Rapid protein stability prediction using deep learning representations

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Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

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Supervised Learning of Protein Melting Temperature: Cross‐Species vs. Species‐Specific Prediction

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Functional assessment of protein variants in structured domains by fluorescence cross-correlation spectroscopy

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Site-saturation functional screens identify PALB2 missense variants associated with increased breast cancer risk

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Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network

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Hydroxylase Thermostability Prediction Based on Self-Trained Semisupervised Iteration and Bayesian Dynamic Tuning

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Precision Chemistry–Guided Peptide and Protein Self‐Assembly

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Discovery to Engineering of a Bacterial Thermostable Secretory Phospholipase A 2 Using Pretrained Computational Models

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A functional map of phosphoprotein phosphatase regulation identifies an evolutionarily conserved reductase for the catalytic metal ions

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Assessing enzyme stability for biocatalysis

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Computationally guided strategies to modulate enzyme pH profiles for industrial biocatalysis and environmental applications

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Effects of residue substitutions on the cellular abundance of proteins

Schulze, T. K., & Lindorff-Larsen, K. (2026) · eLife · doi:10.7554/elife.103721.3 · Cited by 1

Review on strategies for enhancing stability of chondroitinase ABC in the treatment of spinal cord injury

Koushki, S., Askaripour, H., & Bahiraee, S. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06656-4 · Cited by 1

Congenital heart disease missense mutations in the TBX5 DNA-binding domain alter thermal stability and DNA-binding affinity

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Adaptation of protein stability to thermally heterogeneous environments

Priklopil, T., Bomblies, K., & Widmer, A. (2025) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2025.112252 · Cited by 1

Deep mutational scanning reveals a tight correlation between protein degradation and toxicity of thousands of non-native aspartoacylase protein variantsPreprint

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A joint embedding of protein sequence and structure enables robust variant effect predictionsPreprint

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Functionally constrained human proteins are less prone to mutational instability caused by single amino acid substitutionsPreprint

Andrews, T. D., Lehmann, N., May, M., Goodall, L., Cho, E., & Chuah, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.14.603410 · Cited by 1

In-Silico Stability Predictors: Investigation of Performance Towards balanced Experimental DataPreprint

Degn, K., Utichi, M., Besora, P. S.-I., Tiberti, M., & Papaleo, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.28.645695 · Cited by 5

Interpreting the Effects of DNA Polymerase Variants at the Structural Level Using MAVISp and Molecular Dynamics SimulationsPreprint

Arnaudi, M., Krzesińska, K., Beltrame, L., Besora, P. S.-I., Tiberti, M., Nilbert, M. C., Rohlin, A., & Papaleo, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.04.653638 · Cited by 1

Decoding molecular mechanisms for loss-of-function variants in the human proteomePreprint

Cagiada, M., Jonsson, N., & Lindorff‐Larsen, K. (2025) · eLife · doi:10.7554/elife.108160 · Cited by 1

Predicting interaction-specific protein–protein interaction perturbations by missense variants with MutPred-PPIPreprint

Stewart, R., Laval, F., Coppin, G., Spirohn, K., Tixhon, M., Hao, T., Calderwood, M. A., Mort, M. E., Cooper, D. N., Vidal, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.20.695738 · Cited by 2

GYDE: A collaborative drug discovery platform for AI-powered protein design and engineeringPreprint

Down, T. A., Warowny, M., Walker, A., D’Ascenzo, L., Lee, D., Zhou, Z., Cao, S., Bainbridge, T. W., Nicoludis, J. M., Harris, S. F., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.24.714039 · Cited by 1

Deciphering GB1’s Single Mutational Landscape: Insights from MuMi Analysis

Guclu, T. F., Atilgan, A. R., & Atilgan, C. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04916

Genotype sampling for deep-learning assisted experimental mapping of fitness landscapesPreprint

Wagner, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.18.576262

Integrating Deep Learning with Structural Bioinformatics using Next-Generation Protein Stability Prediction

