interactions provides valuable insights for understanding protein folding and de novo protein design.

Protein databases on the internet

Protein databases have become a crucial part of modern biology. Huge amounts of data for protein structures, functions, and particularly sequences are being generated. Searching databases is often the first step in the study of a new protein. Comparison between proteins and between protein families in databases provides information about the relationship between proteins within a genome or across different species, and hence offers much more information than can be obtained by studying only an isolated protein. In addition, secondary databases derived from experimental databases are also widely available. These databases reorganize and annotate the data or provide predictions. The use of multiple databases often helps researchers understand the structure and function of proteins. Although some protein databases are widely known, they are far from being fully utilized in the protein science community. This unit provides a starting point for readers to explore the potential of protein databases on the Internet.

Can Contact Potentials Reliably Predict Stability of Proteins?

The simplest approximation of interaction potential between amino acid residues in proteins is the contact potential, which defines the effective free energy of a protein conformation by a set of amino acid contacts formed in this conformation. Finding a contact potential capable of predicting free energies of protein states across a variety of protein families will aid protein folding and engineering in silico on a computationally tractable time-scale. We test the ability of contact potentials to accurately and transferably (across various protein families) predict stability changes of proteins upon mutations. We develop a new methodology to determine the contact potentials in proteins from experimental measurements of changes in protein's thermodynamic stabilities (DeltaDeltaG) upon mutations. We apply our methodology to derive sets of contact interaction parameters for a hierarchy of interaction models including solvation and multi-body contact parameters. We test how well our models reproduce experimental measurements by statistical tests. We evaluate the maximum accuracy of predictions obtained by using contact potentials and the correlation between parameters derived from different data-sets of experimental (DeltaDeltaG) values. We argue that it is impossible to reach experimental accuracy and derive fully transferable contact parameters using the contact models of potentials. However, contact parameters may yield reliable predictions of DeltaDeltaG for datasets of mutations confined to the same amino acid positions in the sequence of a single protein.

ProTherm, version 4.0: thermodynamic database for proteins and mutants

Release 4.0 of ProTherm, thermodynamic database for proteins and mutants, contains approximately 14,500 numerical data (approximately 450% of the first version) of several thermodynamic parameters along with experimental methods and conditions, and structural, functional and literature information. The sequence and structural information of proteins is connected with thermodynamic data through links between entries in Protein Data Bank, Protein Information Resource and SWISS-PROT and the data in ProTherm. We have separated the Gibbs free energy change obtained at extrapolated temperature from the data on denaturation temperature measured by the thermal denaturation method. We have added the statistics of amino acid replacements and links to homologous structures to each protein. Further, we have improved the search and display options to enhance search capability through the web interface. ProTherm is freely available at http://gibk26. bse.kyutech.ac.jp/jouhou/Protherm/protherm.html.

Structural destabilization of the recombinant thermophilic TthL11 ribosomal protein by a single amino acid substitution

Thermus thermophilus L11 protein has previously been reported to be resistant against tryptic and chymotryptic proteolysis under native conditions. With a single amino acid substitution, namely Trp101Arg, conformational changes were induced that resulted in the exhibition of specific amino acids that served as targets for tryptic and chymotryptic action and rendered the protein highly unstable even during purification. This unexpected process was evidenced by the isolation with size exclusion gel chromatography of the well-structured chymotryptic N-terminal domain in a high amount and its characterization both by Edman degradation and QTOF-EMS spectroscopy. On the other hand, the substitution of Val38Cys, which did not contribute to structural changes, indicates a very possible implication of this amino acid in the protein methylation process. The data reported in this work illustrate the distinctive amino acid dynamics in a thermophilic protein, which, while serving the function common to its counterparts from mesophilic organisms, has had to adapt to the extreme environmental conditions typical of thermophilic organisms.

