Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics

Variation of the genes encoding antioxidant enzymes SOD2 (rs4880), GPX1 (rs1050450), and CAT (rs1001179) and susceptibility to male infertility: a genetic association study and in silico analysis

Enzymatic factors including superoxide dismutase (SOD), glutathione peroxidase (GPX), and catalase (CAT) are among the most important protective antioxidant systems in human semen. This study was conducted to investigate the association between the activities of the mentioned enzymes in semen and also the association between SOD2 rs4880, GPX1 rs1050450, and CAT rs1001179 polymorphisms with male infertility, which was followed by a bioinformatics approach. In a case-control study, 223 infertile men and 154 healthy fertile men were included in the study. After extracting genomic DNA from semen samples, the genotype of rs1001179, rs1050450, and rs4880 polymorphisms was determined using the PCR-RFLP. Next, the activities of SOD, CAT, and GPX enzymes were also measured in semen. Bioinformatics software was used to investigate the effect of polymorphisms on the function of genes. Data analysis indicated that rs1001179 polymorphisms were not associated with male infertility. But our data revealed that the rs1050450 polymorphism is associated with a reduced risk of male infertility as well as asthenozoospermia and teratozoospermia. In addition, rs4880 polymorphism was associated with an increased risk of male infertility as well as teratozoospermia. Further analysis showed that the activity of the CAT enzyme in the infertile group is significantly higher than in the fertile group, but the activity of GPX and SOD enzymes in the infertile group is significantly lower than in the fertile group. Bioinformatic analysis showed that rs1001179 polymorphism affects the transcription factors binding site upstream of the gene, while rs1050450 and rs4880 polymorphisms had an essential role in protein structure and function. On the other hand, rs1050450 (T allele) was exposed to a reduced risk of male infertility and may be a protective factor. And SOD2 rs4880 (C allele) is associated with an increased risk of male infertility, and it is considered a risk factor for male infertility. To reach accurate results, we recommend that the study of SOD2 rs4880 and GPX1 rs1050450 polymorphism effects in the different populations with a larger sample size and meta-analysis are needed.

Two CRISPR/Cas12a-based methods for fast and accurate detection of single-base mutations

Current single-base mutation detection approaches are time-consuming, labor-intensive, and costly. This highlights the critical need for speedy and accurate technology capable of detecting single-base alterations. Using clustered regularly interspaced short palindromic repeats/associated protein 12a (CRISPR/Cas12a), two fundamental approaches for getting 100% differentiation of single-base mutations have been established, by which fluorescence signals could be detected for variants but not for wild strains. The first method required both polymerase chain reaction (PCR) and CRISPR/Cas12a cleavage: By introducing a mismatched base at the 3' end of the primers and adjusting the PCR settings, the wild strain strand amplifications were completely blocked prior to CRISPR/Cas12a cleavage. The parameters for Method 1 (PCR + CRISPR/Cas12a) could be easily controlled and adjusted to attain a sensitivity of one copy (about 6 copies μL-1). The second method included isothermal recombinase polymerase amplification (RPA) and CRISPR/Cas12a cleavage: By introducing an extra mismatched base adjacent to the single-base mutant site by RPA (IMAS-RPA), the RPA products from the wild strains were rendered incapable of triggering the cleavage activity of CRISPR/Cas12a. Method 2 (IMAS-RPA) was rapid and easy to implement (can be finished within 1 h). Because each method has its own set of advantages, the laboratory environment-appropriate methods can be selected independently. Both approaches are expected to aid in clinical diagnosis to some extent in the near future.

