Decoding the protein-ligand interactions using parallel graph neural networks.

Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds.

The prerequisite of therapeutic drug design and discovery is to identify novel molecules and developing lead candidates with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to generate new molecules with desired target properties is by constraining the critical fucntional groups or the core scaffolds in the generation process. To this end, we developed a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We demonstrated that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific data sets, we generate covalent and noncovalent antiviral inhibitors targeting viral proteins. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease (Mpro) and nonstructural protein endoribonuclease (NSP15) targets. Most importantly, our deep learning model performs well with relatively small 3D structural training data and quickly learns to generalize to new scaffolds, highlighting its potential application to other domains for generating target specific candidates.

Rotary manifold for automating a paper-based Salmonella immunoassay.

Foodborne pathogens are responsible for hundreds of thousands of deaths around the world each year. Rapid screening of agricultural products for these pathogens is essential to reduce and/or prevent outbreaks and pinpoint contamination sources. Unfortunately, current detection methods are laborious, expensive, time-consuming and require a central laboratory. Therefore, a rapid, sensitive, and field-deployable pathogen-detection assay is needed. We previously developed a colorimetric sandwich immunoassay utilizing immuno-magnetic separation (IMS) and chlorophenol red-ß-d-galactopyranoside for Salmonella detection on a paper-based analytical device (µPAD); however, the assay required many sample preparation steps prior to the µPAD as well as laboratory equipment, which decreased user-friendliness for future end-users. As a step towards overcoming these limitations in resource-limited settings, we demonstrate a reusable 3D-printed rotational manifold that couples with disposable µPAD layers for semi-automated reagent delivery, washing, and detection in 65 minutes. After IMS to clean the sample, the manifold performs pipette-free reagent delivery and washing steps in a sequential order with controlled volumes, followed by enzymatic amplification and colorimetric detection using automated image processing to quantify color change. Salmonella was used as the target pathogen in this project and was detected with the manifold in growth media and milk with detection limits of 4.4 × 102 and 6.4 × 102 CFU mL-1 respectively. The manifold increases user friendliness and simplifies immunoassays resulting in a practical product for in-field use and commercialization.