AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements

• Published in: Beni-Suef University journal of basic and applied sciences 2024-12, Vol.13 (1), p.46-5, Article 46
• Main Authors: Uzoeto, Henrietta Onyinye, Cosmas, Samuel, Bakare, Toluwalope Temitope, Durojaye, Olanrewaju Ayodeji
• Format: Article
• Language: English
• Subjects: Accuracy; Alphafold-latest; AlphaFold-multimer; Alphafold2; Biology; Biomolecules; CASP; Collaboration; Crystallography; Deep learning; Disease; Empowerment; Google DeepMind; Language; Letter to the Editor; Ligands; Machine learning; Medicine; Medicine & Public Health; Microscopy; NMR; Nuclear magnetic resonance; Nucleic acids; Predictions; Protein structure; Proteins; R&D; Research & development
• ISSN: 2314-8543; 2314-8535; 2314-8543
• DOI: 10.1186/s43088-024-00503-y

Breakthrough achievements in protein structure prediction have occurred recently, mostly due to the advent of sophisticated machine learning methods and significant advancements in algorithmic approaches. The most recent version of the AlphaFold model, known as “AlphaFold-latest,” which expands the functionalities of the groundbreaking AlphaFold2, is the subject of this article. The goal of this novel model is to predict the three-dimensional structures of various biomolecules, such as ions, proteins, nucleic acids, small molecules, and non-standard residues. We demonstrate notable gains in precision, surpassing specialized tools across multiple domains, including protein–ligand interactions, protein–nucleic acid interactions, and antibody–antigen predictions. In conclusion, this AlphaFold framework has the ability to yield atomically-accurate structural predictions for a variety of biomolecular interactions, hence facilitating advancements in drug discovery.