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Biology subjects

Akhter, N.

Publications and source records attributed to Akhter, N..

3 recordsLinked to original sources

Pervasive divergence in protein thermostability is mediated by both structural changes and cellular environments

Temperature is a universal environmental constraint and organisms have evolved diverse mechanisms of thermotolerance. A central feature of thermophiles relative to mesophiles is a universal shift in protein stability, implying that it is a major constituent of thermotolerance. However, organisms have also evolved extensive buffering systems, such as those that disaggregate and refold denatured proteins and enable survival of heat shock. Here, we show that both cellular and protein structural changes contribute to divergence in protein thermostability between two closely related Saccharomyces species that differ by 8{degrees}C in their thermotolerance. Using thermal proteomic profiling we find that 85% of S. cerevisiae proteins are more stable than their S. uvarum homologs and there is an average shift of 1.6{degrees}C in temperature induced protein aggregation. In an interspecific hybrid of the two species, S. cerevisiae proteins retain their thermostability, while the thermostability of their S. uvarum homologs is enhanced, indicating that cellular context contributes to protein stability differences. By purifying orthologous proteins we show that amino acid substitutions underlie melting temperature differences for two proteins, Guk1 and Aha1. Amino acid substitutions are also computationally predicted to contribute to stability differences for most of the proteome. Our results imply that coordinated changes in protein thermostability impose a significant constraint on the time scales over which thermotolerance can evolve.

evolutionary biology↗

Induction of pluripotent oncogenic stem cells from mouse fibroblasts

Natural biological agents that can transform normal somatic cells into cancer stem cells, have not been identified. We earlier reported that cell free chromatin particles (cfChPs) that circulate in blood of cancer patients can horizontally transfer themselves to healthy cells to induce dsDNA breaks and inflammation. Here we show that a single cell clone D5 developed from NIH3T3 mouse fibroblast cells treated with cfChPs isolated from sera of cancer patients exhibited upregulation of stemness related transcription factors and surface markers, and the ability to form spheroids in appropriate culture medium. Transcriptome analysis revealed upregulation of cancer related pathways including invasion, metastasis and stemness. Subcutaneous inoculation into SCID mice resulted in development of malignant tumors which expressed all three germline markers. Our results suggest the cfChPs that circulate in blood of cancer patients are oncogenic and can transform susceptible somatic cells into cancer stem cells with the potential to promote metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/622204v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@4288org.highwire.dtl.DTLVardef@1102d4borg.highwire.dtl.DTLVardef@10daaf6org.highwire.dtl.DTLVardef@53084d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Predicting small-molecule inhibition of protein complexes

MotivationProtein-Protein Interactions (PPIs) are crucial in biological processes and disease mechanisms, underscoring the importance of discovering PPI inhibitors in drug development. Machine learning can expedite this discovery process. Although machine learning techniques for predicting general compound inhibition are available, we are not aware of any that accurately forecast the inhibitory effect of a compound on a specific protein complex, utilizing inputs from both the compound and the protein complex. MethodsWe present the first targeted machine learning based predictor of small molecule based inhibition of protein complexes. Our proposed graph neural network integrates the structure of a protein complex, its protein-protein binding site or interface features and a compounds SMILES representation to predict the potential of the given compound to inhibit the interaction between proteins in the given complex in a targeted manner. ResultsValidated on the 2p2i-DB-v2 database, encompassing 714 inhibitors across 23 complexes with over 12,000 instances, our model achieves superior predictive accuracy (cross-validation AUC-ROC of 0.86), outperforming established kernel methods and pre-trained neural networks. We further tested the predictive performance of our model on two independent external datasets - one collected from recent publications and another consisting of putative inhibitors of the SARS-CoV-2-Spike and Human-ACE2 protein complex with AUC-ROCs of 0.82 and 0.78, respectively. Our targeted predictor introduces a novel approach for PPI inhibitor discovery, laying foundational work for future advancements in addressing this complex and previously unexplored prediction challenge. AvailabilityCode/supplementary material available: https://github.com/adibayaseen/PPI-Inhibitors

bioinformatics↗