skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes
Title: AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes
Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a growing class of natural products biosynthesized from a genetically encoded precursor peptide. The enzymes that install the post-translational modifications on these peptides have the potential to be useful catalysts in the production of natural-product-like compounds and can install non-proteogenic amino acids in peptides and proteins. However, engineering these enzymes has been somewhat limited, due in part to limited structural information on enzymes in the same families that nonetheless exhibit different substrate selectivities. Despite AlphaFold2’s superior performance in single-chain protein structure prediction, its multimer version lacks accuracy and requires high-end GPUs, which are not typically available to most research groups. Additionally, the default parameters of AlphaFold2 may not be optimal for predicting complex structures like RiPP biosynthetic enzymes, due to their dynamic binding and substrate-modifying mechanisms. This study assessed the efficacy of the structure prediction program ColabFold (a variant of AlphaFold2) in modeling RiPP biosynthetic enzymes in both monomeric and dimeric forms. After extensive benchmarking, it was found that there were no statistically significant differences in the accuracy of the predicted structures, regardless of the various possible prediction parameters that were examined, and that with the default parameters, ColabFold was able to produce accurate models. We then generated additional structural predictions for select RiPP biosynthetic enzymes from multiple protein families and biosynthetic pathways. Our findings can serve as a reference for future enzyme engineering complemented by AlphaFold-related tools.  more » « less
Award ID(s):
2216836
PAR ID:
10508038
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Biomolecules
Volume:
13
Issue:
8
ISSN:
2218-273X
Page Range / eLocation ID:
1243
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; (, Bioinformatics)
    Abstract MotivationQuality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. ResultsWe develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. Availability and implementationThe source code is available at https://github.com/BioinfoMachineLearning/EnQA. Supplementary informationSupplementary data are available at Bioinformatics online. 
    more » « less
  2. ; ; ; ; ; (, BMC Genomics)
    Abstract Background Halogenation is a recurring feature in natural products, especially those from marine organisms. The selectivity with which halogenating enzymes act on their substrates renders halogenases interesting targets for biocatalyst development. Recently, CylC – the first predicted dimetal-carboxylate halogenase to be characterized – was shown to regio- and stereoselectively install a chlorine atom onto an unactivated carbon center during cylindrocyclophane biosynthesis. Homologs of CylC are also found in other characterized cyanobacterial secondary metabolite biosynthetic gene clusters. Due to its novelty in biological catalysis, selectivity and ability to perform C-H activation, this halogenase class is of considerable fundamental and applied interest. The study of CylC-like enzymes will provide insights into substrate scope, mechanism and catalytic partners, and will also enable engineering these biocatalysts for similar or additional C-H activating functions. Still, little is known regarding the diversity and distribution of these enzymes. Results In this study, we used both genome mining and PCR-based screening to explore the genetic diversity of CylC homologs and their distribution in bacteria. While we found non-cyanobacterial homologs of these enzymes to be rare, we identified a large number of genes encoding CylC-like enzymes in publicly available cyanobacterial genomes and in our in-house culture collection of cyanobacteria. Genes encoding CylC homologs are widely distributed throughout the cyanobacterial tree of life, within biosynthetic gene clusters of distinct architectures (combination of unique gene groups). These enzymes are found in a variety of biosynthetic contexts, which include fatty-acid activating enzymes, type I or type III polyketide synthases, dialkylresorcinol-generating enzymes, monooxygenases or Rieske proteins. Our study also reveals that dimetal-carboxylate halogenases are among the most abundant types of halogenating enzymes in the phylum Cyanobacteria. Conclusions Our data show that dimetal-carboxylate halogenases are widely distributed throughout the Cyanobacteria phylum and that BGCs encoding CylC homologs are diverse and mostly uncharacterized. This work will help guide the search for new halogenating biocatalysts and natural product scaffolds. 
    more » « less
  3. ; ; (, Proteins: Structure, Function, and Bioinformatics)
    Abstract AlphaFold2 has revolutionized protein structure prediction from amino‐acid sequence. In addition to protein structures, high‐resolution dynamics information about various protein regions is important for understanding protein function. Although AlphaFold2 has neither been designed nor trained to predict protein dynamics, it is shown here how the information returned by AlphaFold2 can be used to predict dynamic protein regions at the individual residue level. The approach, which is termed cdsAF2, uses the 3D protein structure returned by AlphaFold2 to predict backbone NMR NHS2order parameters using a local contact model that takes into account the contacts made by each peptide plane along the backbone with its environment. By combining for each residue AlphaFold2's pLDDT confidence score for the structure prediction accuracy with the predictedS2value using the local contact model, an estimator is obtained that semi‐quantitatively captures many of the dynamics features observed in experimental backbone NMR NHS2order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder. 
    more » « less
  4. ; ; ; ; ; (, Proceedings of the National Academy of Sciences)
    The concept that proteins are selected to fold into a well-defined native state has been effectively addressed within the framework of energy landscapes, underpinning the recent successes of structure prediction tools like AlphaFold. The amyloid fold, however, does not represent a unique minimum for a given single sequence. While the cross-βhydrogen-bonding pattern is common to all amyloids, other aspects of amyloid fiber structures are sensitive not only to the sequence of the aggregating peptides but also to the experimental conditions. This polymorphic nature of amyloid structures challenges structure predictions. In this paper, we use AI to explore the landscape of possible amyloid protofilament structures composed of a single stack of peptides aligned in a parallel, in-register manner. This perspective enables a practical method for predicting protofilament structures of arbitrary sequences: RibbonFold. RibbonFold is adapted from AlphaFold2, incorporating parallel in-register constraints within AlphaFold2’s template module, along with an appropriate polymorphism loss function to address the structural diversity of folds. RibbonFold outperforms AlphaFold2/3 on independent test sets, achieving a mean TM-score of 0.5. RibbonFold proves well-suited to study the polymorphic landscapes of widely studied sequences with documented polymorphisms. The resulting landscapes capture these observed polymorphisms effectively. We show that while well-known amyloid-forming sequences exhibit a limited number of plausible polymorphs on their “solubility” landscape, randomly shuffled sequences with the same composition appear to be negatively selected in terms of their relative solubility. RibbonFold is a valuable framework for structurally characterizing amyloid polymorphism landscapes. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, bioRxiv)
    Abstract In computational biology, accurate prediction of phosphopeptide-protein complex structures is essential for understanding cellular functions and advancing drug discovery and personalized medicine. While AlphaFold has significantly improved protein structure prediction, it faces accuracy challenges in predicting structures of complexes involving phosphopeptides possibly due to structural variations introduced by phosphorylation in the peptide component. Our study addresses this limitation by refining AlphaFold to improve its accuracy in modeling these complex structures. We employed weighted metrics for a comprehensive evaluation across various protein families. The enhanced model notably outperforms the original AlphaFold, showing a substantial increase in the weighted average local distance difference test (lDDT) scores for peptides: from 52.74 to 76.51 in the Top 1 model and from 56.32 to 77.91 in the Top 5 model. These advancements not only deepen our understanding of the role of phosphorylation in cellular signaling but also have extensive implications for biological research and the development of innovative therapies. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email