Photophysics of Molecular Probes for Amyloid-β Detection: Computational Insights into the Roles of Probe Linker and Functional Groups.

In this computational study, density functional theory (DFT) and time-dependent DFT methods (TD-DFT) were employed to study the optical properties of six families of molecules with donor (D), bridge (B), and acceptor (A) fragments that have potential for use as fluorescent molecular probes for the early detection of Alzheimer's disease. After validating our computational method against experimental data, using X-ray and absorption data, the equilibrium geometries and wave functions of the ground and first singlet excited states were systematically studied. Our simulations demonstrate that the S states of these rod-like D-B-A fluorescent probes are twisted intramolecular charge transfer states with a predominant highest occupied molecular orbital-least unoccupied molecular orbital (HOMO-LUMO) character, the former localized primarily at the donor, whereas the latter at the acceptor site.

Fluorescence changes in carbon nanotube sensors correlate with THz absorption of hydration.

Single wall carbon nanotubes (SWCNTs) functionalized with (bio-)polymers such as DNA are soluble in water and sense analytes by analyte-specific changes of their intrinsic fluorescence. Such SWCNT-based (bio-)sensors translate the binding of a molecule (molecular recognition) into a measurable optical signal. This signal transduction is crucial for all types of molecular sensors to achieve high sensitivities.

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 data sets predict the change in the fluorescence emission in response to serotonin, Δ/, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences.

Interfacial Interactions between Nanoplastics and Biological Systems: toward an Atomic and Molecular Understanding of Plastics-Driven Biological Dyshomeostasis.

Micro- and nano-plastics (NPs) are found in human milk, blood, tissues, and organs and associate with aberrant health outcomes including inflammation, genotoxicity, developmental disorders, onset of chronic diseases, and autoimmune disorders. Yet, interfacial interactions between plastics and biomolecular systems remain underexplored. Here, we have examined experimentally, in vitro, in vivo, and by computation, the impact of polystyrene (PS) NPs on a host of biomolecular systems and assemblies.

An Atomic and Molecular Insight into How PFOA Reduces α-Helicity, Compromises Substrate Binding, and Creates Binding Pockets in a Model Globular Protein.

Per- and polyfluoroalkyl substances (PFAS) pose significant health risks due to their widespread presence in various environmental and biological matrices. However, the molecular-level mechanisms underlying the interactions between PFAS and biological constituents, including proteins, carbohydrates, lipids, and DNA, remain poorly understood. Here, we investigate the interactions between a legacy PFAS, viz.

Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening.

In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of ssDNA-SWCNT nanosensors. In the three rounds for higher serotonin sensitivity, we substantially improved sensitivity, achieving a remarkable 2.

Fusion Position-Dependent Aromatic Transitions of Ligand Backbone Rings for Controlling the Redox Energetics of Photoredox Catalysts.

To reveal, quantify, and rationalize the effect of backbone π-extension on ligand redox activity, we studied the ground- and excited-state reduction potentials of eight ruthenium photoredox catalysts with the formula Ru(ppy)L (L is the redox-active ligand of the bipyridine family) using density functional theory. Our research underlines the profound importance of the fusion position of backbone aromatic C rings on the redox activity of ligands in transition metal photoredox catalysts. Namely, certain fusion positions lead to the dearomatization of C rings in ligand-centered electron transfer events, resulting in a thermodynamic penalty equivalent to a half-volt negative shift in the reduction potential.

Triepoxide formation by a flavin-dependent monooxygenase in monensin biosynthesis.

Monensin A is a prototypical natural polyether polyketide antibiotic. It acts by binding a metal cation and facilitating its transport across the cell membrane. Biosynthesis of monensin A involves construction of a polyene polyketide backbone, subsequent epoxidation of the alkenes, and, lastly, formation of cyclic ethers via epoxide-opening cyclization.

Genetically encoded discovery of perfluoroaryl macrocycles that bind to albumin and exhibit extended circulation in vivo.

Peptide-based therapeutics have gained attention as promising therapeutic modalities, however, their prevalent drawback is poor circulation half-life in vivo. In this paper, we report the selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SAr chemistry, with decafluoro-diphenylsulfone (DFS). Testing of the binding of the selected peptides to albumin identified SICRFFC as the lead sequence.

Characterizing the Interactions of Cell-Membrane-Disrupting Peptides with Lipid-Functionalized Single-Walled Carbon Nanotubes.

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.

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.

Mechanistic Understanding of DNA Denaturation in Nanoscale Thermal Gradients Created by Femtosecond Excitation of Gold Nanoparticles.

There is significant interest in developing photothermal systems that can precisely control the structure and function of biomolecules through local temperature modulation. One specific application is the denaturation of double-stranded (ds) DNA through femtosecond (fs) laser pulse optical heating of gold nanoparticles (AuNPs); however, the mechanism of DNA melting in these systems is not fully understood. Here, we utilize 55 nm AuNPs with surface-tethered dsDNA, which are locally heated using fs laser pulses to induce DNA melting.

Discovery of DNA-Carbon Nanotube Sensors for Serotonin with Machine Learning and Near-infrared Fluorescence Spectroscopy.

