Jinbo Xu

Toyota Technological Institute at Chicago

Affiliations

Toyota Technological Institute at Chicago

Jinbo Xu is a Senior Professor at the Toyota Technological Institute at Chicago. His research integrates bioinformatics, protein science, and deep learning, with a focus on protein structure prediction, biomolecular engineering, and computational biology. Recent work includes single-sequence protein structure prediction using protein language models, species-specific codon optimization, and ab initio protein modeling. He has published in the Proceedings of the National Academy of Sciences and Bioinformatics Advances, and his research on nucleic acid binding protein prediction and AI-driven statistical physics scoring highlights his contributions to computational biology.

Honors

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Jinbo Xu

Toyota Technological Institute at Chicago

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bioinformatics computational biology protein science molecular biology systems biology structural biology protein structure prediction biophysics deep learning protein bioinformatics protein modeling protein structure machine learning structural molecular biology biomolecular engineering machine learning research structural bioinformatics proteomics molecular informatics biomedical informatics biomolecular structure genomics proteome research scientific computing computational genomics molecular structure theoretical biology computational intelligence biomolecular structure prediction sequence alignment web server protein contact prediction deep transfer learning genome biology knowledge discovery sequence analysis interactomics protein language model nucleic acids integrative medicine medicinal research structural genomics cancer genetics genomic medicine cancer genomics medical genetics principal component analysis computational biophysics graphical models supervised learning variant interpretation neuroinformatics biological networks template-based protein structure global alignment human mutation clinical impact deep neural networks protein-protein interactions hybrid method accurate prediction multiomics connectomics primate behavior multi-protein assembly protein evolution protein assembly residual stress fragmentation effect structural integrity large scale structure structural composition raptorx web server ultra-deep learning model protein contact map functional orthology detection

Notable Scholarly Works

Notable Federal Grants

Collaborators

Protein Structure-Informed Deep Learning Enables Species-Specific Codon Optimization. bioRxiv (Cold Spring Harbor Laboratory) (2026)

Rescuing True Protein Binders From AI Hallucinations via Zero-Shot, Ensemble-Driven Statistical Physics Scoring. bioRxiv (Cold Spring Harbor Laboratory) (2026)

From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?. ArXiv.org (2025)

Quantamind MD Enables Protein Modeling With Ab Initio Accuracy. bioRxiv (Cold Spring Harbor Laboratory) (2025)

Accurate Prediction of Nucleic Acid Binding Proteins Using Protein Language Model. Bioinformatics Advances (2024)

Siwen WuJinbo XuJun-tao Guo

biomolecular engineeringnucleic acidsbioinformaticsprotein structure predictionaccurate predictionprotein sciencesequence analysissystems biologystructural bioinformaticscomputational biologyprotein language modelprotein modelingmolecular biologyprotein bioinformatics

Prediction of Single-Stranded Dna Binding Proteins With Protein Language Model. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2024)

Siwen WuJinbo XuJun-tao Guo

biomolecular structure predictionbioinformaticsprotein scienceprotein structuremolecular modellingbiomedical informaticssystems biologyprotein modelingsingle-stranded dnacomputational biologymolecular informaticsprotein bioinformaticsstructural biologybiomolecular engineeringprotein structure predictionstructural molecular biologybiomolecular structurestructural bioinformaticsprotein language modelmolecular biology

Single-Sequence Protein Structure Prediction by Integrating Protein Language Models. Proceedings of the National Academy of Sciences (2024)

An End-To-End Deep Learning Method for Protein Side-Chain Packing and Inverse Folding. Proceedings of the National Academy of Sciences (2023)

Antibody Humanization via Protein Language Model and Neighbor Retrieval. bioRxiv (Cold Spring Harbor Laboratory) (2023)

Honggang ZouRongqing YuanBoqiao LaiYang DouLi WeiJinbo Xu

antibody engineeringlinguisticsneighbor retrievalprotein language modelantibody humanization

Caid Prediction Portal: A Comprehensive Service for Predicting Intrinsic Disorder and Binding Regions in Proteins. Nucleic Acids Research (2023)

Alessio Del ConteAdel BouhraouaMahta MehdiabadiDamiano ClementelAlexander Miguel MonzonAlex S HolehouseDaniel GriffithRyan J EmeneckerAshwini PatilRonesh SharmaTatsuhiko TsunodaAlok SharmaYi Jun TangBin LiuClaudio MirabelloBjörn WallnerBurkhard RostDagmar IlzhöferMaria LittmannMichael HeinzingerLea I M KrautheimerMichael BernhoferLiam J McGuffinIsabelle CallebautTristan Bitard FeildelJian LiuJianlin ChengZhiye GuoJinbo XuSheng WangNawar MalhisJörg GsponerChol-Song KimKun-Sop HanMyong-Chol MaLukasz KurganSina GhadermarziAkila KatuwawalaBi ZhaoZhenling PengZhonghua WuGang HuKui WangMd Tamjidul HoqueMd Wasi Ul KabirMichele VendruscoloPietro SormanniMin LiFuhao ZhangPengzhen JiaYida WangMichail Yu LobanovOxana V GalzitskayaWim VrankenAdrián DíazThomas LitfinYaoqi ZhouJack HansonKuldip PaliwalZsuzsanna DosztányiGábor ErdősSilvio C E TosattoDamiano Piovesan

comprehensive serviceintrinsic disordercaid prediction portalprotein structure predictionbioinformaticsprotein sciencestructural bioinformaticsancillary servicemolecular biologystructural biologybinding regions