Evaluating apoenzyme–coenzyme–substrate interactions of methane monooxygenase with an engineered active site for electron harvesting: a computational study

Zhang, Sikai; Karthikeyan, Raghupathy; Fernando, Sandun

doi:10.1007/s00894-018-3876-4

Cited by 4 publications

(3 citation statements)

References 50 publications

(68 reference statements)

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“…Here, given the definition of GIBAC as in Equation ( 1), the discussion of the practical application of synthetic structural data and intermolecular K d data in drug discovery & design focuses on intermolecular binding and interactions. In biological systems, there are a wide range of intermolecular binding pairs, including including enzyme-substrate [61,62], ligand-receptor [63,64], protein-protein [34,65], ion channel-drug [66,67], antibody-antigen [68][69][70], DNA-protein [71], RNA-protein [55,72], RNA-RNA [72], hormone-receptor [73], coenzyme-substrate [74], metal ion-protein [35,75], lipid-protein [76], et cetera. By definition, synthetic structural data and intermolecular K d data can find its use for any binding pair involved in the molecular pathogenesis of human diseases, infectious or non-communicable, including:…”

Section: Broader Implications Of Synthetic Structural Data and Interm...mentioning

confidence: 99%

In Silico Generation of Structural and Intermolecular Binding Affinity Data with Reasonable Accuracy: Expanding Horizons in Drug Discovery and Design

2024

Preprint

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In the domain of drug discovery and design, the acquisition of precise structural and intermolecular Kd data for biological molecules is paramount. However, conventional methods for obtaining such data are often beset by challenges including high costs, time intensiveness, and inherent limitations in scalability and diversity. This article puts forward a high-throughput approach for in silico generation of structural and intermolecular binding affinity (Kd) data, which keeps exploring the uncharted territories of drug discovery & design by harnessing computational methodologies to synthetically generate diverse datasets. Through a meticulously designed workflow encompassing molecular structural modeling, structural biophysics-based calculations of intermolecular binding affinity (Kd), this innovative methodology transcends the constraints of traditional experimentation, offering a cost-effective, scalable, and efficient alternative. By simulating molecular interactions and binding interfaces and predicting binding affinities with reasonable accuracy, this approach not only expedites the drug development process but also enables the exploration of vast molecular space, thereby facilitating the discovery of novel therapeutics beyond conventional drug modality as a form factor. Moreover, the versatility of synthetic data extends beyond virtual screening and lead optimization, encompassing applications such as dataset augmentation, model validation, and benchmarking against experimental data. This article elucidates the conceptual underpinnings, methodological intricacies, validation strategies, and potential ramifications of in silico generated data, heralding a paradigm shift in drug discovery paradigms. By fostering synergy between computational and experimental research domains, this innovative approach promises to accelerate the pace of drug discovery, enhance the robustness of predictive models, and pave the way for transformative advancements in the entire pharmaceutical industry.

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Section: Broader Implications Of Synthetic Structural Data and Interm...mentioning

confidence: 99%

In Silico Generation of Structural and Intermolecular Binding Affinity Data with Reasonable Accuracy: Expanding Horizons in Drug Discovery and Design

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Here, given the definition of GIBAC as in Equation 4and Table 1, the discussion of the practical application of GIBAC in drug discovery & design focuses on intermolecular binding and interactions. In biological systems, there are a wide range of intermolecular binding pairs, including including enzyme-substrate [197,198], ligand-receptor [199,200], protein-protein [55,201], ion channel-drug [202,203], antibody-antigen [18,204,205], DNA-protein [206], RNA-protein [191,207], RNA-RNA [207], hormone-receptor [208], coenzyme-substrate [209], metal ion-protein [134,210], lipid-protein [211], et cetera. By definition, GIBAC can find its use for any binding pair involved in the molecular pathogenesis of human diseases, infectious or non-communicable, including:…”

Section: Application Of Gibac In Drug Discovery and Designmentioning

confidence: 99%

Towards a Truly General Intermolecular Binding Affinity Calculator for Drug Discovery & Design

