The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.     

Neural network-based prediction of mutation-induced protein stability changes in Staphylococcal nuclease at 20 residue positions

Protein-based therapeutics are playing an increasingly important role in the treatment of diseases, including diabetes and cancer. The viability of these treatments, however, are highly dependent on the stability of the therapeutic, since stability affects both the shelf life of the therapeutic as well as its active life in the body. Stability engineering can, therefore, be used to increase the effectiveness of protein-based therapeutics. Computational methods of protein stability prediction have been under development for about a decade, but complex molecular interactions make stability prediction difficult and computationally intensive. A rapid computational method of protein stability prediction is developed using feed-forward neural networks and used to predict mutation-induced stability changes in Staphylococcal nuclease. The input to the neural network consisted of sequences of evolutionarily based amino acid similarity scores that were obtained through the comparison of the amino acids in a mutation containing sequence to their positional counterparts in the baseline wild-type amino acid sequence. A training set was created which consisted of similarity score sequences, for which the stabilities of the corresponding amino acid sequences were known, paired with the relative stabilities of the sequences to that of the baseline. Back-propagation of error was used to train the network to output accurate relative stability scores for the sequences in the training set. Neural network-based relative stability predictions for 55 sequences containing mutation combinations not found in the training set had an accuracy of 92.8%.     

Unfolding energetics and stability of banana lectin

The unfolding pathway of banana lectin from Musa paradisiaca was determined by isothermal denaturation induced by the chaotrope GdnCl. The unfolding was found to be a reversible process. The data obtained by isothermal denaturation provided information on conformational stability of banana lectin. The high values of DeltaG of unfolding at various temperatures indicated the strength of intersubunit interactions. It was found that banana lectin is a very stable and denatures at high chaotrope concentrations only. The basis of the stability may be attributed to strong hydrogen bonds of the order 2.5-3.1 A at the dimeric interface along with the presence of water bridges. This is perhaps very unique example in proteins where subunit association is not a consequence of the predominance of hydrophobic interactions.     

Ligand-induced changes in the conformational stability and flexibility of glutamate dehydrogenase and their role in catalysis and regulation

Bovine glutamate dehydrogenase (GDH) is allosterically regulated and requires substrate‐induced subunit interactions for maximum catalytic activity. Steady‐state and presteady‐state kinetics indicate that the rate‐limiting step depends on the nature of the substrate and are likely associated with conformational fluctuations necessary for optimal hydride transfer. Deuterated glutamate shows a steady‐state isotope effect but no effect on the presteady‐state burst rate, demonstrating that conformational effects are rate limiting for hydride transfer while product release is overall rate limiting for glutamate. Guanidine hydrochloride unfolding, heat inactivation, and differential scanning calorimetry demonstrate the effects of alternative substrates, glutamate and norvaline, on conformational stability. Glutamate has little effect on overall stability, whereas norvaline markedly stabilizes the protein. Limited proteolysis demonstrates that glutamate had a variety of effects on local flexibility, whereas norvaline significantly decreased conformational fluctuations that allow protease cleavage. Dynamic light scattering suggests that norvaline stabilizes all interfaces in the hexamer, whereas glutamate had little effect on trimer–trimer interactions. The substrate glutamate exhibits negative cooperativity and complex allosteric regulation but has only minor effects on global GDH stability, while promoting certain local conformational fluctuations. In contrast, the substrate norvaline does not show negative cooperativity or allow allosteric regulation. Instead, norvaline significantly stabilizes the enzyme and markedly slows or prevents local conformational fluctuations that are likely to be important for cooperative effects and to determine the overall rate of hydride transfer. This suggests that homotropic allosteric regulation by the enzymatic substrate involves changes in both global stability and local flexibility of the protein.     

De novo protein structure prediction by dynamic fragment assembly and conformational space annealing

Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods.     

Average assignment method for predicting the stability of protein mutants

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

Progress and challenges in high-resolution refinement of protein structure models

Achieving atomic level accuracy in de novo structure prediction presents a formidable challenge even in the context of protein models with correct topologies. High-resolution refinement is a fundamental test of force field accuracy and sampling methodology, and its limited success in both comparative modeling and de novo prediction contexts highlights the limitations of current approaches. We constructed four tests to identify bottlenecks in our current approach and to guide progress in this challenging area. The first three tests showed that idealized native structures are stable under our refinement simulation conditions and that the refinement protocol can significantly decrease the root mean square deviation (RMSD) of perturbed native structures. In the fourth test we applied the refinement protocol to de novo models and showed that accurate models could be identified based on their energies, and in several cases many of the buried side chains adopted native-like conformations. We also showed that the differences in backbone and side-chain conformations between the refined de novo models and the native structures are largely localized to loop regions and regions where the native structure has unusual features such as rare rotamers or atypical hydrogen bonding between beta-strands. The refined de novo models typically have higher energies than refined idealized native structures, indicating that sampling of local backbone conformations and side-chain packing arrangements in a condensed state is a primary obstacle.     

Local water bridges and protein conformational stability.

