Folding Free Energy Research Articles

Impact of a non-synonymous Q281R polymorphism on structure of human Lipoprotein-Associated Phospholipase A2 (Lp-PLA2 ).

Non-synonymous single nucleotide polymorphisms (nsSNPs) are genetic variations at single base resulting in an amino acid change which have been associated with various complex human diseases. The human Lipoprotein-associated phospholipase A2 (Lp-PLA2 ) gene harbours a rare Q281R polymorphism which was previously reported to cause loss of enzymatic function. Lp-PLA2 is an important enzyme which catalyzes the hydrolysis of polar phospholipids releasing pro-atherogenic and pro-inflammatory mediators involved in the pathogenesis of atherosclerosis. Our current study is aimed at elucidating the structural and functional consequences of Q281R polymorphism on Lp-PLA2 . The Q281R mutation is classified as deleterious and causes protein instability as deduced from evolutionary, folding free energy changes and Support vector machine (SVM)-based methods. A Q281R mutant structure was deciphered using homology modelling approach and was validated using phi and psi dihedral angles distribution, ERRAT, Verify_3D scores, Protein Structure Analysis (ProSA) energ,y and Z-score. A decreased hydrophobic interactions and weaker substrate binding affinity was observed in the mutant compared to the wild- type (WT) using molecular docking. Further, the mutant displayed enhanced structural flexibility particularly in the low density lipoprotein (LDL) binding domain, decreased solvent accessibility of catalytic residues-Phe274 and Ser273 and increased Cɑ distance between Phe274 and Leu153 and large conformational entropy change as inferred from all-atom molecular dynamics (MD) simulation and essential dynamics (ED) studies. Our results corroborate well with previous experimental studies and thus these aberrations in the Q281R mutant structure may help explain the molecular basis of loss of enzyme activity.

Improving RNA nearest neighbor parameters for helices by going beyond the two-state model

RNA folding free energy change nearest neighbor parameters are widely used to predict folding stabilities of secondary structures. They were determined by linear regression to datasets of optical melting experiments on small model systems. Traditionally, the optical melting experiments are analyzed assuming a two-state model, i.e. a structure is either complete or denatured. Experimental evidence, however, shows that structures exist in an ensemble of conformations. Partition functions calculated with existing nearest neighbor parameters predict that secondary structures can be partially denatured, which also directly conflicts with the two-state model. Here, a new approach for determining RNA nearest neighbor parameters is presented. Available optical melting data for 34 Watson–Crick helices were fit directly to a partition function model that allows an ensemble of conformations. Fitting parameters were the enthalpy and entropy changes for helix initiation, terminal AU pairs, stacks of Watson–Crick pairs and disordered internal loops. The resulting set of nearest neighbor parameters shows a 38.5% improvement in the sum of residuals in fitting the experimental melting curves compared to the current literature set.

Quantification of biases in predictions of protein stability changes upon mutations

Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and have become accurate and fast enough to make computational mutagenesis experiments feasible, even on a proteome scale. Despite these achievements, they still suffer from important issues that must be solved to allow further improving their performances and utilizing them to deepen our insights into protein folding and stability mechanisms. One of these problems is their bias toward the learning datasets which, being dominated by destabilizing mutations, causes predictions to be better for destabilizing than for stabilizing mutations. We thoroughly analyzed the biases in the prediction of folding free energy changes upon point mutations (ΔΔG0) and proposed some unbiased solutions. We started by constructing a dataset Ssym of experimentally measured ΔΔG0s with an equal number of stabilizing and destabilizing mutations, by collecting mutations for which the structure of both the wild-type and mutant protein is available. On this balanced dataset, we assessed the performances of 15 widely used ΔΔG0 predictors. After the astonishing observation that almost all these methods are strongly biased toward destabilizing mutations, especially those that use black-box machine learning, we proposed an elegant way to solve the bias issue by imposing physical symmetries under inverse mutations on the model structure, which we implemented in PoPMuSiCsym. This new predictor constitutes an efficient trade-off between accuracy and absence of biases. Some final considerations and suggestions for further improvement of the predictors are discussed. Supplementary data are available at Bioinformatics online. The article 10.1093/bioinformatics/bty340/, published alongside this paper, also addresses the problem of biases in protein stability change predictions.

