AND/OR branch-and-bound for computational protein design optimizing K*

Bobak Pezeshki, Radu Marinescu, Alexander Ihler, Rina Dechter
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:1602-1612, 2022.

Abstract

The importance of designing proteins, such as high affinity antibodies, has become ever more apparent. Computational Protein Design can cast such design problems as optimization tasks with the objective of maximizing K*, an approximation of binding affinity. Here we lay out a graphical model framework for K* optimization that enables use of compact AND/OR search algorithms. We designed an AND/OR branch-and-bound algorithm, AOBB-K*, for optimizing K* that is guided by a new K* heuristic and can incorporate specialized performance improvements with theoretical guarantees. As AOBB-K* is inspired by algorithms from the well studied task of Marginal MAP, this work provides a foundation for harnessing advancements in state-of-the-art mixed inference schemes and adapting them to protein design.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-pezeshki22a, title = {{AND/OR} branch-and-bound for computational protein design optimizing {K*}}, author = {Pezeshki, Bobak and Marinescu, Radu and Ihler, Alexander and Dechter, Rina}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {1602--1612}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/pezeshki22a/pezeshki22a.pdf}, url = {https://proceedings.mlr.press/v180/pezeshki22a.html}, abstract = {The importance of designing proteins, such as high affinity antibodies, has become ever more apparent. Computational Protein Design can cast such design problems as optimization tasks with the objective of maximizing K*, an approximation of binding affinity. Here we lay out a graphical model framework for K* optimization that enables use of compact AND/OR search algorithms. We designed an AND/OR branch-and-bound algorithm, AOBB-K*, for optimizing K* that is guided by a new K* heuristic and can incorporate specialized performance improvements with theoretical guarantees. As AOBB-K* is inspired by algorithms from the well studied task of Marginal MAP, this work provides a foundation for harnessing advancements in state-of-the-art mixed inference schemes and adapting them to protein design.} }
Endnote
%0 Conference Paper %T AND/OR branch-and-bound for computational protein design optimizing K* %A Bobak Pezeshki %A Radu Marinescu %A Alexander Ihler %A Rina Dechter %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-pezeshki22a %I PMLR %P 1602--1612 %U https://proceedings.mlr.press/v180/pezeshki22a.html %V 180 %X The importance of designing proteins, such as high affinity antibodies, has become ever more apparent. Computational Protein Design can cast such design problems as optimization tasks with the objective of maximizing K*, an approximation of binding affinity. Here we lay out a graphical model framework for K* optimization that enables use of compact AND/OR search algorithms. We designed an AND/OR branch-and-bound algorithm, AOBB-K*, for optimizing K* that is guided by a new K* heuristic and can incorporate specialized performance improvements with theoretical guarantees. As AOBB-K* is inspired by algorithms from the well studied task of Marginal MAP, this work provides a foundation for harnessing advancements in state-of-the-art mixed inference schemes and adapting them to protein design.
APA
Pezeshki, B., Marinescu, R., Ihler, A. & Dechter, R.. (2022). AND/OR branch-and-bound for computational protein design optimizing K*. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:1602-1612 Available from https://proceedings.mlr.press/v180/pezeshki22a.html.

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