Biology subjects

Borsatto, A.

Publications and source records attributed to Borsatto, A..

3 recordsLinked to original sources

bioRxiv2025

CryptoBank: A Resource for the Identification and Prediction of Cryptic Sites in Proteins

Cryptic binding sites in proteins, which are hidden in the absence of a ligand, offer opportunities to modulate targets previously considered undruggable. However, the scarcity of experimentally validated examples limits the development of predictive tools. Here, we introduce CryptoBank, a large-scale database of cryptic sites identified by applying a machine learning model to detect ligand-induced conformational changes in over 5.5 million structural alignments of unbound (apo) and bound (holo) protein pairs from the Protein Data Bank (PDB). Our analysis reveals that cryptic pockets are widespread, occurring in approximately 16.3% of protein clusters. Leveraging this resource, we fine-tuned a protein language model (PLM) to predict cryptic sites directly from protein sequence information. This sequence-based model achieves high precision (PR AUC 0.8) when query sequences share more than 20% identity with CryptoBank entries. Critically, we demonstrate its broader utility by predicting a cryptic site in human TPP1, a protein with less than 20% sequence identity to any CryptoBank entry, and validating its opening using molecular dynamics simulations. CryptoBank and the predictive PLM are publicly accessible via a web server, providing valuable resources for cryptic site discovery and drug development.

bioinformatics↗

bioRxiv2023

SWISH-X, an expanded approach to detect cryptic pockets inproteins and at protein-protein interfaces

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyse a set of well-characterised protein-protein interactions harbouring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we develop a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 {beta}-lactamase.

biophysics↗

bioRxiv2022

Revealing druggable cryptic pockets in the Nsp-1 of SARS-CoV-2 and other β-coronaviruses by simulations and crystallography

Non-structural protein 1 (Nsp1) is a main pathogenicity factor of - and {beta}-coronaviruses. Nsp1 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suppresses the host gene expression by sterically blocking 40S host ribosomal subunits and promoting host mRNA degradation. This mechanism leads to the downregulation of the translation-mediated innate immune response in host cells, ultimately mediating the observed immune evasion capabilities of SARS-CoV-2. Here, by combining extensive Molecular Dynamics simulations, fragment screening and crystallography, we reveal druggable pockets in Nsp1. Structural and computational solvent mapping analyses indicate the partial crypticity of these newly discovered and druggable binding sites. The results of fragment-based screening via X-ray crystallography confirm the druggability of the major pocket of Nsp1. Finally, we show how the targeting of this pocket could disrupt the Nsp1-mRNA complex and open a novel avenue to design new inhibitors for other Nsp1s present in homologous {beta}-coronaviruses.

biophysics↗