{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:53:14Z","timestamp":1787500394398,"version":"build-2736575974"},"reference-count":73,"publisher":"American Chemical Society (ACS)","issue":"12","license":[{"start":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T00:00:00Z","timestamp":1600387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T00:00:00Z","timestamp":1600387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T00:00:00Z","timestamp":1600387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R35GM124952"],"award-info":[{"award-number":["R35GM124952"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence\u2013structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence\u2013structure data. Data, source codes, and trained models are available at https:\/\/github.com\/Shen-Lab\/gcWGAN<\/jats:p>","DOI":"10.1021\/acs.jcim.0c00593","type":"journal-article","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T09:39:15Z","timestamp":1600421955000},"page":"5667-5681","source":"Crossref","is-referenced-by-count":65,"title":["De Novo Protein Design for Novel Folds Using Guided\nConditional Wasserstein Generative Adversarial Networks"],"prefix":"10.1021","volume":"60","author":[{"given":"Mostafa","family":"Karimi","sequence":"first","affiliation":[{"name":"Texas A&M University , , , ,","place":["College\nStation, Texas, United States, 77843"]},{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77840"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaowen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College\nStation, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Cao","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College\nStation, Texas, United States, 77843"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1703-7796","authenticated-orcid":true,"given":"Yang","family":"Shen","sequence":"additional","affiliation":[{"name":"Texas A&M University , , , ,","place":["College\nStation, Texas, United States, 77843"]},{"name":"Texas A&M University , , , ,","place":["College Station, Texas, United States, 77840"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2020,9,18]]},"reference":[{"key":"2026081804365982700_cit1","volume-title":"Essential Cell Biology","author":"Alberts","year":"2015"},{"key":"2026081804365982700_cit2","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1016\/S0021-9258(19)73943-X","article-title":"The reversible reduction of disulfide\nbonds in polyalanyl ribonuclease","volume":"237","author":"Anfinsen","year":"1962","journal-title":"J. Biol. Chem."},{"key":"2026081804365982700_cit3","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1126\/science.181.4096.223","article-title":"Principles\nthat govern the folding of protein chains","volume":"181","author":"Anfinsen","year":"1973","journal-title":"Science"},{"key":"2026081804365982700_cit4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1126\/science.1065659","article-title":"Protein structure prediction\nand structural genomics","volume":"294","author":"Baker","year":"2001","journal-title":"Science"},{"key":"2026081804365982700_cit5","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1038\/301200a0","article-title":"Molecular\ntechnology: designing proteins and peptides","volume":"301","author":"Pabo","year":"1983","journal-title":"Nature"},{"key":"2026081804365982700_cit6","doi-asserted-by":"publisher","first-page":"R105","DOI":"10.1016\/S0969-2126(99)80062-8","article-title":"Computational protein\ndesign","volume":"7","author":"Street","year":"1999","journal-title":"Structure"},{"key":"2026081804365982700_cit7","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1073\/pnas.0805923106","article-title":"Identification\nof direct residue contacts in protein\u2013protein interaction by\nmessage passing","volume":"106","author":"Weigt","year":"2009","journal-title":"Proc. Natl. Acad. Sci. U. S.\nA."},{"key":"2026081804365982700_cit8","doi-asserted-by":"publisher","first-page":"15674","DOI":"10.1073\/pnas.1314045110","article-title":"Assessing the utility of coevolution-based\nresidue\u2013residue contact predictions in a sequence-and structure-rich\nera","volume":"110","author":"Kamisetty","year":"2013","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"2026081804365982700_cit9","doi-asserted-by":"publisher","first-page":"3128","DOI":"10.1093\/bioinformatics\/btu500","article-title":"CCMpred\ue5f8fast\nand precise prediction\nof protein residue\u2013residue contacts from correlated mutations","volume":"30","author":"Seemayer","year":"2014","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit10","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1002\/prot.24374","article-title":"One contact\nfor every twelve residues allows robust and accurate topology-level\nprotein structure modeling","volume":"82","author":"Kim","year":"2014","journal-title":"Proteins: Struct.,\nFunct., Genet."