Structure-based mutant stability predictions on proteins of unknown structure

Functional characterization of HNF4A gene variants identify promoter and cell line specific transactivation effects

Hepatocyte nuclear factor-4 alpha (HNF-4A) regulates genes with roles in glucose metabolism and β-cell development. Although pathogenic HNF4A variants are commonly associated with maturity-onset diabetes of the young (MODY1; HNF4A-MODY), rare phenotypes also include hyperinsulinemic hypoglycemia, renal Fanconi syndrome and liver disease. While the association of rare functionally damaging HNF1A variants with HNF1A-MODY and type 2 diabetes is well established owing to robust functional assays, the impact of HNF4A variants on HNF-4A transactivation in tissues including the liver and kidney is less known, due to lack of similar assays. Our aim was to investigate the functional effects of seven HNF4A variants, located in the HNF-4A DNA binding domain and associated with different clinical phenotypes, by various functional assays and cell lines (transactivation, DNA binding, protein expression, nuclear localization) and in silico protein structure analyses. Variants R85W, S87N and R89W demonstrated reduced DNA binding to the consensus HNF-4A binding elements in the HNF1A promoter (35, 13 and 9%, respectively) and the G6PC promoter (R85W ~10%). While reduced transactivation on the G6PC promoter in HepG2 cells was shown for S87N (33%), R89W (65%) and R136W (35%), increased transactivation by R85W and R85Q was confirmed using several combinations of target promoters and cell lines. R89W showed reduced nuclear levels. In silico analyses supported variant induced structural impact. Our study indicates that cell line specific functional investigations are important to better understand HNF4A-MODY genotype-phenotype correlations, as our data supports ACMG/AMP interpretations of loss-of-function variants and propose assay-specific HNF4A control variants for future functional investigations.

Bioinformatic analysis of the effect of SNPs in the pig TERT gene on the structural and functional characteristics of the enzyme to develop new genetic markers of productivity traits

BACKGROUND

Telomerase reverse transcriptase (TERT) plays a crucial role in synthesizing telomeric repeats that safeguard chromosomes from damage and fusion, thereby maintaining genome stability. Mutations in the TERT gene can lead to a deviation in gene expression, impaired enzyme activity, and, as a result, abnormal telomere shortening. Genetic markers of productivity traits in livestock can be developed based on the TERT gene polymorphism for use in marker-associated selection (MAS). In this study, a bioinformatic-based approach is proposed to evaluate the effect of missense single-nucleotide polymorphisms (SNPs) in the pig TERT gene on enzyme function and structure, with the prospect of developing genetic markers.

RESULTS

A comparative analysis of the coding and amino acid sequences of the pig TERT was performed with corresponding sequences of other species. The distribution of polymorphisms in the pig TERT gene, with respect to the enzyme's structural-functional domains, was established. A three-dimensional model of the pig TERT structure was obtained through homological modeling. The potential impact of each of the 23 missense SNPs in the pig TERT gene on telomerase function and stability was assessed using predictive bioinformatic tools utilizing data on the amino acid sequence and structure of pig TERT.

CONCLUSIONS

According to bioinformatic analysis of 23 missense SNPs of the pig TERT gene, a predictive effect of rs789641834 (TEN domain), rs706045634 (TEN domain), rs325294961 (TRBD domain) and rs705602819 (RTD domain) on the structural and functional parameters of the enzyme was established. These SNPs hold the potential to serve as genetic markers of productivity traits. Therefore, the possibility of their application in MAS should be further evaluated in associative analysis studies.

Protposer: the web server that readily proposes protein stabilizing mutations with high PPV

Protein stability is a requisite for most biotechnological and medical applications of proteins. As natural proteins tend to suffer from a low conformational stability ex vivo, great efforts have been devoted toward increasing their stability through rational design and engineering of appropriate mutations. Unfortunately, even the best currently used predictors fail to compute the stability of protein variants with sufficient accuracy and their usefulness as tools to guide the rational stabilisation of proteins is limited. We present here Protposer, a protein stabilising tool based on a different approach. Instead of quantifying changes in stability, Protposer uses structure- and sequence-based screening modules to nominate candidate mutations for subsequent evaluation by a logistic regression model, carefully trained to avoid overfitting. Thus, Protposer analyses PDB files in search for stabilization opportunities and provides a ranked list of promising mutations with their estimated success rates (eSR), their probabilities of being stabilising by at least 0.5 kcal/mol. The agreement between eSRs and actual positive predictive values (PPV) on external datasets of mutations is excellent. When Protposer is used with its Optimal kappa selection threshold, its PPV is above 0.7. Even with less stringent thresholds, Protposer largely outperforms FoldX, Rosetta and PoPMusiC. Indicating the PDB file of the protein suffices to obtain a ranked list of mutations, their eSRs and hints on the likely source of the stabilization expected. Protposer is a distinct, straightforward and highly successful tool to design protein stabilising mutations, and it is freely available for academic use at http://webapps.bifi.es/the-protposer.

Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation

This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R² of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.

Rational Design of Adenylate Kinase Thermostability through Coevolution and Sequence Divergence Analysis

Protein engineering is actively pursued in industrial and laboratory settings for high thermostability. Among the many protein engineering methods, rational design by bioinformatics provides theoretical guidance without time-consuming experimental screenings. However, most rational design methods either rely on protein tertiary structure information or have limited accuracies. We proposed a primary-sequence-based algorithm for increasing the heat resistance of a protein while maintaining its functions. Using adenylate kinase (ADK) family as a model system, this method identified a series of amino acid sites closely related to thermostability. Single- and double-point mutants constructed based on this method increase the thermal denaturation temperature of the mesophilic Escherichia coli (E. coli) ADK by 5.5 and 8.3 °C, respectively, while preserving most of the catalytic function at ambient temperatures. Additionally, the constructed mutants have improved enzymatic activity at higher temperature.

PremPS: Predicting the impact of missense mutations on protein stability

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

The development of computational methods to accurately predict the impacts of amino acid substitutions on protein stability is of paramount importance for the field of protein design and understanding the roles of missense mutations in disease. However, most of the available methods have very limited predictive accuracy for mutations increasing stability and few could consistently perform well across different test cases. Here we present a new computational approach PremPS, which is capable of predicting the effects of single point mutations on protein stability. PremPS employs only ten evolutionary- and structure-based features and is trained on a symmetrical dataset consisting of the same number of cases of stabilizing and destabilizing mutations. Our method was tested against numerous blind datasets and shows a considerable improvement especially in evaluating the effects of stabilizing mutations, outperforming previously developed methods. PremPS is freely available as a user-friendly web server at http://lilab.jysw.suda.edu.cn/research/PremPS/, which is fast enough to handle the large number of cases.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

Analysis of protein missense alterations by combining sequence- and structure-based methods

BACKGROUND

Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play.

METHODS

Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB.

RESULTS

We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions.

CONCLUSION

Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.

Atomic insights into the effects of pathological mutants through the disruption of hydrophobic core in the prion protein

Destabilization of prion protein induces a conformational change from normal prion protein (PrP^C) to abnormal prion protein (PrP^SC). Hydrophobic interaction is the main driving force for protein folding, and critically affects the stability and solvability. To examine the importance of the hydrophobic core in the PrP, we chose six amino acids (V176, V180, T183, V210, I215, and Y218) that make up the hydrophobic core at the middle of the H2-H3 bundle. A few pathological mutants of these amino acids have been reported, such as V176G, V180I, T183A, V210I, I215V, and Y218N. We focused on how these pathologic mutations affect the hydrophobic core and thermostability of PrP. For this, we ran a temperature-based replica-exchange molecular dynamics (T-REMD) simulation, with a cumulative simulation time of 28 μs, for extensive ensemble sampling. From the T-REMD ensemble, we calculated the protein folding free energy difference between wild-type and mutant PrP using the thermodynamic integration (TI) method. Our results showed that pathological mutants V176G, T183A, I215V, and Y218N decrease the PrP stability. At the atomic level, we examined the change in pair-wise hydrophobic interactions from valine-valine to valine-isoleucine (and vice versa), which is induced by mutation V180I, V210I (I215V) at the 180^th-210^th (176^th-215^th) pair. Finally, we investigated the importance of the π-stacking between Y218 and F175.

