On the binding affinity of macromolecular interactions: daring to ask why proteins interact

An Experimental Framework for Developing Point-of-Need Biosensors: Connecting Bio-Layer Interferometry and Electrochemical Impedance Spectroscopy

Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2

Dengue fever (DF) continues to be one of the tropical and subtropical health concerns. Its prevalence tends to increase in some places in these regions. This disease is caused by the dengue virus (DENV), which is transmitted through the mosquitoes Aedes aegypti and A. albopictus. The treatment of DF to date is only supportive and there is no definitive vaccine to prevent this disease. The non-structural DENV protein, RNA-dependent RNA Polymerase (RdRp), is involved in viral replication. The RdRp-derived peptides can be used in the construction of a universal dengue vaccine. These peptides can be utilized as epitopes to induce immunity. This study was an in silico evaluation of the affinity of the potential epitope for the universal dengue vaccine to dendritic cells and the bonds between the epitope and the dendritic cell receptor. The peptide sequence MGKREKKLGEFGKAKG generated from dengue virus subtype 2 (DENV-2) RdRp was antigenic, did not produce allergies, was non-toxic, and had no homology with the human genome. The potential epitope-based vaccine MGKREKKLGEFGKAKG binds stably to dendritic cell receptors with a binding free energy of −474,4 kcal/mol. This epitope is anticipated to induce an immunological response and has the potential to serve as a universal dengue virus vaccine candidate.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Critical Design Factors for Electrochemical Aptasensors Based on Target-Induced Conformational Changes: The Case of Small-Molecule Targets

Nucleic-acid aptamers consisting in single-stranded DNA oligonucleotides emerged as very promising biorecognition elements for electrochemical biosensors applied in various fields such as medicine, environmental, and food safety. Despite their outstanding features, such as high-binding affinity for a broad range of targets, high stability, low cost and ease of modification, numerous challenges had to be overcome from the aptamer selection process on the design of functioning biosensing devices. Moreover, in the case of small molecules such as metabolites, toxins, drugs, etc., obtaining efficient binding aptamer sequences proved a challenging task given their small molecular surface and limited interactions between their functional groups and aptamer sequences. Thus, establishing consistent evaluation standards for aptamer affinity is crucial for the success of these aptamers in biosensing applications. In this context, this article will give an overview on the thermodynamic and structural aspects of the aptamer-target interaction, its specificity and selectivity, and will also highlight the current methods employed for determining the aptamer-binding affinity and the structural characterization of the aptamer-target complex. The critical aspects regarding the generation of aptamer-modified electrodes suitable for electrochemical sensing, such as appropriate bioreceptor immobilization strategy and experimental conditions which facilitate a convenient anchoring and stability of the aptamer, are also discussed. The review also summarizes some effective small molecule aptasensing platforms from the recent literature.

Symmetrization in the Calculation Pipeline of Gauss Function-Based Modeling of Hydrophobicity in Protein Structures

In this paper, we show, discuss, and compare the effects of symmetrization in two calculation subroutines of the Fuzzy Oil Drop model, a coarse-grained model of density of hydrophobicity in proteins. In the FOD model, an input structure is enclosed in an axis-aligned ellipsoid called a drop. Two profiles of hydrophobicity are then calculated for its residues: theoretical (based on the 3D Gauss function) and observed (based on pairwise hydrophobic interactions). Condition of the hydrophobic core is revealed by comparing those profiles through relative entropy, while analysis of their local differences allows, in particular, determination of the starting location for the search for protein–protein and protein–ligand interaction areas. Here, we improve the baseline workflow of the FOD model by introducing symmetry to the hydrophobicity profile comparison and ellipsoid bounding procedures. In the first modification (FOD–JS), Kullback–Leibler divergence is enhanced with its Jensen–Shannon variant. In the second modification (FOD-PCA), the molecule is optimally aligned with the axes of the coordinate system via principal component analysis, and the size of its drop is determined by the standard deviation of all its effective atoms, making it less susceptible to structural outliers. Tests on several molecules with various shapes and functions confirm that the proposed modifications improve the accuracy, robustness, speed, and usability of Gauss function-based modeling of the density of hydrophobicity in protein structures.

Unveiling the underpinnings of various non-conventional ELISA variants: a review article

INTRODUCTION

Enzyme-linked immunosorbent assay (ELISA) is a key bio-analytical technique used for the detection of a large array of antigenic substances of scientific, clinical, food safety, and environmental importance. The assay primarily involves capturing and detecting target analytes using specific antigen-antibody interactions. The wide usage of ELISA shoulders on its high specificity and reproducibility. Notwithstanding, the conventional microwell plate-based format of ELISA has some major drawbacks, such as long assay time (4 - 18 h), large sample volumes requirement (100 - 200 μL), lack of multiplicity, and burdensome procedures that limit its utility in rapid and affordable diagnostics.

AREAS COVERED

Here, we reviewed microfluidic-ELISA, paper-ELISA, aptamer-ELISA, and those based on novel incubation such as heat-ELISA, pressure-ELISA, microwave-ELISA, and sound-ELISA. Further, the current trends and future prospects of these ELISA protocols in clinical diagnostics are discussed.

EXPERT OPINION

The reviewed non-conventional ELISA formats are relatively rapid, require low reagent volumes, are multiplexable, and could be performed in a low-cost setup. In our opinion, these non-conventional variants of ELISA are on a par with the conventional format for clinical diagnostics and fundamental biological research and hold added clinical translational potential for quick, inexpensive, and convenient measurements.

