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Special Issue "Biophysics of Human Genetic Diseases: Understanding Molecular Effects of Mutations"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (31 January 2019).

Special Issue Editor

Prof. Dr. Emil Alexov
E-Mail Website
Guest Editor
Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, SC 29634, USA
Interests: computational modeling of biological macromolecules; predicting molecular effects of disease-causing mutations; personalized medicine; macromolecular interactions; electrostatics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is planned to be a collection of papers describing cutting edge research achievements in the field of understanding molecular effects associated with human genetic diseases. These include the effects of macromolecular stability and interactions, pH-dependence, conformational dynamics and allosteric pathways.

Prof. Dr. Emil Alexov
Guest Editor

Manuscript Submission Information

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Keywords

  • Disease-causing mutations
  • Macromolecular stability
  • Macromolecular interactions
  • Personalized medicine
  • Disease diagnostics
  • Genetic disorders
  • Missense mutations
  • Single nucleotide polymorphism

Published Papers (11 papers)

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Research

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Open AccessArticle
Prioritization of Variants for Investigation of Genotype-Directed Nutrition in Human Superpopulations
Int. J. Mol. Sci. 2019, 20(14), 3516; https://doi.org/10.3390/ijms20143516 - 18 Jul 2019
Abstract
Dietary guidelines recommended by key health agencies are generally designed for a global population. However, ethnicity affects human disease and environment-gene interactions, including nutrient intake. Historically, isolated human populations with different genetic backgrounds have adapted to distinct environments with varying food sources. Ethnicity [...] Read more.
Dietary guidelines recommended by key health agencies are generally designed for a global population. However, ethnicity affects human disease and environment-gene interactions, including nutrient intake. Historically, isolated human populations with different genetic backgrounds have adapted to distinct environments with varying food sources. Ethnicity is relevant to the interaction of food intake with genes and disease susceptibility; yet major health agencies generally do not recommend food and nutrients codified by population genotypes and their frequencies. In this paper, we have consolidated published nutrigenetic variants and examine their frequencies in human superpopulations to prioritize these variants for future investigation of population-specific genotype-directed nutrition. The nutrients consumed by individuals interact with their genome and may alter disease risk. Herein, we searched the literature, designed a data model, and manually curated hundreds of papers. The resulting database houses 101 variants that reached significance (p < 0.05), from 35 population studies. Nutrigenetic variants associated with modified nutrient intake have the potential to reduce the risk of colorectal cancer, obesity, metabolic syndrome, type 2 diabetes, and several other diseases. Since many nutrigenetic studies have identified a major variant in some populations, we suggest that superpopulation-specific genotype-directed nutrition modifications be prioritized for future study and evaluation. Genotype-directed nutrition approaches to dietary modification have the potential to reduce disease risk in select human populations. Full article
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Open AccessArticle
Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces
Int. J. Mol. Sci. 2019, 20(7), 1583; https://doi.org/10.3390/ijms20071583 - 29 Mar 2019
Abstract
One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are [...] Read more.
One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments. Full article
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Open AccessArticle
Functional and Structural Features of Disease-Related Protein Variants
Int. J. Mol. Sci. 2019, 20(7), 1530; https://doi.org/10.3390/ijms20071530 - 27 Mar 2019
Abstract
Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In [...] Read more.
Modern sequencing technologies provide an unprecedented amount of data of single-nucleotide variations occurring in coding regions and leading to changes in the expressed protein sequences. A significant fraction of these single-residue variations is linked to disease onset and collected in public databases. In recent years, many scientific studies have been focusing on the dissection of salient features of disease-related variations from different perspectives. In this work, we complement previous analyses by updating a dataset of disease-related variations occurring in proteins with 3D structure. Within this dataset, we describe functional and structural features that can be of interest for characterizing disease-related variations, including major chemico-physical properties, the strength of association to disease of variation types, their effect on protein stability, their location on the protein structure, and their distribution in Pfam structural/functional protein models. Our results support previous findings obtained in different data sets and introduce Pfam models as possible fingerprints of patterns of disease related single-nucleotide variations. Full article
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Open AccessArticle
Methylation-Based Classification of Cervical Squamous Cell Carcinoma into Two New Subclasses Differing in Immune-Related Gene Expression
Int. J. Mol. Sci. 2018, 19(11), 3607; https://doi.org/10.3390/ijms19113607 - 15 Nov 2018
Cited by 1
Abstract
Cervical cancer is traditionally classified into two major histological subtypes, cervical squamous cell carcinoma (CSCC) and cervical adenocarcinoma (CA). However, heterogeneity exists among patients, comprising possible subpopulations with distinct molecular profiles. We applied consensus clustering to 307 methylation samples with cervical cancer from [...] Read more.
Cervical cancer is traditionally classified into two major histological subtypes, cervical squamous cell carcinoma (CSCC) and cervical adenocarcinoma (CA). However, heterogeneity exists among patients, comprising possible subpopulations with distinct molecular profiles. We applied consensus clustering to 307 methylation samples with cervical cancer from The Cancer Genome Atlas (TCGA). Fisher’s exact test was used to perform transcription factors (TFs) and genomic region enrichment. Gene expression profiles were downloaded from TCGA to assess expression differences. Immune cell fraction was calculated to quantify the immune cells infiltration. Putative neo-epitopes were predicted from somatic mutations. Three subclasses were identified: Class 1 correlating with the CA subtype and Classes 2 and 3 dividing the CSCC subtype into two subclasses. We found the hypomethylated probes in Class 3 exhibited strong enrichment in promoter region as compared with Class 2. Five TFs significantly enriched in the hypomethylated promoters and their highly expressed target genes in Class 3 functionally involved in the immune pathway. Gene function analysis revealed that immune-related genes were significantly increased in Class 3, and a higher level of immune cell infiltration was estimated. High expression of 24 immune genes exhibited a better overall survival and correlated with neo-epitope burden. Additionally, we found only two immune-related driver genes, CARD11 and JAK3, to be significantly increased in Class 3. Our analyses provide a classification of the largest CSCC subtype into two new subclasses, revealing they harbored differences in immune-related gene expression. Full article
