부산대학교 도서관

Deciphering coevolutionary signals in protein sequences and applying deep learning methods such as AlphaFold have led to breakthroughs in modeling protein structures and interactions. The accuracy of interaction partner detection and structural modeling or protein complexes by computational methods now approaches experimental methods, and we are entering a new era where computation will play an essential role in both tasks. We expect rapid progress in characterizing human PPIs, thus enabling biomedical applications such as interpreting pathogenic variants, developing drugs to target PPIs, and designing protein binders to regulate protein function. We still face challenges in modeling transient and weak interactions, understanding the interactions mediated by intrinsically disordered regions (IDRs), expanding to other molecules such as polysaccharides and lipids, and moving towards modeling the entire cell. Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale. [ABSTRACT FROM AUTHOR]