Prediction Of Stability Changes Research Articles

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network

Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.

Transfer learning to leverage larger datasets for improved prediction of protein stability changes

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.

Novel missense mutation c.797T>C (p.Met266Thr) gives rise to the rare B(A) phenotype in a Chinese family.

B(A) phenotype is usually formed by nucleotide mutations in the ABO*B.01 allele, with their products exhibiting glycosyltransferases (GTs) A and B overlapping functionality. We herein report a B(A) allele found in a Chinese family. The entire ABO genes of the probands, including flanking regulatory regions, were sequenced through PacBio third-generation long-read single-molecule real-time sequencing. 3D molecular models of the wild-type and mutant GTB were generated using the DynaMut web server. The effect of the mutation on the enzyme function was predicted by PROVEAN and PolyPhen2. The predictions of stability changes were performed using DynaMut and SNPeffect. Based on serological and sequencing features, we concluded the two probands as possible cases of the B(A) phenotype. Crystallization analysis showed that Thr266 substitution does not disrupt the hydrogen bonds. However, some changes in interatomic contacts, such as loss of ionic interactions and hydrophobic contacts, and addition of weak hydrogen bonds, may have affected protein stability to some extent. This mutation was predicted to have a benign effect on enzyme function and slightly reduce protein stability. The probands had the same novel B(A) allele with a c.797T>C (p.Met266Thr) mutation on the ABO*B.01 backbone.

Predicting protein thermal stability changes upon single and multi-point mutations via restricted attention subgraph neural network

Accurate prediction of protein stability changes due to mutations is instrumental for understanding mechanisms of disease and drug failure, as well as for engineering tailored protein-based materials. In recent years, computational tools using machine learning have been developed to predict stability changes and thereby supplement available experimental methods that may be time consuming and costly for bulk mutational analysis. Existing tools are limited, however, to predicting single point mutations and showing antisymmetric bias in direct/reverse mutations. Here, we develop structure and sequence-based models to predict protein stability changes via Gibbs free energy change upon single or multi-point mutations via two parallel augmented gated attention graph neural networks integrated with global attention blocks receiving subgraphs. These subgraphs are related to mutation sites and constructed through predicted protein contact maps to capture spatial structural information. We train our model on direct and reverse mutations obtained from the S5294 dataset and test on one independent test set and eight most commonly used test sets, including S350, P53, Ssym, S669, S1925, S250, and myoglobin. Our approach shows considerable improvement in estimating the impacts of stabilizing mutations, and consistently outperforms other methods by at least a 5.2% improvement in root mean square error. This approach can be employed for finding functionally important protein variants, helping to design new proteins with vast untapped potential for broad pharmaceutical applications.

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for protein design and understanding the phenotypic variation. Recent studies have shown that protein embedding will be particularly powerful at modeling sequence information with context dependence, such as subcellular localization, variant effect, and secondary structure prediction. Herein, we introduce a novel method, ProSTAGE, which is a deep learning method that fuses structure and sequence embedding to predict protein stability changes upon single point mutations. Our model combines graph-based techniques and language models to predict stability changes. Moreover, ProSTAGE is trained on a larger data set, which is almost twice as large as the most used S2648 data set. It consistently outperforms all existing state-of-the-art methods on mutation-affected problems as benchmarked on several independent data sets. The protein embedding as the prediction input achieves better results than the previous results, which shows the potential of protein language models in predicting the effect of mutations on proteins. ProSTAGE is implemented as a user-friendly web server.

Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability

BackgroundComputational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability.ResultsWe show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation.ConclusionsUsing a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

Prediction of protein stability changes upon single-point variant using 3D structure profile

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

KVarPredDB: a database for predicting pathogenicity of missense sequence variants of keratin genes associated with genodermatoses

