  Prediction of protein structures using computational approaches has been
explored for over two decades, paving a way for more focused research and
development of algorithms in comparative modelling, ab intio modelling and
structure refinement protocols. A tremendous success has been witnessed in
template-based modelling protocols, whereas strategies that involve
template-free modelling still lag behind, specifically for larger proteins (>
150 a.a.). Various improvements have been observed in ab initio protein
structure prediction methodologies overtime, with recent ones attributed to the
usage of deep learning approaches to construct protein backbone structure from
its amino acid sequence. This review highlights the major strategies undertaken
for template-free modelling of protein structures while discussing few tools
developed under each strategy. It will also briefly comment on the progress
observed in the field of ab initio modelling of proteins over the course of
time as seen through the evolution of CASP platform.
