Biology subjects

Glukhov, E.

Publications and source records attributed to Glukhov, E..

7 recordsLinked to original sources

bioRxiv2026

AI-Accelerated Structure Elucidation of Boavistamides A-C, Cyclic Depsipeptides from a Marine Filamentous Cyanobacterium Collected in Cabo Verde

Boavistamide A (1), a new alkyne-containing cyclic depsipeptide featuring the rare 3-amino-2-methyl-7-octynoic acid (AMOYA) moiety, was discovered along with two structurally related analogs, boavistamides B and C (2 and 3), from a filamentous marine cyanobacterium collected on Boa Vista Island, Cabo Verde. Their isolation was guided by antiplasmodial activity, GNPS MS/MS molecular networking, LC-MS profiling, and dereplication using the MarinLit database. The planar structures of boavistamides A-C (1-3) were elucidated through comprehensive HRMS and 1D/2D NMR analyses, with annotation support from AI-based tools SMART-NMR 2.1 and DeepSAT. The absolute configurations were established using Marfeys analysis and L-Phe-OMe coupling, complemented by NMR-based conformational studies. Boavistamides A and B exhibited moderate antiplasmodial activity with no mammalian cell cytotoxicity. Microscopic observations and metagenomic binning identified the producer strain as belonging to the genus Okeania (Microcoleaceae). These results expand the chemical diversity of AMOYA-containing cyanobacterial metabolites and highlight the utility of integrated metabolomics and AI-assisted workflows for natural product discovery from environmental samples. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/732064v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@6577feorg.highwire.dtl.DTLVardef@1a8d656org.highwire.dtl.DTLVardef@18fc299org.highwire.dtl.DTLVardef@130d3d0_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Phospho-Tune: Enhanced Structural Modeling of Phosphorylated Protein Interactions

In computational biology, accurate prediction of phosphopeptide-protein complex structures is essential for understanding cellular functions and advancing drug discovery and personalized medicine. While AlphaFold has significantly improved protein structure prediction, it faces accuracy challenges in predicting structures of complexes involving phosphopeptides possibly due to structural variations introduced by phosphorylation in the peptide component. Our study addresses this limitation by refining AlphaFold to improve its accuracy in modeling these complex structures. We employed weighted metrics for a comprehensive evaluation across various protein families. The enhanced model notably outperforms the original AlphaFold, showing a substantial increase in the weighted average local distance difference test (lDDT) scores for peptides: from 52.74 to 76.51 in the Top 1 model and from 56.32 to 77.91 in the Top 5 model. These advancements not only deepen our understanding of the role of phosphorylation in cellular signaling but also have extensive implications for biological research and the development of innovative therapies.

bioinformatics↗

bioRxiv2023

MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction

The precise prediction of Major Histocompatibility Complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFolds capabilities by fine-tuning it with a specialized dataset comprised by exclusively high-resolution MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFolds original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of MHC-peptide interaction prediction. A comparative analysis was conducted against the homology-modeling-based method Pandora [13], as well as the AlphaFold multimer model [8]. Our results demonstrate that our fine-tuned model outperforms both in terms of RMSD (median value is 0.65 [A]) but also provides enhanced predicted lDDT scores, offering a more reliable assessment of the predicted structures. These advances have substantial implications for computational immunology, potentially accelerating the development of novel therapeutics and vaccines by providing a more precise computational lens through which to view MHC-peptide interactions.

bioinformatics↗

bioRxiv2023

Structure and Biosynthesis of Hectoramide B, a Linear Depsipeptide from the Marine Cyanobacterium Moorena producens JHB Discovered via Co-culture with Candida albicans

The tropical marine cyanobacterium Moorena producens JHB is a prolific source of secondary metabolites with potential biomedical utility. Previous studies of this strain led to the discovery of several novel compounds such as the hectochlorins and jamaicamides; however, bioinformatic analyses of its genome suggested that there were many more cryptic biosynthetic gene clusters yet to be characterized. To potentially stimulate the production of novel compounds from this strain, it was co-cultured with Candida albicans. From this experiment, we observed the increased production of a new compound that we characterize here as hectoramide B. Bioinformatic analysis of the M. producens JHB genome enabled the identification of a putative biosynthetic gene cluster responsible for hectoramide B biosynthesis. This work demonstrates that co-culture competition experiments can be a valuable method to facilitate the discovery of novel natural products from cyanobacteria. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/547815v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1b507d4org.highwire.dtl.DTLVardef@152376org.highwire.dtl.DTLVardef@1cb410dorg.highwire.dtl.DTLVardef@11bcabc_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Development of Highly Selective Epoxyketone-based Plasmodium Proteasome Inhibitors with Negligible Cytotoxicity

Here we present remarkable epoxyketone-based proteasome inhibitors with low nanomolar in vitro potency for blood-stage Plasmodium falciparum and low cytotoxicity for human cells. Our best compound has more than 2,600-fold greater selectivity for erythrocytic-stage P. falciparum over HepG2 cells, which is largely driven by the accommodation of the parasite proteasome for a O_SCPLOWDC_SCPLOW-amino acid in the P3 position and the preference for a difluorobenzyl group in the P1 position. These compounds also significantly reduce parasitemia in a P. berghei mouse infection model and prolong survival of animals by an average of 6 days. The current epoxyketone inhibitors are ideal starting compounds for orally bioavailable anti-malarial drugs.

biochemistry↗

bioRxiv2021

A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters

Microbial specialized metabolites are an important source of and inspiration for many pharmaceutical, biotechnological products and play key roles in ecological processes. However, most bioactivity-guided isolation and identification methods widely employed in metabolite discovery programs do not explore the full biosynthetic potential of an organism. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical compound class). Our pipeline offers an efficient method to link biosynthetic genes to known, analogous, or cryptic metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired data sets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra to their corresponding previously experimentally validated biosynthetic genes (i.e., via NMR or genetic engineering). Finally, we demonstrated that this new approach is a substantial step towards making in silico (and even de novo) structure predictions for peptidic metabolites and a glycosylated terpene. Altogether, we conclude that NPOmix minimizes the need for culturing and facilitates specialized metabolite isolation and structure elucidation based on integrative omics mining. SignificanceThe pace of natural product discovery has remained relatively constant over the last two decades. At the same time, there is an urgent need to find new therapeutics to fight antibiotic-resistant bacteria, cancer, tropical parasites, pathogenic viruses, and other severe diseases. Here, we introduce a new machine learning algorithm that can efficiently connect metabolites to their biosynthetic genes. Our Natural Products Mixed Omics (NPOmix) tool provides access to genomic information for bioactivity, class, (partial) structure, and stereochemistry predictions to prioritize relevant metabolite products and facilitate their structural elucidation. Our approach can be applied to biosynthetic genes from bacteria (used in this study), fungi, algae, and plants where (meta)genomes are paired with corresponding mass fragmentation data.

bioinformatics↗

bioRxiv2017

MetaRiPPquest: A Peptidogenomics Approach for the Discovery of Ribosomally Synthesized and Post-translationally Modified Peptides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that include many antibiotics and a variety of other bioactive compounds. While recent breakthroughs in RiPP discovery raised the challenge of developing new algorithms for their analysis, peptidogenomic-based identification of RiPPs by combining genome/metagenome mining with analysis of tandem mass spectra remains an open problem. We present here MetaRiPPquest, a software tool for addressing this challenge that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just six genomic and metagenomic datasets, MetaRiPPquest identified 27 known and discovered 5 novel RiPP natural products.

bioinformatics↗