Identification of Hotspot Residues in Binding of SARS-CoV-2 Spike and Human ACE2 Proteins

Coronaviruses are a large family of viruses that can cause respiratory infections with varying severity from common cold to severe diseases such as novel coronavirus disease (COVID-19). COVID-19 has been declared as a global pandemic by the World Health Organization on March 11, 2020 and with the development of vaccines it slowed down as of this date. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses its spike glycoprotein (Sgp) to bind human angiotensin-converting enzyme 2 (hACE2) receptor, and mediates membrane fusion and virus entry. The recognition of Sgp to human ACE2 and its high affinity for it has been of great importance since this provides the first step in viral entry to human cells. Therefore, it is crucial to identify key residues (hotspots) in this process. In this study, computational alanine scanning has been performed for Sgp and hACE2. The residues identified with significance in binding and other residues in close proximity were studied further through molecular mechanics-based protein binding free energy change prediction methods. Moreover, the interfacial residues in both proteins were investigated for their cooperative binding. Additionally, folding free energy changes upon mutation to Ala were calculated to assess their effect on stability of Sgp and hACE2. Our results taken together with findings from previous studies revealed the residues that are most significant and are relatively significant in binding of Sgp to human ACE2 protein.

Internal Standardization of the Interpretation and Reporting of Sequence Variants in Hematologic Neoplasms

INTRODUCTION

Accurate classification of somatic genetic alterations detected by next-generation sequencing (NGS) assays is of paramount importance to ensure the provision of high-quality clinical data. Clinical significance of variants can be assessed and tiered based on guidelines from the Association for Molecular Pathology (AMP), the American Society of Clinical Oncology, and the College of American Pathology for the interpretation of somatic sequence variants identified in cancer.

METHODS

We sought to develop a formal structured approach for the classification of somatic variants in hematologic neoplasms, to account for both a variant's clinical significance and its ability to drive tumorigenesis, by adapting elements from these existing guidelines. However, we additionally utilized key criteria from the American College of Medical Genetics/AMP standards for variant reporting to focus evaluation into specific categories of evidence and to gauge the effect of a given variant on tumorigenesis.

RESULTS

The combined approach was applied to the annotation of 87 variants identified by a targeted NGS panel for myeloid neoplasms. In the application of our variant evaluation, we classified 2/87 variants as benign, 6/87 as likely benign, 56/87 as variants of unknown significance (VUS), 13/87 variants as likely pathogenic, and 10/87 variants as pathogenic.

CONCLUSION

Well-established oncogenic alterations were accurately classified as pathogenic. Although there is no defined benchmark for the remaining variants, drawing from two existing guidelines enabled the creation of a modified curation process for variant interpretation that emphasizes systematic review of relevant evidence.

Independent and Interactive Effect of CYPs and GSTs Genetic Variants and Tobacco Smoking on the Risk of Non-Small Cell Lung Carcinoma

BACKGROUND

CYP and GST gene families detoxify tobacco carcinogens and have been linked to the risk of non-small cell lung carcinoma (NSCLC).

AIM

Independent and combined effects of CYP and GST genetic variations and smoking on the risk of non-small cell lung carcinoma (NSCLC) and its sub-histological types.

METHODS

We modelled an epistatic interaction via the effects of particular genotypes in two genes as OR (odds ratio), OR1, and OR2, a combination of both genotypes were characterized as OR_combine. In contrast, the two ORs' epistatic interaction for the individual genotypes has been represented as OR_interaction = OR_combine/(OR1 × OR2).

RESULTS

The variant genotypes of CYP2A6 (OR:4.2, p <0.001), GSTT1 (OR:3.9, p <0.001), and GSTM1 (OR: 4.5, p <0.001) were showed a significant risk with NSCLC. GSTM1 (del.)/CYP2A6 (variant) genotype was associated with a higher risk of NSCLC (OR:12.5, p <0.001). GSTM1 (del.)/CYP2A6 (Ser/Pro+Pro/Pro) and GSTM1 (del.)/CYP2A13 (CT+TT) interacted redundantly (OR_intraction = 0.66 and 0.64). A co-suppressive interaction was observed between GSTT1 (del.)/CYP2A6 (Ser/Pro+Pro/Pro) (OR_intraction = 0.41). Simultaneously, both GSTT1/GSTM1 del. genotype was associated with a significantly higher risk to NSCLC. In contrast, GSTT1 del./GSTM1 del. genotype interaction displayed a co-suppressive effect (OR_intraction = 0.15). CYP1A1(TC+CC)/CYP2A13(CT+TT)mutually interacted synergistically (OR_intraction = 1.27).CYP1A1 (TC+CC)/GSTP1 (Val/Val+Ile/Val) genotype demonstrated an additive (OR_intraction = 1) effect. GSTP1(Val/Val+Ile/Val) interacts with GSTT1 (del.) genotype exerted a suppressive effect (OR_intraction = 0.69). CYP2A6 in smokers increased risk by 4.2 (p = 0.001) to 5.6 fold (p <0.001), while GSTM1 and GSTT1 were independent of smoking.