BackgroundGermline variants of ten keratin genes (K1, K2, K5, K6A, K6B, K9, K10, K14, K16, and K17) have been reported for causing different types of genodermatoses with an autosomal dominant mode of inheritance. Among all the variants of these ten keratin genes, most of them are missense variants. Unlike pathogenic and likely pathogenic variants, understanding the clinical importance of novel missense variants or variants of uncertain significance (VUS) is the biggest challenge for clinicians or medical geneticists. Functional characterization is the only way to understand the clinical association of novel missense variants or VUS but it is time consuming, costly, and depends on the availability of patient’s samples. Existing databases report the pathogenic variants of the keratin genes, but never emphasize the systematic effects of these variants on keratin protein structure and genotype-phenotype correlation.ResultsTo address this need, we developed a comprehensive database KVarPredDB, which contains information of all ten keratin genes associated with genodermatoses. We integrated and curated 400 reported pathogenic missense variants as well as 4629 missense VUS. KVarPredDB predicts the pathogenicity of novel missense variants as well as to understand the severity of disease phenotype, based on four criteria; firstly, the difference in physico-chemical properties between the wild type and substituted amino acids; secondly, the loss of inter/intra-chain interactions; thirdly, evolutionary conservation of the wild type amino acids and lastly, the effect of the substituted amino acids in the heptad repeat. Molecular docking simulations based on resolved crystal structures were adopted to predict stability changes and get the binding energy to compare the wild type protein with the mutated one. We use this basic information to determine the structural and functional impact of novel missense variants on the keratin coiled-coil heterodimer. KVarPredDB was built under the integrative web application development framework SSM (SpringBoot, Spring MVC, MyBatis) and implemented in Java, Bootstrap, React-mutation-mapper, MySQL, Tomcat. The website can be accessed through http://bioinfo.zju.edu.cn/KVarPredDB. The genomic variants and analysis results are freely available under the Creative Commons license.ConclusionsKVarPredDB provides an intuitive and user-friendly interface with computational analytical investigation for each missense variant of the keratin genes associated with genodermatoses.

A bioinformatic approach to investigating cytokine genes and their receptor variants in relation to COVID-19 progression.

Severe acute respiratory syndrome coronavirus 2 infection produces a wide spectrum of manifestations, ranging from no symptom to viral pneumonia. This study aimed to determine the genetic variations in cytokines and their receptors in relation to COVID‐19 pathogenesis using bioinformatic tools. Single nucleotide polymorphisms (SNPs) of genes encoding the cytokines and cytokine receptors elevated in patients with COVID‐19 were determined from the National Biotechnology Information Center website (using the dbSNP database). Missense variants were found in 3 cytokine genes and 10 cytokine receptor genes. Computational analyses were conducted to detect the effects of these missense SNPs via cloud‐based software tools. Also, the miRSNP database was used to explore whether SNPs in the 3′‐UTR altered the miRNA binding efficiency for genes of cytokines and their receptors. Our in silico studies revealed that one SNP in the vascular endothelial growth factor receptor 2 (VEGFR2) gene was predicted as deleterious using sorting intolerant from tolerant. Also, the stability of VEGFR2 decreased in the I‐Mutant2.0 (biotool for predicting stability changes upon mutation from the protein sequence or structure) prediction. It was suggested that the decrease in VEGFR2 function (due to the rs1870377 polymorphism) may be correlated with the progression of COVID‐19 or contribute to the pathogenesis. Moreover, 27 SNPs were determined to affect miRNA binding for the genes of cytokine receptors. CXCR2 rs1126579, TNFRSF1B rs1061624 and IL10RB rs8178562 SNPs were predicted to break the miRNA‐mRNA binding sites for miR‐516a‐3, miR‐720 and miR‐328, respectively. These miRNAs play an important role in immune regulation and lung damage repair. Further studies are needed to evaluate the importance of these miRNAs and the SNPs.

Computation-aided engineering of starch-debranching pullulanase from Bacillus thermoleovorans for enhanced thermostability.