Germline variation in the oxidative DNA repair genes NUDT1 and OGG1 is not associated with hereditary colorectal cancer or polyposis

The causal association of NUDT1 (=MTH1) and OGG1 with hereditary colorectal cancer (CRC) remains unclear. Here, we sought to provide additional evidence for or against the causal contribution of NUDT1 and OGG1 mutations to hereditary CRC and/or polyposis. Mutational screening was performed using pooled DNA amplification and targeted next-generation sequencing in 529 families (441 uncharacterized MMR-proficient familial nonpolyposis CRC and 88 polyposis cases). Cosegregation, in silico analyses, in vitro functional assays, and case-control associations were carried out to characterize the identified variants. Five heterozygous carriers of novel (n = 1) or rare (n = 4) NUDT1 variants were identified. In vitro deleterious effects were demonstrated for c.143G>A p.G48E (catalytic activity and protein stability) and c.403G>T p.G135W (protein stability), although cosegregation data in the carrier families were inconclusive or nonsupportive. The frequency of missense, loss-of-function, and splice-site NUDT1 variants in our familial CRC cohort was similar to the one observed in cancer-free individuals, suggesting lack of association with CRC predisposition. No OGG1 pathogenic mutations were identified. Our results suggest that the contribution of NUDT1 and OGG1 germline mutations to hereditary CRC and to polyposis is inexistent or, at most, negligible. The inclusion of these genes in routine genetic testing is not recommended.

Prediction and interpretation of deleterious coding variants in terms of protein structural stability

The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/.

Association Between Germline Mutations in BRF1, a Subunit of the RNA Polymerase III Transcription Complex, and Hereditary Colorectal Cancer

BACKGROUND & AIMS

Although there is a genetic predisposition to colorectal cancer (CRC), few of the genes that affect risk have been identified. We performed whole-exome sequence analysis of individuals in a high-risk family without mutations in genes previously associated with CRC risk to identify variants associated with inherited CRC.

METHODS

We collected blood samples from 3 relatives with CRC in Spain (65, 62, and 40 years old at diagnosis) and performed whole-exome sequence analyses. Rare missense, truncating or splice-site variants shared by the 3 relatives were selected. We used targeted pooled DNA amplification followed by next generation sequencing to screen for mutations in candidate genes in 547 additional hereditary and/or early-onset CRC cases (502 additional families). We carried out protein-dependent yeast growth assays and transfection studies in the HT29 human CRC cell line to test the effects of the identified variants.

RESULTS

A total of 42 unique or rare (population minor allele frequency below 1%) nonsynonymous genetic variants in 38 genes were shared by all 3 relatives. We selected the BRF1 gene, which encodes an RNA polymerase III transcription initiation factor subunit for further analysis, based on the predicted effect of the identified variant and previous association of BRF1 with cancer. Previously unreported or rare germline variants in BRF1 were identified in 11 of 503 CRC families, a significantly greater proportion than in the control population (34 of 4300). Seven of the identified variants (1 detected in 2 families) affected BRF1 mRNA splicing, protein stability, or expression and/or function.

CONCLUSIONS

In an analysis of families with a history of CRC, we associated germline mutations in BRF1 with predisposition to CRC. We associated deleterious BRF1 variants with 1.4% of familial CRC cases, in individuals without mutations in high-penetrance genes previously associated with CRC. Our findings add additional evidence to the link between defects in genes that regulate ribosome synthesis and risk of CRC.

Prediction of the impact of coding missense and nonsense single nucleotide polymorphisms on HD5 and HBD1 antibacterial activity against Escherichia coli

Defensins confer host defense against microorganisms and are important for human health. Single nucleotide polymorphisms (SNPs) in defensin gene-coding regions could lead to less active variants. Using SNP data available at the dbSNP database and frequency information from the 1000 Genomes Project, two DEFA5 (L26I and R13H) and eight DEFB1 (C35S, K31T, K33R, R29G, V06I, C12Y, Y28* and C05*) missense and nonsense SNPs that are located within mature regions of the coded defensins were retrieved. Such SNPs are rare and population restricted. In order to assess their antibacterial activity against Escherichia coli, two linear regression models were used from a previous work, which models the antibacterial activity as a function of solvation potential energy, using molecular dynamics data. Regarding only the antibacterial predictions, for HD5, no biological differences between wild-type and its variants were observed; while for HBD1, the results suggest that the R29G, K31T, Y28* and C05* variants could be less active than the wild-type one. The data here reported could lead to a substantial improvement in knowledge about the impact of missense SNPs in human defensins and their world distribution. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 633-644, 2016.

