Biology subjects

Goel, G.

Publications and source records attributed to Goel, G..

3 recordsLinked to original sources

bioRxiv2021

Permutational immune analysis reveals architectural similarities between inflammaging, Down syndrome and autoimmunity

People with Down syndrome show cellular and clinical features of dysregulated aging of the immune system, including naive-memory shift in the T cell compartment and increased incidence of autoimmunity. However, a quantitative understanding of how various immune compartments change with age in Down syndrome remains lacking. Here we performed deep immunophenotyping of a cohort of individuals with Down syndrome across the lifespan, selecting for individuals not affected by autoimmunity. We simultaneously interrogated age- and sex-matched healthy neurotypical controls and people with type 1 diabetes, as a representative autoimmune disease. We built a new analytical software, IMPACD, that enabled us to rapidly identify many features of immune dysregulation in Down syndrome that are recapitulated in other autoimmune diseases. We found significant quantitative and qualitative dysregulation of naive CD4+ and CD8+ T cells in Down syndrome and identified IL-6 as a candidate driver of some of these changes, thus extending the consideration of immunopathologic cytokines in Down syndrome beyond interferons. Notably, we successfully used immune cellular composition to generate three quantitative models of aging (i.e. immune clocks) trained on control subjects. All three immune clocks demonstrated significantly advanced immune aging in people with Down syndrome. Notably, one of these clocks, informed by Down syndrome-relevant biology, also showed advanced immune aging in people with type 1 diabetes. Together, our findings demonstrate a novel approach to studying immune aging in Down syndrome which may have implications in the context of other autoimmune diseases. One Sentence SummaryPermutational analysis of immune landscape reveals advanced immune aging in people with Down syndrome and in people with type 1 diabetes.

immunology↗

bioRxiv2021

A linear response theory based method for prediction of large scale protein conformational changes upon ligand binding

Prediction of ligand-induced protein conformational transitions is a challenging task due to a large and rugged conformational space, and limited knowledge of probable direction(s) of structure change. These transitions can involve a large scale, global (at the level of entire protein molecule) structural change and occur on a timescale of milliseconds to seconds, rendering application of conventional molecular dynamics simulations prohibitive even for small proteins. We have developed a computational protocol to efficiently and accurately predict these ligand-induced structure transitions solely from the knowledge of protein apo structure and ligand binding site. Our method involves a series of small scale conformational change steps, where at each step linear response theory is used to predict the direction of small scale global response to ligand binding in the protein conformational space (dLRT) followed by construction of a linear combination of slow (low frequency) normal modes (calculated for the structure from the previous step) that best overlaps with dLRT. Protein structure is evolved along this direction using molecular dynamics with excited normal modes (MDeNM) wherein excitation energy along each normal mode is determined by excitation temperature, mode frequency, and its overlap with dLRT. We show that excitation temperature ({Delta}T) is a very important parameter that allows limiting the extent of structural change in any one step and develop a protocol for automated determination of its optimal value at each step. We have tested our protocol for three protein-ligand systems, namely, adenylate Kinase - di(adenosine-5)pentaphosphate, ribose binding protein - {beta}-D-ribopyranose, and DNA {beta}-glucosyltransferase - uridine-5-diphosphate, that incorporate important differences in type and range of structural changes upon ligand binding. We obtain very accurate prediction for not only the structure of final protein-ligand complex (holo-structure) having a large scale conformational change, but also for biologically relevant intermediates between the apo and the holo structures. Moreover, most relevant set of normal modes for conformational change at each step are an output from our method, which can be used as collective variables for determination of free energy barriers and transition timescales along the identified pathway.

biophysics↗

bioRxiv2021

A Multiscale Model for Quantitative Prediction of Insulin Aggregation Nucleation Kinetics

We combined kinetic, thermodynamic, and structural information from single molecule (protein folding) and two molecule (association) explicit-solvent simulations for determination of kinetic parameters in protein aggregation nucleation with insulin as model protein. A structural bioinformatics approach was developed to account for heterogeneity of aggregation-prone species with the transition complex theory found applicable in modeling association kinetics involving non-native species. We show that a key simplification arises from presence of only a few relevant modes for non-native association kinetics. The kinetic parameters thus obtained were used in a population balance model and accurate predictions for aggregation nucleation time varying over two orders of magnitude with changes in concentration of insulin or an aggregation-inhibitor ligand were obtained while an empirical parameter set was not found to be transferable for prediction of ligand effects. This physically determined kinetic parameter set also allowed identification of the rate-limiting step in aggregation nucleation. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/431119v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@7ff3e4org.highwire.dtl.DTLVardef@653c90org.highwire.dtl.DTLVardef@6b4675org.highwire.dtl.DTLVardef@deab8a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