Biology subjects

Kelich, P.

Publications and source records attributed to Kelich, P..

8 recordsLinked to original sources

bioRxiv2026

Substrate biasing in UCHL5 proteoforms

Proteolytic deubiquitinating enzymes bridge a gap in substrate recognition through complex regulatory mechanisms. A growing portion of these are accomplished through proteoforms that uniquely control association and diverse sets of cleavage capabilities that relay distinct physiological outcomes. This study describes substrate biasing governed by UCHL5 proteoforms. It demonstrates that N-terminal ubiquitination activates the enzyme towards monoubiquitin substrates, a feature that is conserved across UCHL5 homologs. Crystallographic and spectroscopic data suggest that the N-terminal ubiquitin binds intramolecularly in an allosteric binding site and inhibits branched chain substrate cleavage. Association with Rpn13/Adrm1 relieves this inhibition and reestablishes its ability to debranch, potentially controlling nonspecific debranching compared to retention of needed activity on the 26S proteasome. Collectively, this study describes the molecular basis for substrate selectivity in a deubiquitinating enzyme, an unexplored area in the enzymes that counteract ubiquitin E3 ligases.

biochemistry↗

bioRxiv2026

Structure and Mechanism of a Two-component Lanthipeptide Toxin

The enterococcal cytolysin is a two-component lanthipeptide natural product that co-operatively kills mammalian cells and bacteria. It is a virulence factor in typically commensal strains of gut-dwelling Enterococcus faecalis and causes a fatal form of alcoholic hepatitis. Despite its clinical significance, little is known about the mechanism of action. We present here the molecular details of cytolysins remarkable bioactivity. A high-resolution cryo-electron microscopy structure revealed highly ordered tubular assemblies comprising the two subunits. We further demonstrate these structures disrupt the membranes of eukaryotic and bacterial cells. These results provide the first high-resolution structure of two distinct lanthipeptides interacting with one another and offer an explanation for the unique bioactivity of the cytolysin toxin.

biochemistry↗

bioRxiv2024

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors Across Multiple Spectral Wavelengths

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT) based sensors for chemically-specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing datasets predict the change in the fluorescence emission in response to serotonin, {Delta}F/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training dataset leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

bioengineering↗

bioRxiv2023

BinderSpace: A Package for Sequence Space Analyses for Datasets of Affinity-Selected Oligonucleotides and Peptide-Based Molecules

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/528758v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@cf45aforg.highwire.dtl.DTLVardef@1e819eaorg.highwire.dtl.DTLVardef@fd9340org.highwire.dtl.DTLVardef@1ce7f78_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Characterizing the Interactions of Cell Membrane-Disrupting Peptides with Lipid-Functionalized Single-Walled Carbon Nanotube Systems for Antimicrobial Screening

Lipid-functionalized single-walled carbon nanotubes (SWNTs) have garnered significant interest for their potential use in a wide range of biomedical applications. In this work, we used molecular dynamics simulations to study the equilibrium properties of SWNTs surrounded by the phosphatidylcholine (POPC) corona phase, and their interactions with three cell membrane disruptor peptides: colistin, TAT peptide, and crotamine-derived peptide. Our results show that SWNTs favor asymmetrical positioning within the POPC corona, so that one side of the SWNT, covered by the thinnest part of the corona, comes in contact with charged and polar functional groups of POPC and water. We also observed that colistin and TAT insert deeply into POPC corona, while crotamine-derived peptide only adsorbs to the corona surface. Compared to crotamine-derived peptide, colistin and TAT also induce larger perturbations in the thinnest region of the corona, by allowing more water molecules to directly contact the SWNT surface. In separate simulations, we show that three examined peptides exhibit similar insertion and adsorption behaviors when interacting with POPC bilayers, confirming that peptide-induced perturbations to POPC in conjugates and bilayers are similar in nature and magnitude. Furthermore, we observed correlations between the peptide-induced structural perturbations and the near-infrared emission of the lipid-functionalized SWNTs, which suggest that the optical signal of the conjugates transduces the morphological changes in the lipid corona. Overall, our findings indicate that lipid-functionalized SWNTs could serve as simplified cell membrane model systems for pre-screening of new antimicrobial compounds that disrupt cell membranes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/525557v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@4c5645org.highwire.dtl.DTLVardef@1b3cd30org.highwire.dtl.DTLVardef@1643c6corg.highwire.dtl.DTLVardef@1bb096e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Genetically-Encoded Discovery of Perfluoroaryl-Macrocycles that Bind to Albumin and Exhibit Extended Circulation in-vivo

