Structure SNP (StSNP): a web server for mapping and modeling nsSNPs on protein structures with linkage to metabolic pathways

In-silico screening of missense nsSNPs in Delta-opioid receptor protein and their restoring tendency on MCRT interaction; focusing on dynamic nature

Delta-opioid receptor protein (OPRD1) is one of the potential targets for treating pain. The presently available opioid agonists are known to cause unnecessary side effects. To discover a novel opioid agonist, our research group has synthesized a chimeric peptide MCRT and proved its potential activity through in vivo analysis. Non-synonymous SNPs (nsSNPs) missense mutations affect the functionality and stability of proteins leading to diseases. The current research was focused on understanding the role of MCRT in restoring the binding tendency of OPRD1 nsSNPs missense mutations on dynamic nature in comparison with Deltorphin-II and morphiceptin. The deleterious effects of nsSNPs were analyzed using various bioinformatics tools for predicting structural, functional, and oncogenic influence. The shortlisted nine nsSNPs were predicted for allergic reactions, domain changes, post-translation modification, multiple sequence alignment, secondary structure, molecular dynamic simulation (MDS), and peptide docking influence. Further, the docked complex of three shortlisted deleterious nsSNPs was analyzed using an MDS study, and the highly deleterious shortlisted nsSNP A149T was further analyzed for higher trajectory analysis. MCRT restored the binding tendency influence caused by nsSNPs on the dynamics of stability, functionality, binding affinity, secondary structure, residues connection, motion, and folding of OPRD1 protein.

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein-ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient's genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

N-terminal diversity of Litopenaeus vannamei hemocyanin and immunity

Hemocyanin is primarily a respiratory copper-containing glycoprotein present in the hemolymph of mollusks and arthropods. Recently, hemocyanin has attracted huge research interest due to its multifunctionality and polymorphism. Most previous immune-related studies on shrimp hemocyanin have focused on the C-terminal. Moreover, we previously reported that the C-terminal domain of Litopenaeus vannamei hemocyanin possesses single nucleotide polymorphisms (SNPs), but little is known about the molecular diversity of the N-terminal domain. In the current study, diversity within the N-terminal domain of L. vannamei hemocyanin (LvHMC-N) was explored using bioinformatics and molecular biology techniques as well as immune challenge. Twenty-five LvHMC-N variants were identified using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and DNA sequencing, with multiple sequence alignment showing that the 25 variants shared 87%-99 % sequence homology with LvHMC (AJ250830.1). In different shrimp individuals and different shrimp tissues (i.e., hemocytes, stomach, muscle and hepatopancreas), the LvHMC-N variants were expressed differently. Pathogen challenge could modulate the molecular diversity of LvHMC-N, as three LvHMC-Nr variants (LvHMC-Nr1, LvHMC-Nr2 and LvHMC-Nr3) were identified by sequencing following Vibrio parahaemolyticus challenge. Most importantly, recombinant proteins of these three variants (rLvHMC-Nr1, rLvHMC-Nr2 and rLvHMC- Nr3) had relatively high in vitro agglutinative activities against V. parahaemolyticus, Vibrio alginolyticus and Streptoccocus iniae. Our present data indicates that the N-terminus of L. vannamei hemocyanin also possess molecular diversity, which seems to be associated with immune resistance to pathogenic infections.

Identification and immune-related analysis of SNPs in Litopenaeus vannamei Toll3 receptor

Tolls and Toll-like receptors (TLRs), as innate immune-recognition receptors that recognize molecular patterns associated with microbial pathogens, play a critical role in antimicrobial immune responses. Here, we report on single nucleotide polymorphisms (SNPs) of Litopenaeus vannamei Toll3 (LvToll3). Multiple sequence alignment of the L. vannamei Toll3 Leucine rich repeat C-terminal domain (LvToll3-LRR-CT) with other L. vannamei Tolls LRR-CT domains showed 39.23% - 43.96% homology at the nucleic acid level and 20.31% - 30.00% identity at the amino acid level. Analysis of different shrimp tissues by polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) revealed that LvToll3-LRR-CT had genetic polymorphisms at both the genomic deoxyribonucleic acid (gDNA) and complementary deoxyribonucleic acid (cDNA) levels. Further, high-throughput sequencing analysis confirmed the presence of 8 non-synonymous SNP (nsSNP) and 1 nsSNPs with frequency greater than 1% at the gDNA level, while 13 nsSNPs and 2 nsSNPs with frequency greater than 1% at the cDNA level. In silico analysis revealed that the α-helix secondary structure and tertiary structure of LvToll3 changed when 3 SNPs (C2039T, T2041C, T2228C) were mutated. Interestingly, 2 novel bands on PCR-DGGE, which were identified as 2 nsSNPs (C2140A, T2186A) were observed following challenge with Streptococcus iniae but not with Vibrio parahaemolyticus or White spot syndrome virus (WSSV). Moreover, the secondary and tertiary structures of LvToll3 changed when the nsSNP T2186A was mutated. The present findings therefore provide novel insight into the molecular basis of shrimp innate immune response to pathogens through the generation of specific SNPs.

