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

Microbial-derived salt-tolerant proteases and their applications in high-salt traditional soybean fermented foods: a review

Different microorganisms can produce different proteases, which can adapt to different industrial requirements such as pH, temperature, and pressure. Salt-tolerant proteases (STPs) from microorganisms exhibit higher salt tolerance, wider adaptability, and more efficient catalytic ability under extreme conditions compared to conventional proteases. These unique enzymes hold great promise for applications in various industries including food, medicine, environmental protection, agriculture, detergents, dyes, and others. Scientific studies on microbial-derived STPs have been widely reported, but there has been little systematic review of microbial-derived STPs and their application in high-salt conventional soybean fermentable foods. This review presents the STP-producing microbial species and their selection methods, and summarizes and analyzes the salt tolerance mechanisms of the microorganisms. It also outlines various techniques for the isolation and purification of STPs from microorganisms and discusses the salt tolerance mechanisms of STPs. Furthermore, this review demonstrates the contribution of modern biotechnology in the screening of novel microbial-derived STPs and their improvement in salt tolerance. It highlights the potential applications and commercial value of salt-tolerant microorganisms and STPs in high-salt traditional soy fermented foods. The review ends with concluding remarks on the challenges and future directions for microbial-derived STPs. This review provides valuable insights into the separation, purification, performance enhancement, and application of microbial-derived STPs in traditional fermented foods.

Degradation Mechanism of Soybean Protein B3 Subunit Catalyzed by Prolyl Endopeptidase from Aspergillus niger during Soy Sauce Fermentation

Soy sauce secondary precipitate formed due to the B₃ subunit seriously affects soy sauce's appearance quality. In this study, a prolyl endopeptidase (APE) from Aspergillus niger, which could degrade approximately 50% of the B₃ subunit and increase proline content by 24% in soy sauce, was isolated and identified. The results showed that APE was an acidic salt-tolerant serine protease (62 kDa), which was optimally active at 40 °C and pH 4.0, and retained more than 69% activity in 3 M NaCl solution over 10 days. As a potential substrate of APE, the B₃ subunit contains 10 proline residues. High salinity could not damage the hydrogen bonds, salt bridges, and interior hydrophobicity of APE; thus, the spatial structures and activity of APE in 3 M NaCl solution were stable within 3 days and decreased thereafter. High salinity made the B₃ subunit more rigid and lowered the catalytic activity of APE on the B₃ subunit, hindering complete hydrolysis of the B₃ subunit. This was the first report about the APE capable of degrading the B₃ subunit and reducing the secondary precipitate of soy sauce, providing a new possibility to solve the secondary precipitate of soy sauce.

Prediction and Structural Comparison of Deleterious Coding Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) in Human LEP Gene Associated with Obesity

Leptin is a peptide hormone that regulates fat stores in the body and appetite by controlling the feeling of satiety. This hormone is secreted by the white adipose tissue and plays a role in the storage and mobilization of fatty acids. Mutations of the LEP gene have been associated with obesity in different populations; it is a multifactorial disease that constitutes a major public health problem. In this study, we evaluated the impact of missense SNPs in the LEP gene extracted from dbSNP using 8 computational prediction tools. Out of the total of 4337 SNPs, 93 were nsSNPs (nonsynonymous single nucleotide polymorphisms). Among 93 nsSNPs, 12 (S46L, G59S, D61N, D100N, N103K, C117S, D76V, S88C, P90R, I95N, L161R, and R105W) variants were predicted to be the most deleterious by prediction software. On these 12 deleterious SNPs, 8 variants (S46L, G59S, D61N, D100N, N103K, C117S, L161R, and R105W) were located in the conserved positions and showed a decrease in structure stability which was evaluated by I-Mutant and Mupro. Then, by analyzing the different interactions between different amino acids in wild and mutated proteins, we assessed the structural impact of the deleterious modifications using the YASARA software. Among 8 deleterious nsSNPs, we revealed structure changes in the 6 variants S46L, G59S, D100N, L103K, R105W, L161R, two of which R105W, N103K were previously reported as associated with obesity. Our study suggests 6 deleterious mutations could play an important role in contributing to human obesity and worth to be included in association and functional studies, then may be a drug target.

