The deep learning architecture for the application to globular proteins.

<p>The non-image-like features are incorporated in the multichannel topological convolutional deep neural network by merging the features into the network at one of the fully connected layers.</p

A comparison of behaviors of the GBT based method and the neural network based method.

<p>The plot is for the prediction task of the S350 dataset. The linear fit for GBT prediction [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005690#pcbi.1005690.ref060" target="_blank">60</a>] is <i>y</i> = 0.603<i>x</i> − 0.435 and for TNet-MP-2, <i>y</i> = 0.657<i>x</i> − 0.422.</p

Workflow of the multi-task topological deep learning model.

<p>The multi-task multichannel topological convolutional neural network model shares and transforms topological information for the simultaneous training and prediction of globular protein and membrane protein mutation impacts on protein stability.</p

The multi-task deep learning architecture for membrane proteins.

<p>Using globular protein stability change upon mutation as an auxiliary task to improve the task of membrane protein mutation prediction. The globular protein stability change upon mutation prediction is used as an auxiliary task to improve the task of predicting membrane protein stability changes upon mutation. The solid arrows show the path of information passing when the model is applied for predictions. The dotted and dashed arrows mark the paths of backpropagation when the network is trained with globular protein data set and membrane protein data set respectively.</p

An illustration of barcode changes from wild type to mutant proteins.

<p><b>a</b> The wild type protein (PDB:1hmk) with residue 60 as Trp. <b>b</b> The mutant with residue 60 as Ala. <b>c</b> Wild type protein barcodes for heavy atoms within 6 Å of the mutation site. Three panels from top to bottom are Betti-0, Betti-1, and Betti-2 barcodes, respectively. The horizontal axis is the filtration radius (Å). <b>d</b> Mutant protein barcodes obtained similarly to those of the wild type.</p

Mutation induced protein folding free energy changes.

<p>Mutation induced protein folding free energy changes.</p

Topological representations for protein mutation problem.

<p>Topological representations for protein mutation problem.</p

Performance comparisons of TNet-BP and other methods.

<p>Performance comparisons of TNet-BP and other methods.</p

Performance comparisons of TNet-MMP and other methods.

<p>Performance comparisons of TNet-MMP and other methods.</p

Energy cycle of protein-ligand binding free energy modeling.

<p>Energy cycle of protein-ligand binding free energy modeling.</p