GLASS: A tool to visualize protein structure prediction data in three dimensions and evaluate their consistency

Abstract

When a protein sequence does not share any significant sequence similarity with a protein of known structure, homology modeling cannot be applied. However, many novel and interesting methods, such as secondary structure prediction, fold recognition, and prediction of long-range interactions, are being developed and have been shown to be reasonably successful in predicting protein structures from sequence data and evolutionary information. The a priori evaluation of the correctness of a prediction obtained by one of these methods is however often problematic. Consequently, it is important to use all available information provided by as many different methods as possible and all the available experimental data about the protein of interest, since the consistency of the results is indicative of the reliability of the prediction. Hence the need has arisen for suitable tools able to compare results provided by different methods and evaluate their consistency. We have therefore constructed GLASS, a general platform to read, visualize, compare, and evaluate prediction results from many different sources and to project these prediction results into three dimensions. In addition, GLASS allows the comparison of selected parameters calculated for a model with the distribution observed in real protein structures, thus providing an easy way to test new methods for evaluating the likelihood of different structural models. GLASS can be considered as a "workbench" for structural predictions useful to both experimentalists and theoreticians.

Prediction of the Improvement Effect of Visualization Technology on Online Learning from the Perspective of the Neural Network

“…In reality, visualization has been applied in various fields such as traditional medicine, social media, astronomy, and geography and has achieved certain results. At present, the research on data visualization is still in-depth [18].…”

A Graphic Approach to Evaluate Algorithms of Secondary Structure Prediction

“…The seventh is Q 7 , known as Matthews' coefficient, defined by [7] 830 Zhang and Zhang which has been named historically the correlation coefficient between (positive predicted) and observed as well as (negative predicted) and non-observed (60)(61)(62). Besides the above evaluation methods, there are some other work on the evaluation of secondary structure predictions (63)(64)(65).…”

A platform for integrating threading results with protein family analyses

“…This success in combining outputs from different sources illustrates the need for interactive platforms simplifying such a task (e.g. Miller et al, 1996;Leplae et al, 1998). Here we present our approach towards a general workbench for the evaluation of threading results by assessment of structural and biologically relevant information.…”