Biology subjects

Vukovic, L.

Publications and source records attributed to Vukovic, L..

10 recordsLinked to original sources

bioRxiv2025

Unveiling the structural insights of PFAS-β-lactoglobulin binding mechanism mediating neuronal toxicity in neonates.

The strong, polar-covalent nature of C-F bonds contributes to the forever nature of per- and polyfluoroalkyl (PFAS) substances. PFAS are toxic to humans. Here, we have examined the ability of the small, globular, milk protein {beta}-lactoglobulin to bind PFAS. The protein transports hydrophobic and amphiphilic compounds, including retinol and fatty acids, for vision and brain development; therefore, underscoring its interactions with PFAS is significant. The crystal structures of {beta}-lactoglobulin complexed with PFOA (Perfluorooctanoic acid) at 2.0 ([A]), PFOS (Perfluorooctanesulfonic acid) at 2.5 ([A]), and PFDA (Perfluorodecanoic acid) at 2.0 ([A]) reveal high affinity of the compounds for the central calyx of {beta}-lactoglobulin, which is the canonical retinol and fatty acid binding site. Analyses of the data indicate significant hydrophobic interactions stabilizing the binding of the PFAS hydrophobic "tails" within the calyx and interactions between Lys60 and Lys69 and PFAS polar head groups. Comparative structural analysis revealed the presence of an open conformation of the EF loop containing the Glu89 latch residue in the complexed structures vis-a-vis the apo-form. Molecular dynamics (MD) simulations revealed high stability of the PFAS binding and attainment of energy minima in all complexes. The average binding energy of PFDA in {beta}-lactoglobulin calyx was -25 kcal/mol, which was higher than PFOS (-21 kcal/mol) and PFOA (-23 kcal/mol) due to increased van der Waals interactions of the longer hydrophobic chain of PFDA with {beta}-lactoglobulin. This work advances a mechanism by which {beta}-lactoglobulin can recruit PFAS and act as a transporter for the "forever" chemical, potentially mediating its neurotoxicity.

biophysics↗

bioRxiv2025

A Two-Step Synthesis of Covalent Genetically-Encoded Libraries of Peptide-Derived Macrocycles (cGELs) enables use of electrophiles with diverse reactivity

Genetically-encoded libraries of peptide-derived macrocycles containing electrophile warheads (cGELs) can be used to identify potent and selective covalent ligands for protein targets. Such cGELs are synthesized either by incorporation of unnatural amino acids that display mild electrophiles on their side chains or by chemical post-translational modification (cPTM) of mRNA or phage-displayed peptide libraries. Here we investigate fundamental barriers to the synthesis of cGELs. We observe that a previously reported cPTM that proceeds in neutral-to-basic conditions creates mixtures of regioisomers. The complexity of the resulting mixture scales with the electrophilicity of the warhead used in the linker, with some electrophiles being not suitable for use under basic conditions. In contrast, use of a Knorr-pyrazole cPTM enables attachment of electrophiles in acidic pH, thus preventing unwanted reactions with nucleophilic sidechains. The Electrophile is activated only upon mixture with the desired protein target in neutral pH. We use this approach to generate a cGEL with alkyne-bearing macrocycles and use it to identify covalent macrocyclic ligands for pyruvate kinase 2 (PKM2). Our results suggest that construction of cGELs should be performed in conditions that silence the electrophiles (e.g., acidic environment) to prevent unwanted side reactions. In addition to the Knorr-pyrazole method, many other biocompatible bond-forming processes that proceed in mildly acidic pH are likely to be equally effective in constructing cGELs.

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↗

bioRxiv2020

Binding affinity and conformational preferences influence kinetic stability of short oligonucleotides on carbon nanotubes

DNA-wrapped single walled carbon nanotubes (SWNTs) have found a widespread use in a variety of nanotechnology applications. Yet, the relationship between structural conformation, binding affinity and kinetic stability of these polymers on SWNTs remains poorly understood. Here, we used molecular dynamics (MD) simulations and experiments to explore this relationship for short oligonucleotides adsorbed on SWNTs. First, using classical MD simulations of oligonucleotide-(9,4)-SWNT hybrid complexes, we explored the relationship between ssDNA and ssRNA surface conformation and sequence chemistry. We screened the conformation of 36 sequences of short ssDNA and ssRNA polymers on (9,4) SWNT, where the contour lengths were selected so the polymers can, to a first approximation, wrap once around the SWNT circumference. From these screens, we identified structural motifs that we broadly classified into "rings" and "non-rings." Then, several sequences were selected for detailed investigations. We used temperature replica exchange MD calculations to compute two-dimensional free energy landscapes characterizing the conformations of select sequences. "Ring" conformations seemed to be driven primarily by sequence chemistry. Specifically, strong (n,n+2) nucleotide interactions and the ability of the polymer to form compact structures, as for example, through sharp bends in the nucleotide backbone, correlated with ring-forming propensity. However, ring-formation probability was found to be uncorrelated with free energy of oligonucleotide binding to SWNTs ({Delta}Gbind). Conformational analyses of oligonucleotides, computed free energy of binding of oligonucleotides to SWNTs, and experimentally determined kinetic stability measurements show that {Delta}Gbind is the primary correlate for kinetic stability. The probability of the sequence to adopt a compact, ring-like conformation is shown to play a secondary role that still contributes measurably to kinetic stability. For example, sequences that form stable compact rings (C-rich sequences) could compensate for their relatively lower {Delta}Gbind and exhibit kinetic stability, while sequences with strong {Delta}Gbind (such as (TG)3(GT)3) were found to be kinetically stable despite their low ring formation propensity. We conclude that the stability of adsorbed oligonucleotides is primarily driven by its free energy of binding and that if ring-like structural motifs form, they would contribute positively to stability. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/939918v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@6483e6org.highwire.dtl.DTLVardef@10f9e60org.highwire.dtl.DTLVardef@66c8c3org.highwire.dtl.DTLVardef@68ff4_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2018

Ultralarge Modulation of Single Wall Carbon Nanotube Fluorescence Mediated by Neuromodulators Adsorbed on Arrays of Oligonucleotide Rings

Non-covalent interactions between single-stranded DNA (ssDNA) oligonucleotides and single wall carbon nanotubes (SWNTs) have provided a unique class of tunable chemistries for a variety of applications. However, mechanistic insight into both the photophysical and intermolecular phenomena underlying their utility is lacking, resulting in obligate heuristic approaches for producing ssDNA-SWNT based technologies. In this work, we present an ultrasensitive \"turn-on\" nanosensor for neuromodulators dopamine and norepinephrine with strong {Delta}F/F0 of up to 3500%, a signal appropriate for in vivo imaging, and uncover the photophysical principles and intermolecular interactions that govern the molecular recognition and fluorescence modulation of this nanosensor synthesized from the non-covalent conjugation of (GT)6 ssDNA strands on SWNTs. The fluorescence modulation of the ssDNA-SWNT conjugate is shown to exhibit remarkable sensitivity to the ssDNA sequence chemistry, length, and surface density, providing a wealth of parameters with which to tune nanosensor dynamic range and strength of fluorescence turn-on. We employ classical and quantum mechanical molecular dynamics simulations to rationalize our experimental findings. Calculations show that (GT)6 ssDNA form ordered loops around SWNT, inducing periodic surface potentials that modulate exciton recombination lifetimes. Further evidence is presented to elucidate how analyte binding modulates SWNT fluorescence. We discuss the implications of our findings for SWNT-based molecular sensing applications.

neuroscience↗