Improved Production of Active Streptomyces griseus Trypsin with a Novel Auto-Catalyzed Strategy

N-terminal sequences play crucial roles in regulating expression, translation, activation and enzymatic properties of proteins. To reduce cell toxicity of intracellular trypsin and increase secretory expression, we developed a novel auto-catalyzed strategy to produce recombinant trypsin by engineering the N-terminus of mature Streptomyces griseus trypsin (SGT). The engineered N-terminal peptide of SGT was composed of the thioredoxin, glycine-serine linker, His₆-tag and the partial bovine trypsinogen pro-peptide (DDDDK). Furthermore, we constructed a variant TLEI with insertion of the artificial peptide at N-terminus and site-directed mutagenesis of the autolysis residue R145. In fed-batch fermentation, the production of extracellular trypsin activity was significantly improved to 47.4 ± 1.2 U·ml⁻¹ (amidase activity, 8532 ± 142.2 U·ml⁻¹ BAEE activity) with a productivity of 0.49 U·ml⁻¹·h⁻¹, which was 329% greater than that of parent strain Pichia pastoris GS115-SGT. This work has significant potential to be scaled-up for microbial production of SGT. In addition, the N-terminal peptide engineering strategy can be extended to improve heterologous expression of other toxic enzymes.

Towards a "Golden Standard" for computing globin stability: Stability and structure sensitivity of myoglobin mutants

Fast and accurate computation of protein stability is increasingly important for e.g. protein engineering and protein misfolding diseases, but no consensus methods exist for important proteins such as globins, and performance may depend on the type of structural input given. This paper reports benchmarking of six protein stability calculators (POPMUSIC 2.1, I-Mutant 2.0, I-Mutant 3.0, CUPSAT, SDM, and mCSM) against 134 experimental stability changes for mutations of sperm-whale myoglobin. Six different high-resolution structures were used to test structure sensitivity that may impair protein calculations. The trend accuracy of the methods decreased as I-Mutant 2.0 (R=0.64-0.65), SDM (R=0.57-0.60), POPMUSIC2.1 (R=0.54-0.57), I-Mutant 3.0 (R=0.53-0.55), mCSM (R=0.35-0.47), and CUPSAT (R=0.25-0.48). The mean signed errors increased as SDM<CUPSAT<I-Mutant 2.0<I-Mutant 3.0<POPMUSIC 2.1<mCSM. Mean absolute errors increased as I-Mutant 2.0<I-Mutant 3.0<POPMUSIC 2.1<CUPSAT<SDM<mCSM. Structural sensitivity increased as I-Mutant 3.0 (0.05)<I-Mutant 2.0 (0.09)<POPMUSIC 2.1 (0.12)<SDM (0.18)<mCSM (0.27)<CUPSAT (0.58). Leaving out heterogeneous experimental data did not change conclusions. The distinct performances reveal room for improvement, but I-Mutant 2.0 is proficient for this purpose, as further validated against a data set of related cytochrome c like proteins. The results also emphasize the importance of high-quality crystal structures and reveal structure-dependent effects even in the near-atomic resolution limit.

Atomic Insight into the Altered O6-Methylguanine-DNA Methyltransferase Protein Architecture in Gastric Cancer

O⁶-methylguanine-DNA methyltransferase (MGMT) is one of the major DNA repair protein that counteracts the alkalyting agent-induced DNA damage by replacing O⁶-methylguanine (mutagenic lesion) back to guanine, eventually suppressing the mismatch errors and double strand crosslinks. Exonic alterations in the form of nucleotide polymorphism may result in altered protein structure that in turn can lead to the loss of function. In the present study, we focused on the population feared for high exposure to alkylating agents owing to their typical and specialized dietary habits. To this end, gastric cancer patients pooled out from the population were selected for the mutational screening of a specific error prone region of MGMT gene. We found that nearly 40% of the studied neoplastic samples harbored missense mutation at codon¹⁵¹ resulting into Serine to Isoleucine variation. This variation resulted in bringing about the structural disorder, subsequently ensuing into a major stoichiometric variance in recognition domain, substrate binding and selectivity loop of the active site of the MGMT protein, as observed under virtual microscope of molecular dynamics simulation (MDS). The atomic insight into MGMT protein by computational approach showed a significant change in the intra molecular hydrogen bond pattern, thus leading to the observed structural anomalies. To further examine the mutational implications on regulatory plugs of MGMT that holds the protein in a DNA-Binding position, a MDS based analysis was carried out on, all known physically interacting amino acids essentially clustered into groups based on their position and function. The results generated by physical-functional clustering of protein indicated that the identified mutation in the vicinity of the active site of MGMT protein causes the local and global destabilization of a protein by either eliminating the stabilizing salt bridges in cluster C3, C4, and C5 or by locally destabilizing the “protein stabilizing hing” mapped on C3-C4 cluster, preceding the active site.