Repertoire-scale measures of antigen binding

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC₅₀ ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC₅₀ of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation

After two years since the outbreak, the COVID-19 pandemic remains a global public health emergency. SARS-CoV-2 variants with substitutions on the spike (S) protein emerge increasing the risk of immune evasion and cross-species transmission. Here, we analyzed the evolution of the S protein as recorded in 276,712 samples collected before the start of vaccination efforts. Our analysis shows that most variants destabilize the S protein trimer, increase its conformational heterogeneity and improve the odds of the recognition by the host cell receptor. Most frequent substitutions promote overall hydrophobicity by replacing charged amino acids, reducing stabilizing local interactions in the unbound S protein trimer. Moreover, our results identify "forbidden" regions that rarely show any sequence variation, and which are related to conformational changes occurring upon fusion. These results are significant for understanding the structure and function of SARS-CoV-2 related proteins which is a critical step in vaccine development and epidemiological surveillance.

In Silico Approach in the Evaluation of Pro-Inflammatory Potential of Polycyclic Aromatic Hydrocarbons and Volatile Organic Compounds through Binding Affinity to the Human Toll-Like Receptor 4

Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are widespread across the globe, existing in the environment in complex mixtures potentially capable of initiating respiratory illnesses. Here, we use an in silico approach to evaluate the potential pro-inflammatory effects of various carcinogenic PAHs and VOCs through their binding affinity towards the human toll-like receptor 4 (TLR4). For receptors and ligands, RCSB Protein Data Bank and PubChem were used in obtaining their 3D structures, respectively. Autodock Vina was utilized to obtain the best docking poses and binding affinities of each PAH and VOC. Out of the 14 PAHs included in this study, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, and benzo[a]pyrene had the highest binding affinity values of −10, −9, and −8.9 kcal/mol, respectively. For the VOCs, out of the 10 compounds studied, benzene, 1,4-dichlorobenzene, and styrene had the highest binding affinity values of −3.6, −3.9, and −4.6 kcal/mol, respectively. Compounds with higher affinity than LPS (−4.1 kcal/com) could potentially induce inflammation, while compounds with lower affinity would be less likely to induce an inflammatory response. Meanwhile, molecular dynamics simulation and RMSF statistical analysis proved that the protein, TLR4, stably preserve its conformation despite ligand interactions. Overall, the structure of the TLR4 was considered inflexible.

Artificial intelligence channelizing protein-peptide interactions pipeline for host-parasite paradigm in IL-10 and IL-12 reciprocity by SHP-1

Identification of molecular targets in any cellular phenomena is a challenge and a path that one endeavors upon independently. We have identified a phosphatase SHP-1 as a point of intervention of IL-10 and IL-12 reciprocity in leishmaniasis. The therapeutic model that we have developed uniquely targets this protein but the pipeline in general can be used by the researchers for their unique targets. Naturally occurring peptides are well known for their biochemical participation in cellular functions hence we were motivated to use this uniqueness of physico-chemical properties of peptides conferred by amino acids through machine learning to channelize a mode of therapeutic exploration in infectious disease. Using computational approaches, we identified high order sequence conservation and similarity in SHP-1 sequence which was also evolutionarily conserved, complete structure of Mouse SHP-1 was predicted and validated, a unique motif of the same was identified against which library of synthetic peptides were designed and validated followed by screening the library by docking them with MuSHP-1 protein structure. Our findings showed 3 peptides had high binding affinity and in future can be validated using cell based and in vivo assays.

Molecular Docking Study of IPBCC.08.610 Glucose Oxidase Mutant for Increasing Gluconic Acid Production

Glucose oxidase (GOD) is an oxidoreductase enzyme that catalyzes the oxidation of glucose to gluconolactone and hydrogen peroxide. Then, gluconolactone will be hydrolyzed to gluconic acid. The wide application of gluconic acid in various industries has increased production demand. However, glucose concentrations higher than 40% (w/w) inhibited the conversion of glucose to gluconic acid due to a decrease in the oxygen solubility concentration at pH 6, 30℃, and 1 bar pressure. Therefore, decreasing the value of Km is predicted to reduce saturation and enhance gluconic acid production. This study aimed to analyze the interaction between the IPBCC.08.610 GOD mutant with β-D-Glucose in improving gluconic acid production by decreasing the Km value. Mutations were performed in silico using Chimera and then docked using AutoDock Vina. The mutations resulted in distinct ligand poses in the binding pocket, different -OH conformations of the ligands, and changes in the T554M/D578P mutant’s hydrophobicity index (554 mutated from threonine to methionine, and 578 mutated from aspartate to proline), and decreased ΔG and Km values in the H559D mutant (559 mutated from histidine to aspartate), D578P and T554M/D578P. This decrease might strengthen the ligand-receptor interaction, increasing gluconic acid production. The H559D was the best mutant to increase production based on the ΔG, Km value, and stability due to the addition of hydrogen bonds.

Fever as an evolutionary agent to select immune complexes interfaces

We herein analyzed all available protein–protein interfaces of the immune complexes from the Protein Data Bank whose antigens belong to pathogens or cancers that are modulated by fever in mammalian hosts. We also included, for comparison, protein interfaces from immune complexes that are not significantly modulated by the fever response. We highlight the distribution of amino acids at these viral, bacterial, protozoan and cancer epitopes, and at their corresponding paratopes that belong strictly to monoclonal antibodies. We identify the “hotspots”, i.e. residues that are highly connected at such interfaces, and assess the structural, kinetic and thermodynamic parameters responsible for complex formation. We argue for an evolutionary pressure for the types of residues at these protein interfaces that may explain the role of fever as a selective force for optimizing antibody binding to antigens.

