Biology subjects

Murad, T.

Publications and source records attributed to Murad, T..

3 recordsLinked to original sources

bioRxiv2023

Viral protease NIa-Pro increases MEDIATOR SUBUNIT16 cleavage and plant susceptibility to turnip mosaic virus and its aphid vector Myzus persicae.

Plant viruses, such turnip mosaic virus (TuMV), both trigger and inhibit host plant defense responses, including defenses that target their insect vectors, such as aphids. TuMV infection and its protein, NIa-Pro (nuclear inclusion protease a), suppress aphid-induced plant defenses, however the mechanisms of this suppression are still largely unknown. In this study, we determined that NIa-Pros protease activity is required to increase aphid performance on host plants and that 40 transcripts with predicted NIa-Pro cleavage sequences are regulated in Arabidopsis plants challenged with aphids and/or virus compared to healthy controls. One of the candidates, MEDIATOR 16 (MED16), regulates the transcription of ethylene (ET)/jasmonic acid (JA)-dependent defense responses against necrotrophic pathogens. Using immunoblots, mutants, bioassays, and qRT-PCR, we show that a nuclear localization signal is cleaved from MED16 in virus-infected plants and in the presence of NIa-Pro along with the presence of aphids, suggesting MED16 functions in the nucleus may be impacted. Consistent with this, aphid induction of the MED16-dependent transcript of PLANT DEFENSIN 1.2 (PDF1.2), was reduced in virus-infected plants and in plants expressing NIa-Pro compared to controls, and NIa-Pros protease activity was required for this reduction. Finally, we show performance of both the virus and the aphid vector was enhanced on med16 mutant Arabidopsis compared to controls. Overall, this study demonstrates MED16 regulates defense responses against both the virus and the aphid and provides insights into the mechanism by which TuMV suppresses anti-virus and anti-herbivore defenses.

plant biology↗

bioRxiv2023

Advancing Protein-DNA Binding Site Prediction: Integrating Sequence Models and Machine Learning Classifiers

Predicting protein-DNA binding sites is a challenging computational problem in the field of bioinformatics. Identifying the specific residues where proteins bind to DNA is of paramount importance, as it enables the modeling of their interactions and facilitates downstream studies. Nevertheless, the development of accurate and efficient computational methods for this task remains a persistent challenge. Accurate prediction of protein-DNA binding sites has far-reaching implications for understanding molecular mechanisms, disease processes, drug discovery, and synthetic biology applications. It helps bridge the gap between genomics and functional biology, enabling researchers to uncover the intricacies of cellular processes and advance our knowledge of the biological world. The method used to predict DNA binding residues in this study is a potent combination of conventional bioinformatics tools, protein language models, and cutting-edge machine learning and deep learning classifiers. On a dataset of protein-DNA binding sites, our model is meticulously trained, and it is then rigorously examined using several experiments. As indicated by higher predictive behavior with AUC values on two benchmark datasets, the results show superior performance when compared to existing models. The suggested model has a strong capacity for generalization and shows specificity for DNA-binding sites. We further demonstrated the adaptability of our model as a universal framework for binding site prediction by training it on a variety of protein-ligand binding site datasets. In conclusion, our innovative approach for predicting protein-DNA binding residues holds great promise in advancing our understanding of molecular interactions, thus paving the way for several groundbreaking applications in the field of molecular biology and genetics. Our approach demonstrated efficacy and versatility underscore its potential for driving transformative discoveries in biomolecular research.

bioinformatics↗

bioRxiv2023

Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection

The genetic code for many different proteins can be found in biological sequencing data, which offers vital insight into the genetic evolution of viruses. While machine learning approaches are becoming increasingly popular for many "Big Data" situations, they have made little progress in comprehending the nature of such data. One such area is the t-distributed Stochastic Neighbour Embedding (t-SNE), a generalpurpose approach used to represent high dimensional data in low dimensional (LD) space while preserving similarity between data points. Traditionally, the Gaussian kernel is used with t-SNE. However, since the Gaussian kernel is not data-dependent, it determines each local bandwidth based on one local point only. This makes it computationally expensive, hence limited in scalability. Moreover, it can misrepresent some structures in the data. An alternative is to use the isolation kernel, which is a data-dependent method. However, it has a single parameter to tune in computing the kernel. Although the isolation kernel yields better performance in terms of scalability and preserving the similarity in LD space, it may still not perform optimally in some cases. This paper presents a perspective on improving the performance of t-SNE and argues that kernel selection could impact this performance. We use 9 different kernels to evaluate their impact on the performance of t-SNE, using SARS-CoV-2 "spike" protein sequences. With three different embedding methods, we show that the cosine similarity kernel gives the best results and enhances the performance of t-SNE.

bioinformatics↗