Structural prediction, whole exome sequencing and molecular dynamics simulation confirms p.G118D somatic mutation of PIK3CA as functionally important in breast cancer patients

To understand the structural and functional importance of PIK3CA somatic mutations, whole exome sequencing, molecular dynamics simulation techniques in combination with in silico prediction algorithms such as SIFT, PolyPhen, Provean and CADD were employed. Twenty out of eighty missense somatic mutations in PIK3CA gene were found to be pathogenic by all the four algorithms. Most recurrent mutations found were known hotspot PIK3CA mutations with known clinical significance like p.E545 K, p.E545A, p.E545 G and p.C420R. A missense mutation p.G118D was found to be recurrently mutated in 5 cases. Interestingly, this mutation was observed in one of the patients who underwent whole exome sequencing and was completely absent from the controls. To see the effect of this mutation on the structure of PIK3CA protein, molecular dynamics simulation was performed. By molecular dynamics approach, we have shown that p.G118D mutation deviated from the native structure which was supported by the decrease in the number of hydrogen bonds, difference in hydrogen bond distance and angle, difference in root mean square deviation between the native and the mutant structures.

Separation, biochemical characterization and salt-tolerant mechanisms of alkaline protease from Aspergillus oryzae

BACKGROUND

The salt tolerance of proteases secreted by Aspergillus oryzae 3.042 closely relates to the utilization of raw materials and the quality of soy sauce. However, little is known about the salt-tolerant proteases and their salt-tolerant mechanisms.

RESULTS

In this study, we isolated and identified a salt-tolerant alkaline protease (AP, approximately 29 kDa) produced by A. oryzae 3.042. It was considered as a metal-ion-independent serine protease. The optimum and stable pH values were both pH 9.0 and the optimum temperature was 40 °C. Over 20% relative activity of AP remained in the presence of 3.0 mol L^-1 NaCl after 7 days, but its K_m and V_max were only mildly influenced by the presence of 3.0 mol L^-1 NaCl, indicating its outstanding salt tolerance. Furthermore, AP was more stable than non-salt-tolerant protease at high salinity. The salt-tolerant mechanisms of AP could be due to more salt bridges, higher proportion of ordered secondary structures and stronger hydrophobic amino acid residues in the interior.

CONCLUSIONS

The above results are vital for maintaining, activating and/or modulating the activity of AP in high-salt environments. They would also provide theoretical guidance for the modification of AP and the engineering of A. oryzae 3.042 so as to secrete more AP. © 2018 Society of Chemical Industry.

In silico identification of lipid-binding α helices of uncoupling protein 1

Uncoupling protein 1 (UCP1) located at the mitochondrial inner membrane serves an important role in adaptive non-shivering thermogenesis. Previous data has demonstrated that membrane lipids regulate the biological functions of membrane proteins. However, how mitochondrial lipids interact with UCP1 still remains elusive. In this study, the interactions between UCP1 and membrane lipids were investigated, using bioinformatic approaches due to the limitations associated with experimental techniques. A total of 8 UCP1 peptide regions with α-helices were identified and related to functional sites of UCP1. These were all novel peptide sequences compared with the known protein-lipid interactions. Among several types of UCP1-binding molecules, cardiolipin appeared to serve as a key interacting molecule of the 8 lipid-binding α-helix regions of UCP1. Two cardiolipin-binding lysines (K¹⁷⁵ and K²⁶⁹) of UCP1 may be crucial for this UCP1-cardiolipin recognition and UCP1 function. The present findings provide novel insight into the associations of UCP1 with lipids and the potential drug targets in UCP1-associated diseases.

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.

