Biology subjects

Phogat, S.

Publications and source records attributed to Phogat, S..

4 recordsLinked to original sources

bioRxiv2023

Characterization and Expression Analysis of Nitrate Reductase 6-1ABD Gene in Hexaploid Bread Wheat Under Different Nitrogen Regime

Nitrate reductase (NR) is the key rate-limiting enzyme of the nitrogen (N) assimilation process in plants, which has not been characterized in bread wheat under nitrogen stress, especially with respect to their homeologues. Total 9 NRs were identified and classified into 3 groups, which showed a close relationship with different wheat ancestors. The occurrence of N-responsive cis-acting regulatory elements like MYB, MYC, G-Box and GATA-motif confirmed their N-responsiveness. Expression of all the three groups of NR under N-stress revealed NR 6-1ABD group to be the most N-responsive, which was characterized further in detail. The study was carried out in two genotypes contrasting for their N-responsiveness (HD 2967: Highly responsive to applied N, and Choti Lerma: Less responsive to applied N) selected on the basis of field evaluation. Homeologous differences within a genotype were found much more than the genotypic differences of a specific homeologue coding sequence. Among the three homeologues, though NR 6-1D homeologue was found most responsive to N-stress, the contribution was maximum for this homeologue followed by NR 6-1A and least by NR 6-1B. We found that the expression of homeologues was linked to the presence of N-responsive cis- elements. All the homeologues of NR 6-1 in Choti Lerma were found less responsive to N-stress, in comparison to HD 2967, which might also be linked to N-use efficiency. Homeologous expression of NR 6-1ABD revealed negligible contribution of B-homeologue to N-stress. Homeologous differences of NR 6-1ABD was found much more than the genotypic differences. Hence, our study on wheat NR will be helpful in manipulating the specific homeologue of the NR gene in the future.

plant biology↗

bioRxiv2022

Protein Language Model for Prediction of Subcellular Localization of Protein Sequences from Gram-negative bacteria (ProtLM.SCL)

The prediction of bacterial protein Sub-Cellular Localization (SCL) is critical for antigen identification and reverse vaccinology, especially when determining protein localization in the lab is time consuming, expensive and not possible for all species. While PSORTb is one of the most widely used tool for predicting SCL, it has several limitations, including the tendency to label a large number of proteins as Unknown. To address these shortcomings, we present a protein language model capable of predicting the subcellular localization of a given protein (ProtLM.SCL) from gram-negative bacteria. By performing 10-fold cross validation on the PSORTb public data set, we demonstrate that ProtLM.SCL is more accurate and precise than PSORTb. When compared to empirically validated published data, our models also outperformed PSORTb, particularly when categorizing difficult occurrences.

bioinformatics↗

bioRxiv2022

T-cell receptor specific protein language model for prediction and interpretation of epitope binding (ProtLM.TCR)

The cellular adaptive immune response relies on epitope recognition by T-cell receptors (TCRs). We used a language model for TCRs (ProtLM.TCR) to predict TCR-epitope binding. This model was pre-trained on a large set of TCR sequences (~62.106) before being fine-tuned to predict TCR-epitope bindings across multiple human leukocyte antigen (HLA) of class-I types. We then tested ProtLM.TCR on a balanced set of binders and non-binders for each epitope, avoiding model shortcuts like HLA categories. We compared pan-HLA versus HLA-specific models, and our results show that while computational prediction of novel TCR-epitope binding probability is feasible, more epitopes and diverse training datasets are required to achieve a better generalized performances in de novo epitope binding prediction tasks. We also show that ProtLM.TCR embeddings outperform BLOSUM scores and hand-crafted embeddings. Finally, we have used the LIME framework to examine the interpretability of these predictions.

bioinformatics↗

bioRxiv2019

Boosting with ALVAC-HIV and AIDSVAX B/E enhances Env constant region 1 and 2 antibody-dependent cellular cytotoxicity

Induction of protective antibodies is a critical goal of HIV-1 vaccine development. One strategy is to induce non-neutralizing antibodies that kill virus-infected cells as these antibody specificities have been implicated in slowing HIV-1 disease progression and in protection. HIV-1 Env constant region 1 and 2 (C1C2) antibodies frequently contain potent antibody dependent cellular cytotoxicity (ADCC) making them a vaccine target. Here we explore the effect of delayed and repetitive boosting of RV144 vaccinee recipients with ALVAC/AIDSVAX B/E on the C1C2-specific antibody repertoire. It was found that boosting increased clonal lineage specific ADCC breadth and potency. A ligand crystal structure of a vaccine-induced broad and potent ADCC-mediating C1C2-specific antibody showed that it bound a highly conserved Env gp120 epitope. Thus, rationally designed boosting strategies to affinity mature these type of IgG C1C2-specific antibody responses may be one method by which to make an improved HIV vaccine with higher efficacy than seen in the RV144 trial.\n\nSignificanceOver one million people become infected with HIV-1 each year making the development of an efficacious HIV-1 vaccine an important unmet medical need. The RV144 human HIV-1 vaccine-regimen is the only HIV-1 clinical trial to date to demonstrate vaccine-efficacy. An area of focus has been on identifying ways by which to improve upon RV144 vaccine-efficacy. The RV305 HIV-1 vaccine-regimen was a follow-up boost of RV144 vaccine-recipients that occurred 6-8 years after the conclusion of RV144. Our studies focused on the effect of delayed boosting in humans on the vaccine-induced antibody repertoire. It was found that boosting with a HIV-1 Env vaccine increased antibody-mediated effector function potency and breadth.

immunology↗