Distance-dependent statistical potentials for discriminating thermophilic and mesophilic proteins

Identification of the characteristic structural patterns responsible for protein thermostability is theoretically important and practically useful but largely remains an open problem. These patterns may be revealed through comparative study on thermophilic and mesophilic proteins that have distinct thermostability. In this study, we constructed several distance-dependant potentials from thermophilic and mesophilic proteins. These potentials were then used to evaluate the structural difference between thermophilic and mesophilic proteins. We found that using the subtraction or division of the potentials derived from thermophilic and mesophilic proteins can dramatically increase the discriminatory ability. This approach revealed that the ability to distinct the subtle structural features responsible for protein thermostability may be effectively enhanced through rationally designed comparative study.     

A similarity distance of diversity measure for discriminating mesophilic and thermophilic proteins

The successful prediction of thermophilic proteins is useful for designing stable enzymes that are functional at high temperature. We have used the increment of diversity (ID), a novel amino acid composition-based similarity distance, in a 2-class K-nearest neighbor classifier to classify thermophilic and mesophilic proteins. And the KNN-ID classifier was successfully developed to predict the thermophilic proteins. Instead of extracting features from protein sequences as done previously, our approach was based on a diversity measure of symbol sequences. The similarity distance between each pair of protein sequences was first calculated to quantitatively measure the similarity level of one given sequence and the other. The query protein is then determined using the K-nearest neighbor algorithm. Comparisons with multiple recently published methods showed that the KNN-ID proposed in this study outperforms the other methods. The improved predictive performance indicated it is a simple and effective classifier for discriminating thermophilic and mesophilic proteins. At last, the influence of protein length and protein identity on prediction accuracy was discussed further. The prediction model and dataset used in this article can be freely downloaded from http://wlxy.imu.edu.cn/college/biostation/fuwu/KNN-ID/index.htm.     

Discrimination of thermophilic and mesophilic proteins

Background

There is a considerable literature on the source of the thermostability of proteins from thermophilic organisms. Understanding the mechanisms for this thermostability would provide insights into proteins generally and permit the design of synthetic hyperstable biocatalysts.

Results

We have systematically tested a large number of sequence and structure derived quantities for their ability to discriminate thermostable proteins from their non-thermostable orthologs using sets of mesophile-thermophile ortholog pairs. Most of the quantities tested correspond to properties previously reported to be associated with thermostability. Many of the structure related properties were derived from the Delaunay tessellation of protein structures.

Conclusions

Carefully selected sequence based indices discriminate better than purely structure based indices. Combined sequence and structure based indices improve performance somewhat further. Based on our analysis, the strongest contributors to thermostability are an increase in ion pairs on the protein surface and a more strongly hydrophobic interior.

     

Density discriminates between thermophilic and mesophilic proteins

Despite an intense interest and a remarkable number of studies on the subject, the relationships between thermostability and (primary, secondary and tertiary) structure of proteins are still not fully understood. Here, comparing the protein density – defined by the ratio between the residue number and protein excluded volume – for a set of thermophilic/mesophilic pairs, we provide evidence that this property is connected to the optimal growth temperature. In particular, our results indicate that thermophilic proteins have – in general – a lower density with respect to the mesophilic counterparts, being such a correlation more pronounced for optimal growth temperature differences greater than 40°C. The effect of the protein thermostability changes on the molecular shape is also presented.     

Structural differences between thermophilic and mesophilic membrane proteins

The evolutionary adaptations of thermophilic water‐soluble proteins required for maintaining stability at high temperature have been extensively investigated. Little is known about the adaptations in membrane proteins, however. Here, we compare many properties of mesophilic and thermophilic membrane protein structures, including side‐chain burial, packing, hydrogen bonding, transmembrane kinks, loop lengths, hydrophobicity, and other sequence features. Most of these properties are quite similar between mesophiles and thermophiles although we observe a slight increase in side‐chain burial and possibly a slight decrease in the frequency of transmembrane kinks in thermophilic membrane protein structures. The most striking difference is the increased hydrophobicity of thermophilic transmembrane helices, possibly reflecting more stringent hydrophobicity requirements for membrane partitioning at high temperature. In agreement with prior work examining transmembrane sequences, we find that thermophiles have an increase in small residues (Gly, Ala, Ser, and Val) and a strong suppression of Cys. We also find a relative dearth of most strongly polar residues (Asp, Asn, Glu, Gln, and Arg). These results suggest that in thermophiles, there is significant evolutionary pressure to offload destabilizing polar amino acids, to decrease the entropy cost of side chain burial, and to eliminate thermally sensitive amino acids.     