In silico analyses of missense mutations in coagulation factor VIII: identification of severity determinants of haemophilia A

Factor VIII (FVIII) mutations cause haemophilia A (HA), an X-linked recessive coagulation disorder. Over 1000 missense mutations in FVIII are known and they lead to variable clinical phenotypes (severe, moderate and mild). The exact molecular basis of this phenotypic heterogeneity by FVIII missense mutations is elusive to date. In this study, we aimed to identify the severity determinants that cause phenotypic heterogeneity of HA. We compiled and curated a data set of 766 missense mutations from the repertoire of missense mutations in FVIII. We analysed these mutations by computational programs (e.g. Swiss-PdbViewer) and different mutation analysis servers (e.g. SIFT, PROVEAN, CUPSAT, PolyPhen2, MutPred); and various sequence- and structure-based parameters were assessed for any significant distribution bias among different HA phenotypes. Our analyses suggest that 'mutations in evolutionary conserved residues', 'mutations in buried residues', mutation-induced 'steric clash' and 'surface electrostatic potential alteration' act as risk factors towards severe HA. We have developed a grading system for FVIII mutations combining the severity determinants, and the grading pattern correlates with HA phenotype. This study will help to correctly associate the HA phenotype with a mutation and aid early characterization of novel variants.

Anion–π interactions in protein–porphyrin complexes

In this work, we have analyzed the influence of anion–π interactions on the stability of high resolution protein–porphyrin complex crystal structures.

Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

Combining secondary-structure and protein solvent-accessibility predictions in methionine substitution for anomalous dispersion

In X-ray crystallographic analysis, the single-wavelength and multi-wavelength anomalous diffraction (SAD and MAD) methods have been widely used in order to solve the phase problem. Selenium-labelled methionine has been shown to be very effective for anomalous dispersion phasing, and at least one selenomethionine is required for every 100 amino acids. Some proteins, such as the Arabidopsis thaliana thylakoid lumen protein AtTLP18.3, can be overexpressed in an Escherichia coli system and high-quality protein crystals can be obtained. However, AtTLP18.3 contains no methionine residues, and site-directed mutagenesis was required in order to introduce methionine residues into the protein. A criterion for the mutated residues is that they should avoid affecting the structure and function. In this study, several leucine and isoleucine residues were selected for methionine substitution by combining secondary-structure and solvent-accessibility predictions. From the secondary-structure prediction, mutated residues were first determined in the coil or loop regions at the junction of two secondary structures. Since leucine and isoleucine residues are hydrophobic and are normally buried within the protein core, these residues should have a higher solvent-accessibility prediction so that they would be partially buried or exposed in the protein. In addition, five residues (Leu107, Leu202, Ile133, Leu128 and Ile159) of AtTLP18.3 were mutated to methionine residues. After overexpression and purification, only two single-mutant lines, L128M and I159M, could be crystallized. Finally, a double-mutation line of truncated AtTLP18.3 with L128M and I159M mutations was constructed. The structure of the double mutant AtTLP18.3 protein was resolved using the single-wavelength anomalous diffraction method at 2.6 Å resolution. The results indicated that a combination of secondary-structure and solvent-accessibility prediction for methionine substitution is a useful method in SAD and MAD phasing.

Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences

Background

Comparison of short peptides which form amyloid-fibrils with their homologues that may form amorphous β-aggregates but not fibrils, can aid development of novel amyloid-containing nanomaterials with well defined morphologies and characteristics. The knowledge gained from the comparative analysis could also be applied towards identifying potential aggregation prone regions in proteins, which are important for biotechnology applications or have been implicated in neurodegenerative diseases. In this work we have systematically analyzed a set of 139 amyloid-fibril hexa-peptides along with a highly homologous set of 168 hexa-peptides that do not form amyloid fibrils for their position-wise as well as overall amino acid compositions and averages of 49 selected amino acid properties.

