Order parameters versus temperature for amylase tasks:
<p>mean score and variance of scores versus temperature after initial transient, i.e., at steady-state oscillatory BADASS behavior. Markers come from BADASS runs, and lines are fits using Eqs 5–7 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013119#pcbi...
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2025
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| _version_ | 1852019603023069184 |
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| author | Carlos A. Gomez-Uribe (21492788) |
| author2 | Japheth Gado (21492791) Meiirbek Islamov (9733301) |
| author2_role | author author |
| author_facet | Carlos A. Gomez-Uribe (21492788) Japheth Gado (21492791) Meiirbek Islamov (9733301) |
| author_role | author |
| dc.creator.none.fl_str_mv | Carlos A. Gomez-Uribe (21492788) Japheth Gado (21492791) Meiirbek Islamov (9733301) |
| dc.date.none.fl_str_mv | 2025-06-05T17:49:59Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013119.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Order_parameters_versus_temperature_for_amylase_tasks_/29249813 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biochemistry Evolutionary Biology Biological Sciences not elsewhere classified protein engineering approach prevent premature convergence large language model innovative optimization algorithm dynamically adjusting temperature combining evolutionary data sequences containing mutations amino acid sequences div >< p badass may generalize badass efficiently explores loop </ p 000 sequences identified experimental fitness measurements generate diverse high predict fitness landscapes b </ b </ b 000 sequences fitness sequences experimental labels designing diverse design sequences 000 ). badass exceed predicted fitness underlying mechanism top 1 sequence spaces positions entirely performance proteins new semi mutation energies machine learning every cutoff directed evolution alternative models |
| dc.title.none.fl_str_mv | Order parameters versus temperature for amylase tasks: |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>mean score and variance of scores versus temperature after initial transient, i.e., at steady-state oscillatory BADASS behavior. Markers come from BADASS runs, and lines are fits using Eqs 5–7 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013119#pcbi.1013119.s001" target="_blank">S1 Text</a>. These were obtained from cooling then heating runs of our algorithm for the amylase task: on the left using the ESM2 mutant marginal score, and on the right using the machine learning model that predicts fitness for stain removal and dp3 function. We ran the algorithm for 250 iterations, scoring 500 sequences in each iteration, and show all data for iterations larger than 100 to avoid the initial transient. The peak of the variance at intermediate temperatures is striking. Running the algorithm with an even blend of numbers of mutations changes the variance behavior, and was not fit to our equations. The mean and variance traces here are reminiscent of the magnetization and susceptibility in Ising models.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ef340dbf2e9ca11460df5ec025dfddc1 |
| identifier_str_mv | 10.1371/journal.pcbi.1013119.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29249813 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Order parameters versus temperature for amylase tasks:Carlos A. Gomez-Uribe (21492788)Japheth Gado (21492791)Meiirbek Islamov (9733301)BiophysicsBiochemistryEvolutionary BiologyBiological Sciences not elsewhere classifiedprotein engineering approachprevent premature convergencelarge language modelinnovative optimization algorithmdynamically adjusting temperaturecombining evolutionary datasequences containing mutationsamino acid sequencesdiv >< pbadass may generalizebadass efficiently exploresloop </ p000 sequences identifiedexperimental fitness measurementsgenerate diverse highpredict fitness landscapesb </ b</ b000 sequencesfitness sequencesexperimental labelsdesigning diversedesign sequences000 ).badass exceedpredicted fitnessunderlying mechanismtop 1sequence spacespositions entirelyperformance proteinsnew semimutation energiesmachine learningevery cutoffdirected evolutionalternative models<p>mean score and variance of scores versus temperature after initial transient, i.e., at steady-state oscillatory BADASS behavior. Markers come from BADASS runs, and lines are fits using Eqs 5–7 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013119#pcbi.1013119.s001" target="_blank">S1 Text</a>. These were obtained from cooling then heating runs of our algorithm for the amylase task: on the left using the ESM2 mutant marginal score, and on the right using the machine learning model that predicts fitness for stain removal and dp3 function. We ran the algorithm for 250 iterations, scoring 500 sequences in each iteration, and show all data for iterations larger than 100 to avoid the initial transient. The peak of the variance at intermediate temperatures is striking. Running the algorithm with an even blend of numbers of mutations changes the variance behavior, and was not fit to our equations. The mean and variance traces here are reminiscent of the magnetization and susceptibility in Ising models.</p>2025-06-05T17:49:59ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013119.g003https://figshare.com/articles/figure/Order_parameters_versus_temperature_for_amylase_tasks_/29249813CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292498132025-06-05T17:49:59Z |
| spellingShingle | Order parameters versus temperature for amylase tasks: Carlos A. Gomez-Uribe (21492788) Biophysics Biochemistry Evolutionary Biology Biological Sciences not elsewhere classified protein engineering approach prevent premature convergence large language model innovative optimization algorithm dynamically adjusting temperature combining evolutionary data sequences containing mutations amino acid sequences div >< p badass may generalize badass efficiently explores loop </ p 000 sequences identified experimental fitness measurements generate diverse high predict fitness landscapes b </ b </ b 000 sequences fitness sequences experimental labels designing diverse design sequences 000 ). badass exceed predicted fitness underlying mechanism top 1 sequence spaces positions entirely performance proteins new semi mutation energies machine learning every cutoff directed evolution alternative models |
| status_str | publishedVersion |
| title | Order parameters versus temperature for amylase tasks: |
| title_full | Order parameters versus temperature for amylase tasks: |
| title_fullStr | Order parameters versus temperature for amylase tasks: |
| title_full_unstemmed | Order parameters versus temperature for amylase tasks: |
| title_short | Order parameters versus temperature for amylase tasks: |
| title_sort | Order parameters versus temperature for amylase tasks: |
| topic | Biophysics Biochemistry Evolutionary Biology Biological Sciences not elsewhere classified protein engineering approach prevent premature convergence large language model innovative optimization algorithm dynamically adjusting temperature combining evolutionary data sequences containing mutations amino acid sequences div >< p badass may generalize badass efficiently explores loop </ p 000 sequences identified experimental fitness measurements generate diverse high predict fitness landscapes b </ b </ b 000 sequences fitness sequences experimental labels designing diverse design sequences 000 ). badass exceed predicted fitness underlying mechanism top 1 sequence spaces positions entirely performance proteins new semi mutation energies machine learning every cutoff directed evolution alternative models |