Biology subjects

Ahmad, M.

Publications and source records attributed to Ahmad, M..

4 recordsLinked to original sources

bioRxiv2018

Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes

A new method termed \"Relative Principal Components analysis\" (RPCA) is introduced that extracts optimal relevant principal components to describe the change between two data samples representing two macroscopic states. The method is widely applicable in data-driven science. Calculating the components is based on a unified physical framework which introduces the objective function, namely the Kullback-Leibler divergence, appropriate for quantifying the change of the macroscopic state as it is effected by the microscopic features. To demonstrate the applicability of RPCA, we analyze the thermodynamically relevant conformational changes of the protein HIV-1 protease upon binding to different drug molecules. In this case, the RPCA method provides a sound thermodynamic foundation for the analysis of the binding process. The relevant collective (global) conformational changes can be reconstructed from the informative latent variables to exhibit both the enhanced and the restricted conformational fluctuations upon ligand association. Moreover, RPCA characterizes the locally relevant conformational changes which can be presented on the structure of the protein.

biophysics↗

bioRxiv2018

Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: The case of hypertrophic cardiomyopathy

BackgroundInternational guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework.\n\nResultsWe compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF[≥]0.95, and therefore clinically actionable, were identified in 5 established HCM genes. Applying this approach leads to an estimated 14-20% increase in cases with actionable HCM variants.\n\nConclusionsWhen found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a \"likely pathogenic\" classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent, unbiased and equitable approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.

genetics↗

bioRxiv2018

Dynamic Kinetochore Size Regulation Promotes Microtubule Capture And Chromosome Biorientation In Mitosis

Faithful chromosome segregation depends on the ability of sister kinetochores to attach to spindle microtubules. An outer layer of the kinetochore known as the fibrous corona transiently expands in early mitosis and disassembles upon microtubule capture. Neither the functional importance nor the mechanistic basis for this are known. Here we show that the dynein adaptor Spindly and the RZZ kinetochore complex drive fibrous corona formation in a dynein-independent manner. C-terminal farnesylation and MPS1 kinase activity cause conformational changes of Spindly that promote oligomerization of RZZ:Spindly complexes into a corona-like meshwork in cells and in vitro. Concurrent with corona expansion, Spindly potentiates corona shedding by recruiting dynein via three conserved short linear motifs. Expanded, non-sheddable fibrous coronas engage in extensive, long-lived lateral microtubule interactions that persist to metaphase and result in fused sister kinetochores, formation of merotelic attachments and chromosome segregation errors in anaphase. Thus, dynamic kinetochore size regulation in mitosis is coordinated by a single, Spindly-based mechanism that promotes initial microtubule capture and subsequent correct maturation of attachments.

cell biology↗

bioRxiv2017

CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

PurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).\n\nMethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.\n\nResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fishers P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations.\n\nCardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.\n\nConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

genomics↗