Biology subjects

Krokhin, O. V.

Publications and source records attributed to Krokhin, O. V..

3 recordsLinked to original sources

bioRxiv2024

The phage protein paratox is a multifunctional metabolic regulator of Streptococcus

Streptococcus pyogenes, or Group A Streptococcus (GAS), is a commensal bacteria and human pathogen. Central to GAS pathogenesis is the presence of prophage encoded virulence genes. The conserved phage gene for the protein paratox (Prx) is genetically linked to virulence genes, but the reason for this linkage is unknown. Prx inhibits GAS quorum sensing and natural competence by binding the transcription factor ComR. However, inhibiting ComR does not explain the virulence gene linkage. To address this, we took a mass-spectrometry approach to search for other Prx interaction partners. The data demonstrates that Prx binds numerous DNA binding proteins and transcriptional regulators. We show binding of Prx in vitro with the GAS protein EndoS1 (SpyM3_0890) and the phage protein JM3 (SpyM3_1246). An EndoS1:Prx complex X-ray crystal structure reveals that EndoS1 and ComR possess a conserved Prx binding helix. Computational modelling predicts that the Prx binding helix is present in several, but not all, binding partners. Namely, JM3 lacks the Prx binding helix. As Prx is conformationally dynamic, this suggests partner-dependent binding modes. Overall, Prx acts as a metabolic regulator of GAS to maintain the phage genome. As such, Prx maybe a direct contributor to the pathogenic conversion of GAS.

biochemistry↗

bioRxiv2023

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

bioinformatics↗

bioRxiv2020

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry

The contribution of peptide amino-acid sequence to collision cross-section values (CCS) has been investigated using a dataset of [~]134,000 peptides of four different charge states (1+ to 4+). The migration data was acquired using a two-dimensional LC/trapped ion mobility spectrometry/quadrupole/time-of-flight MS analysis of HeLa cell digests created using 7 different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into 8 groups (four charges for both tryptic/non-tryptic peptides). Position dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of [~]0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptides ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position dependent ISP behaviour consistent with electrostatic interaction with the peptide macro-dipole, which affects the peptide helicity.

bioinformatics↗