Using cryo-EM to uncover mechanisms of bacterial transcriptional regulation

Wood, David M; Dobson, Renwick C. J.; Horne, Christopher R

doi:10.1042/bst20210674

Cited by 6 publications

(4 citation statements)

References 126 publications

(192 reference statements)

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“…This has become the bottleneck for developing an effective tool, given the difficulties in solving the structures of the PNA complexes. The use of sophisticated techniques like electron cryo-microscopy to determine PNA complex structures is a way forward; however, the complex structures available as of today are subminimal. , On the other hand, there is a vast amount of experimental data available for the PNAIs; their sequences are known with no available 3D structures. To overcome this, developing DeePNAP was an attempt to extract information from just the sequences and predict the unknown PNA binding interactions.…”

Section: Resultsmentioning

confidence: 99%

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

Pandey,

Behara,

Sharma

et al. 2024

J. Chem. Inf. Model.

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Predicting the protein–nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting K D and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface:

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Section: Resultsmentioning

confidence: 99%

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

Pandey,

Behara,

Sharma

et al. 2024

J. Chem. Inf. Model.

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show abstract

“…This has become the bottleneck for developing an effective tool given the difficulties in solving the structures of the PNA complexes. Use of sophisticated techniques like electron cryo-microscopy to determine protein-NA complex structures is a way forward; however, the complex structures available as of today are subminimal 44,45 . On the other hand, there is a vast amount of experimental data available for the PNA interactions; their sequences are known with no available 3D structures.…”

Section: Resultsmentioning

confidence: 99%

DeePNAP: A deep learning method to predict protein-nucleic acids binding affinity from sequence

Pandey,

Behara,

Sharma

et al. 2023

Preprint

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Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs, while also relying on both sequence and structural information of the PNA complexes for both train/test and also as inputs. As PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to a small training dataset. Additionally, a majority of the tools predict a single parameter such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model trained on a vast and heterogeneous dataset with 14,401 entries (from both eukaryotes and prokaryotes) of ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from the sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting theKDand ΔΔG implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface:http://14.139.174.41:8080/

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“…With the advent of AlphaFold, we are now able to access reliable models for the individual domains of most B. subtilis sigma factors [14]. The positioning of the connecting loops, especially when wrapped around binding partners in large assemblies, is the next structural frontier, and is well on the way to being cracked both experimentally through large high resolution cryo-EM structures [66] and computationally with AlphaFold multimer [67], which is becoming more and more sophisticated at a rate of knots. These developments are unprecedented given the high flexibility of sigma factors and the difficulty associated with the expression and purification of many of them.…”

Section: Discussionmentioning

confidence: 99%

Structural Analysis of Bacillus subtilis Sigma Factors

et al. 2023

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Bacteria use an array of sigma factors to regulate gene expression during different stages of their life cycles. Full-length, atomic-level structures of sigma factors have been challenging to obtain experimentally as a result of their many regions of intrinsic disorder. AlphaFold has now supplied plausible full-length models for most sigma factors. Here we discuss the current understanding of the structures and functions of sigma factors in the model organism, Bacillus subtilis, and present an X-ray crystal structure of a region of B. subtilis SigE, a sigma factor that plays a critical role in the developmental process of spore formation.

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Using cryo-EM to uncover mechanisms of bacterial transcriptional regulation

Cited by 6 publications

References 126 publications

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences

DeePNAP: A deep learning method to predict protein-nucleic acids binding affinity from sequence

Structural Analysis of Bacillus subtilis Sigma Factors

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