Revealing Missing Protein-Ligand Interactions Using AlphaFold Predictions

被引:0
作者
Escobedo, Nahuel [1 ]
Saldano, Tadeo [1 ,2 ]
Mac Donagh, Juan [1 ]
Sawicki, Luciana Rodriguez [1 ]
Palopoli, Nicolas [1 ]
Alberti, Sebastian Fernandez [1 ]
Fornasari, Maria Silvina [1 ]
Parisi, Gustavo [1 ]
机构
[1] Univ Nacl Quilmes, Dept Ciencia & Tecnol, B1876BXD, Bernal, Argentina
[2] Univ Nacl Ctr Prov Buenos Aires, Fac Agron, Dept Ciencias Basicas, RA-B7300 Buenos Aires, Argentina
关键词
predicted interactions; protein-ligand interactions; alphafold; order-disorder transitions; disorder; CONFORMATIONAL ENSEMBLES; DISORDER; ENZYME; TRANSITIONS; DISCOVERY; SEQUENCE; PATHWAY;
D O I
10.1016/j.jmb.2024.168852
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Protein-ligand interactions represent an essential step to understand the bases of molecular recognition, an intense field of research in many scientific areas. Structural biology has played a central role in unveiling protein-ligand interactions, but current techniques are still not able to reliably describe the interactions of ligands with highly flexible regions. In this work, we explored the capacity of AlphaFold2 (AF2) to estimate the presence of interactions between ligands and residues belonging to disordered regions. As these interactions are missing in the crystallographic-derived structures, we called them "ghost interactions". Using a set of protein structures experimentally obtained after AF2 was trained, we found that the obtained models are good predictors of regions associated with order-disorder transitions. Additionally, we found that AF2 predicts residues making ghost interactions with ligands, which are mostly buried and show differential evolutionary conservation with the rest of the residues located in the flexible region. Our findings could fuel current areas of research that consider, given their biological relevance and their involvement in diseases, intrinsically disordered proteins as potentially valuable targets for drug development. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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页数:13
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