Using structural bioinformatics to investigate the impact of non synonymous SNPs and disease mutations: scope and limitations

The MitoAging Project: Single nucleotide polymorphisms (SNPs) in mitochondrial genes and their association to longevity

Mitochondrial dysfunction is a major hallmark of aging. Mitochondrial DNA (mtDNA) mutations (inherited or acquired) may cause a malfunction of the respiratory chain (RC), and thus negatively affect cell metabolism and function. In contrast, certain mtDNA single nucleotide polymorphisms (SNPs) may be beneficial to mitochondrial electron transport chain function and the extension of cellular health as well as lifespan. The goal of the MitoAging project is to detect key physiological characteristics and mechanisms that improve mitochondrial function and use them to develop therapies to increase longevity and a healthy lifespan. We chose to perform a systematic literature review (SLR) as a tool to collect key mtDNA SNPs associated with an increase in lifespan. Then validated our results by comparing them to the MitoMap database. Next, we assessed the effect of relevant SNPs on protein stability. A total of 28 SNPs were found in protein coding regions. These SNPs were reported in Japan, China, Turkey, and India. Among the studied SNPs, the C5178A mutation in the ND2 gene of Complex I of the RC was detected in all the reviewed reports except in Uygur Chinese centenarians. Then, we found that G9055A (ATP6 gene) and A10398G (ND3 gene) polymorphisms have been associated with a protective effect against Parkinson's disease (PD). Additionally, C8414T in ATP8 was significantly associated with longevity in three Japanese reports. Interestingly, using MitoMap we found that G9055A (ATP6 gene) was the only SNP promoting longevity not associated with any pathology. The identification of SNPs associated with an increase in lifespan opens the possibility to better understand individual differences regarding a decrease in illness susceptibility and find strategies that contribute to healthy aging.

Molecular characterization of the vitamin D receptor (VDR) gene in Holstein cows

Vitamin D plays a vital role in calcium homeostasis, growth, and immunoregulation. Because little is known about the vitamin D receptor (VDR) gene in cattle, the aim of the present investigation was to present the molecular characterization of exons 5 and 6 of the VDR gene in Holstein cows. DNA extraction, genomic sequencing, phylogenetic analysis, synteny mapping and single nucleotide gene polymorphism analysis of the VDR gene were performed to assess blood samples collected from 50 clinically healthy Holstein cows. The results revealed the presence of a 450-base pair (bp) nucleotide sequence that resembled exons 5 and 6 with intron 5 enclosed between these exons. Sequence alignment and phylogenetic analysis revealed a close relationship between the sequenced VDR region and that found in Hereford cattle. A close association between this region and the corresponding region in small ruminants was also documented. Moreover, a single nucleotide polymorphism (SNP) that caused the replacement of a glutamate with an arginine in the deduced amino acid sequence was detected at position 7 of exon 5. In conclusion, Holstein and Hereford cattle differ with respect to exon 5 of the VDR gene. Phylogenetic analysis of the VDR gene based on nucleotide sequence produced different results from prior analyses based on amino acid sequence.

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

An Integrated Computational Framework to Assess the Mutational Landscape of α-L-Iduronidase IDUA Gene

Mucopolysaccharidosis type I is a lysosomal genetic disorder caused due to the deficiency of the α-L-iduronidase enzyme (IDUA). Mutations associated with IDUA lead to mild to severe forms of diseases characterized by different clinical features. In the present study, we first performed a comprehensive analysis using various in silico prediction tools to screen and prioritize the missense mutations or nonsynonymous SNPs (nsSNPs) associated with IDUA. Subsequently, statistical analysis was empowered to examine the predictive ability and accuracy of the in silico prediction tool results supporting the disease phenotype ranging from mild to severe. Till date, no study has been carried out in IDUA in analyzing the impact of the nsSNPs at the structural level. In this context with the aid of pathogenic and stability prediction in silico tools, we identified nsSNPs R89Q, R89W, and P533R to be most deleterious and disease-causing having impact on the function of the protein. Extensive molecular dynamics analysis was performed using Gromacs to understand the deleterious nature of the mutants. Variations observed between the trajectory files of native and mutants R89Q, R89W, and P533R using Gromacs utilities enabled us to measure the adverse effects on the protein and could be the underlying reasons for the disease pathogenesis. These findings may be helpful in understanding the genotype-phenotype relationship and molecular basis of the disease to design drugs for better treatment. J. Cell. Biochem. 119: 555-565, 2018. © 2017 Wiley Periodicals, Inc.