Computational analysis for the determination of deleterious nsSNPs in human MTHFR gene

Methylenetetrahydrofolate reductase (MTHFR) is a key enzyme involved in folate metabolism and plays a central role in DNA methylation and biosynthesis. MTHFR mutations may alter the cellular folate supply which in turn affects nucleic acid synthesis, DNA methylation and chromosomal damage. The identification of number of SNPs in the human genome growing nowadays and hence, the evaluation of functional & structural consequences of these SNPs is very laborious by means of experimental analysis. Therefore, in the present study, recently developed various computational algorithms have been used which can predict the functional and structural consequences of the SNPs. Various computational tools like SIFT, PolyPhen2, PROVEAN, SNAP2, nsSNPAnalyzer, SNPs&GO, PhD-SNP, PMut, I-Mutant, iPTREE-STAB and MUpro were used to predict most deleterious SNPs. Additionally, ConSurf was used to find amino acids conservation and NCBI conserved domain search tool to find conserved domains in MTHFR. Post translational modification sites were predicted using ModPred. SPARKS-X was used to generate 3D structure of the native and mutant MTHFR protein, ModRefiner for further refinement, Varify3D and RAMPAGE to validate structure. Ligand binding sites were predicted using FTsite, RaptorX binding and COACH. Three SNPs i.e. R157Q, L323P and W500C predicted the most deleterious in all the tools used for functional and stability analysis. Moreover, both residues R157, L323 and W500 were predicted highly conserved, buried and structural residues by ConSurf. Post translational modification sites were also predicted at R157 and W500. The ligand binding sites were predicted at R157, L323 and W500.

Vitamin D receptor (VDR) non-synonymous single nucleotide polymorphisms (nsSNPs) affect the calcitriol drug response - A theoretical insight

Pharmacogenetics and pharmacogenomics have become presumptive with advancements in next-generation sequencing technology. In complex diseases, distinguishing the feasibility of pathogenic and neutral disease-causing variants is a time consuming and expensive process. Recent drug research and development processes mainly rely on the relationship between the genotype and phenotype through Single nucleotide polymorphisms (SNPs). The SNPs play an indispensable role in elucidating the individual's vulnerability to disease and drug response. The understanding of the interplay between these leads to the establishment of personalized medicine. In order to address this issue, we developed a computational pipeline of vitamin D receptor (VDR) for SNP centered study by application of elegant molecular docking and molecular dynamics simulation approaches. In a few SNPs the volume of the binding cavities has increased in mutant structures when compared to the wild type, indicating a weakening in interaction (699.1 ų in wild type Vs. 738.8 in Leu230Val, 820.7 ų in Arg247Leu). This also differently reflected in the H-bond interactions and binding free energies -169.93 kcal/mol (wild type) Vs -156.43 kcal/mol (R154W), -105.49 kcal/mol (R274L) in Leu230Val and Arg247Leu respectively. Although we could not find noteworthy changes in the binding free energies and binding pocket in the remaining mutations, the H-bond interactions made these SNPs deleterious. Thus, we further analyzed the H-bond interactions and distances using molecular dynamics (MD) simulation studies.

Resources for Interpreting Variants in Precision Genomic Oncology Applications

Precision genomic oncology-applying high throughput sequencing (HTS) at the point-of-care to inform clinical decisions-is a developing precision medicine paradigm that is seeing increasing adoption. Simultaneously, new developments in targeted agents and immunotherapy, when informed by rich genomic characterization, offer potential benefit to a growing subset of patients. Multiple previous studies have commented on methods for identifying both germline and somatic variants. However, interpreting individual variants remains a significant challenge, relying in large part on the integration of observed variants with biological knowledge. A number of data and software resources have been developed to assist in interpreting observed variants, determining their potential clinical actionability, and augmenting them with ancillary information that can inform clinical decisions and even generate new hypotheses for exploration in the laboratory. Here, we review available variant catalogs, variant and functional annotation software and tools, and databases of clinically actionable variants that can be used in an ad hoc approach with research samples or incorporated into a data platform for interpreting and formally reporting clinical results.