Results

Amyloid-fibril forming peptides show distinct preferences and avoidances for amino acid residues to occur at each of the six positions. As expected, the amyloid fibril peptides are also more hydrophobic than non-amyloid peptides. We have used the results of this analysis to develop statistical potential energy values for the 20 amino acid residues to occur at each of the six different positions in the hexa-peptides. The distribution of the potential energy values in 139 amyloid and 168 non-amyloid fibrils are distinct and the amyloid-fibril peptides tend to be more stable (lower total potential energy values) than non-amyloid peptides. The average frequency of occurrence of these peptides with lower than specific cutoff energies at different positions is 72% and 50%, respectively. The potential energy values were used to devise a statistical discriminator to distinguish between amyloid-fibril and non-amyloid peptides. Our method could identify the amyloid-fibril forming hexa-peptides to an accuracy of 89%. On the other hand, the accuracy of identifying non-amyloid peptides was only 54%. Further attempts were made to improve the prediction accuracy via machine learning. This resulted in an overall accuracy of 82.7% with the sensitivity and specificity of 81.3% and 83.9%, respectively, in 10-fold cross-validation method.

Conclusions

Amyloid-fibril forming hexa-peptides show position specific sequence features that are different from those which may form amorphous β-aggregates. These positional preferences are found to be important features for discriminating amyloid-fibril forming peptides from their homologues that don't form amyloid-fibrils.

iStable: off-the-shelf predictor integration for predicting protein stability changes

BACKGROUND

Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users.

RESULTS

We proposed an integrated predictor, iStable, with grid computing architecture constructed by using sequence information and prediction results from different element predictors. In the learning model, several machine learning methods were evaluated and adopted the support vector machine as an integrator, while not just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available with two different input types: structural and sequential. After training and cross-validation, iStable has better performance than all of the element predictors on several datasets. Under different classifications and conditions for validation, this study has also shown better overall performance in different types of secondary structures, relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental conditions.

CONCLUSIONS

The trained and validated version of iStable provides an accurate approach for prediction of protein stability changes. iStable is freely available online at: http://predictor.nchu.edu.tw/iStable.

Arginine and Lysine interactions with π residues in metalloproteins

Metalloproteins have many different functions in cells such as enzymes; signal transduction, transport and storage proteins. About one third of all proteins require metals to carry out their functions. In the present study we have analyzed the roles played by Arg and Lys (cationic side chains) interactions with π (Phe, Tyr or Trp) residues and their role in the structural stability of metalloproteins. These interactions might play an important role in the global conformational stability in metalloproteins. In spite of its lower natural occurrence (1.76%) the number of Trp residues involved in energetically significant interactions is higher in metalloproteins.

Relationship between amino acid properties and functional parameters in olfactory receptors and discrimination of mutants with enhanced specificity

BACKGROUND

Olfactory receptors are key components in signal transduction. Mutations in olfactory receptors alter the odor response, which is a fundamental response of organisms to their immediate environment. Understanding the relationship between odorant response and mutations in olfactory receptors is an important problem in bioinformatics and computational biology. In this work, we have systematically analyzed the relationship between various physical, chemical, energetic and conformational properties of amino acid residues, and the change of odor response/compound's potency/half maximal effective concentration (EC50) due to amino acid substitutions.

RESULTS

We observed that both the characteristics of odorant molecule (ligand) and amino acid properties are important for odor response and EC50. Additional information on neighboring and surrounding residues of the mutants enhanced the correlation between amino acid properties and EC50. Further, amino acid properties have been combined systematically using multiple regression techniques and we obtained a correlation of 0.90-0.98 with odor response/EC50 of goldfish, mouse and human olfactory receptors. In addition, we have utilized machine learning methods to discriminate the mutants, which enhance or reduce EC50 values upon mutation and we obtained an accuracy of 93% and 79% for self-consistency and jack-knife tests, respectively.

CONCLUSIONS

Our analysis provides deep insights for understanding the odor response of olfactory receptor mutants and the present method could be used for identifying the mutants with enhanced specificity.

