Enzyme promiscuous profiles for protein sequence and reaction annotation
Homa MohammadiPeyhaniiD, Anastasia N. SveshnikovaiD, Ljubiša MiškovićiD, Vassily Hatzimanikatis AiD
Abstract
1.1Novel sequencing techniques and biochemical pathway prediction resources provide a wealth of data on novel proteins and computationally predicted enzymatic reactions. Accurate matching of protein sequences to enzymatic activities is crucial for advancing synthetic biology and metabolic engineering efforts. Here we present BridgIT+, a computational workflow that accounts for enzyme promiscuity and accurately predicts protein-reaction and reaction-protein associations. BridgIT+ builds upon the promiscuity-based method for annotating orphan and novel reactions with enzymatic activities, BridgIT, and utilizes position-specific scoring matrices (PSSM). The framework uses sequence alignment and enzyme promiscuity predictions to analyze protein sequences, identify sequence patterns, and create promiscuous protein sequence profiles for each reaction. These profiles allow us to predict the protein sequences most likely involved in the reaction. We showcase BridgIT+ by annotating (i) computationally predicted reactions with proteins and (ii) unannotated proteins of E. coli proteome with enzymatic functions. We demonstrated the performance of BridgIT+ on several biochemical assays and compared it to three current state-of-the-art methods for matching proteins and reactions. We anticipate that the proposed conceptual framework will enhance our understanding of gene-protein-reaction relations and advance biological sequence and reaction annotation in biology and synthetic biology studies.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 2
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Assigning enzyme sequences to orphan and novel reactions using knowledge of substrate reactive sites
Noushin Hadadi, Homa MohammadiPeyhani, … Vassily Hatzimanikatis A·Proceedings of the National Academy of SciencesPeer-reviewed·Mar 25, 2019
- FEDKEA: Enzyme function prediction with a large pretrained protein language model and distance-weighted k-nearest neighbor
Lei Zheng, Bowen Li, … Guanxiang Liang·bioRxiv·Aug 12, 2024
- Predicting Enzyme Functions Using Contrastive Learning with Hierarchical Enzyme Structure Information
Hongyu Duan, Ziyan Li, … Li Charlie Xia·bioRxiv·Jul 7, 2024
- How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning
Xiang Ma, Parnal Joshi, … Qi Li·BioinformaticsPeer-reviewed·Jul 1, 2026
- ActSeekN: A Structural-Motif-Based Pipeline for Interpretable Enzyme Function Annotation
Sandra Castillo, Chunhao Gu, … O.H. Samuli Ollila·bioRxiv·Apr 28, 2026
- EnzFinder: a sustainable alternative to chemical synthesis
Akriti Jain, Nishtha Pandey, Arijit Roy·Digital DiscoveryPeer-reviewed·Jan 1, 2026