{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T19:27:40Z","timestamp":1763580460046,"version":"3.41.2"},"reference-count":102,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":30,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein pre-training has emerged as a transformative approach for solving diverse biological tasks. While many contemporary methods focus on sequence-based language models, recent findings highlight that protein sequences alone are insufficient to capture the extensive information inherent in protein structures. Recognizing the crucial role of protein structure in defining function and interactions, we introduce $\\mathcal{S}$able, a versatile pre-training model designed to comprehensively understand protein structures. $\\mathcal{S}$able incorporates a novel structural encoding mechanism that enhances inter-atomic information exchange and spatial awareness, combined with robust pre-training strategies and lightweight decoders optimized for specific downstream tasks. This approach enables $\\mathcal{S}$able to consistently outperform existing methods in tasks such as generation, classification, and regression, demonstrating its superior capability in protein structure representation. The code and models can be accessed via GitHub repository at https:\/\/github.com\/baaihealth\/Sable.<\/jats:p>","DOI":"10.1093\/bib\/bbaf120","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T12:34:50Z","timestamp":1741869290000},"source":"Crossref","is-referenced-by-count":1,"title":["$\\mathcal{S}$\n          able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8439-7516","authenticated-orcid":false,"given":"Jiashan","family":"Li","sequence":"first","affiliation":[{"name":"Institute for Mathematical Sciences, Renmin University of China , 59 Zhongguancun Street, Beijing 100872 ,","place":["China"]}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Bio Computing Center, Beijing Academy of Artificial Intelligence , 150 Chengfu Road, Beijing 100084 ,","place":["China"]}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[{"name":"Bio Computing Center, Beijing Academy of Artificial Intelligence , 150 Chengfu Road, Beijing 100084 ,","place":["China"]}]},{"given":"Mingliang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Bio Computing Center, Beijing Academy of Artificial Intelligence , 150 Chengfu Road, Beijing 100084 ,","place":["China"]}]},{"given":"Jingcheng","family":"Yu","sequence":"additional","affiliation":[{"name":"Bio Computing Center, Beijing Academy of Artificial Intelligence , 150 Chengfu Road, Beijing 100084 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2802-6176","authenticated-orcid":false,"given":"Xinqi","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute for Mathematical Sciences, Renmin University of China , 59 Zhongguancun Street, Beijing 100872 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4264-5846","authenticated-orcid":false,"given":"Qiwei","family":"Ye","sequence":"additional","affiliation":[{"name":"Bio Computing Center, Beijing Academy of Artificial Intelligence , 150 Chengfu Road, Beijing 100084 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin","key":"2025040101491113300_ref1"},{"key":"2025040101491113300_ref2","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J Mach Learn Res"},{"article-title":"GPT-4 technical report","year":"2023","author":"","key":"2025040101491113300_ref3"},{"key":"2025040101491113300_ref4","doi-asserted-by":"crossref","DOI":"10.1101\/676825","article-title":"Evaluating protein transfer learning with tape","volume-title":"Advances in Neural Information Processing Systems","author":"Rao","year":"2019"},{"key":"2025040101491113300_ref5","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci"},{"article-title":"xTrimoPGLM: unified 100b-scale pre-trained transformer for deciphering the language of protein","year":"2024","author":"Chen","key":"2025040101491113300_ref6"},{"key":"2025040101491113300_ref7","first-page":"8844","article-title":"MSA Transformer","volume-title":"International Conference on Machine Learning","author":"Rao","year":"2021"},{"key":"2025040101491113300_ref8","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: toward understanding the language of life through self-supervised learning","volume":"44","author":"Elnaggar","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"article-title":"Evolutionary-scale prediction of atomiclevel protein structure with a language model","volume-title":"Science","author":"Lin","key":"2025040101491113300_ref9"},{"key":"2025040101491113300_ref10","doi-asserted-by":"crossref","DOI":"10.1101\/2023.01.16.524265","article-title":"Ankh: optimized protein language model unlocks general-purpose modelling","author":"Elnaggar","year":"2023"},{"key":"2025040101491113300_ref11","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1038\/s41587-024-02123-4","article-title":"Designing