  Accurately determining a change in protein binding affinity upon mutations is
important for the discovery and design of novel therapeutics and to assist
mutagenesis studies. Determination of change in binding affinity upon mutations
requires sophisticated, expensive, and time-consuming wet-lab experiments that
can be aided with computational methods. Most of the computational prediction
techniques require protein structures that limit their applicability to protein
complexes with known structures. In this work, we explore the sequence-based
prediction of change in protein binding affinity upon mutation. We have used
protein sequence information instead of protein structures along with machine
learning techniques to accurately predict the change in protein binding
affinity upon mutation. Our proposed sequence-based novel change in protein
binding affinity predictor called PANDA gives better accuracy than existing
methods over the same validation set as well as on an external independent test
dataset. On an external test dataset, our proposed method gives a maximum
Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art
existing protein structure-based method called MutaBind which gives a maximum
Pearson correlation coefficient of 0.59. Our proposed protein sequence-based
method, to predict a change in binding affinity upon mutations, has wide
applicability and comparable performance in comparison to existing protein
structure-based methods. A cloud-based webserver implementation of PANDA and
its python code is available at
https://sites.google.com/view/wajidarshad/software and
https://github.com/wajidarshad/panda.