MAP(2.0)3D: a sequence/structure based server for protein engineering

The Mutagenesis Assistant Program (MAP) is a web-based tool to provide statistical analyses of the mutational biases of directed evolution experiments on amino acid substitution patterns. MAP analysis assists protein engineers in the benchmarking of random mutagenesis methods that generate single nucleotide mutations in a codon. Herein, we describe a completely renewed and improved version of the MAP server, the MAP(2.0)3D server, which correlates the generated amino acid substitution patterns to the structural information of the target protein. This correlation aids in the selection of a more suitable random mutagenesis method with specific biases on amino acid substitution patterns. In particular, the new server represents MAP indicators on secondary and tertiary structure and correlates them to specific structural components such as hydrogen bonds, hydrophobic contacts, salt bridges, solvent accessibility, and crystallographic B-factors. Three model proteins (D-amino oxidase, phytase, and N-acetylneuraminic acid aldolase) are used to illustrate the novel capability of the server. MAP(2.0)3D server is available publicly at http://map.jacobs-university.de/map3d.html.

Investigating the structural impacts of I64T and P311S mutations in APE1-DNA complex: a molecular dynamics approach

Background

Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA.

Principal Findings

In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis.

Significance

Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis.

A comparative study of the second-order hydrophobic moments for globular proteins: the consensus scale of hydrophobicity and the CHARMM partial atomic charges

In this paper, the second-order hydrophobic moment for fifteen globular proteins in 150 nonhomologous protein chains was performed in a comparative study involving two sets of hydrophobicity: one selected from the consensus scale and the other derived from the CHARMM partial atomic charges. These proteins were divided into three groups, based on their number of residues (N) and the asphericity (δ). Proteins in Group I were spherical and those in Groups II and III were prolate. The size of the proteins is represented by the mean radius of gyration (R_g ), which follows the Flory scaling law, R_g ∝ N^ν. The mean value of v was 0.35, which is similar to a polymer chain in a poor solvent. The spatial distributions of the second-order moment for each of the proteins, obtained from the two sets of hydrophobicity, were compared using the Pearson correlation coefficient; the results reveal that there is a strong correlation between the two data sets for each protein structure when the CHARMM partial atomic charges, |q_i| ≥ 0.3, assigned for polar atoms, are used. The locations at which these distributions vanish and approach a negative value are at approximately 50% of the percentage of solvent accessibility, indicating that there is a transition point from hydrophobic interior to hydrophilic exterior in the proteins. This may suggest that there is a position for the proteins to determine the residues at exposed sites beyond this range.

Computed structures of point deletion mutants and their enzymatic activities

Point deletions in enzymes can vary in effect from negligible to complete loss of activity; however, these effects are not generally predictable. Deletions are widely observed in nature and often result in diseases such as cancer, cystic fibrosis, or osteogenesis imperfecta. Here, we have developed an algorithm to model the perturbed structures of deletion mutants with the ultimate goal of predicting their activities. The algorithm works by deleting the specified residue from the wild-type structure, creating a gap that is closed using a combination of local and global moves that change the backbone torsion angles of the protein structure. On a set of five proteins for which both wild-type and deletion mutant x-ray crystal structures are available, the algorithm produces deep, narrow energy funnels within 1.5 Å of the crystal structure for the deletion mutants. To assess the ability of our algorithm to predict activity from the predicted structures, we tested the correlation of experimental activity with several measures of the predicted structure ensemble using a set of 45 point deletions from ricin. Estimates incorporating likely prevalence of active and inactive deletion sites suggest that activity can be predicted correctly over 60% of the time from the active site root-mean squared deviation of the lowest energy predicted structures. The predictions are stronger than simple sequence organization measures, but more fundamental work is required in structure prediction and enzyme activity determination to allow consistent prediction of activity.

