Abstract
Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function.
Graphical abstract
ProTSAV brings an efficient unification of available resources for quality assessment and outperforms their individual accuracies. By means of ProTSAV, a reliable and accurate quality assessment of model structures for applicability in different fields is facilitated.
1. Introduction
Protein structure prediction is a primary challenge in structural biology and is essential for gaining better insights into biological function. An understanding of three-dimensional structures is very crucial for rational drug design.
2. Material and methods
Input for the server is a single model structure or multiple models in pdb file format. For single model structure quality assessment, ProTSAV furnishes the user with one cumulative global score depicted through a plot and for multiple model structures ProTSAV generates the scores for respective model structures and performs their ranking.
3. Module selection
ProTSAV server is developed based on ten previously reported, well-known and thoroughly tested methods.
4. Class definition
The selected dataset is classified into three classes based on rmsd values from the corresponding native structures. The first class has structures with rmsds of range 0–2 Å. It comprises all the experimentally solved structures and some predicted model structures with rmsds < 2 Å (9264 structures).
5. Score generation
All the selected modules are run for the quality assessment of the selected dataset of protein structures and raw scores are generated for individual modules. These raw scores are normalized between 0 and 1 based on the observed minimum and maximum values for each module. A comparison of raw scores and the computed normalized scores is shown Fig.