The structural effects of mutations can aid in differential phenotype prediction of beta-myosin heavy chain (Myosin-7) missense variants

MOTIVATION

High-throughput sequencing platforms are increasingly used to screen patients with genetic disease for pathogenic mutations, but prediction of the effects of mutations remains challenging. Previously we developed SAAPdap (Single Amino Acid Polymorphism Data Analysis Pipeline) and SAAPpred (Single Amino Acid Polymorphism Predictor) that use a combination of rule-based structural measures to predict whether a missense genetic variant is pathogenic. Here we investigate whether the same methodology can be used to develop a differential phenotype predictor, which, once a mutation has been predicted as pathogenic, is able to distinguish between phenotypes-in this case the two major clinical phenotypes (hypertrophic cardiomyopathy, HCM and dilated cardiomyopathy, DCM) associated with mutations in the beta-myosin heavy chain (MYH7) gene product (Myosin-7).

RESULTS

A random forest predictor trained on rule-based structural analyses together with structural clustering data gave a Matthews' correlation coefficient (MCC) of 0.53 (accuracy, 75%). A post hoc removal of machine learning models that performed particularly badly, increased the performance (MCC = 0.61, Acc = 79%). This proof of concept suggests that methods used for pathogenicity prediction can be extended for use in differential phenotype prediction.

AVAILABILITY AND IMPLEMENTATION

Analyses were implemented in Perl and C and used the Java-based Weka machine learning environment. Please contact the authors for availability.

CONTACTS

andrew@bioinf.org.uk or andrew.martin@ucl.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

StructMAn: annotation of single-nucleotide polymorphisms in the structural context

The next generation sequencing technologies produce unprecedented amounts of data on the genetic sequence of individual organisms. These sequences carry a substantial amount of variation that may or may be not related to a phenotype. Phenotypically important part of this variation often comes in form of protein-sequence altering (non-synonymous) single nucleotide variants (nsSNVs). Here we present StructMAn, a Web-based tool for annotation of human and non-human nsSNVs in the structural context. StructMAn analyzes the spatial location of the amino acid residue corresponding to nsSNVs in the three-dimensional (3D) protein structure relative to other proteins, nucleic acids and low molecular-weight ligands. We make use of all experimentally available 3D structures of query proteins, and also, unlike other tools in the field, of structures of proteins with detectable sequence identity to them. This allows us to provide a structural context for around 20% of all nsSNVs in a typical human sequencing sample, for up to 60% of nsSNVs in genes related to human diseases and for around 35% of nsSNVs in a typical bacterial sample. Each nsSNV can be visualized and inspected by the user in the corresponding 3D structure of a protein or protein complex. The StructMAn server is available at http://structman.mpi-inf.mpg.de.

Effect of Structural Changes in Proteins Derived from GATA4 Nonsynonymous Single Nucleotide Polymorphisms in Congenital Heart Disease

Congenital heart disease is the most common type of birth defect. The single nucleotide polymorphism in GATA4 is associated with various congenital heart disease phenotypes. In the present study, we analysed the nonsynonymous single nucleotide polymorphism of GATA4, which are involved in congenital heart disease by predicting the changes in protein structures. Total of 49 nonsynonymous single nucleotide polymorphisms of GATA4 was screened from congenital heart disease patients of Mysore and also globally reported nonsynonymous single nucleotide polymorphisms. To understand the role of nonsynonymous single nucleotide polymorphisms, we mutated the sequence and translated into amino acids. Further the mutated protein secondary structure is predicted and tertiary structure is predicted using homology modeling. The quantitative evaluation of protein structure quality was verified with Volume Area Dihedral Angle Reporter server. Results revealed the secondary, tertiary structural changes along with changes in free energy of folding, volume and accessible surface area. Thus, the structural changes in the mutated proteins impaired the normal function of GATA4.

