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TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling

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Pan Tan, Mingchen Li, Liang Zhang, Zhiqiang Hu, Liang Hong

Quantitative Methods · Submitted 7 Apr 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Consequently, TemPL offers considerable promise for protein engineering applications, facilitating the design of mutation sequences with enhanced stability and activity.

how it is stated · hedged possibility 0.83 read the record →

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arXiv ID2304.03780 (version 5)
Pith IDJBLU6AKV, Pith's permanent record of this paper and its review
CitationLiang Hong, Liang Zhang, Mingchen Li, Pan Tan, Zhiqiang Hu. "TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling." arXiv:2304.03780 (2023). https://pith.science/paper/JBLU6AKV
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,268 input tokens.

What the abstract itself states, as probabilities: claims first 0.18 · states limitation 0.08 · human participants 0.03 · controlled comparison 0.65 · randomized assignment 0.01 · states quantitative result 0.71 · compares with prior results 0.96 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v5. Results and the main claim are underlined; .

We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an extensive dataset of 96 million sequence-host bacterial strain optimal growth temperatures (OGTs) and {\Delta}Tm data for point mutations under consistent experimental conditions, we effectively compared TemPL with state-of-the-art models. Notably, TemPL demonstrated superior performance in predicting protein stability. An ablation study was conducted to elucidate the influence of OGT prediction and language modeling modules on TemPL's performance, revealing the importance of integrating both components. Consequently, TemPL offers considerable promise for protein engineering applications, facilitating the design of mutation sequences with enhanced stability and activity.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. contribution 0.99 0.90 0.12 asserted 0.99
s1 By assembling an extensive dataset of 96 million sequence-host bacterial strain optimal growth temperatures (OGTs) and {\Delta}Tm data for point mutations under consistent experimental conditions, we effectively compared TemPL with state-of-the-art models. method 0.98 0.95 0.04 asserted 1.00
s2 Notably, TemPL demonstrated superior performance in predicting protein stability. result 1.00 0.95 0.32 asserted 1.00
s3 An ablation study was conducted to elucidate the influence of OGT prediction and language modeling modules on TemPL's performance, revealing the importance of integrating both components. result 0.55 0.96 0.07 asserted 1.00
s4 Consequently, TemPL offers considerable promise for protein engineering applications, facilitating the design of mutation sequences with enhanced stability and activity. interpretation 0.99 0.30 0.82 hedged possibility 0.83

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.45.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an extensive dataset of 96 million sequence-host bacterial strain optimal growth temperatures (OGTs) and {\Delta}Tm data for point mutations under consistent experimental conditions, we effectively compared TemPL with state-of-the-art models. Notably, TemPL demonstrated superior performance in predicting protein stability. An ablation study was conducted to elucidate the influence of OGT prediction and language modeling modules on TemPL's performance, revealing the importance of integrating both components. Consequently, TemPL offers considerable promise for protein engineering applications, facilitating the design of mutation sequences with enhanced stability and activity.

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