2026-10-10T01:25:12Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33940585 2026-09-18T18:15:01Z category_1 category_915 category_7 category_14 category_24 category_734 category_931 category_132 portal_63 item_type_3 month_year_09_2026

Examining Protein Residue-Level Stability Using Theory and Experiment Andrew D. Sanders (25073671) Rickey Y. Yada (591206) Derek R. Dee (591204) Biophysics Physical Sciences not elsewhere classified Medicine Molecular Biology Evolutionary Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Infectious Diseases level interactions relative empirical data enables amino acid interactions protein folding funnels experiment protein behavior theoretical predictions regarding local frustration quantifies examining protein residue local frustration protein sequence protein frustratometer theoretical principles whether evolved tools across silico </ remained elusive intentionally designed experimental variance experimental validation evaluated metrics energetic measures bridge sequence 42 ). 26 ), 178 proteins 000 positions Protein behavior, whether evolved in nature or intentionally designed, is governed by the energetics of amino acid interactions that bridge sequence to function. As an extension of the theory of protein folding funnels, local frustration quantifies the optimality of these residue-level interactions relative to alternatives. Although this framework has existed for decades, experimental validation has remained elusive. Past deep mutational scanning datasets enable experimental assessment across nearly 8,000 positions from 178 proteins. Results provide experimental evidence for theoretical predictions regarding the relationship between local frustration and protein sequence, structure, and evolution. An evaluation of three <i>in silico</i> methods shows modest agreement with benchmarks (r = 0.03 to 0.26), with the predominant local frustration predictor (Protein Frustratometer) capturing under 3% of the experimental variance. A recent deep learning model, Pythia, outperformed other tools across all evaluated metrics (r = 0.42). Finally, this scale of empirical data enables an evaluation of the strengths and limitations of energetic measures themselves, inspiring a complementary Sequence Probability INverse (SPIN) framework, which characterizes optimality through a Boltzmann-weighted selection probability within an ensemble of sequences. These findings help provide experimental grounding for the theoretical principles that govern protein sequence energetics. 2026-09-18T00:00:00Z Dataset Dataset 10.1021/acsomega.6c05138.s001 https://figshare.com/articles/dataset/Examining_Protein_Residue-Level_Stability_Using_Theory_and_Experiment/33940585 CC BY-NC 4.0