Biology subjects

Amarasekara, D. L.

Publications and source records attributed to Amarasekara, D. L..

5 recordsLinked to original sources

bioRxiv2025

Precision control of nanoparticle delivery with engineered biomimetic protein coronas

Nanoparticle delivery to tumors remains inefficient, with current nanomedicines achieving only 0.7% injected dose per gram (ID/g) of tumor tissue due to uncontrolled protein corona formation that redirects nanoparticles away from target sites. We engineered biomimetic protein coronas to control nanoparticle-protein interactions and enhance tumor targeting. Competitive binding studies using NMR spectroscopy revealed that transferrin (Tf) and fibronectin (Fn) outcompete albumin (BSA) and immunoglobulin G (IgG) for 15 nm gold nanoparticle surfaces, establishing a binding hierarchy that enables predictable corona composition. Pre-coating nanoparticles with a four-protein combination (BSA+Tf+Fn+IgG) created coronas that selectively enhanced cancer cell uptake while reducing macrophage recognition in vitro. When administered to tumor-bearing mice, these engineered coronas achieved 13 ppm/g tumor accumulation--equivalent to 4% ID/g--representing 6.5-fold improvement over bare nanoparticles and 2.6-fold improvement over PEGylated formulations. Proteomics analysis of secondary coronas formed in human serum revealed that engineered nanoparticles selectively recruit transport and adhesion proteins while limiting immune recognition signatures. The pre-formed coronas maintained targeting protein retention and reduced complement binding compared to controls. Circular dichroism confirmed minimal protein structural perturbation, preserving receptor-binding functionality for active targeting. The strategy harnesses natural protein adsorption processes to create "smart" biological interfaces that simultaneously evade immune clearance and promote tumor cell recognition through multiple receptor pathways. This approach demonstrates the feasibility of treating the coron as a programmable interface, addressing delivery limitations that have hindered clinical translation of cancer nanomedicines. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/693224v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@5dd106org.highwire.dtl.DTLVardef@1460e3corg.highwire.dtl.DTLVardef@50c251org.highwire.dtl.DTLVardef@5604f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Using a Bacterial Protein to Selectively Target Bacterial Biofilms: Treatment of S. epidermidis Biofilms with Targeted Photothermal Gold Nanoparticles

Biofilm-related infections are associated with high mortality and morbidity combined with increased treatment costs. Traditional antibiotics are becoming less effective due to the emergence of drug-resistant bacterial strains. The need to treat biofilms on medical implants is particularly acute, and one persistent challenge is selectively directing nanoparticles to the biofilm site. Here, we present a protein-based functionalization strategy that targets the extracellular matrix of biofilms. The protein, derived from the extracellular Staphylococcus epidermidis autolysin, directs nanoparticles to S. epidermidis cell wall components, which are not expected to be present in mammalian tissues. This functionalization is applied to a gold nanoparticle (AuNP) core, along with elastin-like polypeptides (ELPs), which generate a robust photothermal response. In addition to biofilm targeting, the particles exhibit low protein binding, and the photothermal conversion can be modulated by changing the ELP transition temperature. These functionalized AuNPs strongly interact with biofilms under static and flow conditions but exhibit weak interactions with serum-coated surfaces. Near-infrared laser irradiation resulted in a 10,000-fold improvement in killing efficiency compared to untreated controls (p < 0.0001). The targeting strategy utilized here represents a versatile approach to targeting drug-resistant infections and could be readily expanded to other anti-biofilm nanoparticle platforms. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/610983v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@a7bf65org.highwire.dtl.DTLVardef@106855aorg.highwire.dtl.DTLVardef@11cdd5dorg.highwire.dtl.DTLVardef@9325f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Near-Infrared Photothermal Ablation of Biofilms using Protein-Functionalized Gold Nanospheres with a Tunable Temperature Response