Understanding the functional role of membrane confinements in TNF-mediated signaling by multiscale simulations

The interaction between TNFα and TNFR1 is essential in maintaining tissue development and immune responses. While TNFR1 is a cell surface receptor, TNFα exists in both soluble and membrane-bound forms. Interestingly, it was found that the activation of TNFR1-mediated signaling pathways is preferentially through the soluble form of TNFα, which can also induce the clustering of TNFR1 on plasma membrane of living cells. We developed a multiscale simulation framework to compare receptor clustering induced by soluble and membrane-bound ligands. Comparing with the freely diffusive soluble ligands, we hypothesize that the conformational dynamics of membrane-bound ligands are restricted, which affects the clustering of ligand-receptor complexes at cell-cell interfaces. Our simulation revealed that only small clusters can form if TNFα is bound on cell surface. In contrast, the clustering triggered by soluble TNFα is more dynamic, and the size of clusters is statistically larger. We therefore demonstrated the impact of membrane-bound ligand on dynamics of receptor clustering. Moreover, considering that larger TNFα-TNFR1 clusters is more likely to provide spatial platform for downstream signaling pathway, our studies offer new mechanistic insights about why the activation of TNFR1-mediated signaling pathways is not preferred by membrane-bound form of TNFα.

Protein-Based Hydrogels: Promising Materials for Tissue Engineering

The successful design of a hydrogel for tissue engineering requires a profound understanding of its constituents' structural and molecular properties, as well as the proper selection of components. If the engineered processes are in line with the procedures that natural materials undergo to achieve the best network structure necessary for the formation of the hydrogel with desired properties, the failure rate of tissue engineering projects will be significantly reduced. In this review, we examine the behavior of proteins as an essential and effective component of hydrogels, and describe the factors that can enhance the protein-based hydrogels' structure. Furthermore, we outline the fabrication route of protein-based hydrogels from protein microstructure and the selection of appropriate materials according to recent research to growth factors, crucial members of the protein family, and their delivery approaches. Finally, the unmet needs and current challenges in developing the ideal biomaterials for protein-based hydrogels are discussed, and emerging strategies in this area are highlighted.

Principles of Amino Acid and Nucleotide Revealed by Binding Affinities between Homogeneous Oligopeptides and Single-stranded DNA Molecule s

We have determined the binding strengths between nucleotides of adenine, thymine, guanine and cytosine in homogeneous single stranded DNAs and homo-octapeptides consisting of 20 common amino acids. We use a bead-based fluorescence assay for these measurements in which octapeptides are immobilized on the bead surface and ssDNAs are in solutions. The results provide a molecular basis for analyzing selectivity, specificity and polymorphisms of amino-acid-nucleotide interactions. Comparative analyses of the distribution of the binding energies reveal unique binding strengths patterns assignable to each pair of DNA nucleotide and amino acid originating from the chemical structures. Pronounced favorable (such as Arg-G , etc.) and unfavorable (such as Ile-T , etc.) binding interactions can be identified in selected groups of amino acid and nucleotide pairs that could provide basis to elucidate energetics of amino-acid-nucleotide interactions. Such interaction selectivity, specificity and polymorphism manifest the contributions from DNA backbone, DNA bases, as well as main chain and side chain of the amino acids.

Molecular Structure-Based Screening of the Constituents of Calotropis procera Identifies Potential Inhibitors of Diabetes Mellitus Target Alpha Glucosidase

Diabetes mellitus is a disorder characterized by higher levels of blood glucose due to impaired insulin mechanisms. Alpha glucosidase is a critical drug target implicated in the mechanisms of diabetes mellitus and its inhibition controls hyperglycemia. Since the existing standard synthetic drugs have therapeutic limitations, it is imperative to identify new potent inhibitors of natural product origin which may slow carbohydrate digestion and absorption via alpha glucosidase. Since plant extracts from Calotropis procera have been extensively used in the treatment of diabetes mellitus, the present study used molecular docking and dynamics simulation techniques to screen its constituents against the receptor alpha glucosidase. Taraxasterol, syriogenin, isorhamnetin-3-O-robinobioside and calotoxin were identified as potential novel lead compounds with plausible binding energies of −40.2, −35.1, −34.3 and −34.3 kJ/mol against alpha glucosidase, respectively. The residues Trp⁴⁸¹, Asp⁵¹⁸, Leu⁶⁷⁷, Leu⁶⁷⁸ and Leu⁶⁸⁰ were identified as critical for binding and the compounds were predicted as alpha glucosidase inhibitors. Structurally similar compounds with Tanimoto coefficients greater than 0.7 were reported experimentally to be inhibitors of alpha glucosidase or antidiabetic. The structures of the molecules may serve as templates for the design of novel inhibitors and warrant in vitro assaying to corroborate their antidiabetic potential.