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 ∼10 unique DNA-SWNT candidates, are used to train machine learning (ML) models to predict DNA sequences with strong optical response to neurotransmitter serotonin.

Computational Modeling of the Virucidal Inhibition Mechanism for Broad-Spectrum Antiviral Nanoparticles and HPV16 Capsid Segments.

Solid core nanoparticles (NPs) coated with sulfonated ligands that mimic heparan sulfate proteoglycans (HSPGs) can exhibit virucidal activity against many viruses that utilize HSPG interactions with host cells for the initial stages of infection. How the interactions of these NPs with large capsid segments of HSPG-interacting viruses lead to their virucidal activity has been unclear. Here, we describe the interactions between sulfonated NPs and segments of the human papilloma virus type 16 (HPV16) capsids using atomistic molecular dynamics simulations.

Photochemical control of bacterial gene expression based on encoded genetic switches.

Controlling gene expression by light with fine spatiotemporal resolution not only allows understanding and manipulating fundamental biological processes but also fuels the development of novel therapeutic strategies. In complement to exploiting optogenetic tools, photochemical strategies mostly rely on the incorporation of photo-responsive small molecules into the corresponding biomacromolecular scaffolds. Therefore, generally large synthetic effort is required and the switching of gene expression in both directions within a single system remains a challenge.

Genetically Encoded Fragment-Based Discovery from Phage-Displayed Macrocyclic Libraries with Genetically Encoded Unnatural Pharmacophores.

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.

Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2.

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the past decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of adaptive (smart) therapeutics. Here, a computational strategy is developed to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2).

Surfactant-Controlled Mobility of Oil Droplets in Mineral Nanopores.

Polymer flooding is one of the widely used enhanced oil recovery (EOR) methods. However, tuning polymer properties to achieve improved performance in porous mineral rocks of diverse oil reservoirs remains one of the challenges of EOR processes. Here, we use molecular dynamics (MD) simulations to examine decane/water mixtures with surfactant additives in calcite and kaolinite mineral nanopores and characterize surfactant properties associated with increased fluid mobility and improved wettability in planar and constricted nanopore geometries.

Adaptive Evolution of Peptide Inhibitors for Mutating SARS-CoV-2.

The SARS-CoV-2 virus is currently causing a worldwide pandemic with dramatic societal consequences for the humankind. In the last decades, disease outbreaks due to such zoonotic pathogens have appeared with an accelerated rate, which calls for an urgent development of
adaptive (smart) therapeutics. Here, we develop a computational strategy to adaptively evolve peptides that could selectively inhibit mutating S protein receptor binding domains (RBDs) of different SARS-CoV-2 viral strains from binding to their human host receptor, angiotensin-converting enzyme 2 (ACE2).

Modified cyclodextrins as broad-spectrum antivirals.

Viral infections kill millions of people and new antivirals are needed. Nontoxic drugs that irreversibly inhibit viruses (virucidal) are postulated to be ideal. Unfortunately, all virucidal molecules described to date are cytotoxic.

Computational screening of nanoparticles coupling to Aβ40 peptides and fibrils.

Blocking the formation, growth, and breaking of amyloid fibrils by synthetic nanosystems could provide a treatment of neurodegenerative diseases. With this in mind, here atomistic molecular dynamics simulations are used to screen for nanoparticles (NPs), covered with different mixtures of ligands, including positively and negatively charged ligands, Aβ40-cut-peptide, and synthetic inhibitor ligands, in their selective coupling to Aβ40 peptides and their fibrils. The simulations reveal that only Aβ40-cut-peptide-covered NPs have strong and selective coupling to Aβ40 monomers.

Structural Properties of Small Single-Stranded Circular Nucleic Acids.

One strategy to avoid rapid degradation of small nucleic acids in biomedical applications is to covalently link their 3'- and 5'-ends, turning them into circular nucleic acids (circNAs). Here, we examine structural properties of flexible non-base-paired circNAs, containing 6-48 nucleotides, in aqueous solution, using microsecond long molecular dynamics simulations. Analyses of conformational ensembles of circular DNA (circDNA) and RNA (circRNA) molecules of different lengths and sequences reveal how their structures and dynamics are affected by the constraints of their geometries.

Configurations of Nanocubes Floating and Clustering on Liquid Surfaces.

The configurations of nanoparticles (NPs) floating on liquid surfaces can be largely affected by the NP shapes in combination with different NP-liquid coupling strengths. Here, the behavior of ligated nanocubes (NCs) on liquid surfaces is studied as an example of such NP floating by analytical methods and molecular dynamics simulations. Depending on the NC-liquid coupling strength, NCs can sit on the liquid surface (weak), be partly immersed in a tilted orientation (intermediate), or be fully immersed except for the top facet (strong).

Catalytic transport of molecular cargo using diffusive binding along a polymer track.

Transport at the molecular scale is a prerequisite for the development of future molecular factories. Here, we have designed oligoanionic molecular sliders on polycationic tracks that exploit Brownian motion and diffusive binding to transport cargo without using a chemical fuel. The presence of the polymer tracks increases the rate of bimolecular reactions between modified sliders by over two orders of magnitude.