THE CONSENSUS PREDICTION IN SILICO OF PHARMACOKINETIC PREFERENCE OF MULTI-TARGET RAGE INHIBITORS

Using a neural network model based on docking, among 87 new synthesized substances of ten structurally diverse chemical classes, ten compounds with predicted high RAGE-inhibitory activity were found, and for these by means of Qik Prop, PASS programs and on-line resources admetSAR, pkCSM, SwissADME and ADMET-PreServ a consensus in silico estimation of 14 pharmacokinetic ADMET characteristics was carried out. Based on these indicators, consensus integral estimates of pharmacokinetic preferences of these compounds were calculated and substances with favorable pharmacokinetic properties were identified.

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

Theory of active chromatin remodeling

Nucleosome positioning controls the accessible regions of chromatin and plays essential roles in DNA-templated processes. ATP driven remodeling enzymes are known to be crucial for its establishment in vivo, but their non-equilibrium nature has hindered the development of a unified theoretical framework for nucleosome positioning. Using a perturbation theory, we show that the effect of these enzymes can be well approximated by effective equilibrium models with rescaled temperatures and interactions. Numerical simulations support the accuracy of the theory in predicting both kinetic and steady-state quantities, including the effective temperature and the radial distribution function, in biologically relevant regimes. The energy landscape view emerging from our study provides an intuitive understanding for the impact of remodeling enzymes in either reinforcing or overwriting intrinsic signals for nucleosome positioning, and may help improve the accuracy of computational models for its prediction in silico.