Evolutionary Analysis of Dipeptidyl Peptidase I

Human dipeptidyl peptidase I (DPPI) belongs to the family of papain-like cysteine peptidases. Its distinctive features are the unique exclusion domain which enables the eponymous activity and homotetramerization of DPPI, and its dependence on chloride ions for enzymatic activity. The oligomeric state of DPPI is unique in this family of predominantly monomeric peptidases. However, a distant DPPI ortholog from Plasmodium falciparum has been shown to be monomeric, indicating that the oligomeric state of DPPI varies between lineages. The aim of this work was to study the evolution of DPPI, with particular attention to the structural features that determine its characteristic enzymatic activity and preferences, and to reconstruct the evolution of its oligomerization. We analyzed fifty-seven selected sequences of DPPI and confirmed its presence in three lineages, namely, Amorphea (including animals and Amoebozoa), Alveolates and the metamonad Giardia. The amino acid residues that bind the chloride ion are highly conserved in all species, indicating that the dependence on chloride ions for activity is an evolutionarily conserved feature of DPPI. The number of N-glycosylation sites is significantly increased in animals, particularly vertebrates. Analysis of homology models and subunit contacts suggests that oligomerization is likely restricted to DPPIs in the Amorphea group.

Detection of SARS-CoV-2 in COVID-19 Patient Nasal Swab Samples Using Signal Processing

This work presents an opto-electrical method that measures the viral nucleocapsid protein and anti-N antibody interactions to differentiate between SARS-CoV-2 negative and positive nasal swab samples. Upon light exposure of the patient nasal swab sample mixed with the anti-N antibody, charge transfer (CT) transitions within the altered protein folds are initiated between the charged amino acids side chain moieties and the peptide backbone that play the role of donor and acceptor groups. A Figure of Merit (FOM) was introduced to correlate the relative variations of the samples with and without antibody at two different voltages. Empirically, SARS-CoV-2 in patient nasal swab samples was detected within two minutes, if an extracted FOM threshold of >1 was achieved; otherwise, the sample wasconsidered negative.

Tag-mediated single-step purification and immobilization of recombinant proteins toward protein-engineered advanced materials

Background

The potential applications of protein-engineered functional materials are so wide and exciting that the interest in these eco-friendly advanced materials will further expand in the future. Tag-mediated protein purification/immobilization technologies have emerged as green and cost-effective approaches for the fabrication of such materials. Strategies that combine the purification and immobilization of recombinant proteins/peptides onto/into natural, synthetic or hybrid materials in a single-step are arising and attracting increasing interest.

Aim of Review

This review highlights the most significant advances of the last 5 years within the scope of tag-mediated protein purification/immobilization and elucidates their contributions for the development of efficient single-step purification and immobilization strategies. Recent progresses in the field of protein-engineered materials created using innovative protein-tag combinations and future opportunities created by these new technologies are also summarized and identified herein.

Key Scientific Concepts of Review

Protein purification/immobilization tags present a remarkable ability to establish specific non-covalent/covalent interactions between solid materials and biological elements, which prompted the creation of tailor-made and advanced functional materials, and of next-generation hybrid materials. Affinity tags can bind to a wide range of materials (of synthetic, natural or hybrid nature), being most suitable for protein purification. Covalently binding tags are most suitable for long-term protein immobilization, but can only bind naturally to protein-based materials. Hybrid affinity-covalently binding tags have allowed efficient one-step purification and immobilization of proteins onto different materials, as well as the development of innovative protein-engineered materials. Self-aggregating tags have been particularly useful in combination with other tags for generating protein-engineered materials with self-assembling, flexible and/or responsive properties. While these tags have been mainly explored for independent protein purification, immobilization or functionalization purposes, efficient strategies that combine tag-mediated purification and immobilization/functionalization in a single-step will be essential to guarantee the sustainable manufacturing of advanced protein-engineered materials.

Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity

The surfaces of pristine nanoparticles become rapidly coated by proteins in biological fluids, forming the so-called protein corona. The corona modifies key physicochemical characteristics of nanoparticle surfaces that modulate its biological and pharmacokinetic activity, biodistribution, and safety. In the two decades since the protein corona was identified, the importance of nanoparticles surface properties in regulating biological responses have been recognized. However, there is still a lack of clarity about the relationships between physiological conditions and corona composition over time, and how this controls biological activities/interactions. Here we review recent progress in characterizing the structure and composition of protein corona as a function of biological fluid and time. We summarize the influence of nanoparticle characteristics on protein corona composition and discuss the relevance of protein corona to the biological activity and fate of nanoparticles. The aim is to provide a critical summary of the key factors that affect protein corona formation (e.g. characteristics of nanoparticles and biological environment) and how the corona modulates biological activity, cellular uptake, biodistribution, and drug delivery. In addition to a discussion on the importance of the characterization of protein corona adsorbed on nanoparticle surfaces under conditions that mimic relevant physiological environment, we discuss the unresolved technical issues related to the characterization of nanoparticle-protein corona complexes during their journey in the body. Lastly, the paper offers a perspective on how the existing nanomaterial toxicity data obtained from in vitro studies should be reconsidered in the light of the presence of a protein corona, and how recent advances in fields, such as proteomics and machine learning can be integrated into the quantitative analysis of protein corona components.

Membrane nanodomains modulate formin condensation for actin remodeling in Arabidopsis innate immune responses

The assembly of macromolecules on the plasma membrane concentrates cell surface biomolecules into nanometer- to micrometer-scale clusters (nano- or microdomains) that help the cell initiate or respond to signals. In plant-microbe interactions, the actin cytoskeleton undergoes rapid remodeling during pathogen-associated molecular pattern-triggered immunity (PTI). The nanoclustering of formin-actin nucleator proteins at the cell surface has been identified as underlying actin nucleation during plant innate immune responses. Here, we show that the condensation of nanodomain constituents and the self-assembly of remorin proteins enables this mechanism of controlling formin condensation and activity during innate immunity in Arabidopsis thaliana. Through intrinsically disordered region-mediated remorin oligomerization and formin interaction, remorin gradually recruits and condenses formins upon PTI activation in lipid bilayers, consequently increasing actin nucleation in a time-dependent manner postinfection. Such nanodomain- and remorin-mediated regulation of plant surface biomolecules is expected to be a general feature of plant innate immune responses that creates spatially separated biochemical compartments and fine tunes membrane physicochemical properties for transduction of immune signals in the host.

Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, "hot spots", as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ-TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

EXCESP: A Structure-Based Online Database for Extracellular Interactome of Cell Surface Proteins in Humans

The interactions between ectodomains of cell surface proteins are vital players in many important cellular processes, such as regulating immune responses, coordinating cell differentiation, and shaping neural plasticity. However, while the construction of a large-scale protein interactome has been greatly facilitated by the development of high-throughput experimental techniques, little progress has been made to support the discovery of extracellular interactome for cell surface proteins. Harnessed by the recent advances in computational modeling of protein-protein interactions, here we present a structure-based online database for the extracellular interactome of cell surface proteins in humans, called EXCESP. The database contains both experimentally determined and computationally predicted interactions among all type-I transmembrane proteins in humans. All structural models for these interactions and their binding affinities were further computationally modeled. Moreover, information such as expression levels of each protein in different cell types and its relation to various signaling pathways from other online resources has also been integrated into the database. In summary, the database serves as a valuable addition to the existing online resources for the study of cell surface proteins. It can contribute to the understanding of the functions of cell surface proteins in the era of systems biology.

Free Energy-Based Computational Methods for the Study of Protein-Peptide Binding Equilibria

This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies: (1) alchemical absolute binding free energy estimation with implicit solvation, (2) alchemical relative binding free energy estimation with explicit solvation, and (3) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method.

Implementation of molecular matching in transplantation requires further characterization of both immunogenicity and antigenicity of individual HLA epitopes

Over the past decade, high HLA epitope mismatch scores have been associated with inferior transplant outcomes using several tools, of which HLAMatchmaker is most well-known. This software uses theoretically defined polymorphic amino acid configurations, called eplets, for HLA compatibility analysis. Although consideration of eplet mismatch loads has potential for immunological risk stratification of transplant patients, the use of eplet matching in organ allocation algorithms is hindered by lacking knowledge of the immunogenicity of individual eplets, and the possibility that single mismatched amino acids, rather than complete eplets, are responsible for HLA antibody induction. There are several approaches to define eplet immunogenicity, such as antibody verification of individual eplets, and data-driven approaches using large datasets that correlate specific eplet mismatches to donor specific antibody formation or inferior transplant outcomes. Data-driven approaches can also be used to define whether single amino acid mismatches may be more informative than eplet mismatches for predicting HLA antibody induction. When using epitope knowledge for the assignment of unacceptable antigens, it important to realize that alleles sharing an eplet to which antibodies have formed are not automatically all unacceptable since multiple contact sites determine the binding strength and thus biological function and pathogenicity of an antibody, which may differ between reactive alleles. While the future looks bright for using HLA epitopes in clinical decision making, major steps need to be taken to make this a clinical reality.

Femtomolar detection of dopamine using surface plasmon resonance sensor based on chitosan/graphene quantum dots thin film

Due to the crucial role of dopamine (DA) in health and peripheral nervous systems, it is particularly important to develop an efficient and accurate sensor to monitor and determine DA concentrations for diagnostic purposes and diseases prevention. Up to now, using surface plasmon resonance (SPR) sensors in DA determination is very limited and its application still at the primary stage. In this work, a simple and ultra-sensitive SPR sensor was constructed for DA detection by preparation of chitosan- graphene quantum dots (CS-GQDs) thin film as the sensing layer. Other SPR measurements were conducted using different sensing layers; GQDs, CS for comparison. The proposed thin films were prepared by spin coating technique. The developed CS-GQDs thin film-based SPR sensor was successfully tested in DA concentration range from 0 fM to 1 pM. The designed SPR sensor showed outstanding performance in detecting DA sensitively (S = 0.011°/fM, R² = 0.8174) with low detection limit of 1.0 fM has been achieved for the first time. The increased angular shift of SPR dip, narrow full width half maximum of the SPR curves, excellent signal-to-noise ratio and figure of merit, and a binding affinity constant (K_A) of 2.962 PM^-1 demonstrated the potential of this sensor to detect DA with high accuracy. Overall, it was concluded that the proposed sensor would serve as a valuable tool in clinical diagnostic for the serious neurological disorders. This in turns has a significant socio-economic impact.

The role of temperature in the binding of the disordered epitope region of human thrombopoietin to antibody: A molecular dynamics simulations study

The N-terminal domain (163 residues) of Human thrombopoietin (hTPO) is highly conserved and responsible for the receptor-binding. The crystal structure of free hTPO is not yet available, but the crystal structure of its receptor-binding domain (hTPO₁₆₃) is available in complex with the TN1-Fab antibody. According to a thermodynamic study of hTPO₁₆₃ binding to TN1-Fab Ab, the ΔH value for binding becomes more negative with an increase in temperature from 283 K to 303 K. The objective of our study is to understand how the free hTPO₁₆₃ behaves dynamically and to study the effect of temperature on the association of hTPO₁₆₃ to TN1-Fab antibody through molecular dynamics simulations. We studied the Ag-Ab interactions at two different temperatures 298 K and 303 K. The discontinuous epitope region (residues 98-115) of free hTPO₁₆₃ displays a conformational switch and it gets stabilized upon binding to the Ab at 303 K. Based on our results, it may be surmised that the epitope region 98-115 is behaving like a disordered epitope. The disordered epitopes are known to be more efficient in binding with the antibody. We also find that, there is an increase in number of hydrogen-bonding interactions and hydrophobic contacts with an increase in the temperature from 298 K to 303 K. Thus, this observation explains a possible reason behind the more negative value of ΔH at the higher temperature 303 K as compared to 298 K.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

Geometrical and electro-static determinants of protein-protein interactions

Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100 thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range and followed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces the importance of geometrical and electrostatic complementarity as the main determinants for PPIs.

