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Updated: Jun 14, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Accelerating BRPF1b hit identification with BioPhysical and Active Learning Screening (BioPALS).

Sandeep Pal¹, Zandile Nare¹, Vincenzo A Rao¹

¹Concept Life Sciences, Frith Knoll Road, Chapel-en-le-Frith, SK23 0PG, High Peak, UK.

|February 19, 2024

Summary

This summary is machine-generated.

We developed BioPhysical and Active Learning Screening (BioPALS), an AI-driven method for rapid drug discovery. This approach efficiently identifies novel micromolar binders with good ADMET properties for biological targets like BRPF1b.

Keywords:

BRPF1 Grating Coupled Interferometry Hit identification Machine Learning Virtual Screening

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Last Updated: Jun 14, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

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Area of Science:

Drug discovery and development
Computational chemistry and cheminformatics
Biophysics and structural biology

Background:

Efficient hit identification is crucial for drug discovery.
Traditional screening methods can be time-consuming and costly.
Integrating artificial intelligence with biophysical assays offers a promising alternative.

Purpose of the Study:

To develop and validate a novel, rapid hit identification protocol named BioPhysical and Active Learning Screening (BioPALS).
To apply BioPALS to identify novel binders for the BRPF1b bromodomain.
To assess the efficiency, versatility, and data richness of the BioPALS workflow.

Main Methods:

Development of the BioPALS protocol, combining AI-powered virtual screening with GCI-driven biophysical confirmation.
Application of BioPALS to screen for binders of the BRPF1b bromodomain.
Determination of binding kinetics and prediction of binding topologies for identified hits.

Main Results:

Successful identification of novel micromolar binders for the BRPF1b bromodomain.
Demonstration of a high in silico/in vitro confirmation rate for BioPALS.
Characterization of binding kinetics and predicted binding topologies for all identified hits.
Identification of binders with favorable ADMET properties.

Conclusions:

BioPALS is a rapid, versatile, and data-rich protocol for hit identification.
The protocol integrates AI virtual screening with biophysical confirmation effectively.
BioPALS is applicable to a wide range of biological targets, accelerating drug discovery efforts.