},{"key":"2026081804365982700_cit11","doi-asserted-by":"publisher","first-page":"e03430","DOI":"10.7554\/eLife.03430","article-title":"Sequence co-evolution\ngives 3D contacts and structures of protein complexes","volume":"3","author":"Hopf","year":"2014","journal-title":"eLife"},{"key":"2026081804365982700_cit12","doi-asserted-by":"publisher","first-page":"e02030","DOI":"10.7554\/eLife.02030","article-title":"Robust and accurate prediction of\nresidue\u2013residue interactions across protein interfaces using\nevolutionary information","volume":"3","author":"Ovchinnikov","year":"2014","journal-title":"eLife"},{"key":"2026081804365982700_cit13","doi-asserted-by":"publisher","first-page":"e1005324","DOI":"10.1371\/journal.pcbi.1005324","article-title":"Accurate de novo prediction\nof protein contact map by ultra-deep learning model","volume":"13","author":"Wang","year":"2017","journal-title":"PLoS Comput. Biol."},{"key":"2026081804365982700_cit14","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved protein structure prediction using potentials\nfrom deep learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"2026081804365982700_cit15","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1038\/nature11600","article-title":"Principles for designing ideal protein\nstructures","volume":"491","author":"Koga","year":"2012","journal-title":"Nature"},{"key":"2026081804365982700_cit16","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.sbi.2016.03.006","article-title":"Algorithms for protein design","volume":"39","author":"Gainza","year":"2016","journal-title":"Curr. Opin. Struct. Biol."},{"key":"2026081804365982700_cit17","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1093\/protein\/15.10.779","article-title":"Protein design is NP-hard","volume":"15","author":"Pierce","year":"2002","journal-title":"Protein\nEng., Des. Sel."},{"key":"2026081804365982700_cit18","first-page":"87","article-title":"OSPREY: Protein Design with Ensembles, Flexibility,\nand Provable Algorithms","volume-title":"Methods in Enzymology","author":"Gainza","year":"2013"},{"key":"2026081804365982700_cit19","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1093\/bioinformatics\/btt374","article-title":"A new framework\nfor computational\nprotein design through cost function network optimization","volume":"29","author":"Traore\u0301","year":"2013","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit20","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1089\/cmb.2015.0188","article-title":"COMETS (Constrained Optimization\nof Multistate Energies\nby Tree Search): A provable and efficient protein design algorithm\nto optimize binding affinity and specificity with respect to sequence","volume":"23","author":"Hallen","year":"2016","journal-title":"J. Comput. Biol."},{"key":"2026081804365982700_cit21","doi-asserted-by":"publisher","first-page":"i811","DOI":"10.1093\/bioinformatics\/bty564","article-title":"iCFN: an efficient exact algorithm for multistate protein design","volume":"34","author":"Karimi","year":"2018","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit22","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1093\/bioinformatics\/bti144","article-title":"Solving and analyzing\nside-chain\npositioning problems using linear and integer programming","volume":"21","author":"Kingsford","year":"2005","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit23","doi-asserted-by":"publisher","first-page":"i214","DOI":"10.1093\/bioinformatics\/btn168","article-title":"A computational framework to empower\nprobabilistic\nprotein design","volume":"24","author":"Fromer","year":"2008","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit24","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1002\/pro.5560030405","article-title":"De novo\nprotein design using pairwise potentials and a genetic algorithm","volume":"3","author":"Jones","year":"1994","journal-title":"Protein Sci."