Search papers, labs, and topics across Lattice.
This review examines the advancements and limitations of AlphaFold (AF) in predicting biomacromolecule structures, highlighting its success with rigid proteins but shortcomings in capturing dynamics and interactions. It analyzes recent modifications and applications of AF, while also charting a course for future development towards comprehensive proteome-wide structure and interaction prediction. The authors advocate for a combined in silico and in vitro approach to bridge the gap between static and dynamic protein features for advancing structural biology.
AlphaFold's revolution in protein structure prediction is just the beginning: integrating it with in vitro methods is the key to unlocking protein dynamics and function.
ABSTRACT Introduction The DeepMind’s AlphaFold (AF) has revolutionized biomedical and biocience research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules. Areas covered In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF. We also provide a detailed analysis its of strengths and limitations, and explore more recent iterations, modifications, and practical applications of this strategy. Moreover, we map the path forward for expanding the landscape of AF toward predicting structures of every protein and peptide, and their interactions in the proteome in the most physiologically relevant form. This discussion is based on an extensive literature search performed using PubMed and Google Scholar. Expert opinion While significant progress has been made to enhance AF’s modeling capabilities, we argue that a combined approach integrating both various in silico and in vitro methods will be most beneficial for the future of structural biology, bridging the gaps between static and dynamic features of proteins and their functions.
