Automated Multiconformer Modeling Improves Fit to Experimental Data and Geometry in X-ray Crystallography and Cryo-EM

Grunnleggende konsepter

Automated computational strategy qFit can incorporate protein conformational heterogeneity into models built from high-resolution X-ray crystallography and cryo-EM data, improving model fit to experimental data and geometry compared to manually built single-conformer models.

Sammendrag

The content describes the development and evaluation of the qFit algorithm, which automatically generates multiconformer protein models from high-resolution X-ray crystallography and cryo-EM data. Key highlights:

qFit leverages powerful optimization algorithms to identify alternative protein conformations that better explain the experimental density maps compared to traditional single-conformer models.
Algorithmic improvements in qFit, including Bayesian information criterion (BIC) scoring and B-factor sampling, lead to multiconformer models with superior Rfree and geometry metrics across a diverse test set of high-resolution X-ray structures.
Evaluation on synthetic data shows qFit can accurately recapitulate manually built alternative conformations in high-resolution (better than 2 Å) data, but performance degrades at lower resolutions.
Application of qFit to high-resolution cryo-EM structures demonstrates its ability to identify previously unmodeled alternative conformations, though challenges remain in standardizing cryo-EM data processing and validation.
The multiconformer models generated by qFit can be manually edited and further refined using standard software, lowering the barrier to incorporating conformational heterogeneity into structural models.

Statistikk

"The qFit model has a lower (improved) Rfree value for 76% (109/144) of structures."
"On average, there is an absolute decrease of Rfree value by 0.6% (median deposited models Rfree: 18.1%, median qFit models Rfree: 17.5%)."
"Only 2.9% of residues in the deposited models were multiconformers (two or more alternative conformations, n=970). In contrast, 40.7% (n=11,049) of residues in the qFit models were multiconformers."

Sitater

"Ideally, we would accurately model the complete ensemble of protein conformations reflected in experimental data."
"Multiconformer models are notably easier to modify and more interpretable in software like Coot, unlike ensemble methods that generate multiple complete protein copies."
"With the improvements in model quality outlined here, qFit can now increasingly be used for finalizing high-resolution models to derive ensemble-function insights."

Viktige innsikter hentet fra

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

by Wankowicz,S.... klokken www.biorxiv.org 06-28-2023

https://www.biorxiv.org/content/10.1101/2023.06.28.546963v4

Dypere Spørsmål

How could the qFit algorithm be further improved to better model larger-scale conformational changes, such as alternative loop conformations or coordinated shifts in secondary structural elements?

To enhance the qFit algorithm for modeling larger-scale conformational changes, such as alternative loop conformations or coordinated shifts in secondary structural elements, several key improvements could be implemented:

Backbone Sampling Enhancements: Introduce more sophisticated algorithms for backbone sampling to capture a wider range of backbone conformations, especially in regions prone to larger-scale conformational changes like loops or secondary structural elements. This could involve incorporating more diverse backbone movements and increasing the granularity of sampling to better represent the conformational space.

Improved Scoring Functions: Develop advanced scoring functions that can accurately evaluate the fit of alternative conformations for larger-scale structural changes. These scoring functions should consider not only the local fit to density but also the overall impact on the protein structure and interactions.

Enhanced Optimization Algorithms: Implement optimization algorithms that can efficiently search the conformational space for larger-scale changes, ensuring that the algorithm can explore complex structural rearrangements while maintaining computational efficiency.

Integration of Experimental Data: Incorporate additional experimental data, such as NMR or hydrogen-deuterium exchange data, to guide the modeling of larger-scale conformational changes. By integrating multiple sources of information, qFit can better capture the dynamic nature of protein structures.

User-Driven Flexibility: Provide users with more control and flexibility in defining and modeling larger-scale conformational changes. This could involve interactive tools that allow users to guide the modeling process based on their domain knowledge and insights into the protein structure.

By implementing these enhancements, qFit can better model larger-scale conformational changes, enabling more accurate representation of protein ensembles and dynamics.