Thermodynamic properties of several soil- and sediment-derived natural organic materials

Improved understanding of the structure of soil- and sediment-derived organic matter is critical to elucidating the mechanisms that control the reactivity and transport of contaminants in the environment. This work focuses on an experimental investigation of thermodynamic properties that are a function of the macromolecular structure of natural organic matter (NOM). A suite of thermal analysis instruments were employed to quantify glass transition temperatures (Tg), constant-pressure specific heat capacities (Cp), and thermal expansion coefficients (alpha) of several International Humic Substances Society (IHSS) soil-, sediment-, and aquatic-derived NOMs. Thermal mechanical analysis (TMA) of selected NOMs identified Tgs between 36 and 72 degrees C, and alphas ranging from 11 mum/m degrees C below the Tg to 242 mum/m degrees C above the Tg. Standard differential scanning calorimetry (DSC) and temperature-modulated differential scanning calorimetry (TMDSC) measurements provided additional evidence of glass transition behavior, including identification of multiple transition behavior in two aquatic samples. TMDSC also provided quantitative measures of Cp at 0 and 25 degrees C, ranging from 1.27 to 1.44 J/g degrees C. Results from TMA, DSC, and TMDSC analyses are consistent with glass transition theories for organic macromolecules, and the glass transition behavior of other NOM materials reported in previous studies. Discussion of the importance of quantifying these thermodynamic properties is presented in terms of improved physical and chemical characterization of NOM structures, and in terms of providing constraints to molecular simulation models of NOM structures.

Role of cation-pi interactions to the stability of thermophilic proteins

Elucidating the factors responsible for exhibiting extreme thermal stability of thermophilic proteins is very important for an understanding of the mechanism of protein stability, as well as to design stable proteins. In this work, we have analyzed the influence of cation-pi interactions to enhance the stability from mesophilic to thermophilic proteins. The favorable residue pairs forming such a system of interactions have been brought out. We found that the Tyr has a greater number of such interactions with Lys in thermophilic proteins. Specifically, the same Lys would experience a greater number of cation-pi interactions with several Tyr residues in thermophiles. On the other hand, the influence of Phe in making cation-pi interactions is higher in mesophiles than in thermophiles. Further, a network of cation-pi interactions are maintained by Lys in thermophiles, whereas Arg plays a major role in mesophilic proteins. Moreover, atoms that have a substantial positive charge in both Lys and Arg make a more significant contribution for cation-pi interactions than do cationic group atoms.

Importance of mutant position in Ramachandran plot for predicting protein stability of surface mutations

Understanding the mechanisms by which mutations affect protein stability is one of the most important problems in molecular biology. In this work, we analyzed the relationship between changes in protein stability caused by surface mutations and changes in 49 physicochemical, energetic, and conformational properties of amino acid residues. We found that the hydration entropy was the major contributor to the stability of surface mutations in helical segments; other properties responsible for size and volume of molecule also correlated significantly with stability. Classification of coil mutations based on their locations in the (phi-psi) map improved the correlation significantly, demonstrating the existence of a relationship between stability and strain energy, which indicates that the role of strain energy is very important for the stability of surface mutations. We observed that the inclusion of sequence and structural information raised the correlation, indicating the influence of surrounding residues on the stability of surface mutations. Further, we examined the previously reported "inverse relationship" between stability and hydrophobicity, and observed that the inverse hydrophobic effect was generally applicable only to coil mutations. The present study leads to a simple method for predicting protein stability changes caused by amino acid substitutions, which will be useful for protein engineering in designing novel proteins with increased stability and altered function.

Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations

We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at http://fold-x.embl-heidelberg.de

ProTherm, Thermodynamic Database for Proteins and Mutants: developments in version 3.0

The current release of ProTherm, Thermodynamic Database for Proteins and Mutants, contains more than 10 000 numerical data (300% of the first version) of several thermodynamic parameters, experimental methods and conditions, reversibility of folding, details about the surrounding residues in space for all mutants, structural, functional and literature information. In the current version, we have added information about the source of each protein, identification codes for SWISS-PROT and Protein Information Resource and unique Protein Data Bank (PDB) code for proteins with relevant source. We have also provided additional options to search for data based on PDB code, number of states and reversibility. ProTherm is cross-linked with other sequence, structural, functional and literature databases, and the mutant sites and surrounding residues are automatically mapped on the structure. The ProTherm database is freely available at http://www.rtc.riken.go.jp/jouhou/protherm/protherm.html.