MAESTRO--multi agent stability prediction upon point mutations

BACKGROUND

Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.

RESULTS

We aim to contribute a novel method for predicting changes in stability upon point mutation in proteins called MAESTRO. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (Δ ΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds. The predictive power of MAESTRO for single point mutations and stabilizing disulfide bonds is comparable to similar methods.

CONCLUSIONS

MAESTRO is a versatile tool in the field of stability change prediction upon point mutations. Executables for the Linux and Windows operating systems are freely available to non-commercial users from http://biwww.che.sbg.ac.at/MAESTRO.

Novel α-L-arabinofuranosidase from Cellulomonas fimi ATCC 484 and its substrate-specificity analysis with the aid of computer

In the process of gene mining for novel α-L-arabinofuranosidases (AFs), the gene Celf_3321 from Cellulomonas fimi ATCC 484 encodes an AF, termed as AbfCelf, with potent activity, 19.4 U/mg under the optimum condition, pH 6.0 and 40 °C. AbfCelf can hydrolyze α-1,5-linked oligosaccharides, sugar beet arabinan, linear 1,5-α-arabinan, and wheat flour arabinoxylan, which is partly different from some previously well-characterized GH 51 AFs. The traditional substrate-specificity analysis for AFs is labor-consuming and money costing, because the substrates include over 30 kinds of various 4-nitrophenol (PNP)-glycosides, oligosaccharides, and polysaccharides. Hence, a preliminary structure and mechanism based method was applied for substrate-specificity analysis. The binding energy (ΔG, kcal/mol) obtained by docking suggested the reaction possibility and coincided with the experimental results. AbfA crystal 1QW9 was used to test the rationality of docking method in simulating the interaction between enzyme and substrate, as well the credibility of the substrate-specificity analysis method in silico.

The fundamental tradeoff in genomes and proteomes of prokaryotes established by the genetic code, codon entropy, and physics of nucleic acids and proteins

Background

Mutations in nucleotide sequences provide a foundation for genetic variability, and selection is the driving force of the evolution and molecular adaptation. Despite considerable progress in the understanding of selective forces and their compositional determinants, the very nature of underlying mutational biases remains unclear.

Results

We explore here a fundamental tradeoff, which analytically describes mutual adjustment of the nucleotide and amino acid compositions and its possible effect on the mutational biases. The tradeoff is determined by the interplay between the genetic code, optimization of the codon entropy, and demands on the structure and stability of nucleic acids and proteins.

Conclusion

The tradeoff is the unifying property of all prokaryotes regardless of the differences in their phylogenies, life styles, and extreme environments. It underlies mutational biases characteristic for genomes with different nucleotide and amino acid compositions, providing foundation for evolution and adaptation.

Reviewers

This article was reviewed by Eugene Koonin, Michael Gromiha, and Alexander Schleiffer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-014-0029-2) contains supplementary material, which is available to authorized users.

Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.

Stability curve prediction of homologous proteins using temperature-dependent statistical potentials

The unraveling and control of protein stability at different temperatures is a fundamental problem in biophysics that is substantially far from being quantitatively and accurately solved, as it requires a precise knowledge of the temperature dependence of amino acid interactions. In this paper we attempt to gain insight into the thermal stability of proteins by designing a tool to predict the full stability curve as a function of the temperature for a set of 45 proteins belonging to 11 homologous families, given their sequence and structure, as well as the melting temperature () and the change in heat capacity () of proteins belonging to the same family. Stability curves constitute a fundamental instrument to analyze in detail the thermal stability and its relation to the thermodynamic stability, and to estimate the enthalpic and entropic contributions to the folding free energy. In summary, our approach for predicting the protein stability curves relies on temperature-dependent statistical potentials derived from three datasets of protein structures with targeted thermal stability properties. Using these potentials, the folding free energies () at three different temperatures were computed for each protein. The Gibbs-Helmholtz equation was then used to predict the protein's stability curve as the curve that best fits these three points. The results are quite encouraging: the standard deviations between the experimental and predicted 's, 's and folding free energies at room temperature () are equal to 13 , 1.3 ) and 4.1 , respectively, in cross-validation. The main sources of error and some further improvements and perspectives are briefly discussed.

