Biology subjects

Salaikumaran, M. R.

Publications and source records attributed to Salaikumaran, M. R..

3 recordsLinked to original sources

bioRxiv2026

Lipoproteins modulate the uptake and biological functionof cationic cell-penetrating peptides across the animal lineage.

Cationic cell-penetrating peptides (+CPPs) are pervasive killer peptides produced by all animals and found in innate immune systems, venoms and neurodegenerative diseases. Despite their ubiquity, the mechanisms underlying their uptake remain opaque and intensely debated. Here, we interrogate +CPPs spanning 600 million years of animal evolution and identify endocytosis as a conserved/convergent uptake mechanism across cell types and organisms, at physiological concentrations. By combining multiplex imaging, genetic screening, cryo-electron tomography, and biophysical methods, we uncover that +CPPs seemingly universally enter eukaryote cells via 'hitchhiking' on lipoproteins. We further show that the interaction of +CPPs with lipoproteins modulates their antimicrobial function. Combined, our findings establish a unified molecular framework describing a pan-eukaryote cell penetrance mechanism. As hyperlipidemia is a common comorbidity, our findings have direct implications for human health. Lastly, the insights gleaned from this work highlight design principles that may inform the future engineering of peptide-based therapeutics and delivery vehicles.

cell biology↗

bioRxiv2023

Rational Design of TDP-43 Derived α-Helical Peptide Inhibitors: an In-Silico Strategy to Prevent TDP-43 Aggregation in Neurodegenerative Disorders

TDP-43, an essential RNA/DNA-binding protein, is central to the pathology of neurodegenerative diseases such as Amyotrophic Lateral Sclerosis and Frontotemporal Dementia. Pathological mislocalization and aggregation of TDP-43 disrupts RNA splicing, mRNA stability, and mRNA transport, thereby impairing neuronal function and survival. The formation of amyloid-like TDP-43 filaments is largely facilitated by the destabilization of an -helical segment within the disordered C-terminal region. In this study, we hypothesized that preventing the destabilization of the -helical domain could potentially halt the growth of these pathological filaments. To explore this, we utilized a range of in-silico techniques to design and evaluate peptide-based therapeutics. Various pathological TDP-43 amyloid-like filament crystal structures were selected for their potential to inhibit the binding of additional TDP-43 monomers to the growing filaments. Our computational approaches included biophysical and secondary structure property prediction, molecular docking, 3D structure prediction, and molecular dynamics simulations. Through these techniques, we were able to assess the structure, stability, and binding affinity of these peptides in relation to pathological TDP-43 filaments. The results of our in-silico analyses identified a selection of promising peptides, which displayed a stable -helical structure, exhibited an increased number of intramolecular hydrogen bonds within the helical domain, and demonstrated high binding affinities for pathological TDP-43 amyloid-like filaments. Additionally, molecular dynamics simulations provided further support for the stability of these peptides, as they exhibited lower root mean square deviations in their helical propensity over 100ns. These findings establish -helical propensity peptides as potential lead molecules for the development of novel therapeutics against TDP-43 aggregation. This structure-based computational approach for rational design of peptide inhibitors opens a new direction in the search for effective interventions for ALS, FTD, and other related neurodegenerative diseases. The peptides identified as the most promising candidates in this study are currently subject to further testing and validation through both in vitro and in vivo experiments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/564235v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@a25d6forg.highwire.dtl.DTLVardef@1a45a21org.highwire.dtl.DTLVardef@11938a7org.highwire.dtl.DTLVardef@d66ffe_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Epitope order Matters in multi-epitope-based peptide (MEBP) vaccine design: An in silico study

With different countries facing multiple waves, with some SARS-CoV-2 variants more deadly and virulent, the COVID-19 pandemic is becoming more dangerous by the day and the world is facing an even more dreadful extended pandemic with exponential positive cases and increasing death rates. There is an urgent need for more efficient and faster methods of vaccine development against SARS-CoV-2. Compared to experimental protocols, the opportunities to innovate are very high in immunoinformatics/in silico approaches especially with the recent adoption of structural bioinformatics in peptide vaccine design. In recent times, multi-epitope-based peptide vaccine candidates (MEBPVCs) have shown extraordinarily high humoral and cellular responses to immunization. Most of the publications claim that respective reported MEBPVC(s) assembled using a set of in silico predicted epitopes, to be the computationally validated potent vaccine candidate(s) ready for experimental validation. However, in this article, for a given set of predicted epitopes, it is shown that the published MEBPVC is one among the many possible variants and there is high likelihood of finding more potent MEBPVCs than the published candidate. To test the same, a methodology is developed where novel MEBP variants are derived by changing the epitope order of the published MEBPVC. Further, to overcome the limitations of current qualitative methods of assessment of MEBPVC, to enable quantitative comparison, ranking, and the discovery of more potent MEBPVCs, novel predictors, Percent Epitope Accessibility (PEA), Receptor specific MEBP vaccine potency(RMVP), MEBP vaccine potency(MVP) are introduced. The MEBP variants indeed showed varied MVP scores indicating varied immunogenicity. When the MEBP variants were ranked in descending order of their MVP scores, the published MEBPVC had the least MVP score. Further, the MEBP variants with IDs, SPVC_387 and SPVC_206, had the highest MVP scores indicating these variants to be more potent MEBPVCs than the published MEBPVC and hence should be prioritized for experimental testing and validation. Through this method, more vaccine candidates will be available for experimental validation and testing. This study also opens the opportunity to develop new software tools for designing more potent MEBPVCs in less time. The computationally validated top-ranked MEBPVCs must be experimentally tested, validated, and verified. The differences and deviations between experimental results and computational predictions provide an opportunity for improving and developing more efficient algorithms and reliable scoring schemes and software.

biochemistry↗