Hydrogen-bonding classes in proteins and their contribution to the unfolding reaction

This paper proposes to assess hydrogen-bonding contributions to the protein stability, using a set of model proteins for which both X-ray structures and calorimetric unfolding data are known. Pertinent thermodynamic quantities are first estimated according to a recent model of protein energetics based on the dissolution of alkyl amides. Then it is shown that the overall free energy of hydrogen-bond formation accounts for a hydrogen-bonding propensity close to helix-forming tendencies previously found for individual amino acids. This allows us to simulate the melting curve of an alanine-rich helical 50-mer with good precision. Thereafter, hydrogen-bonding enthalpies and entropies are expressed as linear combinations of backbone-backbone, backbone-side-chain, side-chain-backbone, and side-chain-side-chain donor-acceptor contributions. On this basis, each of the four components shows a different free energy versus temperature trend. It appears that structural preference for side-chain-side-chain hydrogen bonding plays a major role in stabilizing proteins at elevated temperatures.

Role of medium- and long-range interactions to the stability of the mutants of T4 lysozyme

Inter-residue interactions play an important role to the folding and stability of protein molecules. In this work, we analyze the role of medium- and long-range interactions to the stability of T4 lysozyme mutants. We found that, in buried mutations, the increase in long-range contacts upon mutations destabilizes the protein, whereas, in surface mutations, the increase in long-range contacts increases the stability, indicating the importance of surrounding polar residues to the stability of surface mutations. Further, the increase in medium-range contacts decreases the stability of buried and surface mutations and a direct relationship is observed between the increase of medium-range contacts and increase in stability for partially buried/exposed mutations. Moreover, the relationship between amino acid properties and stability of T4 lysozyme mutants at positions Ile3, Phe53, and Leu99 showed that the effect of medium- and long-range contacts is less for buried mutations and the inter-residue contacts have significant correlation with the stability of partially buried mutations.

Important inter-residue contacts for enhancing the thermal stability of thermophilic proteins

Proteins from thermophilic organisms exhibit high thermal stability, but have structures that are very similar to their mesophilic homologues. In order to gain insight into the basis of thermostability, we have analyzed the medium- and long-range contacts in mesophilic and thermophilic proteins of 16 different families. We found that the thermophiles prefer to have contacts between residues with hydrogen-bond-forming capability. Apart from hydrophobic contacts, more contacts are observed between polar and non-polar residues in thermophiles than mesophiles. Residue-wise analysis showed that Tyr has good contacts with several other residues, and Cys has considerably higher long-range contacts in thermophiles compared with mesophiles. Furthermore, the residues occurring in the range of 31-34 residues apart in the sequence contribute significant long-range contacts to the stability of thermophilic proteins.

Factors influencing the stability of alpha-helices and beta-strands in thermophilic ribonuclease H

Understanding the influence of structural parameters is crucial to enhance the thermal stability of proteins. In this work, the stability (deltaG) of residues in different secondary structures of Ribonuclease H (RNase H) has been analyzed with 48 amino acid properties. The properties reflecting hydrophobicity show a good correlation with stability. Further, the linear distribution of surrounding hydrophobicity in alpha-helices, obtained from the three dimensional structure of thermophilic RNase H, agrees well with experimental deltaG values. Moreover, the stability parameters correlate better in alpha-helices than those did in beta-strand segments. Multiple regression analysis, incorporating combinations of three properties from among all possible combinations of the 48 properties, increased the correlation coefficient to 0.77.

Increasing the thermostability of staphylococcal nuclease: implications for the origin of protein thermostability

Seven hyper-stable multiple mutants have been constructed in staphylococcal nuclease by various combinations of eight different stabilizing single mutants. The stabilities of these multiple mutants determined by guanidine hydrochloride denaturation were 3.4 to 5.6 kcal/mol higher than that of the wild-type. Their thermal denaturation midpoint temperatures were 12.6 to 22.9 deg. C higher than that of the wild-type. These are among the greatest increases in protein stability and thermal denaturation midpoint temperature relative to the wild-type yet attained. There has been great interest in understanding how proteins found in thermophilic organisms are stabilized. One frequently cited theory is that the packing of hydrophobic side-chains is improved in the cores of proteins isolated from thermophiles when compared to proteins from mesophiles. The crystal structures of four single and five multiple stabilizing mutants of staphylococcal nuclease were solved to high resolution. No large overall structural change was found, with most changes localized around the sites of mutation. Rearrangements were observed in the packing of side-chains in the major hydrophobic core, although none of the mutations was in the core. It is surprising that detailed structural analysis showed that packing had improved, with the volume of the mutant protein's hydrophobic cores decreasing as protein stability increased. Further, the number of van der Waals interactions in the entire protein showed an experimentally significant increase correlated with increasing stability. These results indicate that optimization of packing follows as a natural consequence of increased protein thermostability and that good packing is not necessarily the proximate cause of high stability. Another popular theory is that thermostable proteins have more electrostatic and hydrogen bonding interactions and these are responsible for the high stabilities. The mutants here show that increased numbers of electrostatic and hydrogen bonding interactions are not obligatory for large increases in protein stability.