The prediction of protein stability remains one of the key goals of protein science. Despite the significant efforts of the last decades, faster and more accurate stability predictors on the proteomic-wide scale are currently demanded. The determination and control of protein stability are indeed fundamental steps on the path towards de novo design. In this paper we develop a method for predicting the stability curve of proteins. This curve encodes the temperature dependence of the folding free energy (). Its knowledge is important in the study of protein stability since all the thermodynamic parameters characterizing the folding transition can be extracted from it. Our prediction method is based on temperature-dependent mean force potentials and uses the tertiary structure of the target protein as well as the melting temperature () and the heat capacity change () of some other proteins belonging to the same family. From the predicted stability curves, the , the and the at room temperature can be inferred. The predictions obtained are compared with experimental data and show reasonable performances.

Computing stability effects of mutations in human superoxide dismutase 1

Protein stability is affected in several diseases and is of substantial interest in efforts to correlate genotypes to phenotypes. Superoxide dismutase 1 (SOD1) is a suitable test case for such correlations due to its abundance, stability, available crystal structures and thermochemical data, and physiological importance. In this work, stability changes of SOD1 mutations were computed with five methods, CUPSAT, I-Mutant2.0, I-Mutant3.0, PoPMuSiC, and SDM, with emphasis on structural sensitivity as a potential issue in structure-based protein calculation. The large correlation between experimental literature data of SOD1 dimers and monomers (r = 0.82) suggests that mutations in separate protein monomers are mostly additive. PoPMuSiC was most accurate (typical MAE ~ 1 kcal/mol, r ~ 0.5). The relative performance of the methods was not very structure-dependent, and the more accurate methods also displayed less structural sensitivity, with the standard deviation from different high-resolution structures down to ~0.2 kcal/mol. Structures of variable resolution and number of protein copies locally affected specific sites, emphasizing the use of state-relevant crystal structures when such sites are of interest, but had little impact on overall batch estimates. Protein-interaction effects (as a mimic of crystal packing) were small for the more accurate methods. Thus, batch computations, relevant to, e.g., comparisons of disease/nondisease mutant sets or different clades in phylogenetic trees, are much more significant than single mutant calculations and may be the only meaningful way to computationally bridge the genotype-phenotype gap of proteomics. Finally, mutations involving glycine were most difficult to model, of relevance to future method improvement. This could be due to structure changes (glycine has a low structural propensity) or water colocalization with glycine.

A frequent splicing mutation and novel missense mutations color the updated mutational spectrum of classic galactosemia in Portugal

Classic galactosemia is an autosomal recessive disorder caused by deficient galactose-1-phosphate uridylyltransferase (GALT) activity. Patients develop symptoms in the neonatal period, which can be ameliorated by dietary restriction of galactose. Many patients develop long-term complications, with a broad range of clinical symptoms whose pathophysiology is poorly understood. The high allelic heterogeneity of GALT gene that characterizes this disorder is thought to play a determinant role in biochemical and clinical phenotypes. We aimed to characterize the mutational spectrum of GALT deficiency in Portugal and to assess potential genotype-phenotype correlations. Direct sequencing of the GALT gene and in silico analyses were employed to evaluate the impact of uncharacterized mutations upon GALT functionality. Molecular characterization of 42 galactosemic Portuguese patients revealed a mutational spectrum comprising 14 nucleotide substitutions: ten missense, two nonsense and two putative splicing mutations. Sixteen different genotypic combinations were detected, half of the patients being p.Q188R homozygotes. Notably, the second most frequent variation is a splicing mutation. In silico predictions complemented by a close-up on the mutations in the protein structure suggest that uncharacterized missense mutations have cumulative point effects on protein stability, oligomeric state, or substrate binding. One splicing mutation is predicted to cause an alternative splicing event. This study reinforces the difficulty in establishing a genotype-phenotype correlation in classic galactosemia, a monogenic disease whose complex pathogenesis and clinical features emphasize the need to expand the knowledge on this "cloudy" disorder.

