From sequence to enzyme mechanism using multi-label machine learning

Ferrari, Luna De; Mitchell, John B. O.

doi:10.1186/1471-2105-15-150

Cited by 18 publications

(21 citation statements)

References 55 publications

(59 reference statements)

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“…Identification of unknown protein functions is essential for understanding biological processes and beyond [ 1 , 2 ]. Enzymes are proteins whose function is to catalyse chemical reactions in a living cell.…”

Section: Resultsmentioning

confidence: 99%

“…Enzymes are proteins whose function is to catalyse chemical reactions in a living cell. Ascertaining enzymatic mechanisms can have important applications for pharmaceutical and industrial processes in which catalysts are involved [ 1 ]. For example, identifying the catalytic mechanism(s) of an enzyme could lead to designing new biocatalysts that give significant cost savings over non-biological alternatives in sectors such as laundry, deodorants, foods and agriculture [ 1 ].…”

Section: Resultsmentioning

confidence: 99%

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Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces

2015

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BackgroundWe recently reported that one may be able to predict with high accuracy the chemical mechanism of an enzyme by employing a simple pattern recognition approach: a k Nearest Neighbour rule with k = 1 (k1NN) and 321 InterPro sequence signatures as enzyme features. The nearest-neighbour rule is known to be highly sensitive to errors in the training data, in particular when the available training dataset is small. This was the case in our previous study, in which our dataset comprised 248 enzymes annotated against 71 enzymatic mechanism labels from the MACiE database. In the current study, we have carefully re-analysed our dataset and prediction results to “explain” why a high variance k1NN rule exhibited such remarkable classification performance.ResultsWe find that enzymes with different chemical mechanism labels in this dataset reside in barely overlapping subspaces in the feature space defined by the 321 features selected. These features contain the appropriate information needed to accurately classify the enzymatic mechanisms, rendering our classification problem a basic look-up exercise. This observation dovetails with the low misclassification rate we reported.ConclusionOur results provide explanations for the “anomaly”—a basic nearest-neighbour algorithm exhibiting remarkable prediction performance for enzymatic mechanism despite the fact that the feature space was large and sparse. Our results also dovetail well with another finding we reported, namely that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also suggest simple rules that might enable one to inductively predict whether a novel enzyme possesses any of our 71 predefined mechanisms.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces

2015

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“…Indeed, the circularity of the combined process of propagating annotations and then predicting function, based on the same annotations and homologies, may be problematic. Sequence-based enzyme function predictions based on EC number annotations in databases can indeed give very impressive results [35] and such predictive exercises can be extended to include mechanism [36], both processes usually operating mostly via the detection of homology -although 3D structure-based methods also exist [37,38,39]. Using mechanisms and catalytic chains as defined in MACiE, the corresponding UniProt sequences are interrogated against InterPro signatures [29] to re-express the MACiE entries in terms of the signatures present in them.…”

Section: Protein Function Predictionmentioning

confidence: 99%

“…Using mechanisms and catalytic chains as defined in MACiE, the corresponding UniProt sequences are interrogated against InterPro signatures [29] to re-express the MACiE entries in terms of the signatures present in them. This information forms the input into a machine learning exercise [36] to associate test sequences with enzymatic mechanisms, as shown in Figure 2. Recently, the success of different groups' approaches to protein function prediction has been evaluated in the CAFA (Critical Assessment of Functional Annotation) exercises, of which the second [40] assessed predictions made in late 2013 and focussed on predicting the Gene Ontology (GO) [41] terms associated with proteins.…”

Section: Protein Function Predictionmentioning

confidence: 99%

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Enzyme function and its evolution

Mitchell

2017

Current Opinion in Structural Biology

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With rapid increases over recent years in the determination of protein sequence and structure, alongside knowledge of thousands of enzyme functions and hundreds of chemical mechanisms, it is now possible to combine breadth and depth in our understanding of enzyme evolution. Phylogenetics continues to move forward, though determining correct evolutionary family trees is not trivial. Protein function prediction has spawned a variety of promising methods that offer the prospect of identifying enzymes across the whole range of chemical functions and over numerous species. This knowledge is essential to understand antibiotic resistance, as well as in protein re-engineering and de novo enzyme design.

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Classifying metal‐binding sites with neural networks

et al. 2023

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To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine‐tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe‐2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub‐classifications of the [2Fe‐2S] cofactor: (1) a standard [2Fe‐2S] cofactor and (2) a Rieske [2Fe‐2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal‐binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal‐binding site identification in proteins.

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From sequence to enzyme mechanism using multi-label machine learning

Cited by 18 publications

References 55 publications

Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces

Enzyme mechanism prediction: a template matching problem on InterPro signature subspaces

Enzyme function and its evolution

Classifying metal‐binding sites with neural networks

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