Multiscale computation delivers organophosphorus reactivity and stereoselectivity to immunoglobulin scavengers

Dynamic interchange between two protonation states is characteristic of active sites of cholinesterases

Cholinesterases are well-known and widely studied enzymes crucial to human health and involved in neurology, Alzheimer's, and lipid metabolism. The protonation pattern of active sites of cholinesterases influences all the chemical processes within, including reaction, covalent inhibition by nerve agents, and reactivation. Despite its significance, our comprehension of the fine structure of cholinesterases remains limited. In this study, we employed enhanced-sampling quantum-mechanical/molecular-mechanical calculations to show that cholinesterases predominantly operate as dynamic mixtures of two protonation states. The proton transfer between two non-catalytic glutamate residues follows the Grotthuss mechanism facilitated by a mediator water molecule. We show that this uncovered complexity of active sites presents a challenge for classical molecular dynamics simulations and calls for special treatment. The calculated proton transfer barrier of 1.65 kcal/mol initiates a discussion on the potential existence of two coupled low-barrier hydrogen bonds in the inhibited form of butyrylcholinesterase. These findings expand our understanding of structural features expressed by highly evolved enzymes and guide future advances in cholinesterase-related protein and drug design studies.

Dynamic Phosphorus: Thiolate Exchange Cascades with Higher Phosphorothioates

In this study, we introduce phosphorus, a pnictogen, as an exchange center for dynamic covalent chemistry. Cascade exchange of neutral phosphorotri- and -tetrathioates with thiolates is demonstrated in organic solvents, aqueous micellar systems, and in living cells. Exchange rates increase with the pH value, electrophilicity of the exchange center, and nucleophilicity of the exchangers. Molecular walking of the dynamic phosphorus center along Hammett gradients is simulated by the sequential addition of thiolate exchangers. Compared to phosphorotrithioates, tetrathioates are better electrophiles with higher exchange rates. Dynamic phosphorotri- and -tetrathioates are non-toxic to HeLa Kyoto cells and participate in the dynamic networks that account for thiol-mediated uptake into living cells.

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of Acidithiobacillia

Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.

Modern non-polarizable force fields diverge in modeling the enzyme-substrate complex of a canonical serine protease

Classical molecular dynamics simulation is a powerful and established method of modern computational chemistry. Being able to obtain accurate information on molecular behavior is crucial to get valuable insights into structure-function relationships that translate into fundamental findings and practical applications. Active sites of enzymes are known to be particularly intricate, therefore, simpler non-polarizable force fields may provide an inaccurate description. In this work, we addressed this hypothesis in a case of a canonical serine triad protease trypsin in its complex with a substrate-mimicking inhibitor. We tested six modern and popular force fields to find that significantly diverging results may be obtained. Amber FB-15 and OPLS-AA/M turned out to model the active site incorrectly. Amber ff19sb and ff15ipq demonstrated mixed performance. The best performing force fields were CHARMM36m and Amber ff99sb-ildn, therefore, they are recommended for use with this and related systems. We speculate that a similar lack of cross-force field convergence may be characteristic of other enzymatic systems. Therefore, we advocate for careful consideration of different force fields in any study within the field of computational enzymology.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

Between Protein Fold and Nucleophile Identity: Multiscale Modeling of the TEV Protease Enzyme-Substrate Complex