},{"key":"2026081804365982700_cit25","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/B978-0-12-381270-4.00019-6","article-title":"Rosetta3: An Object-Oriented Software Suite for the\nSimulation and Design of Macromolecules","volume-title":"Computer Methods, Part C","author":"Leaver-Fay","year":"2011"},{"key":"2026081804365982700_cit26","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/nature19946","article-title":"The coming\nof age of de novo protein\ndesign","volume":"537","author":"Huang","year":"2016","journal-title":"Nature"},{"key":"2026081804365982700_cit27","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1126\/science.aah7389","article-title":"Principles for designing proteins\nwith cavities formed by curved \u03b2 sheets","volume":"355","author":"Marcos","year":"2017","journal-title":"Science"},{"key":"2026081804365982700_cit28","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1126\/science.aau3775","article-title":"De novo design of self-assembling\nhelical protein filaments","volume":"362","author":"Shen","year":"2018","journal-title":"Science"},{"key":"2026081804365982700_cit29","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1038\/s41586-018-0509-0","article-title":"De novo design of a\nfluorescence-activating \u03b2-barrel","volume":"561","author":"Dou","year":"2018","journal-title":"Nature"},{"key":"2026081804365982700_cit30","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/s41586-018-0830-7","article-title":"De novo design of potent and selective mimics of IL-2 and IL-15","volume":"565","author":"Silva","year":"2019","journal-title":"Nature"},{"key":"2026081804365982700_cit31","doi-asserted-by":"publisher","first-page":"16189","DOI":"10.1038\/s41598-018-34533-1","article-title":"Design\nof metalloproteins and novel\nprotein folds using variational autoencoders","volume":"8","author":"Greener","year":"2018","journal-title":"Sci. Rep."},{"key":"2026081804365982700_cit32","doi-asserted-by":"crossref","unstructured":"Strokach, A.; Becerra, D.; Corbi-Verge, C.; Perez-Riba, A.; Kim, P.\n          Fast and Flexible Design of Novel\nProteins Using Graph Neural Networks; bioRxiv, 2020.","DOI":"10.1101\/868935"},{"key":"2026081804365982700_cit33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24760-x","article-title":"Computational protein design with\ndeep learning neural networks","volume":"8","author":"Wang","year":"2018","journal-title":"Sci. Rep."},{"key":"2026081804365982700_cit34","unstructured":"Anand, N.; Huang, P.\n          Generative\nModeling for Protein\nStructures. In Advances in Neural Information\nProcessing Systems 31; Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R., Eds.; 32nd Conference on Neural Information Processing Systems;\nMontreal, Canada, 2018; pp 7494\u20137505."},{"key":"2026081804365982700_cit35","unstructured":"Ingraham, J.; Garg, V.; Barzilay, R.; Jaakkola, T.\n          Generative\nModels for Graph-Based Protein Design In Advances\nin Neural Information Processing Systems 32; Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R., Eds.; 33rd Conference\non Neural Information Processing Systems; Vancouver, Canada, 2019; pp 15820\u201315831."},{"key":"2026081804365982700_cit36","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1021\/acs.jcim.0c00043","article-title":"DenseCPD: Improving the Accuracy of Neural-Network-Based\nComputational Protein Sequence Design with DenseNet","volume":"60","author":"Qi","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"2026081804365982700_cit37","unstructured":"Anand, N.; Eguchi, R. R.; Derry, A.; Altman, R. B.; Huang, P.\n          Protein Sequence\nDesign\nwith a Learned Potential; bioRxiv, 2020."},{"key":"2026081804365982700_cit38","unstructured":"Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y.\n          Generative Adversarial\nNets. In Advances in Neural Information Processing\nSystems 27; Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D., Weinberger, K. Q., Eds.; 28th Annual Conference on Neural Information Processing Systems;\nMontreal, Canada, 2014; pp 2672\u20132680."},{"key":"2026081804365982700_cit39","unstructured":"Kingma, D. P.; Welling, M.\n          Auto-Encoding Variational\nBayes; arXiv:1312.6114, 2013."},{"key":"2026081804365982700_cit40","unstructured":"Gupta, A.; Zou, J.\n          Feedback GAN (FBGAN) for DNA: A Novel\nFeedback-Loop Architecture for Optimizing Protein Functions; arXiv:1804.01694, 2018,."},{"key":"2026081804365982700_cit41","unstructured":"Killoran, N.; Lee, L.\nJ.; Delong, A.; Duvenaud, D.; Frey, B. J.\n          Generating and Designing DNA with\nDeep Generative Models; arXiv:1712.06148, 2017,."},{"key":"2026081804365982700_cit42","doi-asserted-by":"publisher","first-page":"e1006176","DOI":"10.1371\/journal.pcbi.1006176","article-title":"Solving the RNA design problem with\nreinforcement learning","volume":"14","author":"Eastman","year":"2018","journal-title":"PLoS Comput. Biol."},{"key":"2026081804365982700_cit43","doi-asserted-by":"publisher","first-page":"eaap7885","DOI":"10.1126\/sciadv.aap7885","article-title":"Deep reinforcement\nlearning for de\nnovo drug design","volume":"4","author":"Popova","year":"2018","journal-title":"Sci. Adv."