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Open AccessArticle
A Functional Mutation in KIAA1462 Promoter Decreases Glucocorticoid Receptor Affinity and Affects Egg-Laying Performance in Yangzhou Geese
Int. J. Mol. Sci. 2018, 19(5), 1531; https://doi.org/10.3390/ijms19051531 - 21 May 2018
Abstract
The identification of genetic markers is valuable for improving the egg-laying performance in goose production. The single-nucleotide polymorphism (SNP) rs1714766362 in an intron of the goose KIAA1462 gene was found to be relevant to laying performance in our previous study. However, its function [...] Read more.
The identification of genetic markers is valuable for improving the egg-laying performance in goose production. The single-nucleotide polymorphism (SNP) rs1714766362 in an intron of the goose KIAA1462 gene was found to be relevant to laying performance in our previous study. However, its function remains unclear. In this study, the full-length coding sequence of KIAA1462 gene was firstly characterized in Yangzhou geese. Q-PCR (Quantitative Real Time Polymerase Chain Reaction) results showed that KIAA1462 was highly expressed in the liver, ovary, and mature F1 follicles. For SNP rs1714766362, geese with the AA genotype showed better laying performance than the TT ones and exhibited a higher KIAA1462 expression level in the ovary. Gain- and loss-of function experiments in granulosa cells revealed that KIAA1462 affected the expression of the apoptosis marker gene caspase-3. Considering that rs1714766362 locates in an intron area, we compared the KIAA1462 promoter regions of AA and TT individuals and identified the SNP c.-413C>G (Genbank ss2137504176), which was completely linked to SNP rs1714766362. According to the transcription factor prediction results, the glucocorticoid receptor (GR) would bind to the SNP site containing the C but not the G allele. In this study, we proved this hypothesis by an electrophoretic mobility shift assay (EMSA). In summary, we identified a novel mutation in the promoter of KIAA1462 gene which can modulate GR binding affinity and affect the laying performance of geese. Full article
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Open AccessArticle
PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality
Int. J. Mol. Sci. 2018, 19(4), 1009; https://doi.org/10.3390/ijms19041009 - 28 Mar 2018
Cited by 8
Abstract
Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for [...] Read more.
Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the investigated phenomenon. Available machine learning algorithms for variant stability have all been trained with ProTherm data. We noticed a number of issues with the contents, quality and relevance of the database. There were errors, but also features that had not been clearly communicated. Consequently, all machine learning variant stability predictors have been trained on biased and incorrect data. We obtained a corrected dataset and trained a random forests-based tool, PON-tstab, applicable to variants in any organism. Our results highlight the importance of the benchmark quality, suitability and appropriateness. Predictions are provided for three categories: stability decreasing, increasing and those not affecting stability. Full article
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Open AccessArticle
Unravelling the Effects of the Mutation m.3571insC/MT-ND1 on Respiratory Complexes Structural Organization
Int. J. Mol. Sci. 2018, 19(3), 764; https://doi.org/10.3390/ijms19030764 - 07 Mar 2018
Cited by 1
Abstract
Mammalian respiratory complex I (CI) biogenesis requires both nuclear and mitochondria-encoded proteins and is mostly organized in respiratory supercomplexes. Among the CI proteins encoded by the mitochondrial DNA, NADH-ubiquinone oxidoreductase chain 1 (ND1) is a core subunit, evolutionary conserved from bacteria to mammals. [...] Read more.
Mammalian respiratory complex I (CI) biogenesis requires both nuclear and mitochondria-encoded proteins and is mostly organized in respiratory supercomplexes. Among the CI proteins encoded by the mitochondrial DNA, NADH-ubiquinone oxidoreductase chain 1 (ND1) is a core subunit, evolutionary conserved from bacteria to mammals. Recently, ND1 has been recognized as a pivotal subunit in maintaining the structural and functional interaction among the hydrophilic and hydrophobic CI arms. A critical role of human ND1 both in CI biogenesis and in the dynamic organization of supercomplexes has been depicted, although the proof of concept is still missing and the critical amount of ND1 protein necessary for a proper assembly of both CI and supercomplexes is not defined. By exploiting a unique model in which human ND1 is allotopically re-expressed in cells lacking the endogenous protein, we demonstrated that the lack of this protein induces a stall in the multi-step process of CI biogenesis, as well as the alteration of supramolecular organization of respiratory complexes. We also defined a mutation threshold for the m.3571insC truncative mutation in mitochondrially encoded NADH:ubiquinone oxidoreductase core subunit 1 (MT-ND1), below which CI and its supramolecular organization is recovered, strengthening the notion that a certain amount of human ND1 is required for CI and supercomplexes biogenesis. Full article
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Open AccessArticle
Computational Investigation of the Missense Mutations in DHCR7 Gene Associated with Smith-Lemli-Opitz Syndrome
Int. J. Mol. Sci. 2018, 19(1), 141; https://doi.org/10.3390/ijms19010141 - 04 Jan 2018
Cited by 3
Abstract
Smith-Lemli-Opitz syndrome (SLOS) is a cholesterol synthesis disorder characterized by physical, mental, and behavioral symptoms. It is caused by mutations in 7-dehydroxycholesterolreductase gene (DHCR7) encoding DHCR7 protein, which is the rate-limiting enzyme in the cholesterol synthesis pathway. Here we demonstrate that [...] Read more.
Smith-Lemli-Opitz syndrome (SLOS) is a cholesterol synthesis disorder characterized by physical, mental, and behavioral symptoms. It is caused by mutations in 7-dehydroxycholesterolreductase gene (DHCR7) encoding DHCR7 protein, which is the rate-limiting enzyme in the cholesterol synthesis pathway. Here we demonstrate that pathogenic mutations in DHCR7 protein are located either within the transmembrane region or are near the ligand-binding site, and are highly conserved among species. In contrast, non-pathogenic mutations observed in the general population are located outside the transmembrane region and have different effects on the conformational dynamics of DHCR7. All together, these observations suggest that the non-classified mutation R228Q is pathogenic. Our analyses indicate that pathogenic effects may affect protein stability and dynamics and alter the binding affinity and flexibility of the binding site. Full article
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Review

