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https://github.com/bayer-science-for-a-better-life/pKAI

pKAI: a fast and interpretable deep learning approach to accurate electrostatics-driven pKa prediction
https://github.com/bayer-science-for-a-better-life/pKAI

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pKAI: a fast and interpretable deep learning approach to accurate electrostatics-driven pKa prediction

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# pKAI



A fast and interpretable deep learning approach to accurate electrostatics-driven pKa prediction



```

@article{pkai,

author = {Reis, Pedro B. P. S. and Bertolini, Marco and Montanari, Floriane and Machuqueiro, Miguel and Clevert, Djork-Arné},

title = {pKAI: A fast and interpretable deep learning approach to accurate electrostatics-driven pKa prediction},

note = {in preparation}

}

```



### Installation & Basic Usage



We recommend installing pKAI on a conda enviroment. The pKAI+ model will be downloaded on the first execution and saved for subsequent runs.



```

python3 -m pip install pKAI



pKAI 

```



It can also be used as python function,

```

from pKAI.pKAI import pKAI



pks = pKAI(pdb)

```

where each element of the returned list is a tuple of size 4. (chain, resnumb, resname, pk)



## pKAI+ vs pKAI models



pKAI+ (default model) aims to predict experimental pKa values from a single conformation. To do such, the interactions characterized in the input structure are given less weight and, as a consequence, the predictions are closer to the pKa values of the residues in water. This effect is comparable to an increase in the dielectric constant of the protein in Poisson-Boltzmann models. In these models, the dielectric constant tries to capture, among others, electronic polarization and side-chain reorganization. When including conformational sampling explicitly, one should use a lower value for the dielectric constant of the protein. Likewise, one should use pKAI -- instead of pKAI+ -- as in this model there is no penalization of the interactions' impact on the predicted pKa values.



tl;dr version

- use pKAI+ for pKa predictions arising from a single structure

- use pKAI for pKa predictions arising from multiple conformations



Change the model to be used in the calculation by evoking the `model` argument:

```

pKAI  --model pKAI

```



## Benchmark



Performed on 736 experimental values taken from the PKAD database1.



| Method                | RMSE | MAE  | Quantile 0.9  | Error < 0.5 (%)  |

|-----------------------|------|------|---------------|------------------|

| Null2      | 1.09 | 0.72 |          1.51 |             52.3 |

| PROPKA3    | 1.11 | 0.73 |          1.58 |             51.1 |

| PypKa4     | 1.07 | 0.71 |          1.48 |             52.6 |

| pKAI                  | 1.15 | 0.75 |          1.66 |             49.3 |

| pKAI+                 | 0.98 | 0.64 |          1.37 |             55.0 |



[1] Pahari, Swagata et al. "PKAD: a database of experimentally measured pKa values of ionizable groups in proteins." doi:10.1093/database/baz024



[2] Thurlkill, Richard L et al. “pK values of the ionizable groups of proteins.” doi:10.1110/ps.051840806



[3] Olsson, Mats H M et al. “PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa Predictions.” doi:10.1021/ct100578z



[4] Reis, Pedro B P S et al. “PypKa: A Flexible Python Module for Poisson-Boltzmann-Based pKa Calculations.” doi:10.1021/acs.jcim.0c00718



## License



This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.



## Contacts

Please submit a github issue to report bugs and to request new features. Alternatively, you may  email the developer directly.