proteins with language models","volume":"42","author":"Ruffolo","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2025040101491113300_ref12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1038\/s41594-017-0011-7","article-title":"Structure and dynamics of GPCR signaling complexes","volume":"25","author":"Hilger","year":"2018","journal-title":"Nat Struct Mol Biol"},{"key":"2025040101491113300_ref13","doi-asserted-by":"publisher","first-page":"e1005324","DOI":"10.1371\/journal.pcbi.1005324","article-title":"Accurate de novo prediction of protein contact map by ultra-deep learning model","volume":"13","author":"Wang","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"2025040101491113300_ref14","doi-asserted-by":"crossref","DOI":"10.26434\/chemrxiv-2022-jjm0j-v4","article-title":"Uni-Mol: a universal 3D molecular representation learning framework","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhou","year":"2023"},{"key":"2025040101491113300_ref15","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","article-title":"Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning","volume":"17","author":"Gainza","year":"2020","journal-title":"Nat Methods"},{"key":"2025040101491113300_ref16","first-page":"15272","article-title":"Fast end-to-end learning on protein surfaces","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Sverrisson","year":"2021"},{"key":"2025040101491113300_ref17","doi-asserted-by":"publisher","first-page":"e4750","DOI":"10.7717\/peerj.4750","article-title":"EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation","volume":"6","author":"Amidi","year":"2018","journal-title":"PeerJ"},{"key":"2025040101491113300_ref18","doi-asserted-by":"publisher","first-page":"4046","DOI":"10.1093\/bioinformatics\/bty494","article-title":"Deep convolutional networks for quality assessment of protein folds","volume":"34","author":"Derevyanko","year":"2018","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref19","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1038\/s41467-021-23303-9","article-title":"Structure-based protein function prediction using graph convolutional networks","volume":"12","author":"Vladimir Gligorijevi\u0107","year":"2021","journal-title":"Nat Commun"},{"key":"2025040101491113300_ref20","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1093\/bioinformatics\/btaa714","article-title":"GraphQA: protein model quality assessment using graph convolutional networks","volume":"37","author":"Baldassarre","year":"2021","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref21","doi-asserted-by":"publisher","first-page":"bbac614","DOI":"10.1093\/bib\/bbac614","article-title":"High-accuracy protein model quality assessment using attention graph neural networks","volume":"24","author":"Zhang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025040101491113300_ref22","article-title":"Iterative refinement graph neural network for antibody sequence-structure co-design","volume-title":"International Conference on Learning Representations","author":"Jin","year":"2022"},{"key":"2025040101491113300_ref23","article-title":"Learning from protein structure with geometric vector perceptrons","volume-title":"International Conference on Learning Representations","author":"Jing","year":"2021"},{"key":"2025040101491113300_ref24","doi-asserted-by":"crossref","first-page":"6832","DOI":"10.1038\/s41598-022-10775-y","article-title":"LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction","volume":"12","author":"Wang","year":"2022","journal-title":"Sci Rep"},{"key":"2025040101491113300_ref25","article-title":"Orientation-aware graph neural networks for protein structure representation learning","volume-title":"arXiv preprint arXiv:2201.13299","author":"Li","year":"2025"},{"key":"2025040101491113300_ref26","article-title":"Protein representation learning by geometric structure pretraining","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhang","year":"2023"},{"key":"2025040101491113300_ref27","article-title":"Generative models for graph-based protein design","volume-title":"Advances in Neural Information Processing Systems","author":"Ingraham","year":"2019"},{"key":"2025040101491113300_ref28","first-page":"8946","article-title":"Learning inverse folding from millions of predicted structures","volume-title":"International Conference on Machine Learning","author":"Hsu","year":"2022"},{"key":"2025040101491113300_ref29","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1126\/science.add2187","article-title":"Robust deep learning based protein sequence design using ProteinMPNN","volume":"378","author":"Dauparas","year":"2022","journal-title":"Science (New York, NY)"},{"key":"2025040101491113300_ref30","article-title":"PiFold: toward