Development of Electrochemical Impedance Spectroscopy Based Malaria Aptasensor Using HRP‐II as Target Biomarker

AbstractCurrent demand for a stable, low cost and sensitive malaria sensor has prompted to explore novel recognition systems that can substitute widely used protein based labile biorecognition elements to be used in point of care diagnostic devices. Here, we report a novel ssDNA aptamer of 90 mer sequence developed by SELEX process against HRP‐II, a specific biomarker for Plasmodium falciparum strains. High stability of the secondary structure of the isolated aptamer was discerned from its free energy of folding of −20.40 kcal mole−1. The binding constant (Kd) of the aptamer with HRP‐II analysed by isothermal titration calorimetry was ∼1.32 μM. Circular dichroism studies indicated B form of the aptamer DNA. The aptamer was chemically immobilized on a gold electrode surface through a self‐assembled monolayer of dithio‐bis(succinimidyl) propionate to produce the aptasensor. The step wise modification of the layers over the gold electrode during fabrication of the aptasensor was confirmed by cyclic voltammetry. The aptasensor was then challenged with different concentration of HRP‐II and analysed the interaction signals through electrochemical impedance spectroscopy. The impedance signal behaved reciprocally with the increasing concentrations of the target in the sample from which a dynamic range of 1 pM–500 pM (R2=0.99) and LOD of ∼3.15 pM were discerned. The applicability of the developed aptasensor to detect HRP‐II in mimicked real sample was also validated.

Structural effects of point mutations in proteins.

A structural database of 11 families of chains differing by a single amino acid substitution has been built. Another structural dataset of 5 families with identical sequences has been used for comparison. The RMSD computed after a global superimposition of the mutated protein on each native one is smaller than the RMSD calculated among proteins of identical sequences. The effect of the perturbation is very local, and not necessarily the highest at the position of the mutation. A RMSD between mutated and native proteins is computed over a 3-residue or a 7-residue window at each position. To separate the effects of structural fluctuations due to point mutations from other sources, pair RMSD have been translated into P values which themselves are included in a score called P-RANK. This score allows highlighting small backbone distortions by comparing these RMSD between mutated and native positions to the RMSD at the same positions in the absence of a mutation. It results from the P-RANK that 38% of all mutations produce a significant effect on the displacement. When compared with a random distribution of RMSD at un-mutated positions, we show that, even if the RMSD is greater when the mutation is in loops than in regular secondary structure, the relative effect is more important for regular secondary structures and for buried positions. We confirm the absence of correlation between RMSD and the predicted variation of free energy of folding but we found a small correlation between high RMSD and the error in the prediction of ΔΔG.

Identification and characterization of novel promoters for recombinant protein production in yeast Pichia pastoris.

Pichia pastoris expression system has been widely used in recombinant protein production. So far the majority of heterologous proteins are expressed by methanol inducible promoter PAOX1 and constitutive promoter PGAP . The use of other promoters is rather limited. Here we selected 16 potentially efficient and regulatory promoter candidates based on the RNA-seq and RNA folding free energy ΔG data. GFP and recombinant amylase were inserted after these promoters to reveal their strength and efficiency under different carbon sources and culture scales. Two novel promoters were successfully identified and could possibly be applied in recombinant protein expression: the methanol-inducible promoter P0547 and the constitutive promoter P0472 .

Force feedback effects on single molecule hopping and pulling experiments.

Single-molecule experiments with optical tweezers have become an important tool to study the properties and mechanisms of biological systems, such as cells and nucleic acids. In particular, force unzipping experiments have been used to extract the thermodynamics and kinetics of folding and unfolding reactions. In hopping experiments, a molecule executes transitions between the unfolded and folded states at a preset value of the force [constant force mode (CFM) under force feedback] or trap position [passive mode (PM) without feedback] and the force-dependent kinetic rates extracted from the lifetime of each state (CFM) and the rupture force distributions (PM) using the Bell-Evans model. However, hopping experiments in the CFM are known to overestimate molecular distances and folding free energies for fast transitions compared to the response time of the feedback. In contrast, kinetic rate measurements from pulling experiments have been mostly done in the PM while the CFM is seldom implemented in pulling protocols. Here, we carry out hopping and pulling experiments in a short DNA hairpin in the PM and CFM at three different temperatures (6 °C, 25 °C, and 45 °C) exhibiting largely varying kinetic rates. As expected, we find that equilibrium hopping experiments in the CFM and PM perform well at 6 °C (where kinetics are slow), whereas the CFM overestimates molecular parameters at 45 °C (where kinetics are fast). In contrast, nonequilibrium pulling experiments perform well in both modes at all temperatures. This demonstrates that the same kind of feedback algorithm in the CFM leads to more reliable determination of the folding reaction parameters in irreversible pulling experiments.

