Biology subjects

Leng, Q.

Publications and source records attributed to Leng, Q..

4 recordsLinked to original sources

bioRxiv2024

Filamentation and proline inhibition of glutamate kinase

Glutamate kinase (GK) is the first committed enzyme in the proline biosynthesis pathway. Belonging to amino acid kinase (AAK) superfamily, most prokaryotic GKs have an additional PseudoUridine synthase and Archaeosine transglycosylase (PUA) domain at the C-terminus, while the function of the PUA domain in GK is poorly understood. Here, we find that Escherichia coli GK (EcGK) assembles into filaments and bundles in the state of apo and proline binding. Using cryogenic electron microscopy, we determine the high-resolution structures of EcGK filaments and bundles. The PUA domain is necessary for EcGK filaments and bundles, and the main interfaces have been clearly defined. The feedback inhibitor proline binds at the same pocket as substrate glutamate, inducing conformational changes on nearby regulatory loop which facilitate proline binding. The PUA domain stabilizes the regulatory loop and contributes to proline feedback inhibition. This study reports the special filament-based assembly of EcGK at apo and proline binding state. The first proline binding structure in the GK family illustrates the feedback inhibition mechanism. Intriguingly, the PUA domain is involved in both filamentation and feedback inhibition of EcGK, revealing the versatility of this ancient domain.

biochemistry↗

bioRxiv2023

Dynamic atP5CS2 Filament Facilitates Substrate Channeling

In plants, the rapid accumulation of proline is a common response to combat abiotic stress. Delta-1-pyrroline-5-carboxylate synthase (P5CS) is a rate-limiting enzyme in proline synthesis, catalyzing the initial two-step conversion from glutamate to proline. Here, we determine the first structure of plant P5CS. Our results show that Arabidopsis thaliana P5CS2 (atP5CS2) can form enzymatic filaments in a substrate-sensitive manner. The destruction of atP5CS filaments by mutagenesis leads to a significant reduction in enzymatic activity. Furthermore, separate activity tests on two domains reveals that filament-based substrate channeling is essential for maintaining the high catalytic efficiency of atP5CS. Our study demonstrates the unique mechanism for the efficient catalysis of P5CS, shedding light on the intricate mechanisms underlying plant proline metabolism and stress response. Therefore these findings provide potential avenues for crop genetically modified breeding.

biochemistry↗

bioRxiv2020

Antigenic evolution on global scale reveals potential natural selection of SARS-CoV-2 by pre-existing cross-reactive T cell immunity

The mutation pattern of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is constantly changing with the places of transmission, but the reason remains to be revealed. Here, we presented the study that comprehensively analyzed the potential selective pressure of immune system restriction, which can drive mutations in circulating SARS-CoV-2 isolates. The results showed that the most common mutation sites of SARS-CoV-2 proteins were located on the non-structural protein ORF1ab and the structural protein Spike. Further analysis revealed mutations in cross-reactive epitopes between SARS-CoV-2 and seasonal coronavirus may help SARS-CoV-2 to escape cellular immunity under the long-term and large-scale community transmission. Meanwhile, the mutations on Spike protein may enhance the ability of SARS-CoV-2 to enter the host cells and escape the recognition of B-cell immunity. This study will increase the understanding of the evolutionary direction and warn about the potential immune escape ability of SARS-CoV-2, which may provide important guidance for the potential vaccine design.

bioinformatics↗

bioRxiv2019

ProGeo-neo: a Customized Proteogenomic Workflow for Neoantigen Prediction and Selection

Neoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Currently almost all neoantigen prediction tools are based on genomics data. Here we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeo-neo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only. The pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.

bioinformatics↗