Cheonggukjang-Specific Component 1,3-Diphenyl-2-Propanone as a Novel PPARα/γ Dual Agonist: An In Vitro and In Silico Study

Background: Cheonggukjang is a traditional fermented soybean paste that is mostly consumed in Korea. However, the biological activities of Cheonggukjang specific compounds have not been studied. Thus, we aimed to discover a novel dual agonist for PPARα/γ from dietary sources such as Cheonggukjang specific volatile compounds and explore the potential role of PPARα/γ dual agonists using in vitro and in silico tools. Methods: A total of 35 compounds were selected from non-fermented and fermented soybean products cultured with Bacillus subtilis, namely Cheonggukjang, for analysis by in vitro and in silico studies. Results: Molecular docking results showed that 1,3-diphenyl-2-propanone (DPP) had the lowest docking score for activating PPARα (1K7L) and PPARγ (3DZY) with non-toxic effects. Moreover, DPP significantly increased the transcriptional activities of both PPARα and PPARγ and highly activated its expression in Ac2F liver cells, in vitro. Here, we demonstrated for the first time that DPP can act as a dual agonist of PPARα/γ using in vitro and in silico tools. Conclusions: The Cheonggukjang-specific compound DPP could be a novel PPARα/γ dual agonist and it is warranted to determine the therapeutic potential of PPARα/γ activation by dietary intervention and/or supplementation in the treatment of metabolic disorders without causing any adverse effects.

Binding Characterization of Cyclic Peptide Ligands to Target Proteins and Chemical Epitopes Using ELISA and Fluorescence Polarization Assays

Enzyme-linked immunosorbent assay (ELISA) is a plate-based immunological assay designed to detect and quantify peptides, proteins, antibodies, and hormones. Fluorescence polarization (FP) is a solution-phase technique that can be used to determine equilibrium dissociation constant of ligand for the protein of interest. Here we describe the protocols for different ELISAs and for Fluorescence Polarization, and how they can be used to determine relative or absolute binding of macrocyclic peptides to the target proteins. In ELISA, the target protein is used as the antigen, and the binding of antigen is quantified using cyclic peptides and enzyme-linked antibodies. In Fluorescence Polarization assays, a cyclic ligand is fluorescent dye-labeled and titrated with serial concentrations of the non-labeled target protein to determine the equilibrium dissociation constant (K_D) of ligand for protein. Detailed descriptions of sample preparation and the ELISA and FP experiments are provided in this chapter.

Optical Detection of SARS-CoV-2 Utilizing Antigen-Antibody Binding Interactions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus disease (COVID-19) pandemic, is sweeping the world today. This study investigates the optical detection of SARS-CoV-2, utilizing the antigen-antibody binding interactions utilizing a light source from a smart phone and a portable spectrophotometer. The proof-of-concept is shown by detecting soluble preparations of spike protein subunits from SARS-CoV-2, followed by detection of the actual binding potential of the SARS-CoV-2 proteins with their corresponding antigens. The measured binding interactions for RBD and NCP proteins with their corresponding antibodies under different conditions have been measured and analyzed. Based on these observations, a “hump or spike” in light intensity is observed when a specific molecular interaction takes place between two proteins. The optical responses could further be analyzed using the principle component analysis technique to enhance and allows precise detection of the specific target in a multi-protein mixture.

Molecularly imprinted polymers by epitope imprinting: a journey from molecular interactions to the available bioinformatics resources to scout for epitope templates

The molecular imprinting of proteins is the process of forming biomimetics with entailed protein-recognition by means of a template-assisted synthesis. Protein-imprinted polymers (pMIPs) have been successfully employed in separations, assays, sensors, and imaging. From a technical point of view, imprinting a protein is both costly, for protein expression and purification, and challenging, for the preservation of the protein's structural properties. In fact, the imprinting process needs to guarantee the preservation of the same protein three-dimensional conformation that later would be recognized. So far, the captivating idea to imprint just a portion of the protein, i.e., an epitope, instead of the whole, proved successful, offering reduced costs, compatibility with many synthetic conditions (solvents, pH, temperatures), and fine-tuning of the peptide sequence so to target specific physiological and functional conditions of the protein, such as post-translational modifications. Here, protein-protein interactions and the biochemical features of the epitopes are inspected, deriving lessons to prepare more effective pMIPs. Epitopes are categorized in linear or structured, immunogenic or not, located at the protein's surface or buried in its core and the imprinting strategies are discussed. Moreover, attention is given to freely available online bioinformatics resources that might offer key tools to gain further rationale amid the selection process of suitable epitopes templates.

