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About 1,831 papers for “enzyme function prediction”

Enzyme Function Prediction with Interpretable Models

Umar Syed, Golan Yona · Methods in molecular biology · 2009

Enzymes play central roles in metabolic pathways, and the prediction of metabolic pathways in newly sequenced genomes usually starts with the assignment of genes to enzymatic reactions. However, genes with similar catalytic activity are not necessarily similar in sequence, and therefore the traditional sequence similarity-based approach often fails to identify the relevant enzymes, thus hindering efforts to map the metabolome of an organism.Here we study the direct relationship between basic protein properties and their function. Our goal is to develop a new tool for functional prediction (e.g., prediction of Enzyme Commission number), which can be used to complement and support other techniques based on sequence or structure information. In order to define this mapping we collected a set of 453 features and properties that characterize proteins and are believed to be related to structural and functional aspects of proteins. We introduce a mixture model of stochastic decision trees to learn the set of potentially complex relationships between features and function. To study these correlations, trees are created and tested on the Pfam classification of proteins, which is based on sequence, and the EC classification, which is based on enzymatic function. The model is very effective in learning highly diverged protein families or families that are not defined on the basis of sequence. The resulting tree structures highlight the properties that are strongly correlated with structural and functional aspects of protein families, and can be used to suggest a concise definition of a protein family.

Cited by 46

SEFP: Structure-Based Enzyme Function Prediction

Ziqi Zhang, Guan Yu Yu, Zhaohong Deng et al. · IEEE Transactions on Computational Biology and Bioinformatics · 2025

Traditional biological experimental methods to determine enzyme properties are time-consuming and costly, leading to an increasing interest in computational models for enzyme function prediction. However, the existing computational methods are insufficient and inefficient to exploit enzyme structure. In this work, we introduce SEFP, a novel method leveraging enzyme point clouds for enzyme function prediction. The structure encoder of SEFP uses a tailored enzyme point cloud network to analyze the three-dimensional arrangement of atoms within the enzyme, integrating hierarchical residue global features through a residue feature adapter to extract detailed enzyme point features. Additionally, the Bio-BCS residue feature encoder extracts enzyme residue features with channel and spatial weights using a specially designed attention mechanism. Finally, SEFP fuses point and residue features to generate the final prediction results. Comparative evaluations show that SEFP outperforms various recent computational methods, demonstrating superior performance. On the RSCB enzyme structure dataset, SEFP achieves an f1-score of 95.85, outperforming two representative structure-based methods, EnzyNet and DeepFri. On the HECNet dataset, SEFP maintains its superiority over all comparison sequence-based methods, yielding an f1-score of 94.29. Ablation studies are conducted to confirm the effectiveness of individual modules within SEFP. These findings underscore the potential of SEFP for reliable and precise enzyme function prediction, offering advancements in bioinformatics and computational biology.

Cited by 1

Enzyme function prediction using contrastive learning

Tianhao Yu, Haiyang Cui, Jianan Canal Li et al. · Science · 2023

Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning-enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers-functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis.

Cited by 340

Towards structured output prediction of enzyme function

Katja Astikainen, Liisa Holm, Esa Pitkänen et al. · BMC Proceedings · 2008

Structured output prediction with GTG features is shown to be computationally feasible and to have accuracy on par with state-of-the-art approaches in enzyme function prediction.

Cited by 45Open access

DAROGAN : enzyme function prediction from multiple sequence alignments

Russell S. Hamilton · ERA · 2006

Genome sequencing projects are leading to large numbers of protein sequences being deposited in the sequence databases.Unfortunately the amount of accessible functional and structural information for these sequences is advancing more slowly, leaving an ever widening gap in our knowledge.To experimentally characterise each of the sequences would be a massive undertaking, being both time consuming and expensive.Hence there is an ever increasing need for computational tools to bridge this gap.A tool for predicting the function of a putative enzyme from its sequence alone, for example, would be extremely valuable, yet a universally successful method remains elusive.I would like to thank my supervisor Dietlind Gerloff for her enthusiasm and guidance over the last three (and a bit) years.Special thanks go

Cited by 0Open access

Chapter 17 Enzyme Function Prediction with Interpretable Models

Umar Syed, Golan Yona · 2011

Enzymes play central roles in metabolic pathways, and the prediction of metabolic pathways in newly sequenced genomes usually starts with the assignment of genes to enzymatic reactions. However, genes with similar catalytic activity are not necessarily similar in sequence, and therefore the traditional sequence similarity-based approach often fails to identify the relevant enzymes, thus hindering efforts to map the metabolome of an organism. Here we study the direct relationship between basic protein properties and their function. Our goal is to develop a new tool for functional prediction (e.g., prediction of Enzyme Commission number), which can be used to complement and support other techniques based on sequence or structure information. In order to define this mapping we collected a set of 453 features and properties that characterize proteins and are believed to be related to structural and functional aspects of proteins. We introduce a mixture model of stochastic decision trees to learn the set of potentially complex relationships between features and function. To study these correlations, trees are created and tested on the Pfam classification of proteins, which is based on sequence, and the EC classification, which is based on enzymatic function. The model is very effective in learning highly diverged protein families or families that are not defined on the basis of sequence. The resulting tree structures highlight the properties that are strongly correlated with structural and functional aspects of protein families, and can be used to suggest a concise definition of a protein family. Key words: Sequence–function relationships, functional prediction, decision trees, enzyme classification. 1.

