Predicting enzymatic reactions with a molecular transformer

Kreutter, David; Schwaller, Philippe; Reymond, Jean‐Louis

doi:10.1039/d1sc02362d

Cited by 55 publications

(41 citation statements)

References 60 publications

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“…Implicitly, we thus assume that conserved substructures indicate the importance of the respective structures to the mechanism of the reaction or to specific interactions with amino acids in the active pocket of the enzyme. Enzymes usually react only with certain types of substrates, whereas chemical reagents are typically only specific to a functional group, 44 so that inferring information about important substructures in known substrates is especially relevant for biocatalytic transformations.…”

Section: Methodsmentioning

confidence: 99%

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

Heid

Goldman

Sankaranarayanan

et al. 2021

J. Chem. Inf. Model.

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Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

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Section: Methodsmentioning

confidence: 99%

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

Heid

Goldman

Sankaranarayanan

et al. 2021

J. Chem. Inf. Model.

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“…As intimated above, the exponential increase in computer power will eventually allow the methods of molecular dynamics to admit these "measurements" entirely by calculations based on simple force fields, de novo. In addition, given the success of so-called deep learning [628][629][630] methods in predicting the structures of proteins [631][632][633][634][635][636][637][638][639][640][641][642][643][644][645], novel receptor-ligand interactions [646][647][648][649][650], and a variety of other protein and small molecule properties (e.g., [201,[651][652][653][654][655][656][657][658][659][660][661][662][663][664][665]), it seems likely that we shall soon have available in silico methods for predicting transporter substrates directly from protein sequences, and the likeliest transporters from candidate substrate structures of interest.…”

Section: Looking To the Futurementioning

confidence: 99%

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Kell

2021

Molecules

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Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport “phospholipid bilayer transport is negligible”.

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“…ML methods have been coupled with chemoinformatic molecular descriptors [106] and structure-based reactivity estimation approaches to predict reaction outcomes [107,108]. Deep learning, which is a subset of ML, has been used in chemical reaction prediction [109] and to predict reaction yields [110] using interfaces like IBM RXN for Chemistry [111,112], which can be further modified to predict enzymatic reactions [113], and being open-source, these approaches can be readily modified to meet user requirements. Meuwly [114] reviewed the utility of ML methods for chemical reactions.…”

Section: Use Of Machine Learning (Ml) For Understanding Crnsmentioning

confidence: 99%

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

Sharma

Arya

Cruz

et al. 2021

Life

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Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

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Predicting enzymatic reactions with a molecular transformer

Abstract: The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a...

Cited by 55 publications

References 60 publications

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

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