Mechanically transduced immunosorbent assay to measure protein-protein interactions

Measuring protein-protein interaction (PPI) affinities is fundamental to biochemistry. Yet, conventional methods rely upon the law of mass action and cannot measure many PPIs due to a scarcity of reagents and limitations in the measurable affinity ranges. Here, we present a novel technique that leverages the fundamental concept of friction to produce a mechanical signal that correlates to binding potential. The mechanically transduced immunosorbent (METRIS) assay utilizes rolling magnetic probes to measure PPI interaction affinities. METRIS measures the translational displacement of protein-coated particles on a protein-functionalized substrate. The translational displacement scales with the effective friction induced by a PPI, thus producing a mechanical signal when a binding event occurs. The METRIS assay uses as little as 20 pmols of reagents to measure a wide range of affinities while exhibiting a high resolution and sensitivity. We use METRIS to measure several PPIs that were previously inaccessible using traditional methods, providing new insights into epigenetic recognition.

Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes

Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.

Integrative Computational Approaches to Inform Relative Bioaccumulation Potential of Per- and Polyfluoroalkyl Substances Across Species

Predictive toxicology is increasingly reliant on innovative computational methods to address pressing questions in chemicals assessment. Of importance is the evaluation of contaminant impact differences across species to inform ecosystem protection and identify appropriate model species for human toxicity studies. Here we evaluated 2 complementary tools to predict cross-species differences in binding affinity between per- and polyfluoroalkyl substances (PFAS) and the liver fatty acid-binding protein (LFABP): the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and molecular dynamics (MD). SeqAPASS determined that the structure of human LFABP, a key determinant of PFAS bioaccumulation, was conserved in the majority of vertebrate species, indicating these species would have similar PFAS bioaccumulation potentials. Level 3 SeqAPASS evaluation identified several potentially destabilizing amino acid differences across species, which were generally supported by DUET stability change predictions. Nine single-residue mutations and 7 whole species sequences were selected for MD evaluation. One mutation (F50V for PFNA) showed a statistically significant difference with stronger affinity than wild-type human LFABP. Predicted binding affinities for 9 different PFAS across 7 species showed human, rat, chicken, and rainbow trout had similar binding affinities to one another for each PFAS, whereas Japanese medaka and fathead minnow had significantly weaker LFABP-binding affinity for some PFAS. Based on these analyses, the combined use of SeqAPASS and MD provides rapid screening for potential species differences with deeper structural insight. This approach can be easily extended to other important biological receptors and potential ligands.

Aspergillomarasmine A inhibits metallo-β-lactamases by selectively sequestering Zn2

Class B metallo-β-lactamases (MBLs) are Zn2+-dependent enzymes that catalyze the hydrolysis of β-lactam antibiotics to confer resistance in bacteria. Several problematic groups of MBLs belong to subclass B1, including the binuclear New Delhi MBL (NDM), Verona integrin-encoded MBL, and imipenemase-type enzymes, which are responsible for widespread antibiotic resistance. Aspergillomarasmine A (AMA) is a natural aminopolycarboxylic acid that functions as an effective inhibitor of class B1 MBLs. The precise mechanism of action of AMA is not thoroughly understood, but it is known to inactivate MBLs by removing one catalytic Zn2+ cofactor. We investigated the kinetics of MBL inactivation in detail and report that AMA is a selective Zn2+ scavenger that indirectly inactivates NDM-1 by encouraging the dissociation of a metal cofactor. To further investigate the mechanism in living bacteria, we used an active site probe and showed that AMA causes the loss of a Zn2+ ion from a low-affinity binding site of NDM-1. Zn2+-depleted NDM-1 is rapidly degraded, contributing to the efficacy of AMA as a β-lactam potentiator. However, MBLs with higher metal affinity and stability such as NDM-6 and imipenemase-7 exhibit greater tolerance to AMA. These results indicate that the mechanism of AMA is broadly applicable to diverse Zn2+ chelators and highlight that leveraging Zn2+ availability can influence the survival of MBL-producing bacteria when they are exposed to β-lactam antibiotics.

Measurement of Low Concentration of Micro-Plastics by Detection of Bioaffinity-Induced Particle Retention Using Surface Plasmon Resonance Biosensors

The issue of micro-plastics is becoming more and more important due to their ubiquity and the harm they cause to the human body. Therefore, evaluating the biological–physical interaction of micro-plastics with health cells has become the focus of many research efforts. This study focuses on the movement mode and low concentration detection development for micro-plastics in surface plasmon resonance (SPR). Firstly, 20-micrometer micro-plastics were prepared by grinding and filtering, and the movement mode was explored; then, the characteristics were investigated by SPR. Chromatographic analysis showed that the surface charge of micro-plastics dominated the elution time, and estrogen receptors (ERs) played a supporting role. A difference of micro-plastics in SPR sensorgram was observed, inferring the micro-plastics’ movement in rolling mode on the ERs. Characteristics analysis indicated that the low particle number of micro-plastics on SPR showed a linear relationship with the response unit (RU). When ERs were immobilized on the biosensor, the force of the binding of micro-plastics to ERs under an ultra-low background was equivalent to the dissociation rate constant shown as follows: PS (0.05 nM) > PVC (0.09 nM) > PE (0.14 nM). The ELISA-like magnetic beads experiment verified the specificity between ERs and micro-plastics. Therefore, by using the SPR technique, a biological-derived over-occupation of PS was found via higher binding force with ERs and longer retention time. In the future, there will be considerable potential for micro-plastics issues, such as identification in natural samples, biomarking, real-time detection in specific environments/regions and human health subject.