Sugil, A. J., Merriliance, K., & Soundiraraj, N. (2024) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict60155.2024.10544908

AI-enabled alkaline-resistant evolution of protein to apply in mass production

Kang, L., Wu, B., Zhou, B., Tan, P., Kang, Y. (., Yan, Y., Zong, Y., Li, S., Liu, Z., & Hong, L. (2025) · eLife · doi:10.7554/elife.102788.3

Supervised learning of protein melting temperatures: cross-species vs species-specific predictionPreprint

López, S. G., Salomon, J., & Boomsma, W. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.12.617972

Functionally important residues from graph analysis of coevolved dynamic couplings

Xu, M., Dantu, S. C., Garnett, J. A., Bonomo, R. A., Pandini, A., & Haider, S. M. (2025) · eLife · doi:10.7554/elife.105005.3

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass ProductionPreprint

Kang, L., Wu, B., Zhong, B., Tan, P., Kang, Y. H., Yan, Y., Zong, Y., Li, S., Liu, Z., & Hong, L. (2024) · eLife · doi:10.7554/elife.102788.1

An Antigen Design Method Based on Molecular Dynamics and Deep Learning

Li, H., & Zhou, S. (2024) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai62885.2024.00084

Functionally Important Residues from Graph Analysis of Coevolved Dynamic couplingsPreprint

Xu, M., Dantu, S. C., Garnett, J. A., Bonomo, R. A., Pandini, A., & Haider, S. M. (2025) · eLife · doi:10.7554/elife.105005.1

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass ProductionPreprint

Kang, L., Wu, B., Zhong, B., Tan, P., Kang, Y. H., Yan, Y., Zong, Y., Li, S., Liu, Z., & Liang, H. (2025) · eLife · doi:10.7554/elife.102788.2

Functionally Important Residues from Graph Analysis of Coevolved Dynamic couplingsPreprint

Xu, M., Dantu, S. C., Garnett, J. A., Bonomo, R. A., Pandini, A., & Haider, S. M. (2025) · eLife · doi:10.7554/elife.105005.2

Composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection

Dong, S., & Ding, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-3349-4_18

Title

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Mechanistic Insights into Proteomic Mutation-Phenotype Linkages from Tiling Mutagenesis ScreensPreprint

He, W., Huang, J., Wang, Y., Hayward, S. B., Leuzzi, G., Fu, R., Wang, S., Vaitsiankova, A., Bedford, M. T., Guérois, R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.17.649336

The effects of retinal disease on intrinsic protein disorder and liquid–liquid-phase separation

Hadzijahic, N., Kim, C. K., Djulbegovic, M. B., Antonietti, M., Gonzalez, D. J. T., Uversky, V. N., Pulido, J. S., & Karp, C. L. (2025) · Journal of Proteins and Proteomics · doi:10.1007/s42485-025-00188-6

Probe-based identification of metal-binding sites using deep learning representations

Xu, S., & Onoda, A. (2026) · Nature Communications · doi:10.1038/s41467-026-74657-x · Cited by 2

Sculpting the future: harnessing artificial intelligence for revolutionary drug discovery

Lavecchia, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33885-4.00003-2

Hybrid deep learning framework for enhanced prediction of linear B-cell epitopes using forward-based feature filtering

Rajmane, P. R., & Khiani, S. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00588-z

A pipeline for data-driven learning of topological features with applications to protein stability prediction

Mishra, A., & Motta, F. C. (2025) · Journal of Applied and Computational Topology · doi:10.1007/s41468-025-00225-9

Integrative computational analysis of IFNG gene variants: Identifying high-risk mutations affecting interferon-gamma structure and function

Al-Zaalan, A. R., Aziz, H. S., & Abdulabbas, N. F. (2025) · Asia-Pacific Journal of Molecular Biology and Biotechnology · doi:10.35118/apjmbb.2025.033.4.23

LoMuS: Low-Rank Adaptation with Multimodal Representations Improves Protein Stability PredictionPreprint

