Background:
Prediction of proteins’ secondary structure is one of the major steps in the generation
of homology models. These models provide structural information which is used to design suitable
ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary
Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored
methods and tools, within various contexts.
Objective:
A systematic review was performed for a comprehensive access to recent (2013-2016) studies
which used or recommended protein SSP tools.
Methods:
Three databases, Web of Science, PubMed and Scopus were systematically searched and 99
out of the 209 studies were finally found eligible to extract data.
Results:
Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features
of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and
(IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular
tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied
second and third places of popularity in categories I and II. JPred was only found in the two first
categories, while PHD was present in three fields.
Conclusion:
This study provides a comprehensive insight into the recent usage of SSP tools which
could be helpful for selecting a proper tool.
Identification and application of the concepts important for accurate and reliable protein secondary structure prediction
