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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. 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.

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.Abstract Figure

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.

DNA-wrapped single walled carbon nanotube (SWNT) conjugates have distinct 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 data sets for ∼100 DNA-SWNT conjugates, narrowed down by a selective evolution protocol starting from a pool of ∼10sup10/supunique DNA-SWNT candidates, are used to train machine learning (ML) models to predict 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 neural network classifiers (convolutional or artificial) and SVM regression models leads to the best predictions of both high and low response sequences. With our ML approaches, we discovered five DNA-SWNT sensors with higher fluorescence intensity response to serotonin than obtained previously. Overall, the explored ML approaches, shown to predict useful DNA sequences, can be used for discovery of DNA-based sensors and nanobiotechnologies.

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-diphenylsulfone-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 µM 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.

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 μM 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.

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. After equilibration, distances and angles between capsid proteins in the capsid segments are larger for the systems in which the nanoparticles bind at interfaces of capsid proteins. Over time, 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.

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.Abstract Figure

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

Genetically encoded macrocyclic peptide libraries with unnatural pharmacophores are valuable sources for the discovery of ligands for many targets of interest. Traditionally, generation of such libraries employs "early stage" incorporation of unnatural building blocks into the chemically or translationally produced macrocycles. Here, we describe a divergent late-stage approach to such libraries starting from readily available starting material: genetically encoded libraries of peptides. A diketone linchpin 1,5-dichloropentane-2,4-dione converts peptide libraries displayed on phage to 1,3-diketone bearing macrocyclic peptides (DKMP): shelf-stable precursors for Knorr pyrazole synthesis. Ligation of diverse hydrazine derivatives onto DKMP libraries displayed on phage that carries silent DNA-barcodes yields macrocyclic libraries in which the amino acid sequence and the pharmacophore are encoded by DNA. Selection of this library against carbonic anhydrase enriched macrocycles with benzenesulfonamide pharmacophore and nanomolar Kd. The methodology described in this manuscript can graft diverse pharmacophores into many existing genetically encoded phage libraries and significantly increase the value of such libraries in molecular discoveries.

The effects of nanofiller chemistry and geometry on static and dynamic properties of an aromatic polyester, poly (trimethylene terephthalate), were addressed thanks to long-run classical molecular dynamics simulation. Two carbon nanofillers, graphene and carbon nanotube, were employed, where graphene was used in pristine and functionalized forms and carbon nanotube was used in two different diameters. The nanofiller geometry and chemistry were found to exert significant effects on conformation and dynamic behavior of PTT chain at the interface within the time scale the simulation was performed. It was found that PTT chain underwent interaction of van der Waals type with nanofiller via two subsequent phases, adsorption and orientation. The former stage, with definite characteristic time, involved translation of polymer chain toward interface while the latter was controlled by vibrational motions of chain atoms. The consequence of interaction was an increase in conformational order of polymer chain by transition to folded shape being favorable for any subsequent structural ordering (crystallization). The interaction of polymer with nanofiller gave rise to a reduction in overall mobility of polymer chain characterized by crossover from normal diffusive motion to subdiffusive mode.

Conformation and dynamics of poly (2-ethyl-2-oxazoline) (PEtOx) chains attached at one end to graphene nanosheet (GNS) in absence of solvent was studied using atomistic molecular dynamics (MD) simulations. The MD computations of the PEtOx brushes were carried out within canonical ensemble at 300 K for 10 ns under gradient graft density and chain length regimes. Additionally, the influence of PEtOx-GNS interactions on the brush structure was explored through system visualization and monitoring of the total energy, gyration radius, and mean square displacement. The end-tethered PEtOx chains were found to reveal strong tendency to be adsorbed to the GNS surface due primarily to Van der Waals interactions between PEtOx and GNS atoms. The PEtOx brush structure was also found to be influenced by PEtOx chain length and graft density significantly. PEtOx chains dynamics was investigated and learned to be dependent, in terms of mode of movement, on polymer length and graft density.

The conformational properties of semiflexible polymer chains at their interface with nanoscale organic fillers are classified as a challenging subject due to the vast complexities involved, apart from being the most critical phase of transcrystallization. An extensive computational effort based on atomistic molecular dynamics has been undertaken in this study in order to elucidate the behavior of a single chain of a model aliphatic polyester, poly(ethylene succinate), in the vicinity of a single-walled carbon nanotube to gain new physical insights into the conformational characteristics of this type of polymer. The core finding is that the polymer chain transits from a coiled to a folded shape in the process of adsorption on the nanotube surface being a compromise of the dominant van der Waals interactions and the chain configurational entropy. Varying temperature and chain length, as well as the nanotube curvature, has been highlighted to be influential on chain order parameter and spatial arrangement. C...