},{"key":"2026081804365982700_cit44","unstructured":"Guimaraes, G. L.; Sanchez-Lengeling, B.; Outeiral, C.; Farias, P. L. C.; Aspuru-Guzik, A.\n          Objective-Reinforced\nGenerative Adversarial Networks (ORGAN) for Sequence Generation Models; arXiv:1705.10843, 2017."},{"key":"2026081804365982700_cit45","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1021\/acs.jcim.7b00414","article-title":"Recurrent Neural Network Model for\nConstructive Peptide Design","volume":"58","author":"Muller","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"2026081804365982700_cit46","doi-asserted-by":"publisher","first-page":"D475","DOI":"10.1093\/nar\/gky1134","article-title":"SCOPe:\nclassification of large macromolecular\nstructures in the structural classification of proteins\ue5f8extended\ndatabase","volume":"47","author":"Chandonia","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2026081804365982700_cit47","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1146\/annurev-biophys-083012-130432","article-title":"On the universe of\nprotein folds","volume":"42","author":"Kolodny","year":"2013","journal-title":"Annu. Rev. Biophys."},{"key":"2026081804365982700_cit48","volume-title":"Structural\nBioinformatics","author":"Gu","year":"2009"},{"key":"2026081804365982700_cit49","unstructured":"Arjovsky, M.; Chintala, S.; Bottou, L.\n          Wasserstein Generative Adversarial\nNetworks. In Proceedings of the 34th International\nConference on Machine Learning, Sydney,  Australia, 2017; pp 214\u2013223."},{"key":"2026081804365982700_cit50","unstructured":"Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; Courville, A. C.\n          Improved Training of Wasserstein GANs. In Advances in Neural Information Processing Systems; von Luxburg, U., Guyon, I., Bengio, S., Wallach, H., Fergus, R., Eds.; 31st Conference on Neural Information\nProcessing Systems; Long Beach, CA, USA, 2017; pp 5767\u20135777."},{"key":"2026081804365982700_cit51","unstructured":"Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; Chen, X.\n          Improved Techniques\nfor Training GANs. In Advances in Neural Information\nProcessing Systems; Lee, D. D., von Luxburg, U., Garnett, R., Sugiyama, M., Guyon, I., Eds.; 30th Conference on Neural Information\nProcessing Systems; Barcelonma, Spain, 2016; pp 2234\u20132242."},{"key":"2026081804365982700_cit52","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.ins.2019.10.014","article-title":"Conditional Wasserstein generative adversarial network-gradient penalty-based\napproach to alleviating imbalanced data classification","volume":"512","author":"Zheng","year":"2020","journal-title":"Inf. Sci."},{"key":"2026081804365982700_cit53","doi-asserted-by":"publisher","first-page":"2535","DOI":"10.1109\/EMBC.2018.8512865","article-title":"EEG data augmentation for emotion recognition using\na conditional\nWasserstein GAN","author":"Luo","year":"2018","journal-title":"2018 40th Annual International\nConference of the IEEE Engineering in Medicine and Biology Society\n(EMBC)"},{"key":"2026081804365982700_cit54","unstructured":"Mei, Q.; Gu\u0308l, M.\n          A Conditional\nWasserstein\nGenerative Adversarial Network for Pixel-level Crack Detection using\nVideo Extracted Images; arXiv:1907.06014, 2019."},{"key":"2026081804365982700_cit55","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1186\/s13638-018-1196-0","article-title":"Improved Wasserstein\nconditional generative adversarial network speech\nenhancement","volume":"2018","author":"Qin","year":"2018","journal-title":"EURASIP Journal on Wireless Communications\nand Networking"},{"key":"2026081804365982700_cit56","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1093\/bioinformatics\/btx780","article-title":"DeepSF: deep\nconvolutional neural\nnetwork for mapping protein sequences to folds","volume":"34","author":"Hou","year":"2018","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit57","doi-asserted-by":"publisher","first-page":"e1000205","DOI":"10.1371\/journal.pbio.1000205","article-title":"Exploration\nof uncharted regions\nof the protein universe","volume":"7","author":"Jaroszewski","year":"2009","journal-title":"PLoS Biol."},{"key":"2026081804365982700_cit58","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/BFb0020217","article-title":"Kernel\nprincipal component analysis","volume":"1327","author":"Scho\u0308lkopf","year":"1997","journal-title":"Neural Networks"},{"key":"2026081804365982700_cit59","doi-asserted-by":"publisher","first-page":"2386","DOI":"10.1073\/pnas.2628030100","article-title":"A global representation\nof the protein fold space","volume":"100","author":"Hou","year":"2003","journal-title":"Proc. Natl. Acad.