Pullulanases are widely used in food, medicine, and other industries because they specifically hydrolyze α-1,6-glycosidic linkages in starch and oligosaccharides. In addition, high-temperature thermostable pullulanase has multiple advantages, including decreasing saccharification solution viscosity accompanied with enhanced mass transfer and reducing microbial contamination in starch hydrolysis. However, thermophilic pullulanase availability remains limited. Additionally, most do not meet starch-manufacturing requirements due to weak thermostability. Here, we developed a computation-aided strategy to engineer the thermophilic pullulanase from Bacillus thermoleovorans. First, three computational design predictors (FoldX, I-Mutant 3.0, and dDFIRE) were combined to predict stability changes introduced by mutations. After excluding conserved and catalytic sites, 17 mutants were identified. After further experimental verification, we confirmed six positive mutants. Among them, the G692M mutant had the highest thermostability improvement, with 3.8°C increased Tm and 2.1-fold longer half-life than the wild type at 70°C. We then characterized the mechanism underlying increased thermostability, such as rigidity enhancement, closer conformation, and strengthened motion correlation using root mean square fluctuation (RMSF), principal component analysis (PCA), dynamic cross-correlation map (DCCM), and free energy landscape (FEL) analysis. KEY POINTS: • A computation-aided strategy was developed to engineer pullulanase thermostability. • Seventeen mutants were identified by combining three computational design predictors. • The G692M mutant was obtained with increased Tmand half-life at 70°C.

Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting.

Accurately predicting the impact of point mutation on protein stability has crucial roles in protein design and engineering. In this study, we proposed a novel method (BoostDDG) to predict stability changes upon point mutations from protein sequences based on the extreme gradient boosting. We extracted features comprehensively from evolutional information and predicted structures and performed feature selection by a strategy of sequential forward selection. The features and parameters were optimized by homologue-based cross-validation to avoid overfitting. Finally, we found that 14 features from six groups led to the highest Pearson correlation coefficient (PCC) of 0.535, which is consistent with the 0.540 on an independent test. Our method was indicated to consistently outperform other sequence-based methods on three precompiled test sets, and 7363 variants on two proteins (PTEN and TPMT). These results highlighted that BoostDDG is a powerful tool for predicting stability changes upon point mutations from protein sequences.

Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign point mutations in the transmembrane regions of proteins with known 3D structure. The method, called BorodaTM (BOosted RegressiOn trees for Disease-Associated mutations in TransMembrane proteins), was trained on sequence-, structure-, and energy-derived descriptors. When compared with the state-of-the-art methods, BorodaTM is superior in classifying point mutations in transmembrane regions. Using BorodaTM we have conducted a large-scale mutation analysis in the transmembrane regions of human proteins with known 3D structures. For each protein we generated structural models for all point mutations by replacing each residue to 19 possible residue types. We classified ~1.8 millions point mutations as benign or diseased-associated and made all predictions available as a Web-server at https://www.iitm.ac.in/bioinfo/MutHTP/boroda.php.

Prediction of stability changes upon mutation in an icosahedral capsid.

ABSTRACTIdentifying the contributions to thermodynamic stability of capsids is of fundamental and practical importance. Here we use simulation to assess how mutations affect the stability of lumazine synthase from the hyperthermophile Aquifex aeolicus, a T = 1 icosahedral capsid; in the simulations the icosahedral symmetry of the capsid is preserved by simulating a single pentamer and imposing crystal symmetry, in effect simulating an infinite cubic lattice of icosahedral capsids. The stability is assessed by estimating the free energy of association using an empirical method previously proposed to identify biological units in crystal structures. We investigate the effect on capsid formation of seven mutations, for which it has been experimentally assessed whether they disrupt capsid formation or not. With one exception, our approach predicts the effect of the mutations on the capsid stability. The method allows the identification of interaction networks, which drive capsid assembly, and highlights the plasticity of the interfaces between subunits in the capsid. Proteins 2015; 83:1733–1741. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc

Feature-based multiple models improve classification of mutation-induced stability changes.