Comparative Analysis of Muscle Transcriptome between Pig Genotypes Identifies Genes and Regulatory Mechanisms Associated to Growth, Fatness and Metabolism

Iberian ham production includes both purebred (IB) and Duroc-crossbred (IBxDU) Iberian pigs, which show important differences in meat quality and production traits, such as muscle growth and fatness. This experiment was conducted to investigate gene expression differences, transcriptional regulation and genetic polymorphisms that could be associated with the observed phenotypic differences between IB and IBxDU pigs. Nine IB and 10 IBxDU pigs were slaughtered at birth. Morphometric measures and blood samples were obtained and samples from Biceps femoris muscle were employed for compositional and transcriptome analysis by RNA-Seq technology. Phenotypic differences were evident at this early age, including greater body size and weight in IBxDU and greater Biceps femoris intramuscular fat and plasma cholesterol content in IB newborns. We detected 149 differentially expressed genes between IB and IBxDU neonates (p < 0.01 and Fold-Change > 1. 5). Several were related to adipose and muscle tissues development (DLK1, FGF21 or UBC). The functional interpretation of the transcriptomic differences revealed enrichment of functions and pathways related to lipid metabolism in IB and to cellular and muscle growth in IBxDU pigs. Protein catabolism, cholesterol biosynthesis and immune system were functions enriched in both genotypes. We identified transcription factors potentially affecting the observed gene expression differences. Some of them have known functions on adipogenesis (CEBPA, EGRs), lipid metabolism (PPARGC1B) and myogenesis (FOXOs, MEF2D, MYOD1), which suggest a key role in the meat quality differences existing between IB and IBxDU hams. We also identified several polymorphisms showing differential segregation between IB and IBxDU pigs. Among them, non-synonymous variants were detected in several transcription factors as PPARGC1B and TRIM63 genes, which could be associated to altered gene function. Taken together, these results provide information about candidate genes, metabolic pathways and genetic polymorphisms potentially involved in phenotypic differences between IB and IBxDU pigs associated to meat quality and production traits.

Compensated pathogenic deviations

Deleterious or 'disease-associated' mutations are mutations that lead to disease with high phenotype penetrance: they are inherited in a simple Mendelian manner, or, in the case of cancer, accumulate in somatic cells leading directly to disease. However, in some cases, the amino acid that is substituted resulting in disease is the wild-type native residue in the functionally equivalent protein in another species. Such examples are known as 'compensated pathogenic deviations' (CPDs) because, somewhere in the second species, there must be compensatory mutations that allow the protein to function normally despite having a residue which would cause disease in the first species. Depending on the nature of the mutations, compensation can occur in the same protein, or in a different protein with which it interacts. In principle, compensation can be achieved by a single mutation (most probably structurally close to the CPD), or by the cumulative effect of several mutations. Although it is clear that these effects occur in proteins, compensatory mutations are also important in RNA potentially having an impact on disease. As a much simpler molecule, RNA provides an interesting model for understanding mechanisms of compensatory effects, both by looking at naturally occurring RNA molecules and as a means of computational simulation. This review surveys the rather limited literature that has explored these effects. Understanding the nature of CPDs is important in understanding traversal along fitness landscape valleys in evolution. It could also have applications in treating diseases that result from such mutations.

Structural modeling and in silico analysis of non-synonymous single nucleotide polymorphisms of human 3β-hydroxysteroid dehydrogenase type 2

Single-nucleotide polymorphisms (SNPs), a most common type of genetic mutations, result from single base pair alterations. Non-synonymous SNPs (nsSNP) occur in the coding regions of a gene and result in single amino acid substitution which might have the potential to affect the function as well as structure of the corresponding protein. In human the 3β-hydroxysteroid dehydrogenases/Δ^4,5-isomerase type 2 (HSD3B2) is an important membrane-bound enzyme involved in the dehydrogenation and Δ^4,5-isomerization of the Δ⁵-steroid precursors into their respective Δ⁴-ketosteroids in the biosynthesis of steroid hormones such as glucocorticoids, mineralocorticoids, progesterone, androgens, and estrogens in tissues such as adrenal gland, ovary, and testis. Most of the nsSNPs of HSD3B2 are still uncharacterized in terms of their disease causing potential. So, this study has been undertaken to explore and extend the knowledge related to the effect of nsSNPs on the stability and function of the HSD3B2. In this study sixteen nsSNP of HSD3B2 were subjected to in silico analysis using nine different algorithms: SIFT, PROVEAN, PolyPhen, MutPred, SNPeffect, nsSNP Analyzer, PhD SNP, stSNP, and I Mutant 2.0. The results obtained from the analysis revealed that the prioritization of diseases associated amino acid substitution as evident from possible alteration in structure–function relationship. Structural phylogenetic analysis using ConSurf revealed that the functional residues are highly conserved in human HSD3B2; and most of the disease associated nsSNPs are within these conserved residues. Structural theoritical models of HSD3B2 were created using HHPred, Phyre2 and RaptorX server. The predicted models were evaluated to get the best one for structural understanding of amino acid substitutions in three dimensional spaces.