effective and efficient protein inverse folding","volume-title":"The Eleventh International Conference on Learning Representations","author":"Gao","year":"2023"},{"key":"2025040101491113300_ref31","article-title":"Intrinsic-extrinsic convolution and pooling for learning on 3D protein structures","volume-title":"International Conference on Learning Representations","author":"Hermosilla","year":"2021"},{"key":"2025040101491113300_ref32","article-title":"Continuous-discrete convolution for geometry-sequence modeling in proteins","volume-title":"The Eleventh International Conference on Learning Representations","author":"Fan","year":"2022"},{"key":"2025040101491113300_ref33","article-title":"On the bottleneck of graph neural networks and its practical implications","volume-title":"International Conference on Learning Representations","author":"Alon","year":"2021"},{"key":"2025040101491113300_ref34","article-title":"Understanding over-squashing and bottlenecks on graphs via curvature","volume-title":"International Conference on Learning Representations","author":"Topping","year":"2022"},{"key":"2025040101491113300_ref35","doi-asserted-by":"publisher","first-page":"bpae043","DOI":"10.1093\/biomethods\/bpae043","article-title":"Multimodal pretraining for unsupervised protein representation learning","volume":"9","author":"Nguyen","year":"2024","journal-title":"Biol Methods Protoc"},{"key":"2025040101491113300_ref36","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with alphafold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2025040101491113300_ref37","doi-asserted-by":"publisher","first-page":"5852","DOI":"10.1038\/s41598-021-85274-7","article-title":"Antibody design using LSTM based deep generative model from phage display library for affinity maturation","volume":"11","author":"Saka","year":"2021","journal-title":"Sci Rep"},{"key":"2025040101491113300_ref38","doi-asserted-by":"publisher","first-page":"2031482","DOI":"10.1080\/19420862.2022.2031482","article-title":"In silico proof of principle of machine learning-based antibody design at unconstrained scale","volume":"14","author":"Akbar","year":"2022","journal-title":"MAbs"},{"key":"2025040101491113300_ref39","article-title":"Multi-objective molecule generation using interpretable substructures","volume-title":"International Conference on Machine Learning","author":"Jin","year":"2020"},{"key":"2025040101491113300_ref40","doi-asserted-by":"crossref","DOI":"10.1101\/2022.11.14.516404","article-title":"Incorporating pre-training paradigm for antibody sequence-structure co-design","volume-title":"arXiv preprint arXiv:2211.08406","author":"Gao","year":"2022"},{"key":"2025040101491113300_ref41","article-title":"Conditional antibody design as 3D equivariant graph translation","volume-title":"The Eleventh International Conference on Learning Representations","author":"Kong","year":"2023"},{"key":"2025040101491113300_ref42","first-page":"35037","article-title":"Abode: ab initio antibody design using conjoined odes","volume-title":"International Conference on Machine Learning","author":"Verma","year":"2023"},{"key":"2025040101491113300_ref43","first-page":"15222","article-title":"Cross-gate MLP with protein complex invariant embedding is a one-shot antibody designer","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Cheng","year":"2024"},{"key":"2025040101491113300_ref44","doi-asserted-by":"publisher","first-page":"e1006112","DOI":"10.1371\/journal.pcbi.1006112","article-title":"RosettaAntibodyDesign (RAbD): a general framework for computational antibody design","volume":"14","author":"Adolf-Bryfogle","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"2025040101491113300_ref45","article-title":"Antibody-antigen docking and design via hierarchical structure refinement","volume-title":"International Conference on Machine Learning","author":"Jin","year":"2022"},{"key":"2025040101491113300_ref46","article-title":"End-to-end full-atom antibody design","volume-title":"International Conference on Machine Learning","author":"Kong","year":"2023"},{"key":"2025040101491113300_ref47","first-page":"13266","article-title":"Representing long-range context for graph neural networks with global attention","volume":"34","author":"Wu","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"2025040101491113300_ref48","doi-asserted-by":"publisher","first-page":"e1011330","DOI":"10.1371\/journal.pcbi.1011330","article-title":"SPIN-CGNN: improved fixed backbone protein design with contact map-based graph construction and contact graph neural network","volume":"19","author":"Zhang","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2025040101491113300_ref49","doi-asserted-by":"crossref","DOI":"10.1101\/2023.02.03.526917","article-title":"Structure-informed language models are protein designers","volume-title":"International