PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Abstract

Accurately determining a change in protein binding affinity upon mutations is important to find 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 supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon mutation and question the effectiveness of [Formula: see text]-fold cross-validation (CV) across mutations adopted in previous studies to assess the generalization ability of such predictors with no known mutation during training. 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 performs comparably to the existing methods gauged through an appropriate CV scheme and 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. We made PANDA easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/panda , respectively.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease

“…The DTI data were processed using FSL 4 ( Woolrich et al, 2009 ), PANDA 5 ( Abbasi et al, 2021 ), and MRIcron (NITRC: MRIcron: Tool/Resource Info) software in Ubuntu18.04 as follows: (1) The raw DICOM files were converted to NIFITI format (*.nii.gz). (2) The brain templates were estimated based on non-diffusion-weighted b0 images using the bet command.…”

Molecular typing of canine parvovirus type 2 by VP2 gene sequencing and restriction fragment length polymorphism in affected dogs from Egypt

“…To evaluate the influence of mutations on the collective binding potency amid interactions among VP2 protein monomers, computations were carried out to gauge both the binding efficacy and the dissociation constant (Kd). The analysis was performed at "Predicting the change in proteins binding affinity" tool (PANDA) (Abbasi et al, 2021).…”