Analysing the Effect of Mutation on Protein Function and Discovering Potential Inhibitors of CDK4: Molecular Modelling and Dynamics Studies

The cyclin-dependent kinase 4 (CDK4)-cyclin D1 complex plays a crucial role in the transition from the G1 phase to S phase of the cell cycle. Among the CDKs, CDK4 is one of the genes most frequently affected by somatic genetic variations that are associated with various forms of cancer. Thus, because the abnormal function of the CDK4-cyclin D1 protein complex might play a vital role in causing cancer, CDK4 can be considered a genetically validated therapeutic target. In this study, we used a systematic, integrated computational approach to identify deleterious nsSNPs and predict their effects on protein-protein (CDK4-cyclin D1) and protein-ligand (CDK4-flavopiridol) interactions. This analysis resulted in the identification of possible inhibitors of mutant CDK4 proteins that bind the conformations induced by deleterious nsSNPs. Using computational prediction methods, we identified five nsSNPs as highly deleterious: R24C, Y180H, A205T, R210P, and R246C. From molecular docking and molecular dynamic studies, we observed that these deleterious nsSNPs affected CDK4-cyclin D1 and CDK4-flavopiridol interactions. Furthermore, in a virtual screening approach, the drug 5_7_DIHYDROXY_ 2_ (3_4_5_TRI HYDROXYPHENYL) _4H_CHROMEN_ 4_ONE displayed good binding affinity for proteins with the mutations R24C or R246C, the drug diosmin displayed good binding affinity for the protein with the mutation Y180H, and the drug rutin displayed good binding affinity for proteins with the mutations A205T and R210P. Overall, this computational investigation of the CDK4 gene highlights the link between genetic variation and biological phenomena in human cancer and aids in the discovery of molecularly targeted therapies for personalized treatment.

Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Diseases

Protein function has been observed to rely on select essential sites instead of requiring all sites to be indispensable. Small ubiquitin-related modifier (SUMO) conjugation or sumoylation, which is a highly dynamic reversible process and its outcomes are extremely diverse, ranging from changes in localization to altered activity and, in some cases, stability of the modified, has shown to be especially valuable in cellular biology. Motivated by the significance of SUMO conjugation in biological processes, we report here on the first exploratory assessment whether sumoylation related genetic variability impacts protein functions as well as the occurrence of diseases related to SUMO. Here, we defined the SUMOAMVR as sumoylation related amino acid variations that affect sumoylation sites or enzymes involved in the process of connectivity, and categorized four types of potential SUMOAMVRs. We detected that 17.13% of amino acid variations are potential SUMOAMVRs and 4.83% of disease mutations could lead to SUMOAMVR with our system. More interestingly, the statistical analysis demonstrates that the amino acid variations that directly create new potential lysine sumoylation sites are more likely to cause diseases. It can be anticipated that our method can provide more instructive guidance to identify the mechanisms of genetic diseases.

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.

The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations

Background

Understanding and predicting the effects of mutations on protein structure and phenotype is an increasingly important area. Genes for many genetically linked diseases are now routinely sequenced in the clinic. Previously we focused on understanding the structural effects of mutations, creating the SAAPdb resource.

Results

We have updated SAAPdb to include 41% more SNPs and 36% more PDs.

Introducing a hydrophobic residue on the surface, or a hydrophilic residue in the core, no longer shows significant differences between SNPs and PDs. We have improved some of the analyses significantly enhancing the analysis of clashes and of mutations to-proline and from-glycine. A new web interface has been developed allowing users to analyze their own mutations. Finally we have developed a machine learning method which gives a cross-validated accuracy of 0.846, considerably out-performing well known methods including SIFT and PolyPhen2 which give accuracies between 0.690 and 0.785.

Conclusions

We have updated SAAPdb and improved its analyses, but with the increasing rate with which mutation data are generated, we have created a new analysis pipeline and web interface. Results of machine learning using the structural analysis results to predict pathogenicity considerably outperform other methods.

Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a molecular dynamics approach

Background

Recent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7 (CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs.

Methods

Three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations.

Results

By comparing the results of all seven prediction tools, three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins.

