SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

Abstract

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

“…To overcome this, a new machine learning approach, namely SSnet, was used to identify new potential drugs by screening a library of approved drugs from the DrugBank and ZINC databases [ 20 ] targeting two different conformations (open and closed) of the ACE2 receptor as well as ACE2 in complex with the S1 domain of the S protein, that is the protein responsible for binding with human cells [ 21 ]. After cross-validation of the hits using the Autodock Vina scoring function [ 22 ], the SSnet approach was extended to a library of 750,000 molecules in BindingDB to gain additional information regarding de novo drug design.…”

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

“…To overcome this, a new machine learning approach, namely SSnet, was used to identify new potential drugs by screening a library of approved drugs from DrugBank and ZINC databases [19] targeting two different conformations (open and closed) of the ACE2 receptor as well as ACE2 in complex with S1 domain of the S protein, that is the protein responsible for the binding with human cells [20]. After cross-validation of the hits using the Autodock Vina scoring function [21], the SSnet approach was extended to a library of 750,000 molecules in BindingDB to gain additional information regarding de novo drug design.…”

Screening S protein – ACE2 blockers from natural products: Strategies and advances in the discovery of potential inhibitors of COVID-19

“…In addition, using machine learning method in molecular docking can get more reasonable screening model from known data. In recent years, deep learning method based on neural network has shown great potential in the field of VS. SSnet is a deep neural network framework based on end-to-end, which uses protein structure and ligand information to predict protein ligand interaction probability [ 67 ]. SSnet is superior to Atomnet, 3D-CNN, Autodock Vina and other methods in identifying protein ligand complexes with high binding affinity [ 68 ].…”