Design of RNA hairpin modules that predictably tune translation in yeast.

Modular parts for tuning translation are prevalent in prokaryotic synthetic biology but lacking for eukaryotic synthetic biology. Working in Saccharomyces cerevisiae yeast, we here describe how hairpin RNA structures inserted into the 5′ untranslated region (5′UTR) of mRNAs can be used to tune expression levels by 100-fold by inhibiting translation. We determine the relationship between the calculated free energy of folding in the 5′UTR and in vivo protein abundance, and show that this enables rational design of hairpin libraries that give predicted expression outputs. Our approach is modular, working with different promoters and protein coding sequences, and outperforms promoter mutation as a way to predictably generate a library where a protein is induced to express at a range of different levels. With this new tool, computational RNA sequence design can be used to predictably fine-tune protein production for genes expressed in yeast.

Using Tryptophan Mutants To Probe the Structural and Functional Status of BsSCO, a Copper Binding, Cytochrome c Oxidase Assembly Protein from Bacillus subtilis.

The synthesis of cytochrome c oxidase protein from Bacillus subtilis (i.e., BsSCO) binds copper with picomolar affinity, which increases the protein's melting temperature (i.e., TM) by 20 °C. Here two native tryptophans (i.e., W36 and W101) are identified as major contributors to BsSCO's structural form, and their contributions to the stability, intrinsic fluorescence, and copper binding properties of BsSCO are explored. Single mutations of tryptophan to phenylalanine decrease the TM by 10 °C and the folding free energy by 3-4 kcal/mol. A more severe change to alanine (i.e., W36A BsSCO) decreases the TM by 20 °C and the stability by 9 kcal/mol. However, these mutants bind copper with high affinity and assemble cytochrome c oxidase in vivo. Replacing phenylalanine at a position near (∼5 Å) the copper binding site with tryptophan (i.e., F42W) increases the TM of apo-BsSCO by 3 °C but diminishes the effect of copper binding. When both native tryptophans are changed to alanine, apo-BsSCO is unfolded in vitro and is not functional in cytochrome c oxidase assembly in vivo. A double-mutant of BsSCO in which W36A is combined with F42W exhibits a form of metastability. Apo-W36A/F42W BsSCO melts at 37 °C, which upon binding of copper shifts to 65 °C. B. subtilis expressing W36A/F42W BsSCO and grown at 37 °C does not assemble cytochrome c oxidase. However, when these cells are cooled to 25 °C, cytochrome c oxidase activity is recovered. Our results illustrate the subtle relationship between the structural stability and functional properties of BsSCO in the assembly of cytochrome c oxidase.

A comparative study of sequence- and structure-based features of small RNAs and other RNAs of bacteria

ABSTRACTSmall RNAs (sRNAs) in bacteria have emerged as key players in transcriptional and post-transcriptional regulation of gene expression. Here, we present a statistical analysis of different sequence- and structure-related features of bacterial sRNAs to identify the descriptors that could discriminate sRNAs from other bacterial RNAs. We investigated a comprehensive and heterogeneous collection of 816 sRNAs, identified by northern blotting across 33 bacterial species and compared their various features with other classes of bacterial RNAs, such as tRNAs, rRNAs and mRNAs. We observed that sRNAs differed significantly from the rest with respect to G+C composition, normalized minimum free energy of folding, motif frequency and several RNA-folding parameters like base-pairing propensity, Shannon entropy and base-pair distance. Based on the selected features, we developed a predictive model using Random Forests (RF) method to classify the above four classes of RNAs. Our model displayed an overall predictive accuracy of 89.5%. These findings would help to differentiate bacterial sRNAs from other RNAs and further promote prediction of novel sRNAs in different bacterial species.

Insilico direct folding of thrombin-binding aptamer G-quadruplex at all-atom level.