How could the insights from multiconformer modeling using qFit be leveraged to improve the accuracy of protein structure prediction methods like AlphaFold, which currently focus on single-conformer models?

The insights gained from multiconformer modeling using qFit can be leveraged to enhance the accuracy of protein structure prediction methods like AlphaFold in the following ways:

Ensemble-Based Predictions: Incorporate the concept of protein ensembles and conformational heterogeneity into structure prediction algorithms like AlphaFold. By considering multiple conformations and their relative probabilities, the prediction models can better capture the dynamic nature of protein structures.

Improved Sampling Strategies: Utilize the sampling strategies and algorithms developed in qFit for exploring conformational space and identifying alternative conformations. By integrating these techniques into structure prediction pipelines, AlphaFold can generate more diverse and accurate structural predictions.

Validation and Refinement: Use multiconformer models generated by qFit as a validation and refinement tool for predicted protein structures. By comparing predicted structures to experimentally derived multiconformer models, AlphaFold can refine its predictions and improve the overall accuracy of the models.

Incorporation of Experimental Data: Integrate experimental data from multiconformer models into the training and validation of structure prediction algorithms. By leveraging the wealth of information captured in multiconformer models, AlphaFold can enhance its predictive capabilities and generate more reliable protein structures.

Dynamic Structural Representations: Develop methods to represent protein structures dynamically, incorporating information from multiconformer models to depict the inherent flexibility and variability of protein conformations. This dynamic representation can provide a more comprehensive view of protein structures and their functional implications.

By leveraging the insights from multiconformer modeling using qFit, protein structure prediction methods like AlphaFold can advance towards more accurate and comprehensive predictions of protein structures.

What are the potential limitations of the current PDB/mmCIF data formats in representing the full complexity of protein conformational ensembles, and how could these formats be expanded to better capture this information?

The current PDB/mmCIF data formats have limitations in representing the full complexity of protein conformational ensembles due to the following reasons:

Single-Conformer Representation: The PDB/mmCIF formats primarily support the representation of single-conformer models, limiting the ability to capture the full spectrum of conformational heterogeneity present in protein structures. This restricts the representation of alternative conformations and dynamic structural changes.

Limited Altloc Support: While the altloc field in the PDB/mmCIF formats allows for the representation of alternative conformations, it is often underutilized and lacks the flexibility to represent complex ensembles with varying occupancies and interactions between conformers. This hinders the accurate depiction of conformational dynamics.

Ensemble Modeling Challenges: The current formats do not provide explicit support for representing ensemble models that encompass multiple distinct conformations and their relative probabilities. This makes it challenging to capture the full complexity of protein conformational ensembles and their functional implications.

To better capture the information related to protein conformational ensembles, the PDB/mmCIF formats could be expanded in the following ways:

Ensemble Data Structures: Introduce new data structures within the PDB/mmCIF formats to explicitly represent ensemble models, allowing for the storage of multiple conformations, their occupancies, and interactions. This would enable the comprehensive representation of conformational heterogeneity.

Enhanced Altloc Functionality: Enhance the altloc field to support more detailed information about alternative conformations, such as occupancy levels, inter-conformer interactions, and dynamic changes. This would provide a more nuanced representation of protein dynamics and flexibility.

Standardized Metadata: Implement standardized metadata fields within the PDB/mmCIF formats to capture information about conformational ensembles, including experimental validation data, ensemble refinement parameters, and model quality metrics. This would facilitate the interpretation and validation of ensemble models.

Visualization and Analysis Tools: Develop tools and software that can interpret and visualize ensemble models stored in the expanded PDB/mmCIF formats, allowing researchers to explore and analyze the full complexity of protein conformational ensembles. This would enhance the usability and accessibility of ensemble data.

By expanding the capabilities of the PDB/mmCIF formats to better capture the full complexity of protein conformational ensembles, researchers can more effectively study and understand the dynamic nature of protein structures and their functional implications.