Nature of cation-pi interactions and their role in structural stability of immunoglobulin proteins

Cation-pi interactions are known to be important contributors to protein stability and ligand-protein interactions. In this study, we have analyzed the influence of cation-pi interactions in single chain immunoglobulin proteins. We observed 87 cation-pi interactions in a data set of 33 proteins. These interactions are mainly formed by long-range contacts, and there is preference of Arg over Lys in these interactions. Arg-Tyr interactions are predominant among the various pairs analyzed. Despite the scarcity of interactions involving Trp, the average energy for Trp-cation interactions is quite high. This information suggests that the cation-pi interactions involving Trp might be of high relevance to the proteins. Secondary structure analysis reveals that cation-pi interactions are formed preferably between residues in which at least one is in beta-strand. Proteins having beta-strand regions have the highest number of cation-pi interaction-forming residues.

Investigation of cation-π interactions in sugar-binding proteins

Cation-π interaction is a non-covalent binding force that plays a significant role in protein stability and drug-receptor interactions. In this work, we have investigated the structural role of cation-π interactions in sugar-binding proteins (SBPs). We observed 212 cation-π interactions in 53 proteins out of 59 SBPs in dataset. There is an average one energetically significant cation-π interaction for every 66 residues in SBPs. In addition, Arg is highly preferred to form cation-π interactions, and the average energy of Arg-Trp is high among six pairs. Long-range interactions are predominant in the analyzed cation-π interactions. Comparatively, all interaction pairs favor to accommodate in strand conformations. The analysis of solvent accessible area indicates that most of the aromatic residues are found on buried or partially buried whereas cationic residues were found mostly on the exposed regions of protein. The cation-π interactions forming residues were found that around 43% of cation-π residues had highly conserved with the conservation score ≥6. Almost cationic and π-residues equally share in the stabilization center. Sugar-binding site analysis in available complexes showed that the frequency of Trp and Arg is high, suggesting the potential role of these two residues in the interactions between proteins and sugar molecules. Our observations in this study could help to further understand the structural stability of SBPs.

Computational predictions of the mutant behavior of AraC

An algorithm implemented in Rosetta correctly predicts the folding capabilities of the 17-residue N-terminal arm of the AraC gene regulatory protein when arabinose is bound to the protein and the dramatically different structure of this arm when arabinose is absent. The transcriptional activity of 43 mutant AraC proteins with alterations in the arm sequences was measured in vivo and compared with their predicted folding properties. Seventeen of the mutants possessed regulatory properties that could be directly compared with folding predictions. Sixteen of the 17 mutants were correctly predicted. The algorithm predicts that the N-terminal arm sequences of AraC homologs fold to the Escherichia coli AraC arm structure. In contrast, it predicts that random sequences of the same length and many partially randomized E. coli arm sequences do not fold to the E. coli arm structure. The high level of success shows that relatively "simple" computational methods can in some cases predict the behavior of mutant proteins with good reliability.

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.

Sequence and structural analysis of binding site residues in protein-protein complexes

The binding sites in protein-protein complexes have been identified with different methods including atomic contacts, reduction in solvent accessibility and interaction energy between the interacting partners. In our earlier work, we have developed an energy-based criteria for identifying the binding sites in protein-protein complexes, which showed that the interacting residues are different from that obtained with distance-based methods. In this work, we analyzed the binding site residues based on sequence and structural properties, such as, neighboring residues, secondary structure, solvent accessibility, conservation of residues, medium and long-range contacts and surrounding hydrophobicity. Our results showed that the neighboring residues of binding sites in proteins and ligands are different from each other although the interacting pairs of residues have a common behavior. The analysis on surrounding hydrophobicity reveals that the binding residues are less hydrophobic than non-binding sites, which suggests that the hydrophobic core are important for folding and stability whereas the surface seeking residues play a critical role in binding. This tendency has been verified with the number of contacts in binding sites. In addition, the binding site residues are highly conserved compared with non-binding residues. We suggest that the incorporation of sequence and structure-based features may improve the prediction accuracy of binding sites in protein-protein complexes.

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.