The reversible folding of the thrombin-binding DNA aptamer G-quadruplexes (GQs) (TBA-15) starting from fully unfolded states was demonstrated using a prolonged time scale (10–12 μs) parallel tempering metadynamics (PTMetaD) simulation method in conjunction with a modified version of the AMBER bsc1 force field. For unbiased descriptions of the folding free energy landscape of TBA-15, this force field was minimally modified. From this direct folding simulation using the modified bsc1 force field, reasonably converged free energy landscapes were obtained in K+-rich aqueous solution (150 mM), providing detailed atomistic pictures of GQ folding mechanisms for TBA-15. This study found that the TBA folding occurred via multiple folding pathways with two major free energy barriers of 13 and 15 kcal/mol in the presence of several intermediate states of G-triplex variants. The early formation of these intermediates was associated with a single K+ ion capturing. Interestingly, these intermediate states appear to undergo facile transitions among themselves through relatively small energy barriers.

The effects of a neutral cosolute on the B to Z transition for DNA duplexes incorporating both CG and CA steps

ABSTRACTIn the cell, nearly 40% of the volume is occupied by macromolecular crowding agents and smaller osmolytes accumulate in response to environmental stresses. Of particular interest is the influence of osmolytes on the transition of the right-handed B-DNA to the left-handed Z-DNA. Due to the correlation between Z-DNA formation potential and regions of active transcription, Z-DNA is believed to serve a vital role in the transcription process, and changes in osmolyte concentration may influence transcription as a part of the stress response. We utilized circular dichroism spectroscopy to monitor changes in conformation of DNA duplexes containing a full-turn of Z-DNA in the presence and absence of PEG 200. We used PEG 200 as a model neutral cosolute. Sodium ion titrations revealed that PEG 200 influenced the folding of Z-DNA compared to dilute solution conditions by decreasing the free energy of folding, increasing folding cooperativity, and decreasing the in vitro [Na+] and Δn required for folding for all sequences tested, even those that included 40% CA steps instead of the classic CG repeats. Moreover, the presence of 40% PEG 200 induced the Z-form conformation in sequences that would not fully adopt the Z-form structure even in 5 M NaCl. These results suggest that osmolytes may play a significant role in supporting the transient formation of Z-DNA in vivo, and that sequences containing a significant amounts of CA instead of CG repeats may more favorably adopt the Z-conformation as a part of binding and regulatory processes than had been previously considered.

A component analysis of the free energies of folding of 35 proteins: A consensus view on the thermodynamics of folding at the molecular level.

What factors favor protein folding? This is a textbook question. Parsing the experimental free energies of folding/unfolding into diverse enthalpic and entropic components of solute and solvent favoring or disfavoring folding is not an easy task. In this study, we present a computational protocol for estimating the free energy contributors to protein folding semi-quantitatively using ensembles of unfolded and native states generated via molecular dynamics simulations. We tested the methodology on 35 proteins with diverse structural motifs and sizes and found that the calculated free energies correlate well with experiment (correlation coefficient ∼ 0.85), enabling us to develop a consensus view of the energetics of folding. As a more sensitive test of the methodology, we also investigated the free energies of folding of an additional 33 single point mutants and obtained a correlation coefficient of 0.8. A notable observation is that the folding free energy components appear to carry signatures of the fold (SCOP classification) of the protein. © 2017 Wiley Periodicals, Inc.

Selection originating from protein stability/foldability: Relationships between protein folding free energy, sequence ensemble, and fitness

Assuming that mutation and fixation processes are reversible Markov processes, we prove that the equilibrium ensemble of sequences obeys a Boltzmann distribution with exp(4Nem(1−1/(2N))), where m is Malthusian fitness and Ne and N are effective and actual population sizes. On the other hand, the probability distribution of sequences with maximum entropy that satisfies a given amino acid composition at each site and a given pairwise amino acid frequency at each site pair is a Boltzmann distribution with exp(−ψN), where the evolutionary statistical energy ψN is represented as the sum of one body (h) (compositional) and pairwise (J) (covariational) interactions over all sites and site pairs. A protein folding theory based on the random energy model (REM) indicates that the equilibrium ensemble of natural protein sequences is well represented by a canonical ensemble characterized by exp(−ΔGND/kBTs) or by exp(−GN/kBTs) if an amino acid composition is kept constant, where ΔGND≡GN−GD,GN and GD are the native and denatured free energies, and Ts is the effective temperature representing the strength of selection pressure. Thus, 4Nem(1−1/(2N)),−ΔψND(≡−ψN+ψD), and −ΔGND/kBTs must be equivalent to each other. With h and J estimated by the DCA program, the changes (ΔψN) of ψN due to single nucleotide nonsynonymous substitutions are analyzed. The results indicate that the standard deviation of ΔGN(=kBTsΔψN) is approximately constant irrespective of protein families, and therefore can be used to estimate the relative value of Ts. Glass transition temperature Tg and ΔGND are estimated from estimated Ts and experimental melting temperature (Tm) for 14 protein domains. The estimates of ΔGND agree with their experimental values for 5 proteins, and those of Ts and Tg are all within a reasonable range. In addition, approximating the probability density function (PDF) of ΔψN by a log-normal distribution, PDFs of ΔψN and Ka/Ks, which is the ratio of nonsynonymous to synonymous substitution rate per site, in all and in fixed mutants are estimated. The equilibrium values of ψN, at which the average of Δψ in fixed mutants is equal to zero, well match ψN averaged over homologous sequences, confirming that the present methods for a fixation process of mutations and for the equilibrium ensemble of ψN give a consistent result with each other. The PDFs of Ka/Ks at equilibrium confirm that Ts negatively correlates with the amino acid substitution rate (the mean of Ka/Ks) of protein. Interestingly, stabilizing mutations are significantly fixed by positive selection, and balance with destabilizing mutations fixed by random drift, although most of them are removed from population. Supporting the nearly neutral theory, neutral selection is not significant even in fixed mutants.