Importance of surrounding residues for protein stability of partially buried mutations

For understanding the factors influencing protein stability, we have analyzed the relationship between changes in protein stability caused by partially buried mutations and changes in 48 physico-chemical, energetic and conformational properties of amino acid residues. Multiple regression equations were derived to predict the stability of protein mutants and the efficiency of the method has been verified with both back-check and jack-knife tests. We observed a good agreement between experimental and computed stabilities. Further, we have analyzed the effect of sequence window length from 1 to 12 residues on each side of the mutated residue to include the sequence information for predicting protein stability and we found that the preferred window length for obtaining the highest correlation is different for each secondary structure; the preferred window length for helical, strand and coil mutations are, respectively, 0, 9 and 4 residues on both sides of the mutant residues. However, all the secondary structures have significant correlation for a window length of one residue on each side of the mutant position, implying the role of short-range interactions. Extraction of surrounding residue information for various distances (3 to 20A) around the mutant position showed the highest correlation at 8A, 6A and 7A, respectively, for mutations in helical, strand and coil segments. Overall, the information about the surrounding residues within the sphere of 7 to 8A, may explain better the stability in all subsets of partially buried mutations implying that this distance is sufficient to accommodate the residues influenced by major intramolecular interactions for the stability of protein structures.

Structure of glutamate decarboxylase and related PLP-enzymes: computer-graphical studies

Amino acid sequences of E. coli glutamate decarboxylase (GADa) and those of 36 GAD of different origin were compared by pairwise alignment using computer program CLUSTAL. GADalpha and plant enzymes showed 59.8-67.8% subunit homology, GADalpha and other bacterial GAD--49.8-77.6%, whereas GADalpha and animal enzymes--13.9-58.8%. Two PLP domains exhibited higher homology comparing to that of the whole subunit in the case of GAD67, plant (68.4-73.9%), and bacterial (46.7-83.2%) enzymes. The alignment of PLP-domains of 37 GAD, three group II decarboxylases, and two pyridoxal enzymes with known 3D structures (bacterial ORD and mAAT from chicken heart) allowed us to reveal conserved residues of the active sites. Their functional role is discussed. Modelling of the PLP-binding sites in active centers for GADalpha and human brain GAD67 was done using the Swiss-PdbViewer homology modelling program. Although the homology between GADalpha and GAD67 is rather low, structural similarity of their active sites allows us to consider here a functional convergence. Thus, glutamate decarboxylation by GADalpha may be helpful for understanding general mechanism of this reaction.

Thermodynamic and kinetic analyses for understanding sequence-specific DNA recognition

Thermodynamic and kinetic analyses of biomolecular interactions reveal details of the energetic and dynamic features of molecular recognition processes, and complement structural analyses of the free and complexed conformations. The recent improvements in both isothermal titration calorimetry and surface plasmon resonance sensoring provide powerful tools for analysing biomolecular interactions in thermodynamic and kinetic approaches. The thermodynamic and kinetic parameters obtained for binding between protein and DNA indicate the mechanism of specific DNA recognition, in the high-resolution structures of the protein-DNA complexes. The effects of temperature and ionic strength reflect the conformational changes of the protein and DNA molecules upon complex formation, including important contributions of water and solutes. When combined with mutational studies, the interactions can be reduced to several energetic contributions from individual contacts. These studies should be useful to determine general features of protein functions in genetic regulation.

