Using AlphaFold Predictions in Viral Research

Abstract

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

Identification and Genome Characterization of a Novel Nege-like Virus Isolated from Aphids (Aphis gossypii) in Yunnan Province

“…Predicting protein three-dimensional structures was inherently challenging. AlphaFold2, through its deep learning approach, predicted protein distances and torsion angles with high precision, utilizing training data from experimentally verified PDB structures, primary protein sequences, and multiple sequence alignments (MSAs) [ 22 ]. The notable differences observed between the predictions from AlphaFold2 and PSIPRED for ORF3 could be primarily attributed to the non-availability of reference structural data for this protein.…”

Identification of a Potential Entry-Fusion Complex Based on Sequence Homology of African Swine Fever and Vaccinia Virus

“…These similar domains were expected to be exposed at the protein surface, allowing interaction with similar molecules with potentially analogous functional properties. To elucidate predicted structures, 3D protein structural models were generated using the AlphaFold2 (AF2) computational program [ 45 , 46 ], allowing visualization of potentially interacting residues ( Supplementary Figure S3 and Figure 2 ). The observed structural similarities between VACV and ASFV proteins suggested important conserved roles for these proteins during infection [ 47 ].…”

Recent Advances on Targeting Proteases for Antiviral Development

“…Today, peptide-based molecules, including peptidomimetics, are expected to see an increase in market expansion, reaching a value of USD 46.6 million by the end of 2024 [ 33 ]. Together with the emergence of several viral infections in the last years and the elucidation of their protease structures, as well as with advances in artificial intelligence and tools like Alphafold [ 34 , 35 ], development of such inhibitors has gained some traction. A successful example is the recent approved drug Paxlovid ® (nirmatrelvir/ritonavir tablets, co-packaged, 12 ) to treat the COVID-19 disease.…”