Short answer

When developing or utilizing computational models for predicting complex structures, consider incorporating data derived from evolutionary relationships to boost predictive accuracy.

Field: Modelling
Source: Proteins Structure Function and Bioinformatics (2015)
Method: Computational modelling and simulation
Evidence: Strong effect

Incorporating coevolutionary information into computational protein structure prediction models significantly improves their accuracy, especially for proteins lacking homologous structures. This modelling research insight is drawn from a 2015 study published in Proteins Structure Function and Bioinformatics. Using Computational modelling and simulation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When developing or utilizing computational models for predicting complex structures, consider incorporating data derived from evolutionary relationships to boost predictive accuracy.

Study

ModellingHigh ImpactStrong effect

Coevolutionary Data Enhances Protein Structure Prediction Accuracy

Incorporating coevolutionary information into computational protein structure prediction models significantly improves their accuracy, especially for proteins lacking homologous structures.

Proteins Structure Function and Bioinformatics · 2015

Key Findings

01Coevolutionary contact information generally improved model accuracy in Rosetta.
02The improvement was more pronounced when the number of sequences in a protein family exceeded three times the protein's length.
03A human-assisted prediction achieved unprecedented accuracy for a topologically complex target with no known homologs.

Application

Design takeaway

When developing or utilizing computational models for predicting complex structures, consider incorporating data derived from evolutionary relationships to boost predictive accuracy.

How to apply

When building predictive models for molecular structures or complex systems, explore incorporating analogous 'co-occurrence' or 'conservation' data from related systems or historical performance to refine predictions.

Project actions

01When modelling, think about how different components of your design might be influenced by or interact with each other, and if there's any 'evolutionary' or historical data you can use to inform these relationships.
02Consider how to represent and integrate external data sources (like coevolutionary information) into your modelling process.

Method & Evidence

AimTo what extent does the integration of coevolutionary data improve the accuracy of de novo protein structure prediction models?

MethodComputational modelling and simulation

ProcedureThe study utilized the Rosetta structure prediction methodology, incorporating coevolution-derived residue-residue contact information as restraints during conformational sampling and refinement. Both automated and human-assisted protocols were employed, with a focus on iterative hybridization for complex targets.

ContextComputational biology, bioinformatics, protein structure prediction

Variables

IVInclusion of coevolutionary contact information as restraints.

DVAccuracy of predicted protein structure (e.g., RMSD from crystal structure).

CVProtein sequence, Rosetta methodology parameters, conformational sampling algorithms.

Strengths & Limitations

Strengths

+Demonstrated improvement in blind prediction scenarios.
+Successful application to a challenging, topologically complex protein target.

Limitations

The availability and quality of coevolutionary data can be a bottleneck. The computational resources required for extensive sampling can be prohibitive.

Reliability & validity

Reliability is supported by the consistent improvement observed across different targets. Validity is high due to the comparison against experimentally determined crystal structures in a blind prediction context.

Think critically

How might the principles of using coevolutionary data to improve structural prediction be applied to other complex systems modelling, such as predicting the behaviour of materials under stress or the flow dynamics in a novel fluid system?

Design Principles

"Leverage evolutionary conservation and co-occurrence patterns to inform and constrain computational modelling for improved predictive accuracy."

This research highlights a powerful method for improving the fidelity of computational models, which are crucial tools in fields ranging from drug discovery to materials science. By leveraging evolutionary relationships, designers can create more reliable and accurate structural predictions, leading to better-informed design decisions and reduced experimental validation needs.

What This Means for Your Design

This study shows that by looking at how different parts of a protein have changed together over time in different species, we can make computer models of protein shapes much more accurate.

How to use in your project

1.Reference this study when discussing the validation and refinement of computational models used in your design project, particularly if you are using simulation or predictive software.

Add to My Project

Quick Cite

Paragraph starter

The accuracy of computational modelling can be significantly enhanced by integrating external data sources, such as coevolutionary information derived from protein sequence families. This approach, as demonstrated in studies like Ovchinnikov et al. (2015), allows for more precise predictions by providing constraints based on observed evolutionary relationships, proving particularly effective for complex structures or those lacking direct experimental analogues.

Source

Proteins Structure Function and Bioinformatics

Improved de novo structure prediction in <scp>CASP</scp> 11 by incorporating coevolution information into Rosetta

journal · 2015

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Questions About This Research

What does the research say about coevolutionary data enhances protein structure prediction accuracy?: When developing or utilizing computational models for predicting complex structures, consider incorporating data derived from evolutionary relationships to boost predictive accuracy. Evidence: Proteins Structure Function and Bioinformatics (2015).
Why does "Coevolutionary Data Enhances Protein Structure Prediction Accuracy" matter for design?: This research highlights a powerful method for improving the fidelity of computational models, which are crucial tools in fields ranging from drug discovery to materials science. By leveraging evolutionary relationships, designers can create more reliable and accurate structural predictions, leading to better-informed design decisions and reduced experimental validation needs.
How can designers apply this research?: When developing or utilizing computational models for predicting complex structures, consider incorporating data derived from evolutionary relationships to boost predictive accuracy.
What were the main findings?: Coevolutionary contact information generally improved model accuracy in Rosetta.. The improvement was more pronounced when the number of sequences in a protein family exceeded three times the protein's length.. A human-assisted prediction achieved unprecedented accuracy for a topologically complex target with no known homologs.
What research method was used?: Computational modelling and simulation.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2015 journal from Proteins Structure Function and Bioinformatics.
What should I do differently in my next project?: When building predictive models for molecular structures or complex systems, explore incorporating analogous 'co-occurrence' or 'conservation' data from related systems or historical performance to refine predictions.
What are the limitations?: The effectiveness of coevolutionary data integration may vary depending on the specific protein family and the quality/quantity of available sequence data. The computational cost of extensive sampling can be significant.