Cited by 0

Parallel Convolutional Contrastive Learning Method for Enzyme Function Prediction

Xindi Yu, Shusen Zhou, Mujun Zang et al. · IEEE/ACM Transactions on Computational Biology and Bioinformatics · 2024

The function labeling of enzymes has a wide range of application value in the medical field, industrial biology and other fields. Scientists define enzyme categories by enzyme commission (EC) numbers. At present, although there are some tools for enzyme function prediction, their effects have not reached the application level. To improve the precision of enzyme function prediction, we propose a parallel convolutional contrastive learning (PCCL) method to predict enzyme functions. First, we use the advanced protein language model ESM-2 to preprocess the protein sequences. Second, PCCL combines convolutional neural networks (CNNs) and contrastive learning to improve the prediction precision of multifunctional enzymes. Contrastive learning can make the model better deal with the problem of class imbalance. Finally, the deep learning framework is mainly composed of three parallel CNNs for fully extracting sample features. we compare PCCL with state-of-art enzyme function prediction methods based on three evaluation metrics. The performance of our model improves on both two test sets. Especially on the smaller test set, PCCL improves the AUC by 2.57%.

Cited by 1

EnzymeDetector: an integrated enzyme function prediction tool and database

Susanne Quester, Dietmar Schomburg · BMC Bioinformatics · 2011

EnzymeDetector provides a fast and comprehensive overview of the available enzyme function annotations for a genome of interest. The web interface allows the user to work with customisable weighting schemes and cut-offs for the different prediction methods. These customised quality criteria can easily be applied, and the resulting annotation can be downloaded. The summarised view of all used annotation sources provides up-to-date information. Annotation errors that occur in only one of the databases can be recognised (because of their low relevance score). The results are stored in a database and can be accessed at http://enzymedetector.tu-bs.de.

Cited by 54Open access

Rapid Catalytic Template Searching as an Enzyme Function Prediction Procedure

Jerome P. Nilmeier, Daniel Kirshner, Sergio Wong et al. · PLoS ONE · 2013

We present an enzyme protein function identification algorithm, Catalytic Site Identification (CatSId), based on identification of catalytic residues. The method is optimized for highly accurate template identification across a diverse template library and is also very efficient in regards to time and scalability of comparisons. The algorithm matches three-dimensional residue arrangements in a query protein to a library of manually annotated, catalytic residues--The Catalytic Site Atlas (CSA). Two main processes are involved. The first process is a rapid protein-to-template matching algorithm that scales quadratically with target protein size and linearly with template size. The second process incorporates a number of physical descriptors, including binding site predictions, in a logistic scoring procedure to re-score matches found in Process 1. This approach shows very good performance overall, with a Receiver-Operator-Characteristic Area Under Curve (AUC) of 0.971 for the training set evaluated. The procedure is able to process cofactors, ions, nonstandard residues, and point substitutions for residues and ions in a robust and integrated fashion. Sites with only two critical (catalytic) residues are challenging cases, resulting in AUCs of 0.9411 and 0.5413 for the training and test sets, respectively. The remaining sites show excellent performance with AUCs greater than 0.90 for both the training and test data on templates of size greater than two critical (catalytic) residues. The procedure has considerable promise for larger scale searches.

Cited by 31Open access

Prediction of Enzyme function using interpretable optimized Ensemble learning framework

Saikat Dhibar, Subhash C. Basak, Biman Jana · bioRxiv (Cold Spring Harbor Laboratory) · 2025

Abstract Accurate prediction of enzyme function, particularly for newly discovered uncharacterized sequences, is immensely important for modern biology research. Recently machine learning (ML) based methods have shown promises. However, such tools often suffer from complexity in feature extraction, interpretability, and generalization ability. Here we present an interpretable ML method, SOLVE that addresses these issues by using only combination of tokenized subsequences from the protein’s primary sequence for classification. Its optimized ensemble learning framework improves prediction accuracy, distinguishes enzymes from non-enzymes, and predicts enzyme commission (EC) numbers for mono- and multi-functional enzymes. Additionally, SOLVE provides interpretability through Shapley analyses, identifying functional motifs at catalytic and allosteric sites of enzymes. By focusing only on primary sequence data, SOLVE simplifies high-throughput enzyme function prediction for functionally uncharacterized sequences and outperforms existing tools. With high prediction accuracy and its identification ability of functional regions, SOLVE can become a promising tool in different fields of biology and therapeutic drug design.

Cited by 1Open access

Structure-enabled enzyme function prediction unveils elusive terpenoid biosynthesis in archaea

Raman Samusevich, Téo Hebra, Roman Bushuiev et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2024

Abstract The exponential growth of uncharacterized enzyme sequences in genomic repositories demands novel tools for functional annotation. Here, we combined alignment-driven structural domain analysis with protein language models to create EnzymeExplorer, a machine-learning pipeline for enzyme function prediction. We applied this approach to terpene synthases, which present an ideal model case because they catalyze complex carbocationic rearrangements whose product outcomes cannot be predicted from active site residues alone. We detected new structural domains and achieved state-of-the-art performance on function prediction. By analyzing the UniRef90 database, we identified terpene synthases overlooked by current computational methods. Remarkably, we uncovered and experimentally validated the widespread biosynthesis of terpenoids in archaea. Our approach offers a powerful framework for characterizing enzyme “dark matter” in the rapidly expanding genomic and metagenomic datasets.

Cited by 9Open access

Prediction of enzyme function by combining sequence similarity and protein interactions

Espadaler, Jordi, Eswar, Narayanan, Querol, Enrique et al. · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2013

Background: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. Results: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. Conclusion: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

Cited by 0Open access