ProTherm, version 2.0: thermodynamic database for proteins and mutants

ProTherm 2.0 is the second release of the Thermo-dynamic Database for Proteins and Mutants that includes numerical data for several thermodynamic parameters, structural information, experimental methods and conditions, functional and literature information. The present release contains >5500 entries, an approximately 67% increase over the previous version. In addition, we have included information about reversibility of data, details about buffer and ion concentrations and the surrounding residues in space for all mutants. A WWW interface enables users to search data based on various conditions with different sorting options for outputs. Further, ProTherm has links with other structural and literature databases, and the mutation sites and surrounding residues are automatically mapped on the structures and can be directly viewed through 3DinSight developed in our laboratory. The ProTherm database is freely available through the WWW at http://www.rtc.riken.go.jp/protherm.html

On the thermal unfolding character of globular proteins

A theoretical model is presented to study the stepwise thermal unfolding of globular proteins using the stabilizing/destabilizing characters of amino acid residues in protein crystals. A multiple regression relation connecting the melting temperature and the amounts of stabilizing and destabilizing groups of residues in a protein, when used for the thermal behavior of peptide segments, provides reliable results on the stepwise unfolding nature of the protein. In ribonuclease A, the shell residues 16-22 are predicted to unfold earlier in the temperature range 30-45 degrees C; the beta-sheet structures undergo thermal denaturation as a single cooperative unit and there is evidence indicating the segment 106-118 as a nucleation site. In ribonuclease S, the S-peptide unfolds earlier than S-protein. The predicted average and the range of melting temperatures, and the folding pathways of a set of globular proteins, agree very well with the experimental results. The results obtained in the present study indicate that (i) most of the nucleation parts possess high relative thermal stability, (ii) the unfolded state retains some residual structure, and (iii) some segments undergo gradual and overlapping thermal denaturation.

Thermodynamic analysis of biomolecular interactions

Direct measurement of the thermodynamics of biomolecular interactions is now relatively easy. Interpretation of these thermodynamics in simple molecular terms is not. Recent work shows how the multiplicity of weak noncovalent interactions, and the inevitable enthalpy/entropy compensation that these interactions engender, lead to difficulties in teasing out the different components.

Role of structural and sequence information in the prediction of protein stability changes: comparison between buried and partially buried mutations

Predicting mutation-induced changes in protein stability is one of the greatest challenges in molecular biology. In this work, we analyzed the correlation between stability changes caused by buried and partially buried mutations and changes in 48 physicochemical, energetic and conformational properties. We found that properties reflecting hydrophobicity strongly correlated with stability of buried mutations, and there was a direct relation between the property values and the number of carbon atoms. Classification of mutations based on their location within helix, strand, turn or coil segments improved the correlation of mutations with stability. Buried mutations within beta-strand segments correlated better than did those in alpha-helical segments, suggesting stronger hydrophobicity of the beta-strands. The stability changes caused by partially buried mutations in ordered structures (helix, strand and turn) correlated most strongly and were mainly governed by hydrophobicity. Due to the disordered nature of coils, the mechanism underlying their stability differed from that of the other secondary structures: the stability changes due to mutations within the coil were mainly influenced by the effects of entropy. Further classification of mutations within coils, based on their hydrogen-bond forming capability, led to much stronger correlations. Hydrophobicity was the major factor in determining the stability of buried mutations, whereas hydrogen bonds, other polar interactions and hydrophobic interactions were all important determinants of the stability of partially buried mutations. Information about local sequence and structural effects were more important for the prediction of stability changes caused by partially buried mutations than for buried mutations; they strengthened correlations by an average of 27% among all data sets.

Relationship between amino acid properties and protein stability: buried mutations

In order to understand the mechanism of protein stability and to develop a simple method for predicting mutation-induced stability changes, we analyzed the relationship between stability changes caused by buried mutations and changes in 48 amino acid properties. As expected from the importance of hydrophobicity, properties reflecting hydrophobicity are strongly correlated with the stability of proteins. We found that subgroup classification based on secondary structure increased correlations significantly, and mutations within beta-strand segments correlated better than did those in alpha-helical segments, which may result from stronger hydrophobicity of the beta-strands. Multiple regression analyses incorporating combinations of three properties from among all possible combinations of the 48 properties increased the correlation coefficient to 0.88 and by an average of 13% for all data sets. Analyzing the stability of tryptophan synthase mutants with Glu49 replaced by all other residues except Arg revealed that combining buriedness, solvent-accessible surface area for denatured protein, and unfolding Gibbs free energy change increased the correlation to 0.95. Consideration of sequence and structural information (neighboring residues in sequence and in space) did not significantly strengthen the correlations in buried mutations, suggesting that nonspecific interactions dominate in the interior of proteins.