Trillos, S. I., Singh, A., & Kabir, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.15.694540

Mechanistic Interpretability of Fine-Tuned Protein Language Models for Nanobody Thermostability PredictionPreprint

Murakami, T., Hashidate, Y., & Matsunaga, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.22.695878

Effects of residue substitutions on the cellular abundance of proteinsPreprint

Schulze, T. K., & Lindorff‐Larsen, K. (2026) · eLife · doi:10.7554/elife.103721.2 · Cited by 1

Gigantol disrupts AR and iNOS conformation and promotes aggregation through direct molecular interaction to suppress diabetic cataract

Chen, D., Lu, C., Wang, M., & Wei, X. (2026) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2026.119027

Decoding ATG9A Variation: A Comprehensive Structural Investigation of All Missense VariantsPreprint

Utichi, M., Marjault, H., Tiberti, M., & Papaleo, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.03.703515

StabLyzeGraph : High‐throughput screening of combinatorial mutations using graph neural networks

Waqas, M., Natale, B., Roggia, M., Saha, P. P., Mileni, M., & Cosconati, S. (2026) · Protein Science · doi:10.1002/pro.70534

New computational approaches to study the effect of genetic mutations on the topology of protein-protein interaction networks

Behbahani, Y. M. (2022) · theses.fr (ABES) · doi:10.70675/bfdded7czb638z455azaedaz4ae9f8d0a13d

Discriminative Site-Directed Protein Engineering via Lightweight CASPE PlatformPreprint

Deng, Q., Qiao, J., Wang, C., Ni, X., Chang, Y., Zhao, N., Zhai, R., Cui, H., Li, X., & Jin, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.24.720551 · Cited by 1

TxConformal: Controlling False Discoveries in AI-Driven Therapeutic DiscoveryPreprint

Jin, Y., Huang, K., Diamant, N., Buchholz, K. R., Rutherford, S. T., Skelton, N. J., Biancalani, T., Scalia, G., Leskovec, J., & Candès, E. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.27.721076 · Cited by 2

Directed evolution of enzymes at the crossroads of tradition and innovation

Tomková, M., Miroššay, A., & Sedlák, E. (2026) · FEBS Open Bio · doi:10.1002/2211-5463.70271 · Cited by 1

Interpreting the effects of DNA polymerase variants at the structural level

Arnaudi, M., Krzesińska, K., Beltrame, L., Besora, P. S.-I., Tiberti, M., Nilbert, M. C., Rohlin, A., & Papaleo, E. (2026) · Molecular Oncology · doi:10.1002/1878-0261.70255

Multimodal phenotyping defines variant-to-function maps for RBM20 in dilated cardiomyopathyPreprint

Fenzl, K., Müller, L. H., Kornienko, J., Majchrowska, M., Eikmeier, N., Wehnert, B., Mullapudi, E., Floyd, B. J., Clarke, B., Schweimer, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.10.730167

Deciphering protein mutation-phenotype linkages from CRISPR-based tiling mutagenesis screens

He, W., Huang, J., Wang, Y., Hayward, S. B., Leuzzi, G., Fu, R., Wang, S., Vaitsiankova, A., Chen, Y., Bedford, M. T., et al. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101651

Growth factor thermostability dataset derived from experimental and computational approaches to accelerate cultured meat development

Ng, W., Aghakasiri, K., Rebecca, P., Marschallinger, J., Ong, K. J., Rees, S. D., Staszak, D., & Duffy, B. (2026) · Data in Brief · doi:10.1016/j.dib.2026.113042

OrgNet+: towards robust protein stability prediction with convolutional neural networks

Sarycheva, A., Shumilov, A., & Popov, P. A. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag258

LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction

Infante, S., Singh, A., & Kabir, A. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag509

Proteomic investigation of signaling dynamics: from static maps to network rewiring

Dionne, U., Vafaee, K., Hesketh, G. G., & Gingras, A. (2026) · Bioscience Reports · doi:10.1042/bsr20250116