Thermodynamic differences among homologous thermophilic and mesophilic proteins.

Here, we analyze the thermodynamic parameters and their correlations in families containing homologous thermophilic and mesophilic proteins which show reversible two-state folding <--> unfolding transitions between the native and the denatured states. For the proteins in these families, the melting temperatures correlate with the maximal protein stability change (between the native and the denatured states) as well as with the enthalpic and entropic changes at the melting temperature. In contrast, the heat capacity change is uncorrelated with the melting temperature. These and additional results illustrate that higher melting temperatures are largely obtained via an upshift and broadening of the protein stability curves. Both thermophilic and mesophilic proteins are maximally stable around room temperature. However, the maximal stabilities of thermophilic proteins are considerably greater than those of their mesophilic homologues. At the living temperatures of their respective source organisms, homologous thermophilic and mesophilic proteins have similar stabilities. The protein stability at the living temperature of the source organism does not correlate with the living temperature of the protein. We tie thermodynamic observations to microscopics via the hydrophobic effect and a two-state model of the water structure. We conclude that, to achieve higher stability and greater resistance to high and low temperatures, specific interactions, particularly electrostatic, should be engineered into the protein. The effect of these specific interactions is largely reflected in an increased enthalpy change at the melting temperature.     

Using LogitBoost classifier to predict protein structural classes

Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.     

Packing-based difference of structural features between thermophilic and mesophilic proteins

Twenty pairs of thermophilic and mesophilic proteins were compared in terms of residue packing distribution to obtain structural features related to protein thermostability. Based on residue packing concept, structural features of residues such as residue packing distribution, inner/outer position, secondary structure and water solvation were investigated. The statistical tests revealed that higher frequency in well-packed state of residues, lower frequency in exposed state and higher frequency in well-packed state of inner positioned residues, and higher frequency in well-packed state of 3/10 helix residues could be general structural features thermophilic proteins have.     

Discrimination of mesophilic and thermophilic proteins using machine learning algorithms

Discriminating thermophilic proteins from their mesophilic counterparts is a challenging task and it would help to design stable proteins. In this work, we have systematically analyzed the amino acid compositions of 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively. We found that the charged residues Lys, Arg, and Glu as well as the hydrophobic residues, Val and Ile have higher occurrence in thermophiles than mesophiles. Further, we have analyzed the performance of different methods, based on Bayes rules, logistic functions, neural networks, support vector machines, decision trees and so forth for discriminating mesophilic and thermophilic proteins. We found that most of the machine learning techniques discriminate these classes of proteins with similar accuracy. The neural network-based method could discriminate the thermophiles from mesophiles at the five-fold cross-validation accuracy of 89% in a dataset of 4684 proteins. Moreover, this method is tested with 325 mesophiles in Xylella fastidosa and 382 thermophiles in Aquifex aeolicus and it could successfully discriminate them with the accuracy of 91%. These accuracy levels are better than other methods in the literature and we suggest that this method could be effectively used to discriminate mesophilic and thermophilic proteins.     

Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins

Understanding the role of various interactions in enhancing the thermostability of proteins is important not only for clarifying the mechanism of protein stability but also for designing stable proteins. In this work, we have analyzed the thermostability of 16 different families by comparing mesophilic and thermophilic proteins with 48 various physicochemical, energetic and conformational properties. We found that the increase in shape, s (location of branch point in side chain) increases the thermostability, whereas, an opposite trend is observed for Gibbs free energy change of hydration for native proteins, GhN, in 14 families. A good correlation is observed between these two properties and the simultaneous increases of -GhN and s is necessary to enhance the thermostability from mesophile to thermophile. The increase in shape, which tends to increase with increasing number of carbon atoms both for polar and non-polar residues, may generate more packing and compactness, and the position of beta and higher order branches may be important for better packing. On the other hand, the increase in -GhN in thermophilic proteins increases the solubility of the proteins. This tendency counterbalances the increases in insolubility and unfolding heat capacity change due to the increase in the number of carbon atoms. Thus, the present results suggest that the stability of thermophilic proteins may be achieved by a balance between better packing and solubility.     