CONCLUSION

Epistatic interactions revealed that CYPs/GSTs might follow a web of the interactions to modify the risk of NSCLC.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

Influence of LGALS3 and PNPLA3 genes in non-alcoholic steatohepatitis (NASH) in patients undergone bariatric surgery

AIM

This study evaluated the genesPNPLA3 and LGALS3 in patients who have undergone bariatric surgery.

METHODS

Individuals with NAFLD and NASH were evaluated, the DNA was extracted from total blood for genotyping of rs4644, rs4652 from LGALS3 and rs738409 from PNPLA3 genes, the total RNA was obtained from liver biopsy. For the detection of the molecular targets, real-time PCR through Taqman probes was used.

RESULTS

From a total of 46 collected patients, of those 21 (456%) were included as NASH and 25 (544%) as steatosis group. This groups showed significant difference to aspartate aminotransferase (AST), alanine aminotransferase (ALT) and Glutamyl transpeptidase (GGT) (p = 0.0108, p = 0.0090 and p = 0.0044). Regarding to gene expression in studied groups, hepatic steatosis vs NASH, we observed a higher expression of the LGALS3 gene in NASH (p = 0.0273). In addition, patients with C allele in homozygous for rs4644 and rs4652 of LGALS3 gene had higher expression, in NASH group (p = 0.0500 and p = 0.0242, respectively), furthermore for rs4644 both alleles in homozygous showed higher expression (AA/CC vs AC) (p = 0.0500), when analyzed PNPLA3 rs738409, NASH patients with G allele in homozygous had higher expression (p = 0.0494).

CONCLUSIONS

Therefore, an increased expression of the LGALS3 gene in patients with NASH may be important in the etiopathogenesis of the disease, as well as the presence of rs4652 and rs4644 SNPs in the regulation of transcriptional levels of the gene in patients with NAFLD and NASH.

In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia

Background:

Acute myeloid leukemia (AML) is an extremely heterogeneous malignant disorder; AML has been reported as one of the main causes of death in children. The objective of this work was to classify the most deleterious mutation in CCAAT/enhancer-binding protein-alpha (CEBPA) and to predict their influence on the functional, structural, and expression levels by various Bioinformatics analysis tools.

Methods:

The single nucleotide polymorphisms (SNPs) were claimed from the National Center for Biotechnology Information (NCBI) database and then submitted into various functional analysis tools, which were done to predict the influence of each SNP, followed by structural analysis of modeled protein followed by predicting the mutation effect on energy stability; the most damaging mutations were chosen for additional investigation by Mutation3D, Project hope, ConSurf, BioEdit, and UCSF Chimera tools.

Results:

A total of 5 mutations out of 248 were likely to be responsible for the structural and functional variations in CEBPA protein, whereas in the 3′-untranslated region (3′-UTR) the result showed that among 350 SNPs in the 3′-UTR of CEBPA gene, about 11 SNPs were predicted. Among these 11 SNPs, 65 alleles disrupted a conserved miRNA site and 22 derived alleles created a new site of miRNA.

Conclusions:

In this study, the impact of functional mutations in the CEBPA gene was investigated through different bioinformatics analysis techniques, which determined that R339W, R288P, N292S, N292T, and D63N are pathogenic mutations that have a possible functional and structural influence, therefore, could be used as genetic biomarkers and may assist in genetic studies with a special consideration of the large heterogeneity of AML.

