Neural Bandits for Protein Sequence Optimization

Chenyu Wang, Joseph Kim, Le Cong, Mengdi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Protein design involves searching over a large combinatorial sequence space. Evaluating the fitness of new protein sequences often requires wet-lab experiments that are costly and time consuming. In this paper we propose a neural bandits algorithm that utilizes a modified upper-confidence bound algorithm for accelerating the search for optimal designs. The algorithm makes adaptive queries as guided by the kernelized neural bandits. The algorithm is tested on two public protein fitness datasets, the GB1 and WW domain. For both datasets, our algorithm consistently identifies top-fitness protein sequences. Notably, this approach finds a diverse and rich class of high fitness proteins using substantially fewer design queries compared to a range of alternative methods.

Original languageEnglish (US)
Title of host publication2022 56th Annual Conference on Information Sciences and Systems, CISS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages188-193
Number of pages6
ISBN (Electronic)9781665417969
DOIs
StatePublished - 2022
Event56th Annual Conference on Information Sciences and Systems, CISS 2022 - Princeton, United States
Duration: Mar 9 2022Mar 11 2022

Publication series

Name2022 56th Annual Conference on Information Sciences and Systems, CISS 2022

Conference

Conference56th Annual Conference on Information Sciences and Systems, CISS 2022
Country/TerritoryUnited States
CityPrinceton
Period3/9/223/11/22

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

Keywords

  • kernel trick
  • neural bandits
  • protein design

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