RxnSim: a tool to compare biochemical reactions

Giri, Varun; Sivakumar, T.; Cho, Kwang Myung; Kim, Tae Yong; Bhaduri, Anirban

doi:10.1093/bioinformatics/btv416

Cited by 23 publications

(21 citation statements)

References 14 publications

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“…We repeated the analysis from the previous section using the standard reaction difference fingerprint (Methods), which is used in structure similarity methods such as RxnSim (36) and RxnFinder (37), to assess the benefits of introducing the information about the reactive site of substrates into the reaction fingerprints. A comparison of the two sets of predictions on 5,049 non-orphan reactions showed that the predictions obtained with BridgITmodified fingerprints were significantly better than the standard ones.…”

Section: Bridgit Reaction Fingerprints Offer Improved Predictionsmentioning

confidence: 99%

“…In addition, these methods are not suitable for the annotation of de novo reactions since current pathway prediction tools only provide information about enzyme catalytic biotransformations and not about their sequences. These shortcomings motivated the development of alternative computational methods based on the structural similarity of reactants and products for identifying candidate protein sequences for orphan enzymatic reactions (30,33,(36)(37)(38)(39)(40). The idea behind these approaches was to assess the similarity of two enzymatic reactions via the similarity of their reaction fingerprints, i.e., the mathematical descriptors of the structural and topological properties of the participating metabolites (41), which could eliminate the problems associated with non-matching or unassigned protein sequences.…”

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confidence: 99%

“…One class of reaction-fingerprint computational methods compares all of the compounds participating in reactions (40), which includes both reactants and cofactors. The application of this group of methods is restricted to specific enzymatic reactions that do not involve large cofactors (30,33,(36)(37)(38)(39)(40). This is because the structural information of the large cofactors overwhelmingly contributes to the corresponding reconstructed reactionfingerprint, and consequently, reactions with similar cofactors will inaccurately be classified as similar (35)(36)(37)(38).…”

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Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites

Hadadi

MohamadiPeyhani

Mišković

et al. 2017

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All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/210039 doi: bioRxiv preprint first posted online Oct. 27, 2017; 2 AUTHORS SUMMARYThe recent advances in pathway generation tools have resulted in a wealth of de novo hypothetical enzymatic reactions, which lack knowledge of the protein-encoding genes associated with their functionality. Moreover, nearly half of known metabolic enzymes are orphan, i.e., they also lack an associated gene or protein sequence. Proposing genes for catalytic functions of de novo and orphan reactions is critical for their utility in various applications ranging from biotechnology to medicine. In this work, we propose a novel computational method that will bridge the knowledge gap and provide candidate genes for both de novo and orphan reactions. We demonstrate that information about a small chemical structure around the reactive sites of substrates is sufficient to correctly assign genes to the functionality of enzymatic reactions. ABSTRACTThousands of biochemical reactions with characterized biochemical activities are still orphan. Novel reactions predicted by pathway generation tools also lack associated protein sequences and genes.Mapping orphan and novel reactions back to the known biochemistry and proposing genes for their catalytic functions is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for orphan and novel enzymatic reactions. BridgIT introduces, for the first time, the information of the enzyme binding pocket into reaction similarity comparisons. It ascertains the similarity of two reactions by comparing the reactive sites of their substrates and their surrounding structures, along with the structures of the generated products. BridgIT compares orphan and novel reactions to enzymatic reactions with known protein sequences, and then, it proposes protein sequences and genes of the most similar non-orphan reactions as candidates for catalyzing the novel or orphan reactions. We performed BridgIT analysis of orphan reactions from KEGG 2011 (Kyoto Encyclopedia of Genes and Genomes, published in 2011) that became non-orphan in KEGG 2016, and BridgIT correctly predicted enzymes with identical third-and fourth-level EC numbers for 91% and 56% of these reactions, respectively. BridgIT results revealed that it is sufficient to know information about six atoms together with their connecting bonds around the reactive sites of the substrates to match a protein sequence to the catalytic activity of enzymatic reactions with maximal accuracy. Moreover, the same information about only three atoms around the reactive site allowed us to correctly match 87% of the analyzed enzymatic reactions. Finally, we used BridgIT to provide candidate protein sequences for 137000 novel enzymatic reactions from the recently introduced ATLAS of Biochemistry...

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Section: Bridgit Reaction Fingerprints Offer Improved Predictionsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites

Hadadi

MohamadiPeyhani

Mišković

et al. 2017

Preprint

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“…Similarity between chemical reactions, referred to as reaction similarity, can be calculated at multiple levels: Transformation level similarity is computed by considering only the atoms and bonds that are undergoing transformation, at different degrees of neighborhood information [ 12 ]. Reaction level similarity considers molecular information of the entire substrates and products constituting a biochemical reaction [ 13 ]. Assessing reaction similarity as transformation level enables classification of enzyme function based on reaction mechanism [ 14 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

SimCAL: a flexible tool to compute biochemical reaction similarity

et al. 2018

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BackgroundComputation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels: (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation.ResultsTo address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods.ConclusionsSimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2248-5) contains supplementary material, which is available to authorized users.

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“… 14 We have reported an approach to address these limitations using kernel PCA embedded vector representations of molecules and principals of compositional semantics from computational linguistics. 15 While computational methods exist to compare, classify, and search enzymatic reactions 16 – 21 , they frequently rely on direct comparison of molecular fingerprints as well as specific bond or atom changes within the molecule. Our approach allows for the representation of chemical transformations as algebraic expressions of chemical structure vectors.…”

Section: Introductionmentioning

confidence: 99%

Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome

et al. 2017

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Bacteria in the human gut have the ability to activate, inactivate, and reactivate drugs with both intended and unintended effects. For example, the drug digoxin is reduced to the inactive metabolite dihydrodigoxin by the gut Actinobacterium E. lenta, and patients colonized with high levels of drug metabolizing strains may have limited response to the drug. Understanding the complete space of drugs that are metabolized by the human gut microbiome is critical for predicting bacteria-drug relationships and their effects on individual patient response. Discovery and validation of drug metabolism via bacterial enzymes has yielded >50 drugs after nearly a century of experimental research. However, there are limited computational tools for screening drugs for potential metabolism by the gut microbiome. We developed a pipeline for comparing and characterizing chemical transformations using continuous vector representations of molecular structure learned using unsupervised representation learning. We applied this pipeline to chemical reaction data from MetaCyc to characterize the utility of vector representations for chemical reaction transformations. After clustering molecular and reaction vectors, we performed enrichment analyses and queries to characterize the space. We detected enriched enzyme names, Gene Ontology terms, and Enzyme Consortium (EC) classes within reaction clusters. In addition, we queried reactions against drug-metabolite transformations known to be metabolized by the human gut microbiome. The top results for these known drug transformations contained similar substructure modifications to the original drug pair. This work enables high throughput screening of drugs and their resulting metabolites against chemical reactions common to gut bacteria.

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RxnSim: a tool to compare biochemical reactions

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 23 publications

References 14 publications

Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites

Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites

SimCAL: a flexible tool to compute biochemical reaction similarity

Chemical reaction vector embeddings: towards predicting drug metabolism in the human gut microbiome

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