Modeling pKas of unfolded proteins to probe structural models of unfolded state

Modeling unfolded states of proteins has implications for protein folding and stability. Since in unfolded state proteins adopt multiple conformations, any experimentally measured quantity is ensemble averaged, therefore the computed quantity should be ensemble averaged as well. Here, we investigate the possibility that one can model an unfolded state ensemble with the coil model approach, algorithm such as “flexible-meccano” [Ozenne V et al., Flexible-meccano: A tool for the generation of explicit ensemle descriptions of intrinsically disordered proteins and their associated experimental observables, Bioinformatics 28:1463–1470, 2012], developed to generate structures for intrinsically disordered proteins. We probe such a possibility by using generated structures to calculate pKas of titratable groups and compare with experimental data. It is demonstrated that even with a small number of representative structures of unfolded state, the average calculated pKas are in very good agreement with experimentally measured pKas. Also, predictions are made for titratable groups for which there is no experimental data available. This suggests that the coil model approach is suitable for generating 3D structures of unfolded state of proteins. To make the approach suitable for large-scale modeling, which requires limited number of structures, we ranked the structures according to their solvent accessible surface area (SASA). It is shown that in the majority of cases, the top structures with smallest SASA are enough to represent unfolded state.

Forces and Disease: Electrostatic force differences caused by mutations in kinesin motor domains can distinguish between disease-causing and non-disease-causing mutations

The ability to predict if a given mutation is disease-causing or not has enormous potential to impact human health. Typically, these predictions are made by assessing the effects of mutation on macromolecular stability and amino acid conservation. Here we report a novel feature: the electrostatic component of the force acting between a kinesin motor domain and tubulin. We demonstrate that changes in the electrostatic component of the binding force are able to discriminate between disease-causing and non-disease-causing mutations found in human kinesin motor domains using the receiver operating characteristic (ROC). Because diseases may originate from multiple effects not related to kinesin-microtubule binding, the prediction rate of 0.843 area under the ROC plot due to the change in magnitude of the electrostatic force alone is remarkable. These results reflect the dependence of kinesin’s function on motility along the microtubule, which suggests a precise balance of microtubule binding forces is required.

Variation in Mutational Robustness between Different Proteins and the Predictability of Fitness Effects

Random mutations in genes from disparate protein classes may have different distributions of fitness effects (DFEs) depending on different structural, functional, and evolutionary constraints. We measured the fitness effects of 156 single mutations in the genes encoding AraC (transcription factor), AraD (enzyme), and AraE (transporter) used for bacterial growth on l-arabinose. Despite their different molecular functions these genes all had bimodal DFEs with most mutations either being neutral or strongly deleterious, providing a general expectation for the DFE. This contrasts with the unimodal DFEs previously obtained for ribosomal protein genes where most mutations were slightly deleterious. Based on theoretical considerations, we suggest that the 33-fold higher average mutational robustness of ribosomal proteins is due to stronger selection for reduced costs of translational and transcriptional errors. Whereas the large majority of synonymous mutations were deleterious for ribosomal proteins genes, no fitness effects could be detected for the AraCDE genes. Four mutations in AraC and AraE increased fitness, suggesting that slightly advantageous mutations make up a significant fraction of the DFE, but that they often escape detection due to the limited sensitivity of commonly used fitness assays. We show that the fitness effects of amino acid substitutions can be predicted based on evolutionary conservation, but those weakly deleterious mutations are less reliably detected. This suggests that large-effect mutations and the fraction of highly deleterious mutations can be computationally predicted, but that experiments are required to characterize the DFE close to neutrality, where many mutations ultimately fixed in a population will occur.

Detecting the polymorphism of TERF1 gene by an improved PCR-RFLP method

BACKGROUND

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is a common and mature method of detecting the single nucleotide polymorphism (SNP). But, for the polymorphism site rs3863242 of telomeric repeat binding factor 1(TERF1) gene, there is no appropriate restriction enzyme to recognize it, which limits the research between the variants of rs3863242 and human diseases.