Comparison of energy components of proteins from thermophilic and mesophilic organisms

In order to infer the energetic determinants of thermophilic proteins, molecular mechanics calculations were applied to five proteins from thermophilic eubacteria and their mesophilic homologs. The energy function includes a hydration term as well as the electrostatic contribution from the solvent in addition to the usual conformational energy terms. We calculated energy values for three different states of each protein: the native, near-native, and unfolded structures. The energy difference and its components between pairs of these states were compared. The hypothetical near-native structures have almost the same backbone conformation as the native structure but with largely distorted side-chain packing, thus enabling us to extract the energy components important for stabilizing the native backbone topology itself, irrespective of structural details. It was found that the sum of the electrostatic and hydration energies, although of large positive values, were consistently lower for the thermophilic proteins than for their mesophilic counterparts. This trend was observed in the energy difference not only between the native and unfolded structures, but also between the near-native and unfolded structures. In contrast, the energy components regarding side-chain packing did not show any clear tendency. These results suggest that the thermophilic proteins are stabilized so that the precise packing of the native structure does not significantly affect the stability. Implications of this conclusion are also discussed.     

Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins

Summary. Thermophilic proteins show substantially higher intrinsic thermal stability than their mesophilic counterparts. Amino acid composition is believed to alter the intrinsic stability of proteins. Several investigations and mutagenesis experiment have been carried out to understand the amino acid composition for the thermostability of proteins. This review presents some generalized features of amino acid composition found in thermophilic proteins, including an increase in residue hydrophobicity, a decrease in uncharged polar residues, an increase in charged residues, an increase in aromatic residues, certain amino acid coupling patterns and amino acid preferences for thermophilic proteins. The differences of amino acids composition between thermophilic and mesophilic proteins are related to some properties of amino acids. These features provide guidelines for engineering mesophilic protein to thermophilic protein. Authors’ addresses: Yuan-Jiang Pan, Institute of Chemical Biology and Pharmaceutical Chemistry, Zhejiang University, Zhejiang University Road 38, Hangzhou 310027, China; Wei-Fen Li, Microbiology Division, College of Animal Science, Zhejiang University, Hangzhou 310029, China     

Support vector machine for discrimination of thermophilic and mesophilic proteins based on amino acid composition

The identification of the thermostability from the amino acid sequence information would be helpful in computational screening for thermostable proteins. We have developed a method to discriminate thermophilic and mesophilic proteins based on support vector machines. Using self-consistency validation, 5-fold cross-validation and independent testing procedure with other datasets, this module achieved overall accuracy of 94.2%, 90.5% and 92.4%, respectively. The performance of this SVM-based module was better than the classifiers built using alternative machine learning and statistical algorithms including artificial neural networks, Bayesian statistics, and decision trees, when evaluated using these three validation methods. The influence of protein size on prediction accuracy was also addressed.     

Genes and derived amino acid sequences of S-layer proteins from mesophilic,thermophilic, and extremely thermophilic methanococci

Cells of methanococci are covered by a single layer of protein subunits (S-layer) in hexagonal arrangement, which are directly exposed to the environment and which cannot be stabilized by cellular components. We have isolated S-layer proteins from cells of Methanococcus vannielii ( T(opt.)=37 degrees C), Methanococcus thermolithotrophicus ( T(opt.)=65 degrees C), and Methanococcus jannaschii ( T(opt.)=85 degrees C). The primary structure of the S-layer proteins was determined by sequencing the corresponding genes. According to the predicted amino acid sequence, the molecular masses of the S-layer proteins of the different methanococci are in a small range between 59,064 and 60,547 Da. Compared with its mesophilic counterparts, it is worth noting that in the S-layer protein of the extreme thermophile Mc. jannaschii the acidic amino acid Asp is predominant, the basic amino acid Lys occurs in higher amounts, and Cys and His are only present in this organism. Despite the differences in the growth optima and the predominance of some amino acids, the comparative total primary structure revealed a relatively high degree of identity (38%-45%) between the methanococci investigated. This observation indicates that the amino acid sequence of the S-layer proteins is significantly conserved from the mesophilic to the extremely thermophilic methanococci.     

Protein thermostability: structure-based difference of residual properties between thermophilic and mesophilic proteins

Structure-based differences of residual properties between 20 pairs of thermophilic and mesophilic proteins were statistically analyzed to elucidate the factors governing protein thermostability. This study analyzed the distributions of outer residues, inner residues, flexible residues, rigid residues, hydrogen bonds, salt bridges, cation–pi interactions, and disulfide bonds in each protein in terms of residual structural states, which were determined as five kinds of states under residual packing value. Their structural patterns found in thermophilic protein groups were compared with those of mesophilic protein groups for showing distinctive difference of residual properties. The results of statistical tests (t-test) revealed that flexible residues in fully-exposed state and boundary state, salt bridges in exposed state, and hydrogen bonds in well-buried state could be critical factors related with protein thermostability. Such structure-based differences of residual properties would help to develop a strategy for enhancing protein thermostability.     

