De Novo Molecular Design of Caspase-6 Inhibitors by a GRU-Based Recurrent Neural Network Combined with a Transfer Learning Approach.

Due to their potential in the treatment of neurodegenerative diseases, caspase-6 inhibitors have attracted widespread attention. However, the existing caspase-6 inhibitors showed more or less inevitable deficiencies that restrict their clinical development and applications. Therefore, there is an urgent need to develop novel caspase-6 candidate inhibitors.

A simple pan-specific RNN model for predicting HLA-II binding peptides.

The prediction of human leukocyte antigen (HLA) class II binding peptides plays important roles in understanding the mechanism of immune recognition and developing effective epitope-based vaccines. In this work, gated recurrent unit (GRU)-based recurrent neural network (RNN) was successfully employed to establish a pan-specific prediction model of HLA-II-binding peptides by using only the HLA and peptide sequence information. In comparison with the existing pan-specific models of HLA-II-binding peptides, the GRU-based RNN model covered a broad spectrum of HLA-II molecules including 50 HLA-DR, 47 HLA-DQ, and 19 HLA-DP molecules with peptide lengths varying from 8 to 43 mers.

Conformational transitions of caspase-6 in substrate-induced activation process explored by perturbation-response scanning combined with targeted molecular dynamics.

Caspase-6 participates in a series of neurodegenerative pathways, and has aroused widespread attentions as a promising molecular target for the treatment of neurodegeneration. Caspase-6 is a homodimer with 6 central-stranded β-sheets and 5 α-helices in each monomer. Previous crystallographic studies suggested that the 60's, 90's and 130's helices of caspase-6 undergo a distinctive conformational transition upon substrate binding.

Partial Least-Squares Discriminant Analysis and Ensemble-Based Flexible Docking of PD-1/PD-L1 Inhibitors: A Pilot Study.

Although mAbs targeting the programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway have achieved remarkable therapeutic potential against multiple types of cancer, it is still of great interest for researchers to develop small-molecule PD-1/PD-L1 inhibitors without the mAb-related disadvantages of no oral bioavailability and poor solid tumor penetration. However, targeting the PD-1/PD-L1 pathway with small molecules is normally considered challenging because of the flat and large interaction surface of the PD-1/PD-L1 complex. In this paper, a total of 2558 PD-1/PD-L1 inhibitors were compiled from recent patents and literatures and then used for exploring the chemical space and structural features of PD-1/PD-L1 inhibitors by partial least-squares discriminant analysis.

A Pan-Specific GRU-Based Recurrent Neural Network for Predicting HLA-I-Binding Peptides.

Human leukocyte antigens (HLAs) play a critical role in human-acquired immune responses by the recognition of non-self-peptides derived from exogenous bacteria, fungi, virus, and so forth. The accurate prediction of HLA-binding peptides is thus extremely useful for the mechanistic research of cell-mediated immunity and related epitope-based vaccine design. In this work, a simple pan-specific gated recurrent unit (GRU)-based recurrent neural network model was successfully proposed for predicting HLA-I-binding peptides.

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method.

Accumulated evidence suggests that binding kinetic properties-especially dissociation rate constant or drug-target residence time-are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK).

Computational insight into the conformational transition of human toll-like receptor 8 in the agonist-induced activation processes.

Communicated by Ramaswamy H. Sarma.

Subtle differences in chemical pattern between human toll-like receptor 8 agonists and antagonists: Emerging chemical patterns analysis.

Due to the potencies in the treatments of cancer, infectious diseases, and autoimmune diseases, the developments of human TLR8 (hTLR8) agonists and antagonists have attracted widespread attentions. The hTLR8 agonists and antagonists have similar structures but with completely opposite biological effects. Up to date, the subtle differences in the structures between the hTLR8 agonists and antagonists are still unknown.