Searching for the Pareto frontier in multi-objective protein design.

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Using Cooperatively Folded Peptides To Measure Interaction Energies and Conformational Propensities.

The rates and equilibria of the folding of biopolymers are determined by the conformational preferences of the subunits that make up the sequence of the biopolymer and by the interactions that are formed in the folded state in aqueous solution. Because of the centrality of these processes to life, quantifying conformational propensities and interaction strengths is vitally important to understanding biology. In this Account, we describe our use of peptide model systems that fold cooperatively yet are small enough to be chemically synthesized to measure such quantities. The necessary measurements are made by perturbing an interaction or conformation of interest by mutation and measuring the difference between the folding free energies of the wild type (in which the interaction or conformation is undisturbed) and the mutant model peptides (in which the interaction has been eliminated or the conformational propensities modified). With the proper controls and provided that the peptide model system in question folds via a two-state process, these folding free energy differences can be accurate measures of interaction strengths or conformational propensities. This method has the advantage of having high sensitivity and high dynamic range because the energies of interest are coupled to folding free energies, which can be measured with precisions on the order of a few tenths of a kilocalorie by well-established biophysical methods, like chaotrope or thermal denaturation studies monitored by fluorescence or circular dichroism. In addition, because the model peptides can be chemically synthesized, the full arsenal of natural and unnatural amino acids can be used to tune perturbations to be as drastic or subtle as desired. This feature is particularly noteworthy because it enables the use of analytical tools developed for physical organic chemistry, especially linear free energy relationships, to decompose interaction energies into their component parts to obtain a deeper understanding of the forces that drive interactions in biopolymers. We have used this approach, primarily with the WW domain derived from the human Pin1 protein as our model system, to assess hydrogen bond strengths (especially those formed by backbone amides), the dependence of hydrogen bond strengths on the environment in which they form, β-turn propensities of both natural sequences and small molecule β-turn mimics, and the energetics of carbohydrate-protein interactions. In each case, the combination of synthetic accessibility, the ease of measuring folding energies, and the robustness of the structure of the Pin1 WW domain to mutation enabled us to obtain incisive measurements of quantities that have been challenging to measure by other methods.

Influence of Graphene Oxide on Crystallization Behavior and Chain Folding Surface Free Energy of Poly(vinylidenefluoride‐co‐hexafluoropropylene)

Graphene oxide (GO) filled poly(vinylidene fluoride‐co‐hexafluoropropylene) (PVDF‐HPF) copolymer nanocomposites as promising piezoelectric materials are developed, and their crystallization behavior and chain folding free energy are examined. Appropriate distribution of GO nanoparticles in the copolymer is confirmed by transmission electron microscopic analysis. The crystalline structure of nanocomposites is analyzed by wide‐angle X‐ray scattering and Fourier transform infrared spectroscopy. The results show an increase in the β‐phase content of PVDF‐HPF copolymer in the presence of GO. Non‐isothermal crystallization kinetics of the neat polymer and corresponding nanocomposites are studied by a multiple heating rate differential scanning calorimetry using modified Avrami–Jeziorny and Liu models. Moreover, barrier energy of crystallization is calculated by Friedman and Kissinger models. It is found that addition of GO to the copolymer increases nucleation activity of the nanocomposites. Investigation on linear crystal growth via Hoffman's theorem indicates an enhanced nucleation activity upon increasing GO content. The surface folding free energy of the neat polymer is changed from 5.79 × 10−2 to 7.27 × 10−2 J m−2 upon addition of 5 wt% of GO. A bundle‐like mechanism is proposed, which can explain the crystallite growth. Analysis based on Vyazovkin theorem reveals that the crystallization activation energy is independent of GO at elevated temperatures.