Molecular mechanisms of adaptation emerging from the physics and evolution of nucleic acids and proteins

DNA, RNA and proteins are major biological macromolecules that coevolve and adapt to environments as components of one highly interconnected system. We explore here sequence/structure determinants of mechanisms of adaptation of these molecules, links between them, and results of their mutual evolution. We complemented statistical analysis of genomic and proteomic sequences with folding simulations of RNA molecules, unraveling causal relations between compositional and sequence biases reflecting molecular adaptation on DNA, RNA and protein levels. We found many compositional peculiarities related to environmental adaptation and the life style. Specifically, thermal adaptation of protein-coding sequences in Archaea is characterized by a stronger codon bias than in Bacteria. Guanine and cytosine load in the third codon position is important for supporting the aerobic life style, and it is highly pronounced in Bacteria. The third codon position also provides a tradeoff between arginine and lysine, which are favorable for thermal adaptation and aerobicity, respectively. Dinucleotide composition provides stability of nucleic acids via strong base-stacking in ApG dinucleotides. In relation to coevolution of nucleic acids and proteins, thermostability-related demands on the amino acid composition affect the nucleotide content in the second codon position in Archaea.

Structure-based engineering of alkaline α-amylase from alkaliphilic Alkalimonas amylolytica for improved thermostability

This study aimed to improve the thermostability of alkaline α-amylase from Alkalimonas amylolytica through structure-based rational design and systems engineering of its catalytic domain. Separate engineering strategies were used to increase alkaline α-amylase thermostability: (1) replace histidine residues with leucine to stabilize the least similar region in domain B, (2) change residues (glycine, proline, and glutamine) to stabilize the highly conserved α-helices in domain A, and (3) decrease the free energy of folding predicted by the PoPMuSiC program to stabilize the overall protein structure. A total of 15 single-site mutants were obtained, and four mutants - H209L, Q226V, N302W, and P477V - showed enhanced thermostability. Combinational mutations were subsequently introduced, and the best mutant was triple mutant H209L/Q226V/P477V. Its half-life at 60 °C was 3.8-fold of that of the wild type and displayed a 3.2 °C increase in melting temperature compared with that of the wild type. Interestingly, other biochemical properties of this mutant also improved: the optimum temperature increased from 50 °C to 55 °C, the optimum pH shifted from 9.5 to 10.0, the stable pH range expanded from 7.0-11.0 to 6.0-12.0, the specific activity increased by 24 %, and the catalytic efficiency (k cat/K m) increased from 1.8×10(4) to 3.5 × 10(4) l/(g min). Finally, the mechanisms responsible for the increased thermostability were analyzed through comparative analysis of structure models. The structure-based rational design and systems engineering strategies in this study may also improve the thermostability of other industrial enzymes.

Towards precision medicine: advances in computational approaches for the analysis of human variants

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.

Structural and biochemical characterization of an active arylamine N-acetyltransferase possessing a non-canonical Cys-His-Glu catalytic triad

Arylamine N-acetyltransferases (NATs), a class of xenobiotic-metabolizing enzymes, catalyze the acetylation of aromatic amine compounds through a strictly conserved Cys-His-Asp catalytic triad. Each residue is essential for catalysis in both prokaryotic and eukaryotic NATs. Indeed, in (HUMAN)NAT2 variants, mutation of the Asp residue to Asn, Gln, or Glu dramatically impairs enzyme activity. However, a putative atypical NAT harboring a catalytic triad Glu residue was recently identified in Bacillus cereus ((BACCR)NAT3) but has not yet been characterized. We report here the crystal structure and functional characterization of this atypical NAT. The overall fold of (BACCR)NAT3 and the geometry of its Cys-His-Glu catalytic triad are similar to those present in functional NATs. Importantly, the enzyme was found to be active and to acetylate prototypic arylamine NAT substrates. In contrast to (HUMAN) NAT2, the presence of a Glu or Asp in the triad of (BACCR)NAT3 did not significantly affect enzyme structure or function. Computational analysis identified differences in residue packing and steric constraints in the active site of (BACCR)NAT3 that allow it to accommodate a Cys-His-Glu triad. These findings overturn the conventional view, demonstrating that the catalytic triad of this family of acetyltransferases is plastic. Moreover, they highlight the need for further study of the evolutionary history of NATs and the functional significance of the predominant Cys-His-Asp triad in both prokaryotic and eukaryotic forms.

BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations

The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein–protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein–protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.