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- Bryant, Patrick, 1993-
(författare)
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Learning Protein Evolution and Structure
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- By analysing the structure of a protein it is possible to draw conclusions about its function. Obtaining the structure of a protein experimentally is however a time consuming and expensive process. By using evolution it is possible to infer the structure of a protein. AlphaFold2 (AF), the latest AI technology for protein structure prediction, uses evolutionary information to obtain protein structures in minutes instead of years at a fraction of the experimental cost. Here, we develop this technology further to predict the structure of interacting proteins. We create a confidence score, pDockQ, and show that this score rivals high-throughput experiments in distinguishing true and false protein-protein interactions (PPIs). Applying AF and the pDockQ score to a set of 65484 human PPIs we identify 1371 new high-confidence models. These models expand the structural knowledge of human protein complexes and can be used to e.g. develop new drugs or evaluate biological pathways. One limitation of AF is that the accuracy decreases with the number of proteins being predicted together and that the biggest protein complexes do not fit in the memory of the latest GPUs. To circumvent these issues, we predict subcomponents of protein complexes and assemble these together with Monte Carlo Tree search (MCTS). MCTS enables assembling some of the largest protein complexes using only sequence information and stoichiometry. Out of 175 protein complexes with 10-30 chains, 91 can be completely assembled with a median TM-score of 0.51. A third of these (30 complexes) are highly accurate (TM-score ≥0.8). The use of highly accurate protein structure prediction is revolutionising many fiends of biological research only one year after its realisation. Likely, this is only the beginning of a new era; the era of AI.
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| 2. |
- Bryant, Patrick, 1993-
(författare)
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Predicting the structure of large proteincomplexes using AlphaFold and MonteCarlo tree search
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- AlphaFold can predict the structure of single- and multiple-chain proteins with very highaccuracy. However, the accuracy decreases with the number of chains, and the availableGPU memory limits the size of protein complexes which can be predicted. Here we showthat one can predict the structure of large complexes starting from predictions ofsubcomponents. We assemble 91 out of 175 complexes with 10-30 chains from predictedsubcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create ascoring function, mpDockQ, that can distinguish if assemblies are complete and predict theiraccuracy. We find that complexes containing symmetry are accurately assembled, whileasymmetrical complexes remain challenging. The method is freely available and accesibleas a Colab notebookhttps://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb.
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| 3. |
- Bryant, Patrick, 1993-, et al.
(författare)
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Structure prediction of protein-ligand complexes from sequence information with Umol
- 2024
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Ingår i: Nature Communications. - 2041-1723. ; 15
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Tidskriftsartikel (refereegranskat)abstract
- Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol.
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