Protein thermostability: structure-based difference of amino acid between thermophilic and mesophilic proteins

Structural distributions of each amino acid were compared between 20 pairs of thermophilic and mesophilic proteins to obtain thermostable factors. Five kinds of residual structure states such as fully-exposed, exposed, partially exposed (or partially buried), buried, well-buried states were considered for analyzing the structural patterns of amino acids. The statistical tests revealed that lower frequency in partially exposed state of SER, lower frequency in exposed state and higher frequency in well-buried state of ALA, higher frequency in buried state of GLU, higher frequency in exposed state of ARG, etc. could be critical factors related with protein thermostability.     

Primary structure of selected archaeal mesophilic and extremely thermophilic outer surface layer proteins

The archaea are recognized as a separate third domain of life together with the bacteria and eucarya. The archaea include the methanogens, extreme halophiles, thermoplasmas, sulfate reducers and sulfur metabolizing thermophiles, which thrive in different habitats such as anaerobic niches, salt lakes, and marine hydrothermals systems and continental solfataras. Many of these habitats represent extreme environments in respect to temperature, osmotic pressure and pH-values and remind on the conditions of the early earth. The cell envelope structures were one of the first biochemical characteristics of archaea studied in detail. The most common archaeal cell envelope is composed of a single crystalline protein or glycoprotein surface layer (S-layer), which is associated with the outside of the cytoplasmic membrane. The S-layers are directly exposed to the extreme environment and can not be stabilized by cellular components. Therefore, from comparative studies of mesophilic and extremely thermophilic S-layer proteins hints can be obtained about the molecular mechanisms of protein stabilization at high temperatures. First crystallization experiments of surface layer proteins under microgravity conditions were successful. Here, we report on the biochemical features of selected mesophilic and extremely archaeal S-layer (glyco-) proteins.     

Robust determinants of thermostability highlighted by a codon frequency index capable of discriminating thermophilic from mesophilic genomes

Can genome analysis tell us about the lifestyle of an organism? We ask this question considering a thorough cross comparison of thermophilic and mesophilic genomes, since presently the number of available genomes is enough to ensure statistical significance of the results. We analyze, by means of principal component analysis (PCA), the codon composition of a database comprising 116 genomes, selected so as to include one species for each genus and show that a cross genomic approach can allow the extraction of common determinants of thermostability at the genome level. The results of our analysis indicate that all the known features of thermostability can be found in the 64 component loadings of the second principal axis of PCA. By this, we develop an index of thermostability whose discriminative power between mesophiles and thermophiles scores with 98% accuracy at the genome level and with 95% accuracy at the protein sequence level. We also prove that these results are not due to phylogenetic differences between archaea and bacteria.     

Xylan-hydrolysing enzymes from thermophilic and mesophilic fungi

Screening of 40 mesophilic and 13 thermophilic fungi indicated that enzyme activities capable of degrading oat spelt xylan extensively were produced by only a few of the mesophilic species investigated. The relatively low degree of hydrolysis effected by the enzymes from thermophilic organisms could be explained, in part, by their lack of -xylosidase. Several strains of Aspergillus awamori and Aspergillus phoenicis were notable in producing high xylanase and -xylosidase and low protease activities. Of the fungl tested, 13 produced activities capable of removing O-acetyl, arabinosyl, 4-O-methylglucuronyl, feruloyl and coumaroyl substituents from the backbone of xylan polysaccharides as well as endo-1,4--d-xylanase and -1,4-xylosidase. When the growth medium contained oat spelt xylan as carbon source, higher levels of xylanase, -xylosidase and acetyl xylan esterase were found than in cultures containing meadow fescue grass but the latter were richer in ferulic acid and coumaric acid esterases and 4-O-methylglucuronidase. No single organism or carbon source used was capabie of producing high levels of all the debranching enzymes as well as high levels of enzymes capable of cleaving the glycosidic linkages of the xylan backbone. The best ballnce of enzymes was obtained in cultures of A. awamori IMI 142717 and NRRL 2276 and A. phoenicis IMI 214827. Either of these would be suitable for strain improvement studies.The authors are with The Rowett Research Institute. Bucksburn, Aberdeen, AB2 9SB, UK.T.M. Wood is the corresponding author.