METHODS

The reverse primer was designed based on turning the 3rd base T into the mismatch base G. After PCR amplification, a new restriction enzyme site was introduced into the TERF1 gene amplification products. Two hundred forty samples from Chinese Han individuals were genotyped to evaluate this method.

RESULTS

A new restriction enzyme site for CviQI was introduced into the PCR products. The genotype frequencies of 240 samples from Chinese Han individuals were 4.17% for A/A, 29.58% for A/G, 66.25% for G/G respectively. The allele frequencies were 18.96% for A and 81.04% for G respectively. The genotyping results of PCR products were consistent with the gene sequencing result.

CONCLUSIONS

We developed a simple, direct and economical technique for analyzing the polymorphism of TERF1 rs3863242. It may be applied to the colony screening of other SNPs, mutation-screening of tumor-related gene or mutations in some specific genes on a large scale, in the future.

Single-Nucleotide Polymorphism of PPARγ, a Protein at the Crossroads of Physiological and Pathological Processes

Genome polymorphisms are responsible for phenotypic differences between humans and for individual susceptibility to genetic diseases and therapeutic responses. Non-synonymous single-nucleotide polymorphisms (nsSNPs) lead to protein variants with a change in the amino acid sequence that may affect the structure and/or function of the protein and may be utilized as efficient structural and functional markers of association to complex diseases. This study is focused on nsSNP variants of the ligand binding domain of PPARγ a nuclear receptor in the superfamily of ligand inducible transcription factors that play an important role in regulating lipid metabolism and in several processes ranging from cellular differentiation and development to carcinogenesis. Here we selected nine nsSNPs variants of the PPARγ ligand binding domain, V290M, R357A, R397C, F360L, P467L, Q286P, R288H, E324K, and E460K, expressed in cancer tissues and/or associated with partial lipodystrophy and insulin resistance. The effects of a single amino acid change on the thermodynamic stability of PPARγ, its spectral properties, and molecular dynamics have been investigated. The nsSNPs PPARγ variants show alteration of dynamics and tertiary contacts that impair the correct reciprocal positioning of helices 3 and 12, crucially important for PPARγ functioning.

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.

SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations

Predicting the effect of amino acid substitutions on protein–protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.

SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach

Folding free energy is an important biophysical characteristic of proteins that reflects the overall stability of the 3D structure of macromolecules. Changes in the amino acid sequence, naturally occurring or made in vitro, may affect the stability of the corresponding protein and thus could be associated with disease. Several approaches that predict the changes of the folding free energy caused by mutations have been proposed, but there is no method that is clearly superior to the others. The optimal goal is not only to accurately predict the folding free energy changes, but also to characterize the structural changes induced by mutations and the physical nature of the predicted folding free energy changes. Here we report a new method to predict the Single Amino Acid Folding free Energy Changes (SAAFEC) based on a knowledge-modified Molecular Mechanics Poisson-Boltzmann (MM/PBSA) approach. The method is comprised of two main components: a MM/PBSA component and a set of knowledge based terms delivered from a statistical study of the biophysical characteristics of proteins. The predictor utilizes a multiple linear regression model with weighted coefficients of various terms optimized against a set of experimental data. The aforementioned approach yields a correlation coefficient of 0.65 when benchmarked against 983 cases from 42 proteins in the ProTherm database. Availability: the webserver can be accessed via http://compbio.clemson.edu/SAAFEC/.

The Association between Gene-Environment Interactions and Diseases Involving the Human GST Superfamily with SNP Variants

Exposure to environmental hazards has been associated with diseases in humans. The identification of single nucleotide polymorphisms (SNPs) in human populations exposed to different environmental hazards, is vital for detecting the genetic risks of some important human diseases. Several studies in this field have been conducted on glutathione S-transferases (GSTs), a phase II detoxification superfamily, to investigate its role in the occurrence of diseases. Human GSTs consist of cytosolic and microsomal superfamilies that are further divided into subfamilies. Based on scientific search engines and a review of the literature, we have found a large amount of published articles on human GST super- and subfamilies that have greatly assisted in our efforts to examine their role in health and disease. Because of its polymorphic variations in relation to environmental hazards such as air pollutants, cigarette smoke, pesticides, heavy metals, carcinogens, pharmaceutical drugs, and xenobiotics, GST is considered as a significant biomarker. This review examines the studies on gene-environment interactions related to various diseases with respect to single nucleotide polymorphisms (SNPs) found in the GST superfamily. Overall, it can be concluded that interactions between GST genes and environmental factors play an important role in human diseases.

Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding

Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions was assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here it is demonstrated that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease-causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, correlation coefficients (CC) between the disease index (Pd) and relative folding (Ppr,f) and binding (Ppr,b) probability indexes, respectively, was achieved. The obtained CCs demonstrated the applicability of the proposed approach and it served as good indicator for SAV association with disease.

Impact of Rett Syndrome Mutations on MeCP2 MBD Stability

Rett syndrome causing missense mutations in the methyl-CpG-binding domain (MBD) of methyl CpG-binding protein 2 (MeCP2) were investigated both in silico and in vitro to reveal their effect on protein stability. It is demonstrated that the vast majority of frequently occurring mutations in the human population indeed alter the MBD folding free energy by a fraction of a kcal/mol up to more than 1 kcal/mol. While the absolute magnitude of the change of the free energy is small, the effect on the MBD functionality may be substantial since the folding free energy of MBD is about 2 kcal/mol only. Thus, it is emphasized that the effect of mutations on protein integrity should be evaluated with respect to the wild-type folding free energy but not with the absolute value of the folding free energy change. Furthermore, it was observed that the magnitude of the effect is correlated neither with the burial of the mutation sites nor with the basic amino acid physicochemical property change. Mutations that strongly perturb the immediate structural features were found to have little effect on folding free energy, while very conservative mutations resulted in large changes of the MBD stability. This observation was attributed to the protein's ability to structurally relax and reorganize to reduce the effect of mutation. Comparison between in silico and in vitro results indicated that some Web servers perform relatively well, while the free energy perturbation approach frequently overpredicts the magnitude of the free energy change especially when a charged amino acid is involved.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

CoagVDb: a comprehensive database for coagulation factors and their associated SAPs

The current state of the art in medical genetics is to identify and classify the functional (deleterious) or non-functional (neutral) single amino acid substitutions (SAPs), also known as non-synonymous SNPs (nsSNPs). The primary goal is to elucidate the mechanisms through which functional SAPs exert their effects, and ultimately interrogating this information for association with complex phenotypes. This work focuses on coagulation factors involved in the coagulation cascade pathway which plays a vital role in the maintenance of homeostasis in the human system. We developed an integrated coagulation variation database, CoagVDb, which makes use of the biological information from various public databases such as NCBI, OMIM, UniProt, PDB and SAPs (rsIDs/variant). CoagVDb enriched with computational prediction scores classify SAPs as either deleterious or tolerated. Also, various other properties are incorporated such as amino acid composition, secondary structure elements, solvent accessibility, ordered/disordered regions, conservation, and the presence of disulfide bonds. This specialized database provides integration of various prediction scores from different computational methods along with gene, protein, and disease information. We hope our database will act as a useful reference resource for hematologists to reveal protein structure–function relationship and disease genotype–phenotype correlation.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.

Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins

This review emphasizes the effects of naturally occurring mutations on structural features and physico-chemical properties of proteins. The basic protein characteristics considered are stability, dynamics, and the binding of proteins and methods for assessing effects of mutations on these macromolecular characteristics are briefly outlined. It is emphasized that the above entities mostly reflect global characteristics of considered macromolecules, while given mutations may alter the local structural features such as salt bridges and hydrogen bonds without affecting the global ones. Furthermore, it is pointed out that disease-causing mutations frequently involve a drastic change of amino acid physico-chemical properties such as charge, hydrophobicity, and geometry, and are less surface exposed than polymorphic mutations.