The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation

Label-free OIRD detection of protein microarrays on high dielectric constant substrate with enhanced intrinsic sensitivity

Oblique-incidence reflectivity difference (OIRD) is a dielectric constant-sensitive technique and exhibits intriguing applications in label-free and high-throughput detection of protein microarrays. With the outstanding advantage of being compatible with arbitrary substrates, however, the effect of the substrate, particularly its dielectric constant on the OIRD sensitivity has not been fully disclosed. In this paper, for the first time we investigated the dependence of OIRD sensitivity on the dielectric constant of the substrate under top-incident OIRD configuration by combining theoretical modeling and experimental evaluation. Optical modeling suggested that the higher dielectric constant substrate exhibits a higher intrinsic sensitivity. Experimentally, three substrates including glass, fluorine-doped tin oxide (FTO) and silicon (Si) with different dielectric constants were selected as microarray substrates and their detection performances were evaluated. In good agreement with the modeling, high dielectric constant Si-based microarray exhibited the highest sensitivity among three chips, reaching a detection limit of as low as 5 ng mL-1 with streptavidin as the model target. Quantification of captured targets on three chips with on-chip enzyme-linked immunosorbent assay (ELISA) further confirmed that the enhanced performance originates from the high dielectric constant enhanced intrinsic OIRD sensitivity. This work thus provides a new way to OIRD-based label-free microarrays with improved sensitivity.

Shape-Dependent Complementary Ditopic Terpyridine Pair with Two Levels of Self-Recognition for Coordination-Driven Self-Assembly

Molecular recognition in biological systems plays a vital role in the precise construction of biomacromolecules and the corresponding biological activities. Such recognition mainly relies on the highly specific binding of complementary molecular pairs with complementary sizes, shapes, and intermolecular forces. It still remains challenging to develop artificial complementary motif pairs for coordination-driven self-assembly. Herein, a series of shape-dependent complementary motif pairs, based on ditopic 2,2':6',2″-terpyridine (TPY) backbone, are designed and synthesized. The fidelity degrees of self-assemblies from these motifs are carefully evaluated by multi-dimensional mass spectrometry, nuclear magnetic resonance spectroscopy, and molecular modeling. In addition, two levels of self-recognition in both homoleptic and heteroleptic assembly are discovered in the assembled system. Through finely tuning the shape and size of the ligands, a complementary pair is developed with error-free narcissistically self-sorting at two levels of self-recognition, and the intrinsic principle is carefully investigated.

Biotinylation of a MRI/Gd BNCT theranostic agent to access a novel tumour-targeted delivery system

A new biotin based BNCT (Boron Neutron Capture Therapy)-MRI theranostic is here reported (Gd-AL01) in order to exploit the high tumour specificity of biotin and the selectivity of BNCT in a synergistic manner. The key is the preparation of an intermediate where an o-carborane is linked to two amino groups orthogonally protected via the exploitation of two consecutive Mitsunobu reactions. The aim is its functionalisation in two different steps with biotin as the biological vector and Gd-DOTA as the MRI probe and GdNCT agent. Cell uptake was evaluated on HeLa tumour cells overexpressing biotin receptors. The internalised boron is proportional to the concentration of the theranostic agent incubated in the presence of cells. A maximum value of 77 ppm is reached and a well detectable signal intensity increase in the T₁ weighted image of HeLa cells was observed, differently from clinically used GdHPDO3A, where no contrast is detected. These excellent results indicate that Gd-AL01 can be applied as a theranostic probe in BNCT studies.

An ultrasensitive electrochemical immunosensor for hepatitis C antibodies based on one-step-eletrosynthetized polypyrrole-graphene nanocomposite

An ultrasensitive label-free electrochemical immunosensor was developed for hepatitis C antibodies (anti-HCV). Worldwide, it is estimated 71 million people have HCV infection in a chronic stage that may lead to cirrose and cancer. To achieve HCV elimination, health programs should include screening testing based on anti-HCV detection allowing the early-stage treatment. The immunosensor was based on a graphene oxide-polypyrrole (PPy-GO) film one-step electropolymerized on the electrode surface. Ultrasensitive anti-HCV detection was ensured by HCV antigen conjugated to biotin that was immobilized in a great amount on streptavidin-coated nanostructured surface. Analytical responses were obtained by anodic peaks from the square wave voltammetry in the presence of ferrocyanide/ferricyanide as a redox probe. This immunosensor exhibited a linear range from 2 to 14 ng mL^-1 of anti-HCV and a limit of detection in the clinical range (1.63 ng mL^-1). Furthermore, the immunosensor presented an efficient performance for the determination of anti-HCV in spiked serum samples, becoming this developed nanosensor as potential tools for early HCV diagnosis and screening.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10853-022-06992-5.

Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs

The pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world, with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by 23 November 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structural proteins (NSP). Three non-structural proteins, main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp), are believed to have a crucial role in replication of the virus. We applied computational ligand-receptor binding modeling and performed comprehensive virtual screening on FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics rolapitant and ondansetron for Mpro; labetalol and levomefolic acid for PLpro; and leucal and antifungal natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The results of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins-Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.

MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment

For decades, the complicated energy surfaces found in macromolecular protein:ligand structures, which require large amounts of computational time and resources for energy state sampling, have been an inherent obstacle to fast, routine free energy estimation in industrial drug discovery efforts. Beginning in 2013, the Merz research group addressed this cost with the introduction of a novel sampling methodology termed “Movable Type” (MT). Using numerical integration methods, the MT method reduces the computational expense for energy state sampling by independently calculating each atomic partition function from an initial molecular conformation in order to estimate the molecular free energy using ensembles of the atomic partition functions. In this work, we report a software package, the DivCon Discovery Suite with the MovableType module from QuantumBio Inc., that performs this MT free energy estimation protocol in a fast, fully encapsulated manner. We discuss the computational procedures and improvements to the original work, and we detail the corresponding settings for this software package. Finally, we introduce two validation benchmarks to evaluate the overall robustness of the method against a broad range of protein:ligand structural cases. With these publicly available benchmarks, we show that the method can use a variety of input types and parameters and exhibits comparable predictability whether the method is presented with “expensive” X-ray structures or “inexpensively docked” theoretical models. We also explore some next steps for the method. The MovableType software is available at http://www.quantumbioinc.com/

EasyAmber: A comprehensive toolbox to automate the molecular dynamics simulation of proteins

Conformational plasticity of the functionally important regions and binding sites in protein/enzyme structures is one of the key factors affecting their function and interaction with substrates/ligands. Molecular dynamics (MD) can address the challenge of accounting for protein flexibility by predicting the time-dependent behavior of a molecular system. It has a potential of becoming a particularly important tool in protein engineering and drug discovery, but requires specialized training and skills, what impedes practical use by many investigators. We have developed the easyAmber - a comprehensive set of programs to automate the molecular dynamics routines implemented in the Amber package. The toolbox can address a wide set of tasks in computational biology struggling to account for protein flexibility. The automated workflow includes a complete set of steps from the initial "static" molecular model to the MD "production run": the full-atom model building, optimization/equilibration of the molecular system, classical/conventional and accelerated molecular dynamics simulations. The easyAmber implements advanced MD protocols, but is highly automated and easy-to-operate to attract a broad audience. The toolbox can be used on a personal desktop station equipped with a compatible gaming GPU-accelerator, as well as help to manage huge workloads on a powerful supercomputer. The software provides an opportunity to operate multiple simulations of different proteins at the same time, thus significantly increasing work efficiency. The easyAmber takes the molecular dynamics to the next level in terms of usability for complex processing of large volumes of data, thus supporting the recent trend away from inefficient "static" approaches in biology toward a deeper understanding of the dynamics in protein structures. The software is freely available for download at https://biokinet.belozersky.msu.ru/easyAmber, no login required.

Entropic effect and residue specific entropic contribution to the cooperativity in streptavidin-biotin binding

Molecular dynamics (MD) simulations were performed employing the polarized protein-specific charge (PPC) to explore the origin of the cooperativity in streptavidin-biotin systems (wild type, two single mutations and one double-mutation). The results of the experiment found that the existence of cooperativity is mainly the result of the entropic effect. In this study, the entropic contribution to the binding free energy was calculated using the recently developed interaction entropy (IE) method, and computational results are in excellent agreement with the experimental observations and are further verified by the calculation of the thermodynamic integration. Comparison of different force fields in terms of predicted binding strength ordering, cooperativity of energy and the stability of hydrogen bonding suggests that the PPC force field combined IE method is a suitable choice. In addition, the IE method enables us to obtain the residue-specific entropic contributions to the streptavidin-biotin binding affinity and identify ten hot-spot residues providing the dominant contribution to the cooperative binding. Importantly, the overall cooperativity obtained from the ten residues also comes mainly from the entropic effect in our study. The calculation of the potential of mean force shows that the unbinding of streptavidin-biotin is a multi-step process, and each step corresponds to the formation and rupture of the hydrogen bond network. And S45A mutation may increase the rigidity of the linker region, making the flap region relatively difficult to open. The present study provides significant molecular insight into the binding cooperativity of the streptavidin-biotin complex.

Streptavidin/biotin: Tethering geometry defines unbinding mechanics

Macromolecules tend to respond to applied forces in many different ways. Chemistry at high shear forces can be intriguing, with relatively soft bonds becoming very stiff in specific force-loading geometries. Largely used in bionanotechnology, an important case is the streptavidin (SA)/biotin interaction. Although SA's four subunits have the same affinity, we find that the forces required to break the SA/biotin bond depend strongly on the attachment geometry. With AFM-based single-molecule force spectroscopy (SMFS), we measured unbinding forces of biotin from different SA subunits to range from 100 to more than 400 pN. Using a wide-sampling approach, we carried out hundreds of all-atom steered molecular dynamics (SMD) simulations for the entire system, including molecular linkers. Our strategy revealed the molecular mechanism that causes a fourfold difference in mechanical stability: Certain force-loading geometries induce conformational changes in SA's binding pocket lowering the energy barrier, which biotin has to overcome to escape the pocket.