BackgroundReliable prediction of stability changes in protein variants is an important aspect of computational protein design. A number of machine learning methods that allow a classification of stability changes knowing only the sequence of the protein emerged. However, their performance on amino acid substitutions of previously unseen non-homologous proteins is rather limited. Moreover, the performance varies for different types of mutations based on the secondary structure or accessible surface area of the mutation site.ResultsWe proposed feature-based multiple models with each model designed for a specific type of mutations. The new method is composed of five models trained for mutations in exposed, buried, helical, sheet, and coil residues. The classification of a mutation as stabilising or destabilising is made as a consensus of two models, one selected based on the predicted accessible surface area and the other based on the predicted secondary structure of the mutation site. We refer to our new method as Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM). Cross-validation results show that EASE-MM provides a notable improvement to our previous work reaching a Matthews correlation coefficient of 0.44. EASE-MM was able to correctly classify 73% and 75% of stabilising and destabilising protein variants, respectively. Using an independent test set of 238 mutations, we confirmed our results in a comparison with related work.ConclusionsEASE-MM not only outperformed other related methods but achieved more balanced results for different types of mutations based on the accessible surface area, secondary structure, or magnitude of stability changes. This can be attributed to using multiple models with the most relevant features selected for the given type of mutations. Therefore, our results support the presumption that different interactions govern stability changes in the exposed and buried residues or in residues with a different secondary structure.

Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins

BackgroundReliable prediction of stability changes induced by a single amino acid substitution is an important aspect of computational protein design. Several machine learning methods capable of predicting stability changes from the protein sequence alone have been introduced. Prediction performance of these methods is evaluated on mutations unseen during training. Nevertheless, different mutations of the same protein, and even the same residue, as encountered during training are commonly used for evaluation. We argue that a faithful evaluation can be achieved only when a method is tested on previously unseen proteins with low sequence similarity to the training set.ResultsWe provided experimental evidence of the limitations of the evaluation commonly used for assessing the prediction performance. Furthermore, we demonstrated that the prediction of stability changes in previously unseen non-homologous proteins is a challenging task for currently available methods. To improve the prediction performance of our previously proposed method, we identified features which led to over-fitting and further extended the model with new features. The new method employs Evolutionary And Structural Encodings with Amino Acid parameters (EASE-AA). Evaluated with an independent test set of more than 600 mutations, EASE-AA yielded a Matthews correlation coefficient of 0.36 and was able to classify correctly 66% of the stabilising and 74% of the destabilising mutations. For real-value prediction, EASE-AA achieved the correlation of predicted and experimentally measured stability changes of 0.51.ConclusionsCommonly adopted evaluation with mutations in the same protein, and even the same residue, randomly divided between the training and test sets lead to an overestimation of prediction performance. Therefore, stability changes prediction methods should be evaluated only on mutations in previously unseen non-homologous proteins. Under such an evaluation, EASE-AA predicts stability changes more reliably than currently available methods.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-S1-S4) contains supplementary material, which is available to authorized users.

Identifying human disease genes: advances in molecular genetics and computational approaches.

The human genome project is one of the significant achievements that have provided detailed insight into our genetic legacy. During the last two decades, biomedical investigations have gathered a considerable body of evidence by detecting more than 2000 disease genes. Despite the imperative advances in the genetic understanding of various diseases, the pathogenesis of many others remains obscure. With recent advances, the laborious methodologies used to identify DNA variations are replaced by direct sequencing of genomic DNA to detect genetic changes. The ability to perform such studies depends equally on the development of high-throughput and economical genotyping methods. Currently, basically for every disease whose origen is still unknown, genetic approaches are available which could be pedigree-dependent or -independent with the capacity to elucidate fundamental disease mechanisms. Computer algorithms and programs for linkage analysis have formed the foundation for many disease gene detection projects, similarly databases of clinical findings have been widely used to support diagnostic decisions in dysmorphology and general human disease. For every disease type, genome sequence variations, particularly single nucleotide polymorphisms are mapped by comparing the genetic makeup of case and control groups. Methods that predict the effects of polymorphisms on protein stability are useful for the identification of possible disease associations, whereas structural effects can be assessed using methods to predict stability changes in proteins using sequence and/or structural information.