In this paper, we report selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SNAr chemistry. Modification of phage-displayed libraries SXCXnC-phage, n=3-5, where X is any amino acid except for cysteine by decafluoro-diphenylsulfone (DFS), yields genetically-encoded library of octafluoro-diphen-ylsulfone-crosslinked macrocycles (OFS-SXCXnC-phage). Selection from these libraries using albumin as a bait identified a family of significantly enriched perfluoroaryl-macrocycles. Synthesis of perfluoroaryl-macrocycles predicted by phage display and testing their binding properties by 19F NMR and fluorescent polarization identified OFS-macrocycle with SICRFFC sequence as the most potent albumin binder. We observed that OFS-macrocycles slowly react with biological nucleophiles such as glutathione. Replacing decafluoro-diphenylsulfone by nearly isosteric pentafluorophenyl sulfide yielded perfluorophenylsulfide (PFS)-crosslinked macrocycles devoid of undesired reactivity. The augmented lead PFS-macrocycle with SICRFFC sequence exhibited KD = 4-6 M towards human serum albumin and similar affinities towards rat and mouse albumins. When injected in mouse, the PFS-SICRFFCGGG compound was significantly retained in circulation in vivo when compared to control PFS-macrocyclic peptide. The perfluoroaryl-macrocycles with SICRFFC motif are the smallest known peptide macrocycle with significant affinity for human albumin and they are a productive starting point for future development of compact macrocycles with predictable circulation half-life in vivo. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/504611v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@cd54d2org.highwire.dtl.DTLVardef@7bd9aborg.highwire.dtl.DTLVardef@10f7515org.highwire.dtl.DTLVardef@1cfc49a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Computational modeling of virucidal inhibition mechanism for broad-spectrum antiviral nanoparticles and HPV16 capsid segments

Solid core nanoparticles coated with sulfonated ligands that mimic heparan sulfate proteoglycans (HSPG) can exhibit virucidal activity against many viruses that utilize HSPG interactions with host cells for the initial stages of the infection. How the interactions of these nanoparticles with large capsid segments of HSPG-interacting viruses lead to their virucidal activity has been unclear. Here, we describe the interactions between sulfonated nanoparticles and segments of the human papilloma virus type 16 (HPV16) capsids using atomistic molecular dynamics simulations. The simulations demonstrate that nanoparticles primarily bind at interfaces of two HPV16 capsid proteins. Insertions of nanoparticles at these interfaces leads to increased separation in distances and angles between capsid proteins. As the time progresses, the nanoparticle binding can lead to breaking of contacts between two neighboring proteins. The revealed mechanism of nanoparticles targeting the interfaces between pairs of capsid proteins can be utilized for designing new generations of virucidal materials and contribute to the development of new broad-spectrum non-toxic virucidal materials. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/457236v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@16d1f17org.highwire.dtl.DTLVardef@1145513org.highwire.dtl.DTLVardef@1dd9185org.highwire.dtl.DTLVardef@17cee30_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Machine learning enables discovery of DNA-carbon nanotube sensors for serotonin

DNA-wrapped single walled carbon nanotube (SWNT) conjugates have remarkable optical properties leading to their use in biosensing and imaging applications. A critical limitation in the development of DNA-SWNT sensors is the current inability to predict unique DNA sequences that confer a strong analyte-specific optical response to these sensors. Here, near-infrared (nIR) fluorescence response datasets for ~100 DNA-SWNT conjugates, narrowed down by a selective evolution protocol starting from a pool of ~1010 unique DNA-SWNT candidates, are used to train machine learning (ML) models to predict new unique DNA sequences with strong optical response to neurotransmitter serotonin. First, classifier models based on convolutional neural networks (CNN) are trained on sequence features to classify DNA ligands as either high response or low response to serotonin. Second, support vector machine (SVM) regression models are trained to predict relative optical response values for DNA sequences. Finally, we demonstrate with validation experiments that integrating the predictions of ensembles of the highest quality CNN classifiers and SVM regression models leads to the best predictions of both high and low response sequences. With our ML approaches, we discovered five new DNA-SWNT sensors with higher fluorescence intensity response to serotonin than obtained previously. Overall, the explored ML approaches introduce an important new tool to predict useful DNA sequences, which can be used for discovery of new DNA-based sensors and nanobiotechnologies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/457145v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@9e1a2corg.highwire.dtl.DTLVardef@1c84e68org.highwire.dtl.DTLVardef@1939d1eorg.highwire.dtl.DTLVardef@30424e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