Biology subjects

Dhusia, K.

Publications and source records attributed to Dhusia, K..

4 recordsLinked to original sources

bioRxiv2023

Encoding the Space of Protein-protein Binding Interfaces by Artificial Intelligence

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Our study illustrates how artificial intelligence can be used to understand and characterize protein-protein binding interfaces. The method will be potentially useful to search for the conformation of unknown protein-protein interactions. This result demonstrated that the structural space of protein-protein interactions is highly degenerate under the representation of interface fragment pairs. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

bioinformatics↗

bioRxiv2021

Bioengineering of genetically encoded gene promoter repressed by flavonoids for constructing intracellular sensor for molecular events

In recent years, Synthetic Biology has emerged as a new discipline where functions that were traditionally performed by electronic devices are replaced by "cellular devices". Those are genetically encoded circuits, constructed of DNA that are built from biological parts (aka bio-parts). The cellular devices can be used for sensing and responding to natural and artificial signals. However, a major challenge in the field is that the crosstalk between many cellular signaling pathways use the same signaling endogenous molecules that can result in undesired activation. To overcome this problem, we utilized a specific promoter that can activate genes with a natural, non-toxic ligand at a highly-induced transcription level with low background or undesirable off-target expression. Here we used the orphan aryl hydrocarbon receptor (AHR), a ligand-activated transcription factor that upon activation binds to specific AHR response elements (AHRE) of the Cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1) promoter. Flavonoids have been identified as AHR ligands. Data presented here shows successful creation of a synthetic gene "off" switch that can be monitored directly using an optical reporter gene. This is the first step towards bioengineering of a synthetic, nanoscale bio-part for constructing a sensor for molecular events.

bioengineering↗

bioRxiv2020

Mechanistic Dissection of Spatial Organization in NF-κB Signaling Pathways by Hybrid Simulations

The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-{kappa}B) is one of the most important transcription factors involved in the regulation of inflammatory signaling pathways. Inappropriate activation of these pathways has been linked to autoimmunity and cancers. Emerging experimental evidences have been showing the existence of elaborate spatial organizations for various molecular components in the pathways. One example is the scaffold protein tumor necrosis factor receptor associated factor (TRAF). While most TRAF proteins form trimeric quaternary structure through their coiled-coil regions, the N-terminal region of some members in the family can further be dimerized. This dimerization of TRAF trimers can drive them into higher-order clusters as a response to receptor stimulation, which functions as a spatial platform to mediate the downstream poly-ubiquitination. However, the molecular mechanism underlying the TRAF protein clustering and its functional impacts are not well-understood. In this article, we developed a hybrid simulation method to tackle this problem. The assembly of TRAF-based signaling platform at the membrane-proximal region is modeled with spatial resolution, while the dynamics of downstream signaling network, including the negative feedbacks through various signaling inhibitors, is simulated as stochastic chemical reactions. These two algorithms are further synchronized under a multiscale simulation framework. Using this computational model, we illustrated that the formation of TRAF signaling platform can trigger an oscillatory NF-{kappa}B response. We further demonstrated that the temporal patterns of downstream signal oscillations are closely regulated by the spatial factors of TRAF clustering, such as the geometry and energy of dimerization between TRAF trimers. In general, our study sheds light on the basic mechanism of NF-{kappa}B signaling pathway and highlights the functional importance of spatial regulation within the pathway. The simulation framework also showcases its potential of application to other signaling pathways in cells.

systems biology↗

bioRxiv2019

Computational mapping of the differentially expressed gene-lncRNA pairs present at the root nodule developmental stages of Arachis hypogaea

RNA-sequencing (RNA-seq) data analysis of the different stages of root nodules formation in peanut Arachis hypogaea investigate the genetic features. Genes related to the root nodules formations in this plant are extensively studied [1] [2] [3] [4] [5], but less information is present for their relations with long noncoding RNAs (lncRNAs). Bioinformatics techniques are utilised here to identify the novel lncRNAs present in the publically available RNA-seq data reported [6] for the different stages of root nodules formation in this plant. Highly correlated, significant, and Differentially Expressed (DE) gene-lncRNA pairs are also detected to understand the epigenetic control of lncRNA. These pairs are further differentiated between cis and trans antisense lncRNAs and lincRNAs based on their functions and positions from the genes. Obtained results are the catalogue for the highly correlated and significant DE gene-lncRNA pairs related to root nodules formation in A. hypogaea.

bioinformatics↗