Application of the Movable Type Free Energy Method to the Caspase-Inhibitor BindingAffinity Study

Many studies have provided evidence suggesting that caspases not only contribute to the neurodegeneration associated with Alzheimer’s disease (AD) but also play essential roles in promoting the underlying pathology of this disease. Studies regarding the caspase inhibition draw researchers’ attention through time due to its therapeutic value in the treatment of AD. In this work, we apply the “Movable Type” (MT) free energy method, a Monte Carlo sampling method extrapolating the binding free energy by simulating the partition functions for both free-state and bound-state protein and ligand configurations, to the caspase-inhibitor binding affinity study. Two test benchmarks are introduced to examine the robustness and sensitivity of the MT method concerning the caspase inhibition complexing. The first benchmark employs a large-scale test set including more than a hundred active inhibitors binding to caspase-3. The second benchmark includes several smaller test sets studying the relative binding free energy differences for minor structural changes at the caspase-inhibitor interaction interfaces. Calculation results show that the RMS errors for all test sets are below 1.5 kcal/mol compared to the experimental binding affinity values, demonstrating good performance in simulating the caspase-inhibitor complexing. For better understanding the protein-ligand interaction mechanism, we then take a closer look at the global minimum binding modes and free-state ligand conformations to study two pairs of caspase-inhibitor complexes with (1) different caspase targets binding to the same inhibitor, and (2) different polypeptide inhibitors targeting the same caspase target. By comparing the contact maps at the binding site of different complexes, we revealed how small structural changes affect the caspase-inhibitor interaction energies. Overall, this work provides a new free energy approach for studying the caspase inhibition, with structural insight revealed for both free-state and bound-state molecular configurations.

Direction Matters: Monovalent Streptavidin/Biotin Complex under Load

Novel site-specific attachment strategies combined with improvements of computational resources enable new insights into the mechanics of the monovalent biotin/streptavidin complex under load and forced us to rethink the diversity of rupture forces reported in the literature. We discovered that the mechanical stability of this complex depends strongly on the geometry in which force is applied. By atomic force microscopy-based single molecule force spectroscopy we found unbinding of biotin to occur beyond 400 pN at force loading rates of 10 nN/s when monovalent streptavidin was tethered at its C-terminus. This value is about twice as high than that for N-terminal attachment. Steered molecular dynamics simulations provided a detailed picture of the mechanics of the unbinding process in the corresponding force loading geometries. Using machine learning techniques, we connected findings from hundreds of simulations to the experimental results, identifying different force propagation pathways. Interestingly, we observed that depending on force loading geometry, partial unfolding of N-terminal region of monovalent streptavidin occurs before biotin is released from the binding pocket.

Protein-protein interactions of human glyoxalase II: findings of a reliable docking protocol

Glyoxalase II (GlxII) is an antioxidant glutathione-dependent enzyme, which catalyzes the hydrolysis of S-d-lactoylglutathione to form d-lactic acid and glutathione (GSH). The last product is the most important thiol reducing agent present in all eukaryotic cells that have mitochondria and chloroplasts. It is generally known that GSH plays a crucial role not only in the cellular redox state but also in various cellular processes. One of them is protein S-glutathionylation, a process that can occur through an oxidation reaction of proteins' thiol groups by GSH. Changes in protein S-glutathionylation have been associated with a range of human diseases such as diabetes, cardiovascular and pulmonary diseases, neurodegenerative diseases and cancer. Within a major project aimed at elucidating the role of GlxII in the mechanism of S-glutathionylation, a reliable computational protocol consisting of a protein-protein docking approach followed by atomistic Molecular Dynamics (MD) simulations was developed and it was applied to the prediction of molecular associations between human GlxII (in the presence and absence of GSH) and some proteins that are known to be S-glutathionylated in vitro, such as actin, malate dehydrogenase (MDH) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The computational results show a high propensity of GlxII to interact with actin and MDH through its active site and a high stability of the GlxII-protein systems when GSH is present. Moreover, close proximities of GSH with actin and MDH cysteine residues have been found, suggesting that GlxII could be able to perform protein S-glutathionylation by using the GSH molecule present in its catalytic site.

Overview of the SAMPL6 host-guest binding affinity prediction challenge

Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host-guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host-guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host-guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host-guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host-guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.

Role of conformational dynamics in the evolution of novel enzyme function

The free energy landscape concept that describes enzymes as an ensemble of differently populated conformational sub-states in dynamic equilibrium is key for evaluating enzyme activity, enantioselectivity, and specificity. Mutations introduced in the enzyme sequence can alter the populations of the pre-existing conformational states, thus strongly modifying the enzyme ability to accommodate alternative substrates, revert its enantiopreferences, and even increase the activity for some residual promiscuous reactions. In this feature article, we present an overview of the current experimental and computational strategies to explore the conformational free energy landscape of enzymes. We provide a series of recent publications that highlight the key role of conformational dynamics for the enzyme evolution towards new functions and substrates, and provide some perspectives on how conformational dynamism should be considered in future computational enzyme design protocols.