MCSM: predicting the effects of mutations in proteins using graph-based signatures

Motivation: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein–protein and protein–nucleic acid interactions.Results: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario.Availability and implementation: A web server is available at http://structure.bioc.cam.ac.uk/mcsm.Contact: dpires@dcc.ufmg.br; tom@cryst.bioc.cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

In silico prediction of the effects of mutations in the human UDP-galactose 4′-epimerase gene: Towards a predictive framework for type III galactosemia

The enzyme UDP-galactose 4′-epimerase (GALE) catalyses the reversible epimerisation of both UDP-galactose and UDP-N-acetyl-galactosamine. Deficiency of the human enzyme (hGALE) is associated with type III galactosemia. The majority of known mutations in hGALE are missense and private thus making clinical guidance difficult. In this study a bioinformatics approach was employed to analyse the structural effects due to each mutation using both the UDP-glucose and UDP-N-acetylglucosamine bound structures of the wild-type protein. Changes to the enzyme's overall stability, substrate/cofactor binding and propensity to aggregate were also predicted. These predictions were found to be in good agreement with previous in vitro and in vivo studies when data was available and allowed for the differentiation of those mutants that severely impair the enzyme's activity against UDP-galactose. Next this combination of techniques were applied to another twenty-six reported variants from the NCBI dbSNP database that have yet to be studied to predict their effects. This identified p.I14T, p.R184H and p.G302R as likely severely impairing mutations. Although severely impaired mutants were predicted to decrease the protein's stability, overall predicted stability changes only weakly correlated with residual activity against UDP-galactose. This suggests other protein functions such as changes in cofactor and substrate binding may also contribute to the mechanism of impairment. Finally this investigation shows that this combination of different in silico approaches is useful in predicting the effects of mutations and that it could be the basis of an initial prediction of likely clinical severity when new hGALE mutants are discovered.

Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants

BackgroundEven a single amino acid substitution in a protein sequence may result in significant changes in protein stability, structure, and therefore in protein function as well. In the post-genomic era, computational methods for predicting stability changes from only the sequence of a protein are of importance. While evolutionary relationships of protein mutations can be extracted from large protein databases holding millions of protein sequences, relevant evolutionary features for the prediction of stability changes have not been proposed. Also, the use of predicted structural features in situations when a protein structure is not available has not been explored.ResultsWe proposed a number of evolutionary and predicted structural features for the prediction of stability changes and analysed which of them capture the determinants of protein stability the best. We trained and evaluated our machine learning method on a non-redundant data set of experimentally measured stability changes. When only the direction of the stability change was predicted, we found that the best performance improvement can be achieved by the combination of the evolutionary features mutation likelihood and SIFTscore in conjunction with the predicted structural feature secondary structure. The same two evolutionary features in the combination with the predicted structural feature accessible surface area achieved the lowest error when the prediction of actual values of stability changes was assessed. Compared to similar studies, our method achieved improvements in prediction performance.ConclusionAlthough the strongest feature for the prediction of stability changes appears to be the vector of amino acid identities in the sequential neighbourhood of the mutation, the most relevant combination of evolutionary and predicted structural features further improves prediction performance. Even the predicted structural features, which did not perform well on their own, turn out to be beneficial when appropriately combined with evolutionary features. We conclude that a high prediction accuracy can be achieved knowing only the sequence of a protein when the right combination of both structural and evolutionary features is used.

Underexposed polar residues and protein stabilization

Increasing protein stability is interesting for practical reasons and because it tests our understanding of protein energetics. We explore here the feasibility of stabilizing proteins by replacing underexposed polar residues by apolar ones of similar size and shape. We have compared the stability of wild-type apoflavodoxin with that of a few carefully selected mutants carrying Y → F, Q → L, T → V or K → M replacements. Although a clear inverse correlation between native solvent exposures of replaced polar residues and stability of mutants is observed, most mutations fail to stabilize the protein. The promising exceptions are the two Q → L mutations tested, which characteristically combine the greatest reduction in polar burial with the greatest increase in apolar burial relative to wild type. Analysis of published stability data corresponding to a variety of mutant proteins confirms that, unlike Y → F or T → V replacements, Q → L mutations tend to be stabilizing, and it suggests that N → L mutations might be stabilizing as well. On the other hand, we show that the stability changes associated to the apoflavodoxin mutations can be rationalized in terms of differential polar and apolar burials upon folding plus a generic destabilizing penalty term. Simple equations combining these contributions predict stability changes in a large data set of 113 mutants (Y → F, Q → L or T → V) similarly well as more complex algorithms available on the Internet.