Computational analysis for GNAQ mutations: New insights on the molecular etiology of Sturge-Weber syndrome

Somatic activating mutations in the GNAQ have been recently associated with several congenital genetic disorders and tumors; however, the molecular mechanism/etiology that leads to GNAQ somatic mosaic mutation are unknown. Here, we reported a case of Sturge-Weber Syndrome (SWS) manifesting cutaneous vascular malformations (hemifacial Port-wine stain), cerebral and ocular vascular abnormalities (including epilepsy and glaucoma) and harboring a c.548G>A (p.R183Q) somatic mosaic mutation in GNAQ. Computational modeling studies were performed to assistant with the comprehension of the functional impact of p.R183Q and p.Q209L mutations in GNAQ, which encodes a G protein subunit alpha q (Gαq). The p.R183Q mutation was predicted to abolish hydrogen bonds between R183 residue and GDP molecule, destabilizing the inactive GDP-bound conformation of the Gαq mutants. Furthermore, replacement of R183 by Q183 residue was predicted to promote conformation changes in protein surface features affecting the switch I region, a key region that undergoes conformational changes triggered by receptor binding during signal transduction. In addition, replacement of Q209 by L209 residue was predicted to affect the molecular interaction between Gαq and Gβ subunit, impairing formation of the inactive heterotrimeric complex. These findings, in association with PPI network analysis, indicate that p.R183Q and p.Q209L mutations result in the over-activation of different downstream effectors, which in turn will determine the distinct cell responses and phenotype. These findings bring new insights on molecular etiology of vascular malformations associated to SWS and on different mechanisms underlying hyperactivation of downstream pathways to Gαq.

Beyond DNA: An Integrated and Functional Approach for Classifying Germline Variants in Breast Cancer Genes

Genetic testing for hereditary breast cancer is an integral part of individualized care in the new era of precision medicine. The accuracy of an assay is reliant on not only the technology and bioinformatics analysis utilized but also the experience and infrastructure required to correctly classify genetic variants as disease-causing. Interpreting the clinical significance of germline variants identified by hereditary cancer testing is complex and has a significant impact on the management of patients who are at increased cancer risk. In this review we give an overview of our clinical laboratory's integrated approach to variant assessment. We discuss some of the nuances that should be considered in the assessment of genomic variants. In addition, we highlight lines of evidence such as functional assays and structural analysis that can be useful in the assessment of rare and complex variants.

Molecular dynamics-based analyses of the structural instability and secondary structure of the fibrinogen gamma chain protein with the D356V mutation

Mutations in the fibrinogen gamma chain (FGG) gene have been associated with various disorders, such as dysfibrinogenemia, thrombophilia, and hypofibrinogenemia. A literature survey showed that a residue exchange in fibrinogen Milano I from γ Asp to Val at position 330 impairs fibrin polymerization. The D356V (D330V) mutation located in the C-terminus was predicted to be highly deleterious and to affect the function of the protein. The pathogenicity of the altered gene and changes in protein functions were predicted using in silico methods, such as SIFT, PolyPhen 2, I-Mutant 3.0, Align GV-GD, PhD-SNP, and SNPs&GO. The secondary structure of the mutant protein was unwound by the end of the 50-ns simulation period, and a structural change in the helix-turn transition of the alpha-helical (352-356) region residues was observed. Moreover, a change in the length of the helical region was visualized in the mutant trajectory file, indicating the local transient unfolding of the protein. The obtained computational results suggest that the substitution of the neutral amino acid valine for the acidic amino acid aspartic acid at position 356 results in an unwound conformation within 50 ns, which might contribute to defective polymerization. Our analysis also provides insights into the effect of the conformational change in the D356V (D330V) mutant on protein structure and function.