An ensemble learning framework for protein stability prediction with enhanced recognition of stabilizing mutations

Liu, Y., Zhang, J., & Li, M. (2026) · Protein Science · doi:10.1002/pro.70728

A hybrid CNN–GNN–XGB ensemble framework for prediction of mutation-induced protein–protein binding free-energy changes (ΔΔG) in protein engineering

Hari, S., & Babu, R. S. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109303

A unified predictor of protein stability changes across all mutation types via implicit structure learning

Tan, H., Lin, S., & Xiong, Y. (2026) · Chemical Science · doi:10.1039/d6sc02103d

Decoding structure-function synergies in plant proteins using AlphaFold and deep learning tools

Ünal, E. B., Çapanoğlu, E., & Karaça, A. C. (2026) · Food Chemistry · doi:10.1016/j.foodchem.2026.150801

Toward Advanced Quantitative Prediction of Missense Variant Effects

Ye, S., Özkan, S., Díaz-Vásquez, A., Padilla, N., & de la Cruz, X. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27198777

SA-MPNN: A Sequence-Aware ThermoMPNN for Accurate Prediction of Mutational Effects on Protein Thermodynamic Stability

Zhang, X. Y., Zheng, X., Dong, Z. Y., Xiao, J., Zhang, H., Wu, M. C., Liu, Z. M., Li, G. H., Li, J., Wang, W. B., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01644

Amylosucrase as a Versatile Biocatalyst for Next‐Generation Functional Ingredients: Starch Modification and Glycoside Synthesis

Seo, D.-H., Lee, B.-H., & Yoo, S. (2026) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.70656

ProtThermoRank: Sequence-based multi-class prediction model for protein thermal stability changes and its application to CcsZ

Hwang, S., Oh, S., Kim, J., Jeong, K., & Chi, S. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.154559

Mechanistic interpretability of fine-tuned protein language models for nanobody thermostability prediction

Murakami, T., Hashidate, Y., & Matsunaga, Y. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag685

Predicting Protein Thermostability with Equivariant Graph Neural Networks and Contrastive Learning

Han, K., Zhong, K., Song, X., Wang, W., Bin, Y., & Cao, R. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01926

Engineering acid-sensing ion channel 1a with disrupted desensitization: A computer-guided approach

Hassankalhori, M., Hanna, M., Sancho, F., Torras, J., Kellenberger, S. B., & Lucas, M. F. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.154826

Role of machine learning in drug discovery and therapeutic protein design

Ibrahim, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44536-1.00014-6

Benchmarking and Experimental Validation of Machine Learning Strategies for Enzyme Engineering

Zeng, Z., Jin, J., Xu, R., 武玉真, & Luo, X. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01200

ERIS2: A deep-learning platform for predicting mutation-induced protein stability changes

Ekambaram, S., Kundu, D., Ghosh, S., & Dokholyan, N. V. (2026) · Structure · doi:10.1016/j.str.2026.10.003

ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding modelsPreprint

Tan, H., Wei, X., Lin, S., Mao, X., Chen, J., Sun, H., Zhang, Y., Zhou, Z., Wei, D., Lin, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.11.669595 · Cited by 4

StabilitySort: assessment of protein stability changes on a genome-wide scale to prioritize potentially pathogenic genetic variation

Chuah, A., Li, S., Do, A., Field, M., & Andrews, D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac465 · Cited by 5

Metodi di Deep Learning per la predizione di cambiamenti di stabilità di proteine

Pancotti, C. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20787312

Predicting a Protein's Stability under a Million Mutations

Ouyang-Zhang, J., Diaz, D. J., Klivans, A. R., & Krähenbühl, P. (2023) · Neural Information Processing Systems · doi:10.52202/075280-3332 · Cited by 35

Generalizable and scalable protein stability prediction with rewired protein generative models

Li, Z., & Luo, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-67609-4 · Cited by 24