MadraX: Computer-implemented means and methods for the prediction of the stability of proteins and protein complexes

PCT/EP2023/083289The protein structure prediction algorithm AlphaFold [1] started a revolution in computational biology by allowing researchers to rapidly access reliable structural information for virtually any natural protein. The general protein folding problem, however, is still far from being solved; while we can predict the conformations of well studied proteins, inferring the structure of orphan or artificial proteins is still technically impossible. This problem also occurs in cases in which the prediction of a protein structure is not the major prediction task, but it is instrinsically connected with it. This is the case, for instance, for docking, in which the receptor-ligand complex is intrinsically connected with it. This is the case, for instance, for docking, in which the receptor-ligand complex is intrinsically connected with affinity, which is the major prediction task. In all these tasks, machine learning (ML) algorithms struggle to converge to meaningful solutions because of the general scarcity of experimental data, id est the aglorithms must learn complex physical rules from a limited number of examples, which often results in overfitting. Currently, the only currently available solution is to significatly limit the number of trainable parameters in the ML model, therby reducing the risk of overfitting but also limiting the learning ability of the algorithm.The present methods and systems generally relate to the biomedical field and relate to subfields of computational biology and bioinformatics. More, specifically the invention provides a computer-implemented algorithm which can predict the stabilitly of proteins and protein complexes and can be integrated in artificial intelligence tools in an end-to-end fashon.