Cooperation between Magnesium and Metabolite Controls Collapse of the SAM-I Riboswitch

The S-adenosylmethionine (SAM)-I riboswitch is a noncoding RNA that regulates the transcription termination process in response to metabolite (SAM) binding. The aptamer portion of the riboswitch may adopt an open or closed state depending on the presence of metabolite. Although the transition between the open and closed states is critical for the switching process, its atomistic details are not well understood. Using atomistic simulations, we calculate the effect of SAM and magnesium ions on the folding free energy landscape of the SAM-I riboswitch. These molecular simulation results are consistent with our previous wetlab experiments and aid in interpreting the SHAPE probing measurements. Here, molecular dynamics simulations explicitly identify target RNA motifs sensitive to magnesium ions and SAM. In the simulations, we observe that, whereas the metabolite mostly stabilizes the P1 and P3 helices, magnesium serves an important role in stabilizing a pseudoknot interaction between the P2 and P4 helices, even at high metabolite concentrations. The pseudoknot stabilization by magnesium, in combination with P1 stabilization by SAM, explains the requirement of both SAM and magnesium to form the fully collapsed metabolite-bound closed state of the SAM-I riboswitch. In the absence of SAM, frequent open-to-closed conformational transitions of the pseudoknot occur, akin to breathing. These pseudoknot fluctuations disrupt the binding site by facilitating fluctuations in the 5′-end of helix P1. Magnesium biases the landscape toward a collapsed state (preorganization) by coordinating pseudoknot and 5′-P1 fluctuations. The cooperation between SAM and magnesium in stabilizing important tertiary interactions elucidates their functional significance in transcription regulation.

SCooP: an accurate and fast predictor of protein stability curves as a function of temperature.

The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One of the most challenging goals is the development of accurate prediction tools of the temperature dependence of the standard folding free energy ΔG(T). Such predictors have an enormous series of potential applications, which range from drug design in the biopharmaceutical sector to the optimization of enzyme activity for biofuel production. There is thus an important demand for novel, reliable and fast predictors. We present the SCooP algorithm, which is a significant step towards accurate temperature-dependent stability prediction. This automated tool uses the protein structure and the host organism as sole entries and predicts the full T-dependent stability curve of monomeric proteins assumed to follow a two-state folding transition. Equivalently, it predicts all the thermodynamic quantities associated to the folding transition, namely the melting temperature Tm, the standard folding enthalpy ΔHm measured at Tm, and the standard folding heat capacity ΔCp. The cross-validated performances are good, with correlation coefficients between predicted and experimental values equal to [0.80, 0.83, 0.72] for ΔHm, ΔCp and Tm, respectively, which increase up to [0.88, 0.90, 0.78] upon the removal of 10% outliers. Moreover, the stability curve prediction of a target protein is very fast: it takes less than a minute. SCooP can thus potentially be applied on a structurome scale. This opens new perspectives of large-scale analyses of protein stability, which is of considerable interest for protein engineering. The SCooP webserver is freely available at http://babylone.ulb.ac.be/SCooP. fapucci@ulb.ac.be, jkwasigr@ulb.ac.be or mrooman@ulb.ac.be. Supplementary data are available at Bioinformatics online.

Inferring repeat-protein energetics from evolutionary information.

Natural protein sequences contain a record of their history. A common constraint in a given protein family is the ability to fold to specific structures, and it has been shown possible to infer the main native ensemble by analyzing covariations in extant sequences. Still, many natural proteins that fold into the same structural topology show different stabilization energies, and these are often related to their physiological behavior. We propose a description for the energetic variation given by sequence modifications in repeat proteins, systems for which the overall problem is simplified by their inherent symmetry. We explicitly account for single amino acid and pair-wise interactions and treat higher order correlations with a single term. We show that the resulting evolutionary field can be interpreted with structural detail. We trace the variations in the energetic scores of natural proteins and relate them to their experimental characterization. The resulting energetic evolutionary field allows the prediction of the folding free energy change for several mutants, and can be used to generate synthetic sequences that are statistically indistinguishable from the natural counterparts.