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Albumin-Binding Domains in Therapeutic Protein Engineering: A Structural and Computational Perspective on Rational Design. 治疗性蛋白工程中的白蛋白结合域：理性设计的结构和计算视角。

SynBio Pub Date : 2026-03-01 Epub Date: 2026-02-12 DOI: 10.3390/synbio4010005

Matthew J Argyle, Dallin M Chipman, Anna Claire Woolley, Bradley C Bundy, Dennis Della Corte

{"title":"Albumin-Binding Domains in Therapeutic Protein Engineering: A Structural and Computational Perspective on Rational Design.","authors":"Matthew J Argyle, Dallin M Chipman, Anna Claire Woolley, Bradley C Bundy, Dennis Della Corte","doi":"10.3390/synbio4010005","DOIUrl":"10.3390/synbio4010005","url":null,"abstract":"Therapeutic proteins face a critical pharmacokinetic challenge: rapid clearance from circulation limits their clinical efficacy. Albumin-binding domains (ABDs) offer an elegant solution by enabling therapeutic proteins to \"hitchhike\" on serum albumin's favorable 19-day half-life through FcRn-mediated recycling. Clinical validation through approved therapeutics like ozoralizumab demonstrates the success of this approach, with preclinical studies showing fusion to an ABD extended half-life to 18 days. This review provides an analysis of ABD-fusion protein design, integrating structural biology, computational prediction, and rational engineering principles. We catalog the major classes of albumin-binding modalities, including bacterial three-helix bundle domains, engineered peptides, antibody-derived binders, and alternative scaffolds, comparing their binding properties, size contributions, cross-species reactivity, and production cost. Critical examination of linker architectures reveals that flexible glycine-serine linkers (particularly the widely successful (GGGGS)3 motif) provide optimal balance between domain independence and molecular economy, though linker choice profoundly influences not only spatial separation but also binding affinity, folding, stability, and pharmacokinetics. We evaluate the utility and limitations of the structure prediction tools for ABD-fusion design. We establish practical guidelines for integrating computational screening with experimental validation. This review provides protein engineers and synthetic biologists with a comprehensive framework for rational design of albumin-binding therapeutics, emphasizing the synergistic integration of structural insight, computational prediction, and systematic experimental validation to accelerate development of next-generation long-acting biotherapeutics.","PeriodicalId":524142,"journal":{"name":"SynBio","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChronoSort: Revealing Hidden Dynamics in AlphaFold3 Structure Predictions. ChronoSort：揭示隐藏在AlphaFold3结构预测中的动态。

SynBio Pub Date : 2025-12-01 Epub Date: 2025-11-14 DOI: 10.3390/synbio3040018

Matthew J Argyle, William P Heaps, Corbyn Kubalek, Spencer S Gardiner, Bradley C Bundy, Dennis Della Corte

{"title":"ChronoSort: Revealing Hidden Dynamics in AlphaFold3 Structure Predictions.","authors":"Matthew J Argyle, William P Heaps, Corbyn Kubalek, Spencer S Gardiner, Bradley C Bundy, Dennis Della Corte","doi":"10.3390/synbio3040018","DOIUrl":"10.3390/synbio3040018","url":null,"abstract":"Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD (r = 0.53 to 0.84). Principal component analysis reveals that AF3 predictions capture the same collective motion patterns observed in molecular dynamics trajectories, with eigenvector similarities significantly exceeding random distributions. ChronoSort trajectories exhibit structural evolution profiles comparable to MD. These findings suggest that modern AI-based structure prediction tools encode conformational flexibility information that can be systematically extracted without expensive MD. We provide ChronoSort as open-source software to enable broad community adoption. This work offers a novel approach to extracting functional insights from structure prediction tools in minutes, with significant implications for synthetic biology, protein engineering, drug discovery, and structure-function studies.","PeriodicalId":524142,"journal":{"name":"SynBio","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13459805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148715266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0