References of "Iqbal, Sumaiya"
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See detailVariant Score Ranker - a web application for intuitive missense variant prioritization
Du, Juanjiangmeng; Sudarsanam, Monica; Pérez-Palma, Eduardo et al

in Bioinformatics (2019)

The correct classification of missense variants as benign or pathogenic remains challenging. Pathogenic variants are expected to have higher deleterious prediction scores than benign variants in the same ... [more ▼]

The correct classification of missense variants as benign or pathogenic remains challenging. Pathogenic variants are expected to have higher deleterious prediction scores than benign variants in the same gene. However, most of the existing variant annotation tools do not reference the score range of benign population variants on gene level. Here, we present a web-application, Variant Score Ranker, which enables users to rapidly annotate variants and perform gene-specific variant score ranking on the population level. We also provide an intuitive example of how gene- and population-calibrated variant ranking scores can improve epilepsy variant prioritization. [less ▲]

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See detailFunctional Interpretation of Single Amino Acid Substitutions in 1,330 Disease-Associated Genes
Iqbal, Sumaiya; Jespersen, Jakob Berg; Perez-Palma, Eduardo et al

in Biophysical Journal (2019, February 15), 116(3), 420-421

Elucidating molecular consequences of amino-acid-altering missense variants at scale is challenging. In this work, we explored whether features derived from three-dimensional (3D) protein structures can ... [more ▼]

Elucidating molecular consequences of amino-acid-altering missense variants at scale is challenging. In this work, we explored whether features derived from three-dimensional (3D) protein structures can characterize patient missense variants across different protein classes with similar molecular level activities. The identified disease-associated features can advance our understanding of how a single amino acid substitution can lead to the etiology of monogenic disorders. For 1,330 disease-associated genes (>80%, 1,077/1,330 implicated in Mendelian disorders), we collected missense variants from the general population (gnomAD database, N=164,915) and patients (ClinVar and HGMD databases, N=32,923). We in silico mapped the variant positions onto >14k human protein 3D structures. We annotated the protein positions of variants with 40 structural, physiochemical, and functional features. We then grouped the genes into 24 protein classes based on their molecular functions and performed statistical association analyses with the features of population and patient variants. We identified 18 (out of 40) features that are associated with patient variants in general. Specifically, patient variants are less exposed to solvent (p<1.0e-100), enriched on b-sheets (p<2.37e-39), frequently mutate aromatic residues (p<1.0e-100), occur in ligand binding sites (p<1.0e-100) and are spatially close to phosphorylation sites (p<1.0e-100). We also observed differential protein-class-specific features. For three protein classes (signaling molecules, proteases and hydrolases), patient variants significantly perturb the disulfide bonds (p<1.0e-100). Only in immunity proteins, patient variants are enriched in flexible coils (p<1.65e-06). Kinases and cell junction proteins exhibit enrichment of patient variants around SUMOylation (p<1.0e-100) and methylation sites (p<9.29e-11), respectively. In summary, we studied shared and unique features associated with patient variants on protein structure across 24 protein classes, providing novel mechanistic insights. We generated an online resource that contains amino-acid-wise feature annotation-track for 1,330 genes, summarizes the patient-variant-associated features on residue level, and can guide variant interpretation. [less ▲]

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See detailIdentification and Characterization of Variant Intolerant Sites across Human Protein 3-Dimensional Structures
Iqbal, Sumaiya; Berg Jespersen, Jakob; Perez-Palma, Eduardo et al

in Biophysical Journal (2018, February 02), 114(3, Suppl. 1), 664

The functional interpretation of genetic variation in disease-associated genes is far outpaced by data generation. Existing algorithms for prediction of variant consequences do not adequately distinguish ... [more ▼]

The functional interpretation of genetic variation in disease-associated genes is far outpaced by data generation. Existing algorithms for prediction of variant consequences do not adequately distinguish pathogenic variants from benign rare variants. This lack of statistical and bioinformatics analyses, accompanied by an ever-increasing number of identified variants in biomedical research and clinical applications, has become a major challenge. Established methods to predict the functional effect of genetic variation use the degree of amino acid conservation across species in linear protein sequence alignment. More recent methods include the spatial distribution pattern of known patient and control variants. Here, we propose to combine the linear conservation and spatial constrained based scores to devise a novel score that incorporates 3-dimensional structural properties of amino acid residues, such as the solvent-accessible surface area, degree of flexibility, secondary structure propensity and binding tendency, to quantify the effect of amino acid substitutions. For this study, we develop a framework for large-scale mapping of established linear sequence-based paralog and ortholog conservation scores onto the tertiary structures of human proteins. This framework can be utilized to map the spatial distribution of mutations on solved protein structures as well as homology models. As a proof of concept, using a homology model of the human Nav1.2 voltage-gated sodium channel structure, we observe spatial clustering in distinct domains of mutations, associated with Autism Spectrum Disorder (>20 variants) and Epilepsy (>100 variants), that exert opposing effects on channel function. We are currently characterizing all variants (>300k individuals) found in ClinVar, the largest disease variant database, as well as variants identified in >140k individuals from general population. The variant mapping framework and our score, informed with structural information, will be useful in identifying structural motifs of proteins associated with disease risk. [less ▲]

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