Conclusion

This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.

Lay abstract

Cell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases.

Contribution of IKBKE and IFIH1 gene variants to SLE susceptibility

The type I interferon system genes IKBKE and IFIH1 are associated with the risk of systemic lupus erythematosus (SLE). To identify the sequence variants that are able to account for the disease association, we resequenced the genes IKBKE and IFIH1. Eighty-six single-nucleotide variants (SNVs) with potentially functional effect or differences in allele frequencies between patients and controls determined by sequencing were further genotyped in 1140 SLE patients and 2060 controls. In addition, 108 imputed sequence variants in IKBKE and IFIH1 were included in the association analysis. Ten IKBKE SNVs and three IFIH1 SNVs were associated with SLE. The SNVs rs1539241 and rs12142086 tagged two independent association signals in IKBKE, and the haplotype carrying their risk alleles showed an odds ratio of 1.68 (P-value=1.0 × 10(-5)). The risk allele of rs12142086 affects the binding of splicing factor 1 in vitro and could thus influence its transcriptional regulatory function. Two independent association signals were also detected in IFIH1, which were tagged by a low-frequency SNV rs78456138 and a missense SNV rs3747517. Their joint effect is protective against SLE (odds ratio=0.56; P-value=6.6 × 10(-3)). In conclusion, we have identified new SLE-associated sequence variants in IKBKE and IFIH1, and proposed functional hypotheses for the association signals.

Proteome-wide analysis of amino acid variations that influence protein lysine acetylation

Next-generation sequencing (NGS) technologies are yielding ever higher volumes of genetic variation data. Given this large amount of data, it has become both a possibility and a priority to determine what the functional implication of genetic variations is. Considering the essential roles of acetylation in protein functions, it is highly likely that acetylation related genetic variations change protein functions. In this work, we performed a proteome-wide analysis of amino acid variations that could potentially influence protein lysine acetylation characteristics in human variant proteins. Here, we defined the AcetylAAVs as acetylation related amino acid variations that affect acetylation sites or their interacting acetyltransferases, and categorized three types of AcetylAAVs. Using the developed prediction system, named KAcePred, we detected that 50.87% of amino acid variations are potential AcetylAAVs and 12.32% of disease mutations could result in AcetylAAVs. More interestingly, from the statistical analysis, we found that the amino acid variations that directly create new potential lysine acetylation sites have more chance to cause diseases. It can be anticipated that the analysis of AcetylAAVs might be useful to screen important polymorphisms and help to identify the mechanism of genetic diseases. A user-friendly web interface for analysis of AcetylAAVs is now freely available at http://bioinfo.ncu.edu.cn/AcetylAAVs_Home.aspx .

RNA-Seq approach for genetic improvement of meat quality in pig and evolutionary insight into the substrate specificity of animal carbonyl reductases

Changes in meat quality traits are strongly associated with alterations in postmortem metabolism which depend on genetic variations, especially nonsynonymous single nucleotide variations (nsSNVs) having critical effects on protein structure and function. To selectively identify metabolism-related nsSNVs, next-generation transcriptome sequencing (RNA-Seq) was carried out using RNAs from porcine liver, which contains a diverse range of metabolic enzymes. The multiplex SNV genotyping analysis showed that various metabolism-related genes had different nsSNV alleles. Moreover, many nsSNVs were significantly associated with multiple meat quality traits. Particularly, ch7:g.22112616A>G SNV was identified to create a single amino acid change (Thr/Ala) at the 145th residue of H1.3-like protein, very close to the putative 147th threonine phosphorylation site, suggesting that the nsSNV may affect multiple meat quality traits by affecting the epigenetic regulation of postmortem metabolism-related gene expression. Besides, one nonsynonymous variation, probably generated by gene duplication, led to a stop signal in porcine testicular carbonyl reductase (PTCR), resulting in a C-terminal (E281-A288) deletion. Molecular docking and energy minimization calculations indicated that the binding affinity of wild-type PTCR to 5α-DHT, a C₂₁-steroid, was superior to that of C-terminal-deleted PTCR or human carbonyl reductase, which was very consistent with experimental data, reported previously. Furthermore, P284 was identified as an important residue mediating the specific interaction between PTCR and 5α-DHT, and phylogenetic analysis showed that P284 is an evolutionarily conserved residue among animal carbonyl reductases, which suggests that the C-terminal tails of these reductases may have evolved under evolutionary pressure to increase the substrate specificity for C₂₁-steroids and facilitate metabolic adaptation. Altogether, our RNA-Seq revealed that selective nsSNVs were associated with meat quality traits that could be useful for successful marker-assisted selection in pigs and also represents a useful resource to enhance understanding of protein folding, substrate specificity, and the evolution of enzymes such as carbonyl reductase.