\nSci. U. S. A."},{"key":"2026081804365982700_cit60","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","article-title":"UniRef\nclusters: a comprehensive and scalable alternative for improving sequence\nsimilarity searches","volume":"31","author":"Suzek","year":"2015","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit61","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1093\/bioinformatics\/btp163","article-title":"Biopython: freely\navailable Python tools for computational molecular biology and bioinformatics","volume":"25","author":"Cock","year":"2009","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit62","doi-asserted-by":"publisher","first-page":"3174","DOI":"10.1093\/nar\/22.15.3174","article-title":"Hydrophobicity, expressivity and\naromaticity are the\nmajor trends of amino-acid usage in 999 Escherichia coli chromosome-encoded\ngenes","volume":"22","author":"Lobry","year":"1994","journal-title":"Nucleic Acids Res."},{"key":"2026081804365982700_cit63","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0022-2836(82)90515-0","article-title":"A simple\nmethod for displaying the hydropathic character\nof a protein","volume":"157","author":"Kyte","year":"1982","journal-title":"J. Mol. Biol."},{"key":"2026081804365982700_cit64","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/0003-2697(78)90790-X","article-title":"Isoelectric points\nof proteins: a table","volume":"86","author":"Malamud","year":"1978","journal-title":"Anal. Biochem."},{"key":"2026081804365982700_cit65","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1038\/127663b0","article-title":"The molecular weights\nof proteins","volume":"127","author":"Astbury","year":"1931","journal-title":"Nature"},{"key":"2026081804365982700_cit66","doi-asserted-by":"publisher","first-page":"W379","DOI":"10.1093\/nar\/gkz388","article-title":"NetGO: improving large-scale protein function prediction with massive\nnetwork information","volume":"47","author":"You","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"2026081804365982700_cit67","doi-asserted-by":"publisher","first-page":"15107","DOI":"10.1038\/s41598-018-33219-y","article-title":"GOGO: An improved algorithm\nto measure the semantic similarity between\ngene ontology terms","volume":"8","author":"Zhao","year":"2018","journal-title":"Sci. Rep."},{"key":"2026081804365982700_cit68","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1126\/science.aan0693","article-title":"Global analysis of protein folding using massively parallel design,\nsynthesis, and testing","volume":"357","author":"Rocklin","year":"2017","journal-title":"Science"},{"key":"2026081804365982700_cit69","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.jsb.2018.10.010","article-title":"Asymmetric\nprotein design from conserved\nsupersecondary structures","volume":"204","author":"ElGamacy","year":"2018","journal-title":"J. Struct. Biol."},{"key":"2026081804365982700_cit70","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1093\/nar\/gki524","article-title":"TM-align: a protein\nstructure alignment algorithm based\non the TM-score","volume":"33","author":"Zhang","year":"2005","journal-title":"Nucleic Acids Res."},{"key":"2026081804365982700_cit71","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1093\/bioinformatics\/btq066","article-title":"How significant is\na protein structure similarity with TM-score =\n0.5?","volume":"26","author":"Xu","year":"2010","journal-title":"Bioinformatics"},{"key":"2026081804365982700_cit72","unstructured":"Yin, M.; Zhou, M.\n          Semi-Implicit\nVariational Inference; arXiv:1805.11183, 2018."},{"key":"2026081804365982700_cit73","unstructured":"Davidson, T. R.; Falorsi, L.; De Cao, N.; Kipf, T.; Tomczak, J. M.\n          Hyperspherical\nVariational Auto-Encoders; arXiv:1804.00891, 2018."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.0c00593","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/60\/12\/5667\/9618328\/ci0c00593.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/60\/12\/5667\/9618328\/ci0c00593.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:33:23Z","timestamp":1787045603000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/60\/12\/5667\/890741\/De-Novo-Protein-Design-for-Novel-Folds-Using"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,18]]},"references-count":73,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,9,18]]},"published-print":{"date-parts":[[2020,12,28]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.0c00593","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/769919","asserted-by":"object"}]},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,18]]}}}