Dataset Information

Protein rethreading: A novel approach to protein design.

ABSTRACT: Protein engineering is an important tool for the design of proteins with novel and desirable features. Templates from the protein databank (PDB) are often used as initial models that can be modified to introduce new properties. We examine whether it is possible to reconnect a protein in a manner that generates a new topology yet preserves its structural integrity. Here, we describe the rethreading of dihydrofolate reductase (DHFR) from E. coli (wtDHFR). The rethreading process involved the removal of three native loops, and the introduction of three new loops with alternate connections. The structure of the rethreaded DHFR (rDHFR-1) was determined to 1.6?Å, demonstrating the success of the rethreading process. Both wtDHFR and rDHFR-1 exhibited similar affinities towards methotrexate. However, rDHFR-1 showed no reducing activity towards dihydrofolate, and exhibited about ~6-fold lower affinity towards NADPH than wtDHFR. This work demonstrates that protein rethreading can be a powerful tool for the design of a large array of proteins with novel structures and topologies, and that by careful rearrangement of a protein sequence, the sequence to structure relationship can be expanded substantially.

SUBMITTER: Agah S

PROVIDER: S-EPMC4882587 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

Protein rethreading: A novel approach to protein design.

Agah Sayeh S Poulos Sandra S Yu Austin A Kucharska Iga I Faham Salem S

Scientific reports 20160527

Protein engineering is an important tool for the design of proteins with novel and desirable features. Templates from the protein databank (PDB) are often used as initial models that can be modified to introduce new properties. We examine whether it is possible to reconnect a protein in a manner that generates a new topology yet preserves its structural integrity. Here, we describe the rethreading of dihydrofolate reductase (DHFR) from E. coli (wtDHFR). The rethreading process involved the remov ...[more]

PMID: 27229326

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Project description:Generative deep learning models have emerged as a powerful approach for de novo drug design as they aid researchers in finding new molecules with desired properties. Despite continuous improvements in the field, a subset of the outputs that sequence-based de novo generators produce cannot be progressed due to errors. Here, we propose to fix these invalid outputs post hoc. In similar tasks, transformer models from the field of natural language processing have been shown to be very effective. Therefore, here this type of model was trained to translate invalid Simplified Molecular-Input Line-Entry System (SMILES) into valid representations. The performance of this SMILES corrector was evaluated on four representative methods of de novo generation: a recurrent neural network (RNN), a target-directed RNN, a generative adversarial network (GAN), and a variational autoencoder (VAE). This study has found that the percentage of invalid outputs from these specific generative models ranges between 4 and 89%, with different models having different error-type distributions. Post hoc correction of SMILES was shown to increase model validity. The SMILES corrector trained with one error per input alters 60-90% of invalid generator outputs and fixes 35-80% of them. However, a higher error detection and performance was obtained for transformer models trained with multiple errors per input. In this case, the best model was able to correct 60-95% of invalid generator outputs. Further analysis showed that these fixed molecules are comparable to the correct molecules from the de novo generators based on novelty and similarity. Additionally, the SMILES corrector can be used to expand the amount of interesting new molecules within the targeted chemical space. Introducing different errors into existing molecules yields novel analogs with a uniqueness of 39% and a novelty of approximately 20%. The results of this research demonstrate that SMILES correction is a viable post hoc extension and can enhance the search for better drug candidates.

Dataset Information

Protein rethreading: A novel approach to protein design.

Publications

Protein rethreading: A novel approach to protein design.

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