The Bioinformatics Report of Mutation Outcome on NADPH Flavin Oxidoreductase Protein Sequence in Clinical Isolates of H. pylori

frxA gene has been implicated in the metronidazole nitro reduction by H. pylori. Alternatively, frxA is expected to contribute to the protection of urease and to the in vivo survival of H. pylori. The aim of present study is to report the mutation effects on the frxA protein sequence in clinical isolates of H. pylori in our community. Metronidazole resistance was proven in 27 of 48 isolates. glmM and frxA genes were used for molecular confirmation of H. pylori isolates. The primer set for detection of whole sequence of frxA gene for the effect of mutation on protein sequence was used. DNA and protein sequence evaluation and analysis were done by blast, Clustal Omega, and T COFFEE programs. Then, FrxA protein sequences from six metronidazole-resistant clinical isolates were analyzed by web-based bioinformatics tools. The result of six metronidazole-resistant clinical isolates in comparison with strain 26695 showed ten missense mutations. The result with the STRING program revealed that no change was seen after alterations in these sequences. According to consensus data involving four methods, residue substitutions at 40, 13, and 141 increase the stability of protein sequence after mutation, while other alterations decrease. Residue substitutions at 40, 43, 141, 138, 169, and 179 are deleterious, while, V7I, Q10R, V34I, and V96I alterations are neutral. As FrxA contribute to survival of bacterium and in regard to the effect of mutations on protein function, it might affect the survival and bacterium phenotype and it need to be studied more. Also, none of the stability prediction tool is perfect; iStable is the best predictor method among all methods.

Predicting aggregation-prone sequences in proteins

Owing to its association with a diverse range of human diseases, the determinants of protein aggregation are studied intensively. It is generally accepted that the effective aggregation tendency of a protein depends on many factors such as folding efficiency towards the native state, thermodynamic stability of that conformation, intrinsic aggregation propensity of the polypeptide sequence and its ability to be recognized by the protein quality control system. The intrinsic aggregation propensity of a polypeptide sequence is related to the presence of short APRs (aggregation-prone regions) that self-associate to form intermolecular β-structured assemblies. These are typically short sequence segments (5-15 amino acids) that display high hydrophobicity, low net charge and a high tendency to form β-structures. As the presence of such APRs is a prerequisite for aggregation, a plethora of methods have been developed to identify APRs in amino acid sequences. In the present chapter, the methodological basis of these approaches is discussed, as well as some practical applications.

Recent progress on structural bioinformatics research of cytochrome P450 and its impact on drug discovery

Cytochrome P450 is predominantly responsible for human drug metabolism, which is of critical importance for drug discovery and development. Structural bioinformatics focuses on analysis and prediction of three-dimentional structure of biological macromolecules and elucidation of structure-function relationship as well as identification of important binding interactions. Rapid advancement of structural bioinformatics has been made over the last decade. With more information available for CYP structures, the methods of structural bioinformatics may be used in the CYP field. In this review, we demonstrate three previous studies on CYP using the methods of structural bioinformatics, including the investigation of reasons for decrease of enzymatic activity of CYP1A2 caused by a peripheral mutation, the construction of a pharmacophore model specific to active site of CYP1A2 and the prediction of the functional consequences of single residue mutation in CYP. By illustrating these studies we attempt to show the potential role of structural bioinformatics in CYP research and help better understanding the importance of structural bioinformatics in drug designing.

Amino acid changes in disease-associated variants differ radically from variants observed in the 1000 genomes project dataset

The 1000 Genomes Project data provides a natural background dataset for amino acid germline mutations in humans. Since the direction of mutation is known, the amino acid exchange matrix generated from the observed nucleotide variants is asymmetric and the mutabilities of the different amino acids are very different. These differences predominantly reflect preferences for nucleotide mutations in the DNA (especially the high mutation rate of the CpG dinucleotide, which makes arginine mutability very much higher than other amino acids) rather than selection imposed by protein structure constraints, although there is evidence for the latter as well. The variants occur predominantly on the surface of proteins (82%), with a slight preference for sites which are more exposed and less well conserved than random. Mutations to functional residues occur about half as often as expected by chance. The disease-associated amino acid variant distributions in OMIM are radically different from those expected on the basis of the 1000 Genomes dataset. The disease-associated variants preferentially occur in more conserved sites, compared to 1000 Genomes mutations. Many of the amino acid exchange profiles appear to exhibit an anti-correlation, with common exchanges in one dataset being rare in the other. Disease-associated variants exhibit more extreme differences in amino acid size and hydrophobicity. More modelling of the mutational processes at the nucleotide level is needed, but these observations should contribute to an improved prediction of the effects of specific variants in humans.