Conference on Machine Learning","author":"Zheng","year":"2023"},{"key":"2025040101491113300_ref50","doi-asserted-by":"crossref","first-page":"bbae146","DOI":"10.1093\/bib\/bbae146","article-title":"SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition","volume":"25","author":"Wang","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025040101491113300_ref51","doi-asserted-by":"crossref","DOI":"10.1145\/3534678.3539441","article-title":"GBPNet: universal geometric representation learning on protein structures","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Aykent","year":"2022"},{"key":"2025040101491113300_ref52","doi-asserted-by":"publisher","first-page":"W382","DOI":"10.1093\/nar\/gki387","article-title":"The FoldX web server: an online force field","volume":"33","author":"Schymkowitz","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2025040101491113300_ref53","doi-asserted-by":"publisher","first-page":"100939","DOI":"10.1016\/j.isci.2020.100939","article-title":"MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions","volume":"23","author":"Zhang","year":"2020","journal-title":"iScience"},{"key":"2025040101491113300_ref54","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1038\/s42256-020-0149-6","article-title":"A topology-based network tree for the prediction of protein\u2013protein binding affinity changes following mutation","volume":"2","author":"Wang","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2025040101491113300_ref55","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009284","article-title":"Deep geometric representations for modeling effects of mutations on protein-protein binding affinity","volume":"17","author":"Liu","year":"2020","journal-title":"PLoS Comput Biol"},{"key":"2025040101491113300_ref56","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2122954119","article-title":"Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization","volume":"119","author":"Shan","year":"2022","journal-title":"Proc Natl Acad Sci"},{"key":"2025040101491113300_ref57","doi-asserted-by":"publisher","first-page":"33509","DOI":"10.1038\/srep33509","article-title":"ProQ3: improved model quality assessments using Rosetta energy terms","volume":"6","author":"Uziela","year":"2016","journal-title":"Sci Rep"},{"key":"2025040101491113300_ref58","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.1093\/bioinformatics\/btw819","article-title":"ProQ3D: improved model quality assessments using deep learning","volume":"33","author":"Uziela","year":"2017","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref59","doi-asserted-by":"publisher","first-page":"W437","DOI":"10.1093\/nar\/gkz367","article-title":"VoroMQA web server for assessing three-dimensional structures of proteins and protein complexes","volume":"47","author":"Olechnovi\u010d","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025040101491113300_ref60","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.1093\/bioinformatics\/btz122","article-title":"Protein model quality assessment using 3D oriented convolutional neural networks","volume":"35","author":"Pag\u00e8s","year":"2019","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref61","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1038\/s41467-021-21511-x","article-title":"Improved protein structure refinement guided by deep learning based accuracy estimation","volume":"12","author":"Hiranuma","year":"2021","journal-title":"Nat Commun"},{"key":"2025040101491113300_ref62","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1093\/bioinformatics\/btac056","article-title":"DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning","volume":"38","author":"Guo","year":"2022","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref63","doi-asserted-by":"publisher","first-page":"238101","DOI":"10.1103\/PhysRevLett.129.238101","article-title":"State-of-the-art estimation of protein model accuracy using alphafold","volume":"129","author":"Roney","year":"2022","journal-title":"Phys Rev Lett"},{"key":"2025040101491113300_ref64","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41419-020-03314-y","article-title":"Structure, function, and pathology of protein O-glucosyltransferases","volume":"12","author":"Mehboob","year":"2021","journal-title":"Cell Death Dis"},{"key":"2025040101491113300_ref65","doi-asserted-by":"publisher","first-page":"5147","DOI":"10.1021\/cr3000994","article-title":"The amyloid beta peptide: a chemist\u2019s perspective. Role in Alzheimer\u2019s and fibrillization","volume":"112","author":"Hamley","year":"2012","journal-title":"Chem Rev"},{"key":"2025040101491113300_ref66","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1038\/s41467-020-14889-7","article-title":"Nanobody-enabled monitoring of kappa opioid receptor