Selected-fit versus induced-fit protein binding: kinetic differences and mutational analysis

The binding of a ligand molecule to a protein is often accompanied by conformational changes of the protein. A central question is whether the ligand induces the conformational change (induced-fit), or rather selects and stabilizes a complementary conformation from a pre-existing equilibrium of ground and excited states of the protein (selected-fit). We consider here the binding kinetics in a simple four-state model of ligand-protein binding. In this model, the protein has two conformations, which can both bind the ligand. The first conformation is the ground state of the protein when the ligand is off, and the second conformation is the ground state when the ligand is bound. The induced-fit mechanism corresponds to ligand binding in the unbound ground state, and the selected-fit mechanism to ligand binding in the excited state. We find a simple, characteristic difference between the on- and off-rates in the two mechanisms if the conformational relaxation into the ground states is fast. In the case of selected-fit binding, the on-rate depends on the conformational equilibrium constant, whereas the off-rate is independent. In the case of induced-fit binding, in contrast, the off-rate depends on the conformational equilibrium, while the on-rate is independent. Whether a protein binds a ligand via selected-fit or induced-fit thus may be revealed by mutations far from the protein's binding pocket, or other "perturbations" that only affect the conformational equilibrium. In the case of selected-fit, such mutations will only change the on-rate, and in the case of induced-fit, only the off-rate.

Inferring stabilizing mutations from protein phylogenies: application to influenza hemagglutinin

One selection pressure shaping sequence evolution is the requirement that a protein fold with sufficient stability to perform its biological functions. We present a conceptual framework that explains how this requirement causes the probability that a particular amino acid mutation is fixed during evolution to depend on its effect on protein stability. We mathematically formalize this framework to develop a Bayesian approach for inferring the stability effects of individual mutations from homologous protein sequences of known phylogeny. This approach is able to predict published experimentally measured mutational stability effects (ΔΔG values) with an accuracy that exceeds both a state-of-the-art physicochemical modeling program and the sequence-based consensus approach. As a further test, we use our phylogenetic inference approach to predict stabilizing mutations to influenza hemagglutinin. We introduce these mutations into a temperature-sensitive influenza virus with a defect in its hemagglutinin gene and experimentally demonstrate that some of the mutations allow the virus to grow at higher temperatures. Our work therefore describes a powerful new approach for predicting stabilizing mutations that can be successfully applied even to large, complex proteins such as hemagglutinin. This approach also makes a mathematical link between phylogenetics and experimentally measurable protein properties, potentially paving the way for more accurate analyses of molecular evolution.

Mutating a protein frequently causes a change in its stability. As scientists, we often care about these changes because we would like to engineer a protein's stability or understand how its stability is impacted by a naturally occurring mutation. Evolution also cares about mutational stability changes, because a basic evolutionary requirement is that proteins remain sufficiently stable to perform their biological functions. Our work is based on the idea that it should be possible to use the fact that evolution selects for stability to infer from related proteins the effects of specific mutations. We show that we can indeed use protein evolutionary histories to computationally predict previously measured mutational stability changes more accurately than methods based on either of the two main existing strategies. We then test whether we can predict mutations that increase the stability of hemagglutinin, an influenza protein whose rapid evolution is partly responsible for the ability of this virus to cause yearly epidemics. We experimentally create viruses carrying predicted stabilizing mutations and find that several do in fact improve the virus's ability to grow at higher temperatures. Our computational approach may therefore be of use in understanding the evolution of this medically important virus.

Sequence based residue depth prediction using evolutionary information and predicted secondary structure

Background

Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.