In this paper we compare the differences between ‘natural’ and disease-associated amino acid variants at both sequence as well as structural levels. We used data from the 1000 Genomes Project (1 kG), the OMIM database and UniProtKB Humsavar. The results highlight the complex interplay of features from the level of the DNA up to protein sequence and structure. The codon CpG dinucleotide content plays a large role in determining which amino acids mutate. This in turn affects the mutability of amino acids and a clear difference was seen between non-disease and disease variants where amino acids that are naturally very mutable show the opposite trend in the disease-associated data. The current results show evidence for some selection, mainly in that the variants occur slightly more often on the surface of the protein and are much less likely to be annotated as functional than expected by chance. However we should note that even the best definition of functional, taken from structural data, is limited. Even with these caveats, it is clear that the 1 kG variants eschew functional residues as defined here, a trend which is surprisingly even stronger in the OMIM data.

On the effect of protein conformation diversity in discriminating among neutral and disease related single amino acid substitutions

BACKGROUND

Non-synonymous coding SNPs (nsSNPs) that are associated to disease can also be related with alterations in protein stability. Computational methods are available to predict the effect of single amino acid substitutions (SASs) on protein stability based on a single folded structure. However, the native state of a protein is not unique and it is better represented by the ensemble of its conformers in dynamic equilibrium. The maintenance of the ensemble is essential for protein function. In this work we investigated how protein conformational diversity can affect the discrimination of neutral and disease related SASs based on protein stability estimations. For this purpose, we used 119 proteins with 803 associated SASs, 60% of which are disease related. Each protein was associated with its corresponding set of available conformers as found in the Protein Conformational Database (PCDB). Our dataset contains proteins with different extensions of conformational diversity summing up a total number of 1023 conformers.

RESULTS

The existence of different conformers for a given protein introduces great variability in the estimation of the protein stability (ΔΔG) after a single amino acid substitution (SAS) as computed with FoldX. Indeed, in 35% of our protein set at least one SAS can be described as stabilizing, destabilizing or neutral when a cutoff value of ±2 kcal/mol is adopted for discriminating neutral from perturbing SASs. However, when the ΔΔG variability among conformers is taken into account, the correlation among the perturbation of protein stability and the corresponding disease or neutral phenotype increases as compared with the same analysis on single protein structures. At the conformer level, we also found that the different conformers correlate in a different way to the corresponding phenotype.

CONCLUSIONS

Our results suggest that the consideration of conformational diversity can improve the discrimination of neutral and disease related protein SASs based on the evaluation of the corresponding Gibbs free energy change.

Searching for new genetic variations in expression databases for the GABAergic and glutamatergic systems

Changes in gene expression and genetic variations in coding regions have likely functional impact, potentially associated with complex diseases, such as neuropsychiatric conditions. A current need for high throughput analysis of genomic data is leading to the development and improvement of sophisticated bioinformatics approaches, which allows the processing of large amounts of sequence and gene expression data. In this study, we identified new potential genetic variations prioritizing genes related to glutamatergic and GABAergic systems, using different bioinformatics resources. The CLCbio Workbench Combined platform was initially used to build expressed sequence tags and mRNA files retrieved, respectively, from the Goldenpath and National Center for Biotechnology Information databases and latter to perform multiple batches of Smith-Waterman alignments. The PMUT software was used to increase an accurate association between potential variations and pathogenic predictions. The annotation revealed various classes of variations and most of them are deletions ranging from 1 to 7 bp. Bioinformatic pipelines seem to be useful approaches to help screening for genetic variations with potential impact in gene expression. Further analysis will foster this aim to provide celerity at the massive analysis of data currently generated in large scale high throughput experiments.