Interpretation of the consequences of mutations in protein kinases: combined use of bioinformatics and text mining

Protein kinases play a crucial role in a plethora of significant physiological functions and a number of mutations in this superfamily have been reported in the literature to disrupt protein structure and/or function. Computational and experimental research aims to discover the mechanistic connection between mutations in protein kinases and disease with the final aim of predicting the consequences of mutations on protein function and the subsequent phenotypic alterations. In this article, we will review the possibilities and limitations of current computational methods for the prediction of the pathogenicity of mutations in the protein kinase superfamily. In particular we will focus on the problem of benchmarking the predictions with independent gold standard datasets. We will propose a pipeline for the curation of mutations automatically extracted from the literature. Since many of these mutations are not included in the databases that are commonly used to train the computational methods to predict the pathogenicity of protein kinase mutations we propose them to build a valuable gold standard dataset in the benchmarking of a number of these predictors. Finally, we will discuss how text mining approaches constitute a powerful tool for the interpretation of the consequences of mutations in the context of disease genome analysis with particular focus on cancer.

Analyzing effects of naturally occurring missense mutations

Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well.

MSV3d: database of human MisSense Variants mapped to 3D protein structure

The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats.

Database URL:http://decrypthon.igbmc.fr/msv3d

Investigating the structural impacts of I64T and P311S mutations in APE1-DNA complex: a molecular dynamics approach

Background

Elucidating the molecular dynamic behavior of Protein-DNA complex upon mutation is crucial in current genomics. Molecular dynamics approach reveals the changes on incorporation of variants that dictate the structure and function of Protein-DNA complexes. Deleterious mutations in APE1 protein modify the physicochemical property of amino acids that affect the protein stability and dynamic behavior. Further, these mutations disrupt the binding sites and prohibit the protein to form complexes with its interacting DNA.

Principal Findings

In this study, we developed a rapid and cost-effective method to analyze variants in APE1 gene that are associated with disease susceptibility and evaluated their impacts on APE1-DNA complex dynamic behavior. Initially, two different in silico approaches were used to identify deleterious variants in APE1 gene. Deleterious scores that overlap in these approaches were taken in concern and based on it, two nsSNPs with IDs rs61730854 (I64T) and rs1803120 (P311S) were taken further for structural analysis.

Significance

Different parameters such as RMSD, RMSF, salt bridge, H-bonds and SASA applied in Molecular dynamic study reveals that predicted deleterious variants I64T and P311S alters the structure as well as affect the stability of APE1-DNA interacting functions. This study addresses such new methods for validating functional polymorphisms of human APE1 which is critically involved in causing deficit in repair capacity, which in turn leads to genetic instability and carcinogenesis.

Bioinformatics for personal genome interpretation

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.

CanProVar: a human cancer proteome variation database

Identification and annotation of mutated genes or proteins involved in oncogenesis and tumor progression are crucial for both cancer biology and clinical applications. We have developed a human Cancer Proteome Variation Database (CanProVar) by integrating information on protein sequence variations from various public resources, with a focus on cancer-related variations (crVAR). We have also built a user-friendly interface for querying the database. The current version of CanProVar comprises 8,570 crVARs in 2,921 proteins derived from existing genome variation databases and recently published large-scale cancer genome resequencing studies. It also includes 41,541 non-cancer specific variations (ncsVARs) in 30,322 proteins derived from the dbSNP database. CanProVar provides quick access to known crVARs in protein sequences along with related cancer samples, relevant publications, data sources, and functional information such as Gene Ontology (GO) annotations for the proteins, protein domains in which the variation occurs, and protein interaction partners with crVARs. CanProVar also helps reveal functional characteristics of crVARs and proteins bearing these variations. Our analysis showed that crVARs were enriched in certain protein domains. We also showed that proteins bearing crVARs were more likely to interact with each other in the protein interaction network. CanProVar can be accessed from http://bioinfo.vanderbilt.edu/canprovar.