Li,

Vottevor

2023

Preprint

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Intermolecular interactions are the fabrics underlying almost all processes in living organisms, where two cornerstone concepts, intermolecular binding affinity (K d ) and binding energy (ΔG), have long been established to physically describe the strengths of biomolecular interactions, e.g., drug-target K d and ΔG to describe the strength of drug-target interaction. The past two-three years saw a big step forward in the use of artificial intelligence (AI) in structural biology (e.g., AlphaFold for protein structure prediction) and drug discovery & design. In light of the roles of K d and ΔG in drug discovery & design, the speed of this AI progress raises a question of what’s next for its practical application in the pharmaceutical industry, in addition to a system-wide account of biomolecular structures and motions. Last August, the concept of a general intermolecular binding affinity calculator (GIBAC) was for the first time coined and proposed in an MDPI-published preprint. Here, this article puts forward an updated conceptual and practical framework of GIBAC, including its inception, definition, construction, practical applications, technical challenges and limitations, and future directions. Moreover, this article argues that the time is now ripe for the construction of such an accurate, precise and efficient GIBAC to be on the agenda of the entire drug discovery & design community, to ensure its applicability & reliability, and to enhance its value in drug R&D in future.

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“…Histidine 122 was protonated at the δ position, whereas histidine 85, 137, 280, 405, 449, 531, 540, 612, 701, and 759 were protonated at the ε position. Met74 and His176 are coordinated with the heme iron atom, which carries a charge of +0.2 e. 37 In addition, the cofactor FAD bears a charge of −2.0 e, 38 and the force field parameters are derived by Zhang et al 39 The cofactor heme was treated in the oxidation state (consistent with the crystal structure) and bears the charge of +1.4 e. The force field parameters of heme are derived from the CHARMM27 force field. After these treatments, MtCDH carried a net charge of −42 e. All six pairs of disulfide bonds (Cys57−Cys66, Cys129−Cys132, Cys167−Cys211, Cys303−Cys312, Cys779−Cys796, Cys790−Cys806) in MtCDH were preserved during the AAMD simulations.…”

Section: Ptmc Simulationsmentioning

confidence: 99%

Molecular Insights of Cellobiose Dehydrogenase Adsorption on Self-Assembled Monolayers

Jian

2023

Langmuir

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Cellobiose dehydrogenase (CDH) is capable of direct electron transfer (DET) on electrodes and is a promising redox enzyme for bioelectrochemical applications. Its unique two-domain structure makes the function of CDH adsorbed on the surface of the electrode deeply affected by the external environment, such as ion species, strength, pH, and surface charge density. To date, however, the exact mechanism of how the external environment tailors the structure and dynamics of CDH adsorbed on the electrode surface still remains poorly understood. Here, multiscale simulations were performed to look for insight into the effect of Na+ and Ca2+ ions on the activation of CDH on oppositely charged self-assembled monolayer (NH2-SAM and COOH-SAM) surfaces with different surface charge densities (SCDs). Both Na+ and Ca2+ can promote CDH conformation switch from the open state to the closed state, while the promotion effect of Ca2+ is stronger than that of Na+ at the same conditions. However, the high ionic strength (IS) of Ca2+ renders the cytochrome (CYT) domain of CDH away from the NH2-SAM with low SCD. In contrast, whatever the IS, the NH2-SAM surface with high SCD can not only enhance the CYT–surface interaction but also achieve a closed-state conformation due to a similar role of Ca2+. Overall, this study gains molecular-level insights into the role of ion species and surface charge in modulating the structure and conformation of CDH on the SAM surface, thereby tailoring its activity.

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Evaluating apoenzyme–coenzyme–substrate interactions of methane monooxygenase with an engineered active site for electron harvesting: a computational study

Cited by 4 publications

References 50 publications

In Silico Generation of Structural and Intermolecular Binding Affinity Data with Reasonable Accuracy: Expanding Horizons in Drug Discovery and Design

In Silico Generation of Structural and Intermolecular Binding Affinity Data with Reasonable Accuracy: Expanding Horizons in Drug Discovery and Design

Towards a Truly General Intermolecular Binding Affinity Calculator for Drug Discovery & Design

Molecular Insights of Cellobiose Dehydrogenase Adsorption on Self-Assembled Monolayers

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