Strategies for protein structure model generation

TL;DR

This chapter reviews methods for predicting protein 3D structures from sequences, emphasizing template-based and free modeling, and provides a decision flowchart for selecting appropriate strategies based on available data.

Contribution

It offers a comprehensive overview and a practical flowchart for choosing protein structure prediction strategies, highlighting the importance of template detection and alignment quality.

Findings

Template detection is crucial for model quality.

Alignment quality significantly impacts model accuracy.

Incorporating experimental data can improve predictions.

Abstract

This chapter deals with approaches for protein three-dimensional structure prediction, starting out from a single input sequence with unknown struc- ture, the 'query' or 'target' sequence. Both template based and template free modelling techniques are treated, and how resulting structural models may be selected and refined. We give a concrete flowchart for how to de- cide which modelling strategy is best suited in particular circumstances, and which steps need to be taken in each strategy. Notably, the ability to locate a suitable structural template by homology or fold recognition is crucial; without this models will be of low quality at best. With a template avail- able, the quality of the query-template alignment crucially determines the model quality. We also discuss how other, courser, experimental data may be incorporated in the modelling process to alleviate the problem of missing template structures. Finally, we discuss measures to predict the quality of models generated.