states","volume":"11","author":"Che","year":"2020","journal-title":"Nat Commun"},{"key":"2025040101491113300_ref67","doi-asserted-by":"publisher","first-page":"2396","DOI":"10.1038\/s41467-021-22731-x","article-title":"Structural studies of phosphorylation-dependent interactions between the V2R receptor and arrestin-2","volume":"12","author":"He","year":"2021","journal-title":"Nat Commun"},{"key":"2025040101491113300_ref68","article-title":"Learning protein sequence embeddings using information from structure","volume-title":"International Conference on Learning Representations","author":"Bepler","year":"2019"},{"key":"2025040101491113300_ref69","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat Methods"},{"key":"2025040101491113300_ref70","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1038\/s41587-022-01618-2","article-title":"Large language models generate functional protein sequences across diverse families","volume":"41","author":"Madani","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2025040101491113300_ref71","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1002\/prot.25278","article-title":"VoroMQA: assessment of protein structure quality using interatomic contact areas","volume":"85","author":"Olechnovi\u010d","year":"2017","journal-title":"Proteins"},{"key":"2025040101491113300_ref72","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1002\/prot.22030","article-title":"Fast protein tertiary structure retrieval based on global surface shape similarity","volume":"72","author":"Sael","year":"2008","journal-title":"Proteins"},{"key":"2025040101491113300_ref73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-10-407","article-title":"Protein-protein docking using region-based 3D Zernike descriptors","volume":"10","author":"Venkatraman","year":"2009","journal-title":"BMC Bioinformatics"},{"key":"2025040101491113300_ref74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2043-3","article-title":"Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction","volume":"19","author":"Daberdaku","year":"2018","journal-title":"BMC Bioinformatics"},{"article-title":"Lightweight contrastive protein structure-sequence transformation","year":"2023","author":"Zheng","key":"2025040101491113300_ref75"},{"article-title":"Contrastive representation learning for 3D protein structures","year":"2022","author":"Hermosilla","key":"2025040101491113300_ref76"},{"key":"2025040101491113300_ref77","doi-asserted-by":"publisher","first-page":"D1140","DOI":"10.1093\/nar\/gkt1043","article-title":"SAbDab: the structural antibody database","volume":"42","author":"Dunbar","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2025040101491113300_ref78","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.1002\/prot.24620","article-title":"Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles","volume":"82","author":"Li","year":"2014","journal-title":"Proteins"},{"key":"2025040101491113300_ref79","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1021\/acs.jcim.0c00043","article-title":"DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet","volume":"60","author":"Qi","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2025040101491113300_ref80","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/S0022-2836(05)80134-2","article-title":"SCOP: a structural classification of proteins database for the investigation of sequences and structures","volume":"247","author":"Murzin","year":"1995","journal-title":"J Mol Biol"},{"key":"2025040101491113300_ref81","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1093\/bioinformatics\/btx780","article-title":"DeepSF: deep convolutional neural network for mapping protein sequences to folds","volume":"34","author":"Jie","year":"2017","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref82","doi-asserted-by":"publisher","first-page":"D482","DOI":"10.1093\/nar\/gky1114","article-title":"SIFTS: updated structure integration with function, taxonomy and sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins","volume":"47","author":"Jose","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2025040101491113300_ref83","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1093\/nar\/gki524","article-title":"TM-align: a protein structure alignment algorithm based on the TM-score","volume":"33","author":"Zhang","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2025040101491113300_ref84","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/nmeth.2340","article-title":"A large-scale evaluation of computational protein function prediction","volume":"10","author":"Radivojac","year":"2013","journal-title":"Nat Methods"},{"key":"2025040101491113300_ref85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3019-7","article-title":"HH-suite3 for fast remote homology detection and deep protein