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon mutation and question the effectiveness of [Formula: see text]-fold cross-validation (CV) across mutations adopted in previous studies to assess the generalization ability of such predictors with no known mutation during training. We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the change in protein binding affinity upon mutation. Our proposed sequence-based novel change in protein binding affinity predictor called PANDA performs comparably to the existing methods gauged through an appropriate CV scheme and an external independent test dataset. On an external test dataset, our proposed method gives a maximum Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art existing protein structure-based method called MutaBind which gives a maximum Pearson correlation coefficient of 0.59. Our proposed protein sequence-based method, to predict a change in binding affinity upon mutations, has wide applicability and comparable performance in comparison to existing protein structure-based methods. We made PANDA easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/panda, respectively.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

Fungal enzymes for the degradation of polyethylene: Molecular docking simulation and biodegradation pathway proposal

Polyethylene (PE) is one of the most highly consumed petroleum-based polymers and its accumulation as waste causes environmental pollution. In this sense, the use of microorganisms and their enzymes represents the most ecofriendly and effective decontamination approach. In this work, molecular docking simulation for catalytic enzyme degradation of PE was carried out using individual enzymes: laccase (Lac), manganese peroxidase (MnP), lignin peroxidase (LiP) and unspecific peroxygenase (UnP). PE-binding energy, PE-binding affinity and dimensions of PE-binding sites in the enzyme cavity were calculated in each case. Four hypothetical PE biodegradation pathways were proposed using individual enzymes, and one pathway was proposed using a synergic enzyme combination. These results show that in nature, enzymes act in a synergic manner, using their specific features to undertake an extraordinarily effective sequential catalytic process for organopollutants degradation. In this process, Lac (oxidase) is crucial to provide hydrogen peroxide to the medium to ensure pollutant breakdown. UnP is a versatile enzyme that offers a promising practical application for the degradation of PE and other pollutants due to its cavity features. This is the first in silico report of PE enzymatic degradation, showing the mode of interaction of PE with enzymes as well as the degradation mechanism.

Modified Snake α-Neurotoxin Averts β-Amyloid Binding to α7 Nicotinic Acetylcholine Receptor and Reverses Cognitive Deficits in Alzheimer's Disease Mice

Alzheimer's disease (AD) is the most common cause of senile dementia and one of the greatest medical, social, and economic challenges. According to a dominant theory, amyloid-β (Aβ) peptide is a key AD pathogenic factor. Aβ-soluble species interfere with synaptic functions, aggregate gradually, form plaques, and trigger neurodegeneration. The AD-associated pathology affects numerous systems, though the substantial loss of cholinergic neurons and α7 nicotinic receptors (α7AChR) is critical for the gradual cognitive decline. Aβ binds to α7AChR under various experimental settings; nevertheless, the functional significance of this interaction is ambiguous. Whereas the capability of low Aβ concentrations to activate α7AChR is functionally beneficial, extensive brain exposure to high Aβ concentrations diminishes α7AChR activity, contributes to the cholinergic deficits that characterize AD. Aβ and snake α-neurotoxins competitively bind to α7AChR. Accordingly, we designed a chemically modified α-cobratoxin (mToxin) to inhibit the interaction between Aβ and α7AChR. Subsequently, we examined mToxin in a set of original in silico, in vitro, ex vivo experiments, and in a murine AD model. We report that mToxin reversibly inhibits α7AChR, though it attenuates Aβ-induced synaptic transmission abnormalities, and upregulates pathways supporting long-term potentiation and reducing apoptosis. Remarkably, mToxin demonstrates no toxicity in brain slices and mice. Moreover, its chronic intracerebroventricular administration improves memory in AD-model animals. Our results point to unique mToxin neuroprotective properties, which might be tailored for the treatment of AD. Our methodology bridges the gaps in understanding Aβ-α7AChR interaction and represents a promising direction for further investigations and clinical development.

Molecular basis for higher affinity of SARS-CoV-2 spike RBD for human ACE2 receptor

Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) has caused substantially more infections, deaths, and economic disruptions than the 2002‐2003 SARS‐CoV. The key to understanding SARS‐CoV‐2's higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human angiotensin converting enzyme 2 (ACE2) protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV‐2's spike protein stronger than SARS‐CoV's spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X‐ray structures. To go beyond insights gained from X‐ray structures and investigate the role of thermal fluctuations in structure, we employ all‐atom molecular dynamics simulations. Microseconds‐long simulations reveal that while CoV and CoV‐2 spike‐ACE2 interfaces have similar conformational binding modes, CoV‐2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non‐interfacial residues. These results recommend that ongoing efforts to design spike‐ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.

Effect of molecular properties of the protein-ligand complex on the prediction accuracy of AutoDock

Molecular docking approach has been extensively used to predict the ligand's binding conformation in the binding pocket of protein. However, its prediction accuracy is still limited and highly dependent on target protein-ligand complexes. In this study, we investigated the effects of ligand torsion number, ligand hydrophobicity, and binding-site hydrophobicity on the prediction accuracy of Autodock, a popular molecular docking tool, combinatorially as well as respectively. A clear understanding of how these properties affect the prediction accuracy was observed when these properties were studied combinatorially rather than individually. The combination of low ligand torsion number-hydrophilic ligand-hydrophobic binding site provided the best prediction accuracy while the high ligand torsion number-hydrophilic ligand-hydrophobic binding pocket combination showed the least prediction accuracy. This study allowed us to determine the molecular properties of complex, showing relatively higher or low prediction accuracy and can be employed as a reference in the molecular docking studies using Autodock.