Impact of the 237th residue on the folding of human carbonic anhydrase II

The deficiency of human carbonic anhydrase II (HCAII) has been recognized to be associated with a disease called CAII deficiency syndrome (CADS). Among the many mutations, the P237H mutation has been characterized to lead to a significant decrease in the activity of the enzyme and in the Gibbs free energy of folding. However, sequence alignment indicated that the 237th residue of CAII is not fully conserved across all species. The FoldX theoretical calculations suggested that this residue did not significantly contribute to the overall folding of HCAII, since all mutants had small ΔΔG values (around 1 kcal/mol). The experimental determination indicated that at least three mutations affect HCAII folding significantly and the P237H mutation was the most deleterious one, suggesting that Pro237 was important to HCAII folding. The discrepancy between theoretical and experimental results suggested that caution should be taken when using the prediction methods to evaluate the details of disease-related mutations.

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

BACKGROUND

The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.

RESULTS

We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.

CONCLUSIONS

The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

Analysis of disease-linked rhodopsin mutations based on structure, function, and protein stability calculations

Retinitis pigmentosa (RP) refers to a heterogeneous group of inherited diseases that result in progressive retinal degeneration, characterized by visual field constriction and night blindness. A total of 103 mutations in rhodopsin are linked to RP to date, and the phenotypes range from severe to asymptomatic. To study the relation between phenotype and rhodopsin stability in disease mutants, we used a structure-based approach. For 12 of the mutants located at the protein-lipid interphase, we used the von Heijne water-membrane transfer scale, and we find that 9 of the mutations could affect membrane insertion. For 91 mutants, we used the protein design algorithm FoldX. The 3 asymptomatic mutations had no significant reduced stability, 2 were unsuitable for FoldX analysis since the structure was incorrect in this region, 63 mutations had a significant change in protein stability (>1.6 kcal/mol), and 23 mutations had energy change values under the prediction error threshold (<1.6 kcal/mol). Out of these 23, the disease-causing effect could be explained by the involvement in other functions (e.g., glycosylation motifs, the interface with arrestin and transducin, and the cilia-binding motif) for 19 mutants. The remaining 4 mutants were probably incorrectly associated with RP or have functionalities not discovered yet. For destabilizing mutations where clinical data were available, we found a highly significant correlation between FoldX energy changes and the average age of night blindness and between FoldX energy changes and daytime vision loss onset. Our detailed structural, functional, and energetic analysis provides a complete picture of the rhodopsin mutations and can guide mutation-specific therapies.

Cytotoxic aggregation and amyloid formation by the myostatin precursor protein

Myostatin, a negative regulator of muscle growth, has been implicated in sporadic inclusion body myositis (sIBM). sIBM is the most common age-related muscle-wastage disease with a pathogenesis similar to that of amyloid disorders such as Alzheimer's and Parkinson's diseases. Myostatin precursor protein (MstnPP) has been shown to associate with large molecular weight filamentous inclusions containing the Alzheimer's amyloid beta peptide in sIBM tissue, and MstnPP is upregulated following ER stress. The mechanism for how MstnPP contributes to disease pathogenesis is unknown. Here, we show for the first time that MstnPP is capable of forming amyloid fibrils in vitro. When MstnPP-containing Escherichia coli inclusion bodies are refolded and purified, a proportion of MstnPP spontaneously misfolds into amyloid-like aggregates as characterised by electron microscopy and binding of the amyloid-specific dye thioflavin T. When subjected to a slightly acidic pH and elevated temperature, the aggregates form straight and unbranched amyloid fibrils 15 nm in diameter and also exhibit higher order amyloid structures. Circular dichroism spectroscopy reveals that the amyloid fibrils are dominated by beta-sheet and that their formation occurs via a conformational change that occurs at a physiologically relevant temperature. Importantly, MstnPP aggregates and protofibrils have a negative effect on the viability of myoblasts. These novel results show that the myostatin precursor protein is capable of forming amyloid structures in vitro with implications for a role in sIBM pathogenesis.