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Open AccessReview
Twin CHCH Proteins, CHCHD2, and CHCHD10: Key Molecules of Parkinson’s Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia
Int. J. Mol. Sci. 2019, 20(4), 908; https://doi.org/10.3390/ijms20040908 - 20 Feb 2019
Cited by 1
Abstract
Mutations of coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD2) and 10 (CHCHD10) have been found to be linked to Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and/or frontotemporal lobe dementia (FTD). CHCHD2 and CHCHD10 proteins, which are homologous proteins with 54% [...] Read more.
Mutations of coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD2) and 10 (CHCHD10) have been found to be linked to Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and/or frontotemporal lobe dementia (FTD). CHCHD2 and CHCHD10 proteins, which are homologous proteins with 54% identity in amino acid sequence, belong to the mitochondrial coiled-coil-helix-coiled-coil-helix (CHCH) domain protein family. A series of studies reveals that these twin proteins form a multimodal complex, producing a variety of pathophysiology by the disease-causing variants of these proteins. In this review, we summarize the present knowledge about the physiological and pathological roles of twin proteins, CHCHD2 and CHCHD10, in neurodegenerative diseases. Full article
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Open AccessReview
Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases
Int. J. Mol. Sci. 2019, 20(3), 548; https://doi.org/10.3390/ijms20030548 - 28 Jan 2019
Cited by 2
Abstract
Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the [...] Read more.
Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico–chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties. Full article
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Open AccessReview
Computational Approaches to Prioritize Cancer Driver Missense Mutations
Int. J. Mol. Sci. 2018, 19(7), 2113; https://doi.org/10.3390/ijms19072113 - 20 Jul 2018
Cited by 3
Abstract
Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. [...] Read more.
Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations. Full article
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