A structural biology community assessment of AlphaFold2 applications
TLDR
In this paper , the AlphaFold2 (AF2) model was used to predict protein structural elements, including missense variants, function and ligand binding site predictions, and modeling of experimental structural data.Abstract:
Abstract Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research. read more
Citations
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Journal ArticleDOI
Sampling alternative conformational states of transporters and receptors with AlphaFold2
TL;DR: In this article , an approach to drive AlphaFold2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors is presented.
Posted ContentDOI
Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
TL;DR: A large majority of IDR sequences in the proteomes of human and other eukaryotes would be expected to function in the absence of conditional folding, and up to 80% of IDRs in archaea and bacteria are predicted to conditionally fold, but less than 20% of eUKaryotic IDRs.
Journal ArticleDOI
Towards a structurally resolved human protein interaction network
TL;DR: This article used AlphaFold2 to predict structures for 65,484 human protein interactions and identified 3,137 high-confidence models, of which 1,371 have no homology to a known structure.
Journal ArticleDOI
Folding the unfoldable: using AlphaFold to explore spurious proteins
TL;DR: For example, AlphaFold 2.0 has been used to predict the structure of representative protein sequences from all AntiFam 6.0 families as discussed by the authors , and the results showed a trend that the mean structure prediction confidence score pLDDT is higher for shorter sequences.
Journal ArticleDOI
AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor
Fengzhi Ren,Xiao Ding,Min Zheng,Mikhail Korzinkin,Xin Cai,Wei Zhu,Alexey B. Mantsyzov,Alexander Aliper,Vladimir A. Aladinskiy,Zhongying Cao,Shanshan Kong,Xi Long,Bonnie Hei Man Liu,Yingtao Liu,Vladimir Naumov,Anastasia Shneyderman,Ivan V. Ozerov,Ju Wang,Frank Wing Pun,Daniil Polykovskiy,Chong Sun,Michael Levitt,Alán Aspuru-Guzik,Alex Zhavoronkov +23 more
TL;DR: In this paper , the AlphaFold algorithm was used to predict protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology.
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