Results

A new method, RDPred, for the real-value depth prediction from protein sequence is proposed. RDPred combines information extracted from the sequence, PSI-BLAST scoring matrices, and secondary structure predicted with PSIPRED. Three-fold/ten-fold cross validation based tests performed on three independent, low-identity datasets show that the distance based depth (computed using MSMS) predicted by RDPred is characterized by 0.67/0.67, 0.66/0.67, and 0.64/0.65 correlation with the actual depth, by the mean absolute errors equal 0.56/0.56, 0.61/0.60, and 0.58/0.57, and by the mean relative errors equal 17.0%/16.9%, 18.2%/18.1%, and 17.7%/17.6%, respectively. The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509–516]. The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation. We also show that the hydrophilic and flexible residues are predicted more accurately than hydrophobic and rigid residues. Similarly, the charged residues that include Lys, Glu, Asp, and Arg are the most accurately predicted. Our analysis reveals that evolutionary information encoded using PSSM is characterized by stronger correlation with the depth for hydrophilic amino acids (AAs) and aliphatic AAs when compared with hydrophobic AAs and aromatic AAs. Finally, we show that the secondary structure of coils and strands is useful in depth prediction, in contrast to helices that have relatively uniform distribution over the protein depth. Application of the predicted residue depth to prediction of buried/exposed residues shows consistent improvements in detection rates of both buried and exposed residues when compared with the competing method. Finally, we contrasted the prediction performance among distance based (MSMS and DPX) and volume based (SADIC) depth definitions. We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles.

Conclusion

The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method. The predicted depth can be used to provide improved prediction of both buried and exposed residues. The prediction of exposed residues has implications in characterization/prediction of interactions with ligands and other proteins, while the prediction of buried residues could be used in the context of folding predictions and simulations.

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/.

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: Chymotrypsin inhibitor 2 mutants

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.

Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines

This work reports a novel 3D pseudo-folding graph representation of protein sequences for modeling purposes. Amino acids euclidean distances matrices (EDMs) encode primary structural information. Amino Acid Pseudo-Folding 3D Distances Count (AAp3DC) descriptors, calculated from the EDMs of a large data set of 1363 single protein mutants of 64 proteins, were tested for building a classifier for the signs of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) upon single mutations. An optimum support vector machine (SVM) with a radial basis function (RBF) kernel well recognized stable and unstable mutants with accuracies over 70% in crossvalidation test. To the best of our knowledge, this result for stable mutant recognition is the highest ever reported for a sequence-based predictor with more than 1000 mutants. Furthermore, the model adequately classified mutations associated to diseases of human prion protein and human transthyretin.

SIMPLE estimate of the free energy change due to aliphatic mutations: Superior predictions based on first principles

The bioinformatics revolution of the last decade has been instrumental in the development of empirical potentials to quantitatively estimate protein interactions for modeling and design. Although computationally efficient, these potentials hide most of the relevant thermodynamics in 5-to-40 parameters that are fitted against a large experimental database. Here, we revisit this longstanding problem and show that a careful consideration of the change in hydrophobicity, electrostatics, and configurational entropy between the folded and unfolded state of aliphatic point mutations predicts 20-30% less false positives and yields more accurate predictions than any published empirical energy function. This significant improvement is achieved with essentially no free parameters, validating past theoretical and experimental efforts to understand the thermodynamics of protein folding. Our first principle analysis strongly suggests that both the solute-solute van der Waals interactions in the folded state and the electrostatics free energy change of exposed aliphatic mutations are almost completely compensated by similar interactions operating in the unfolded ensemble. Not surprisingly, the problem of properly accounting for the solvent contribution to the free energy of polar and charged group mutations, as well as of mutations that disrupt the protein backbone remains open.

Influence of cation-π interactions on RNA-binding proteins

The energy contribution due to cation-pi interactions has been computed for 37 RNA binding proteins. The contribution of these cation-pi interacting residues in sequential separation, secondary structure involvement, solvent accessibility, and stabilization centers has been evaluated. Sequential separation of the cation-pi involving residues show that, long range contacts predominates in all the proteins studied. Lys and Arg prefers to be in helical structures. Of the cation-pi interacting residues, Arg and Lys were in the exposed regions and the aromatic residues (Phe, Tyr and Trp) were in the buried and partially buried regions in the protein structures. Stabilization centers for these proteins showed that all the five residues found in cation-pi interactions are important in locating one or more of such centers. On the whole, the results presented in this work will be very useful for further investigations on the specificity and selectivity of RNA binding proteins and also for their structural studies.

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.