Recent studies have pointed out the important role of local water structures in protein conformational stability. Here, we present an accurate and computationally effective way to estimate the free energy contribution of the simplest water structure motif--the water bridge. Based on the combination of empirical parameters for accessible protein surface area and the explicit consideration of all possible water bridges with the protein, we introduce an improved protein solvation model. We find that accounting for water bridge formation in our model is essential to understand the conformational behavior of polypeptides in water. The model formulation, in fact, does not depend on the polypeptide nature of the solute and is therefore applicable to other flexible biomolecules (i.e., DNAs, RNAs, polysaccharides, etc.).     

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design

Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side-chain rotamers for wild-type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side-chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics.     

NMR studies of chiral discrimination relevant to the enantioseparation of N-acylarylalkylamines by an (R)-phenylglycinol-derived chiral selector

Recently, it was reported that the chiral recognition ability of (R)-N-3,5-dinitrobenzoyl phenylglycinol derivative was examined as a new HPLC chiral stationary phase (CSP 1) for the resolution of racemic N-acylnaphthylalkylamines. However, the mechanism of chiral discrimination on the CSP remained elusive until now. In this study, a spectroscopic investigation of the chiral discrimination mechanism of CSP 1 was undertaken using mixtures of (R)-N-3,5-dinitrobenzoyl phenylglycinol-derived chiral selector (2) and each of the enantiomers of N-acylnaphthylalkylamines (3) by NMR study. First, the differences in free energy changes (DeltaDeltaG) upon diastereomeric complexation in solution between the complex of each isomer with chiral selector 2 by NMR titration were calculated. The values were then compared with those estimated by chiral HPLC. The chemical shift changes of each proton on the chiral selector and analytes were also checked and it was found that the chemical shift changes decreased continuously as the acyl group on analytes increased in length. This observation was consistent with the HPLC data. From these experimental results, the interaction mechanism of chiral discrimination between the chiral selector and the analytes is more precisely explained.     

Conserved quantitative stability/flexibility relationships (QSFR) in an orthologous RNase H pair

Many reports qualitatively describe conserved stability and flexibility profiles across protein families, but biophysical modeling schemes have not been available to robustly quantify both. Here we investigate an orthologous RNase H pair by using a minimal distance constraint model (DCM). The DCM is an all atom microscopic model [Jacobs and Dallakyan, Biophys J 2005;88(2):903-915] that accurately reproduces heat capacity measurements [Livesay et al., FEBS Lett 2004;576(3):468-476], and is unique in its ability to harmoniously calculate thermodynamic stability and flexibility in practical computing times. Consequently, quantified stability/flexibility relationships (QSFR) can be determined using the DCM. For the first time, a comparative QSFR analysis is performed, serving as a paradigm study to illustrate the utility of a QSFR analysis for elucidating evolutionarily conserved stability and flexibility profiles. Despite global conservation of QSFR profiles, distinct enthalpy-entropy compensation mechanisms are identified between the RNase H pair. In both cases, local flexibility metrics parallel H/D exchange experiments by correctly identifying the folding core and several flexible regions. Remarkably, at appropriately shifted temperatures (e.g., melting temperature), these differences lead to a global conservation in Landau free energy landscapes, which directly relate thermodynamic stability to global flexibility. Using ensemble-based sampling within free energy basins, rigidly, and flexibly correlated regions are quantified through cooperativity correlation plots. Five conserved flexible regions are identified within the structures of the orthologous pair. Evolutionary conservation of these flexibly correlated regions is strongly suggestive of their catalytic importance. Conclusions made herein are demonstrated to be robust with respect to the DCM parameterization.     

Role of phosphorylation in conformational adaptability of bovine myelin basic protein.

Controlled thrombic digestion of a preparation of components 2 + 3 isolated from the 18.5 kDa bovine myelin basic protein (MBP) yielded a polypeptide that was monophosphorylated on threonine 97 (component 3pT97). This is the first posttranslationally phosphorylated MBP isolated in pure form. We studied the effect of this single phosphate on the conformational adaptability of 18.5 kDa bovine MBP by comparing the circular dichroism (CD) spectrum of component 3pT97 with the spectra of highly purified nonphosphorylated components 1 and 2. The CD spectra of nonphosphorylated component 1 and component 2 [monodeamidated form(s) of component 1] were indistinguishable, while component 3pt97 exhibited a different spectrum. The singly phosphorylated MBP component exhibited 13% more ordered conformations than that adopted by nonphosphorylated MBP in dilute aqueous solutions. This was estimated from the CD spectra, and apparently involved about 17 additional amino acid residues in beta-structure(s).     

Pressure-induced changes in the solution structure of the GB1 domain of protein G

The solution structure of the GB1 domain of protein G at a pressure of 2 kbar is presented. The structure was calculated as a change from an energy-minimised low-pressure structure using (1)H chemical shifts. Two separate changes can be characterised: a compression/distortion, which is linear with pressure; and a stabilisation of an alternative folded state. On application of pressure, linear chemical shift changes reveal that the backbone structure changes by about 0.2 A root mean square, and is compressed by about 1% overall. The alpha-helix compresses, particularly at the C-terminal end, and moves toward the beta-sheet, while the beta-sheet is twisted, with the corners closest to the alpha-helix curling up towards it. The largest changes in structure are along the second beta-strand, which becomes more twisted. This strand is where the protein binds to IgG. Curved chemical shift changes with pressure indicate that high pressure also populates an alternative structure with a distortion towards the C-terminal end of the helix, which is likely to be caused by insertion of a water molecule.     

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

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