PhosSNP for systematic analysis of genetic polymorphisms that influence protein phosphorylation

We are entering the era of personalized genomics as breakthroughs in sequencing technology have made it possible to sequence or genotype an individual person in an efficient and accurate manner. Preliminary results from HapMap and other similar projects have revealed the existence of tremendous genetic variations among world populations and among individuals. It is important to delineate the functional implication of such variations, i.e. whether they affect the stability and biochemical properties of proteins. It is also generally believed that the genetic variation is the main cause for different susceptibility to certain diseases or different response to therapeutic treatments. Understanding genetic variation in the context of human diseases thus holds the promise for "personalized medicine." In this work, we carried out a genome-wide analysis of single nucleotide polymorphisms (SNPs) that could potentially influence protein phosphorylation characteristics in human. Here, we defined a phosphorylation-related SNP (phosSNP) as a non-synonymous SNP (nsSNP) that affects the protein phosphorylation status. Using an in-house developed kinase-specific phosphorylation site predictor (GPS 2.0), we computationally detected that approximately 70% of the reported nsSNPs are potential phosSNPs. More interestingly, approximately 74.6% of these potential phosSNPs might also induce changes in protein kinase types in adjacent phosphorylation sites rather than creating or removing phosphorylation sites directly. Taken together, we proposed that a large proportion of the nsSNPs might affect protein phosphorylation characteristics and play important roles in rewiring biological pathways. Finally, all phosSNPs were integrated into the PhosSNP 1.0 database, which was implemented in JAVA 1.5 (J2SE 5.0). The PhosSNP 1.0 database is freely available for academic researchers.

A survey of proteins encoded by non-synonymous single nucleotide polymorphisms reveals a significant fraction with altered stability and activity

On average, each human gene has approximately four SNPs (single nucleotide polymorphisms) in the coding region, half of which are nsSNPs (non-synonymous SNPs) or missense SNPs. Current attention is focused on those that are known to perturb function and are strongly linked to disease. However, the vast majority of SNPs have not been investigated for the possibility of causing disease. We set out to assess the fraction of nsSNPs that encode proteins that have altered stability and activity, for this class of variants would be candidates to perturb cellular function. We tested the thermostability and, where possible, the catalytic activity for the most common variant (wild-type) and minor variants (total of 46 SNPs) for 16 human enzymes for which the three-dimensional structures were known. There were significant differences in the stability of almost half of the variants (48%) compared with their wild-type counterparts. The catalytic efficiency of approx. 14 variants was significantly altered, including several variants of human PKM2 (pyruvate kinase muscle 2). Two PKM2 variants, S437Y and E28K, also exhibited changes in their allosteric regulation compared with the wild-type enzyme. The high proportion of nsSNPs that affect protein stability and function, albeit subtly, underscores the need for experimental analysis of the diverse human proteome.

An integrated approach to the interpretation of single amino acid polymorphisms within the framework of CATH and Gene3D

BACKGROUND

The phenotypic effects of sequence variations in protein-coding regions come about primarily via their effects on the resulting structures, for example by disrupting active sites or affecting structural stability. In order better to understand the mechanisms behind known mutant phenotypes, and predict the effects of novel variations, biologists need tools to gauge the impacts of DNA mutations in terms of their structural manifestation. Although many mutations occur within domains whose structure has been solved, many more occur within genes whose protein products have not been structurally characterized.

RESULTS

Here we present 3DSim (3D Structural Implication of Mutations), a database and web application facilitating the localization and visualization of single amino acid polymorphisms (SAAPs) mapped to protein structures even where the structure of the protein of interest is unknown. The server displays information on 6514 point mutations, 4865 of them known to be associated with disease. These polymorphisms are drawn from SAAPdb, which aggregates data from various sources including dbSNP and several pathogenic mutation databases. While the SAAPdb interface displays mutations on known structures, 3DSim projects mutations onto known sequence domains in Gene3D. This resource contains sequences annotated with domains predicted to belong to structural families in the CATH database. Mappings between domain sequences in Gene3D and known structures in CATH are obtained using a MUSCLE alignment. 1210 three-dimensional structures corresponding to CATH structural domains are currently included in 3DSim; these domains are distributed across 396 CATH superfamilies, and provide a comprehensive overview of the distribution of mutations in structural space.

CONCLUSION

The server is publicly available at http://3DSim.bioinfo.cnio.es/. In addition, the database containing the mapping between SAAPdb, Gene3D and CATH is available on request and most of the functionality is available through programmatic web service access.

From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways

BACKGROUND

Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks.

RESULTS

First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented.

CONCLUSION

In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.