  Determination of binding affinity of proteins in the formation of protein
complexes requires sophisticated, expensive and time-consuming experimentation
which can be replaced with computational methods. Most computational prediction
techniques require protein structures which limit their applicability to
protein complexes with known structures. In this work, we explore sequence
based protein binding affinity prediction using machine learning. Our paper
highlights the fact that the generalization performance of even the state of
the art sequence-only predictor of binding affinity is far from satisfactory
and that the development of effective and practical methods in this domain is
still an open problem. We also propose a novel sequence-only predictor of
binding affinity called ISLAND which gives better accuracy than existing
methods over the same validation set as well as on external independent test
dataset. A cloud-based webserver implementation of ISLAND and its Python code
are available at the URL:
http://faculty.pieas.edu.pk/fayyaz/software.html#island.