The cysteine protease from the tobacco etch virus (TEVp) is a well-known and widely utilized enzyme. TEVp's chymotrypsin-like fold is generally associated with serine catalytic triads that differ in terms of a reaction mechanism from the most well-studied papain-like cysteine proteases. The question of what dominates the TEVp mechanism, nucleophile identity, or structural composition has never been previously addressed. Here, we use enhanced sampling multiscale modeling to uncover that TEVp combines the features of two worlds in such a way that potentially hampers its activity. We show that TEVp cysteine is strictly in the anionic form in a free enzyme similar to papain. Peptide binding shifts the equilibrium toward the nucleophile's protonated form, characteristic of chymotrypsin-like proteases, although the cysteinyl anion form is still present and interconversion is rapid. This way cysteine protonation generates enzyme states that are a diversion from the most effective course of action, with only 13.2% of Michaelis complex sub-states able to initiate the reaction. As a result, we propose an updated view on the reaction mechanism catalyzed by TEVp. We also demonstrate that AlphaFold is able to construct protease-substrate complexes with high accuracy. We propose that our findings open a way for its industrious use in enzymological tasks. Unique features of TEVp discovered in this work open a discussion on the evolutionary history and trade-offs of optimizing serine triad-associated folds to cysteine as a nucleophile.

Antibodies as Biosensors' Key Components: State-of-the-Art in Russia 2020-2021

The recognition of biomolecules is crucial in key areas such as the timely diagnosis of somatic and infectious diseases, food quality control, and environmental monitoring. This determines the need to develop highly sensitive display devices based on the achievements of modern science and technology, characterized by high selectivity, high speed, low cost, availability, and small size. Such requirements are met by biosensor systems—devices for reagent-free analysis of compounds that consist of a biologically sensitive element (receptor), a transducer, and a working solution. The diversity of biological material and methods for its immobilization on the surface or in the volume of the transducer and the use of nanotechnologies have led to the appearance of an avalanche-like number of different biosensors, which, depending on the type of biologically sensitive element, can be divided into three groups: enzyme, affinity, and cellular/tissue. Affinity biosensors are one of the rapidly developing areas in immunoassay, where the key point is to register the formation of an antigen–antibody complex. This review analyzes the latest work by Russian researchers concerning the production of molecules used in various immunoassay formats as well as new fundamental scientific data obtained as a result of their use.

Probing the Suitability of Different Ca2+ Parameters for Long Simulations of Diisopropyl Fluorophosphatase

Organophosphate hydrolases are promising as potential biotherapeutic agents to treat poisoning with pesticides or nerve gases. However, these enzymes often need to be further engineered in order to become useful in practice. One example of such enhancement is the alteration of enantioselectivity of diisopropyl fluorophosphatase (DFPase). Molecular modeling techniques offer a unique opportunity to address this task rationally by providing a physical description of the substrate-binding process. However, DFPase is a metalloenzyme, and correct modeling of metal cations is a challenging task generally coming with a tradeoff between simulation speed and accuracy. Here, we probe several molecular mechanical parameter combinations for their ability to empower long simulations needed to achieve a quantitative description of substrate binding. We demonstrate that a combination of the Amber19sb force field with the recently developed 12-6 Ca2+ models allows us to both correctly model DFPase and obtain new insights into the DFP binding process.

Therapeutic nanoreactors for detoxification of xenobiotics: Concepts, challenges and biotechnological trends with special emphasis to organophosphate bioscavenging

The introduction of enzyme nanoreactors in medicine is relatively new. However, this technology has already been experimentally successful in cancer treatments, struggle against toxicity of reactive oxygen species in inflammatory processes, detoxification of drugs and xenobiotics, and correction of metabolic and genetic defects by using encapsulated enzymes, acting in single or cascade reactions. Biomolecules, e.g. enzymes, antibodies, reactive proteins capable of inactivating toxicants in the body are called bioscavengers. In this review, we focus on enzyme-containing nanoreactors for in vivo detoxification of organophosphorous compounds (OP) to be used for prophylaxis and post-exposure treatment of OP poisoning. A particular attention is devoted to bioscavenger-containing injectable nanoreactors operating in the bloodstream. The nanoreactor concept implements single or multiple enzymes and cofactors co-encapsulated in polymeric semi-permeable nanocontainers. Thus, the detoxification processes take place in a confined space containing highly concentrated bioscavengers. The article deals with historical and theoretical backgrounds about enzymatic detoxification of OPs in nanoreactors, nanoreactor polymeric enveloppes, realizations and advantages over other approaches using bioscavengers.