annotation","volume":"20","author":"Steinegger","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2025040101491113300_ref86","doi-asserted-by":"publisher","first-page":"W276","DOI":"10.1093\/nar\/gkac240","article-title":"Search and sequence analysis tools services from EMBL-EBI in 2022","volume":"50","author":"Madeira","year":"2022","journal-title":"Nucleic Acids Res"},{"volume-title":"Is transfer learning necessary for protein landscape prediction?","year":"2020","author":"Shanehsazzadeh","key":"2025040101491113300_ref87"},{"key":"2025040101491113300_ref88","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"International Conference on Learning Representations","author":"Kipf","year":"2017"},{"key":"2025040101491113300_ref89","article-title":"Graph attention networks","volume-title":"International Conference on Learning Representations","author":"Veli\u010dkovi\u0107","year":"2018"},{"article-title":"Edge contraction pooling for graph neural networks","year":"2019","author":"Diehl","key":"2025040101491113300_ref90"},{"key":"2025040101491113300_ref91","doi-asserted-by":"publisher","first-page":"3460","DOI":"10.1093\/bioinformatics\/btv398","article-title":"Functional classification of CATH superfamilies: a domain-based approach for protein function annotation","volume":"31","author":"Das","year":"2015","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref92","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","article-title":"DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier","volume":"34","author":"Kulmanov","year":"2018","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref93","doi-asserted-by":"crossref","first-page":"31865","DOI":"10.1038\/srep31865","article-title":"FFPred 3: feature-based function prediction for all gene ontology domains","volume":"6","author":"Cozzetto","year":"2016","journal-title":"Sci Rep"},{"key":"2025040101491113300_ref94","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1002\/pro.2829","article-title":"Ab-bind: antibody binding mutational database for computational affinity predictions","volume":"25","author":"Sirin","year":"2016","journal-title":"Protein Sci"},{"key":"2025040101491113300_ref95","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.jmb.2016.11.022","article-title":"BindProfX: assessing mutation-induced binding affinity change by protein interface profiles with pseudo-counts","volume":"429","author":"Xiong","year":"2017","journal-title":"J Mol Biol"},{"key":"2025040101491113300_ref96","article-title":"mCSM-PPI2: predicting the effects of mutations on protein-protein interactions","volume":"47","author":"Rodrigues","year":"2019","journal-title":"Nuclc Acids Res"},{"key":"2025040101491113300_ref97","doi-asserted-by":"publisher","first-page":"2600","DOI":"10.1093\/bioinformatics\/bts489","article-title":"SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models","volume":"28","author":"Moal","year":"2012","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref98","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1093\/bioinformatics\/bty635","article-title":"SKEMPI 2.0: an updated benchmark of changes in protein\u2013protein binding energy, kinetics and thermodynamics upon mutation","volume":"35","author":"Jankauskaite","year":"2018","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref99","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1093\/bioinformatics\/btg224","article-title":"PISCES: a protein sequence culling server","volume":"19","author":"Wang","year":"2003","journal-title":"Bioinformatics"},{"key":"2025040101491113300_ref100","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1016\/j.str.2013.08.005","article-title":"High-resolution comparative modeling with RosettaCM","volume":"21","author":"Song","year":"2013","journal-title":"Structure"},{"key":"2025040101491113300_ref101","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1073\/pnas.1914677117","article-title":"Improved protein structure prediction using predicted interresidue orientations","volume":"117","author":"Yang","year":"2020","journal-title":"Proc Natl Acad Sci"},{"key":"2025040101491113300_ref102","doi-asserted-by":"crossref","DOI":"10.1038\/nbt.3988","article-title":"MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets","volume":"35","author":"Steinegger","year":"2017","journal-title":"Nat Biotechnol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf120\/62822310\/bbaf120.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf120\/62822310\/bbaf120.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T01:54:15Z","timestamp":1743472455000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf120\/8101508"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":102,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf120","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,3]]},"article-number":"bbaf120"}}