Biology subjects

Chakroborty, S.

Publications and source records attributed to Chakroborty, S..

2 recordsLinked to original sources

bioRxiv2024

Kingdom-specific lipid unsaturation shapes up sequence evolution in membrane arm subunits of eukaryotic respiratory complexes

Sequence evolution of protein complexes (PCs) is constrained by protein-protein interactions (PPIs). PPI-interfaces are predominantly conserved and hotspots for disease-related mutations. How lipid-protein interactions (LPIs) constrain sequence evolution of membrane- PCs? We explore Respiratory Complexes (RCs) as a case study as these allow to compare sequence evolution in subunits exposed to both lipid-rich inner-mitochondrial membrane (IMM) and aqueous matrix. We find that lipid-exposed surfaces of the IMM-subunits but not of the matrix subunits are populated with non-PPI disease-causing mutations signifying LPIs in stabilizing RCs. Further, IMM-subunits including their exposed surfaces show high intra- kingdom sequence conservation but remarkably diverge beyond. Molecular Dynamics simulation suggests contrasting LPIs of structurally superimposable but sequence-wise diverged IMM-exposed helices of Complex I (CI) subunit Ndufa1 from human and Arabidopsis depending on kingdom-specific unsaturation of cardiolipin fatty acyl chains. in cellulo assays consolidate inter-kingdom incompatibility of Ndufa1-helices due to the lipid- exposed amino acids. Plant-specific unsaturated fatty acids in human cells also trigger CI- instability. Taken together, we posit that altered LPIs calibrate sequence evolution at the IMM-arms of eukaryotic RCs.

cell biology↗

bioRxiv2024

A Comprehensive Methodological Framework for Anthropometric Head Shape Modeling Using Small Dataset

Detailed anthropometric characterization of complex shapes of human heads can ensure optimal fit, comfort, and effectiveness of head-mounted devices. However, there is a lack of a reliable and systematic approach for head shape classification and modeling for laboratory-based, small, occupation-specific datasets. Therefore, in this study, we proposed a streamlined framework comprising six steps--pre-processing, feature extraction, feature selection, clustering, shape modeling, and validation--for head shape classification and modeling. We collected 36 firefighter 3D head scans and implemented the framework. Different clustering techniques, such as k-means and k-medoids, were evaluated using the squared Euclidean distance of individual head shapes from their cluster centroid. Furthermore, five variations of NURBS and cubic spline methods were assessed to design the representative head shape of each cluster, and their accuracy was evaluated using mean square error (MSE) values. The clustering results indicated that k-means provide better metrics than k-medoids. Among the shape modeling methods, cubic spline least squares displayed the lowest MSE (0.70 cm2)and computational time (0.14 s), whereas NURBS least squares displayed the highest MSE (7.19 cm2). Overall, the framework with k-means clustering and cubic spline least squares modeling techniques proved to be the most efficient for small datasets.

bioengineering↗