Temperature-responsive nanostructures with high antimicrobial efficacy are attractive for therapeutic applications against multi-drug-resistant bacteria. Here, we report temperature-responsive nanospheres (TRNs) that are engineered to undergo self-association and agglomeration above a tunable transition temperature (Tt). Temperature-responsive behavior of the nanoparticles is obtained by functionalizing citrate-capped, spherical gold nanoparticles (AuNPs) with elastin-like polypeptides (ELPs). Using protein design principles, we achieve a broad range of attainable Tt values and photothermal conversion efficiencies ({eta}). Two approaches were used to adjust this range: First, by altering the position of the cysteine residue used to attach ELP to the AuNP, we attained a Tt range from 34-42 {degrees}C. Then, functionalizing the AuNP with an additional small globular protein, we were able to extend this range to 34-50 {degrees}C. Under near-infrared (NIR) light exposure, all TRNs exhibited reversible agglomeration. Moreover, they showed enhanced photothermal conversion efficiency in their agglomerated state relative to the dispersed state. Despite their spherical shape, TRNs have a photothermal conversion efficiency approaching that of gold nanorods ({eta} = 68{+/-}6%), yet unlike nanorods, the synthesis of TRNs requires no cytotoxic compounds. Finally, we tested TRNs for photothermal ablation of biofilms. Above Tt, NIR irradiation of TRNs resulted in a 10,000-fold improvement in killing efficiency compared to untreated controls (p < 0.0001). Below Tt, no enhanced anti-biofilm effect was observed. In conclusion, engineering the interactions between proteins and nanoparticles enables the tunable control of TRNs, resulting in a novel, anti-biofilm nanomaterial with low cytotoxicity. TOC Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/553096v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@ffb632org.highwire.dtl.DTLVardef@12cbb4org.highwire.dtl.DTLVardef@1fbac00org.highwire.dtl.DTLVardef@1d950b6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Protein Binding Leads to Reduced Stability and Solvated Disorder in the Polystyrene Nanoparticle Corona

Understanding the conformation of proteins in the nanoparticle corona has important implications in how organisms respond to nanoparticle-based drugs. These proteins coat the nanoparticle surface, and their properties will influence the nanoparticles interaction with cell targets and the immune system. While some coronas are thought to be disordered, two key unanswered questions are the degree of disorder and solvent accessibility. Here, using a comprehensive thermodynamic approach, along with supporting spectroscopic experiments, we develop a model for protein corona disorder in polystyrene nanoparticles of varying size. For two different proteins, we find that binding affinity decreases as nanoparticle size increases. The stoichiometry of binding, along with changes in the hydrodynamic size, support a highly solvated, disordered protein corona anchored at a small number of enthalpically-driven attachment sites. The scaling of the stoichiometry vs. nanoparticle size is consistent disordered polymer dimensions. Moreover, we find that proteins are destabilized less severely in the presence of larger nanoparticles, and this is supported by measurements of hydrophobic exposure, which becomes less pronounced at lower curvatures. Our observations hold for flat polystyrene surfaces, which, when controlled for total surface area, have the lowest hydrophobic exposure of all systems. Our model provides an explanation for previous observations of increased amyloid fibrillation rates in the presence of larger nanoparticles, and it may rationalize how cell receptors can recognize protein disorder in therapeutic nanoparticles. TOC Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/548033v1_ufig1.gif" ALT="Figure 1"> View larger version (90K): org.highwire.dtl.DTLVardef@ff243corg.highwire.dtl.DTLVardef@a3f010org.highwire.dtl.DTLVardef@e5d2c5org.highwire.dtl.DTLVardef@70bb5d_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Intrinsically disordered regions that drive phase separation form a robustly distinct protein class

Liquid-liquid phase separation (LLPS) of proteins is thought to be a primary driving force for the formation of membraneless organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. LLPS of proteins in cells is primarily, though not exclusively, driven by intrinsically disordered (ID) domains. Accurate identification of ID regions (IDRs) that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and phase-separating (PS) ID sequences. We then used these sequence sets to examine how broadly existing amino acids scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of LLPS. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving LLPS.

biophysics↗