Experimental and Computational Models of Transport of Galectin-3 Through Glycosylated Matrix.
Experimental and Computational Models of Transport of Galectin-3 Through Glycosylated Matrix.
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DOI：
10.1007/s10439-022-02949-6
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发表时间：
2022-06
影响因子：
3.8
通讯作者：
Simmons, Chelsey S.
中科院分区：
文献类型：
--
作者：
Pineiro-Llanes, Janny;Rodriguez, Camille D.;Farhadi, Shaheen A.;Hudalla, Gregory A.;Sarntinoranont, Malisa;Simmons, Chelsey S.
细胞外基质（ECM）的改变是许多纤维增生性疾病（包括某些癌症）的标志。 ECM改变的富含聚糖成分的高发病率使得使用聚糖结合蛋白（例如Galectin-3（G3））是一种有希望的治疗策略。 ECM作为具有不同糖基化状态的丰富3D蛋白质网络的复杂性使得确定在改变的ECM环境中的聚糖结合蛋白的保留率具有挑战性。能够预测ECM改变的聚糖结合蛋白运输的计算模型可以使此类蛋白质的设计和测试和相关的新型治疗策略受益。但是，这样的计算模型需要许多动力学参数，这些参数无法从传统的2D药代动力学测定中估算。为了验证G3在3D ECM构建体中的转运性能，我们开发了一种物种传输模型，其中包括扩散和基质结合成分，以预测G3融合蛋白在富含聚糖的ECM中的保留。通过迭代地将我们的计算模型与实验结果进行比较，我们能够确定G3传输的强大计算模型的合理参数范围。我们预计建立数据驱动模型的总体方法可以转换为其他靶向ECM的治疗策略。
Altered extracellular matrix (ECM) production is a hallmark of many fibroproliferative diseases, including certain cancers. The high incidence of glycan-rich components within altered ECM makes the use of glycan-binding proteins such as Galectin-3 (G3) a promising therapeutic strategy. The complexity of ECM as a rich 3D network of proteins with varied glycosylation states makes it challenging to determine the retention of glycan-binding proteins in altered ECM environments. Computational models capable of predicting the transport of glycan-binding proteins in altered ECM can benefit the design and testing of such proteins and associated novel therapeutic strategies. However, such computational models require many kinetic parameters that cannot be estimated from traditional 2D pharmacokinetic assays. To validate transport properties of G3 in 3D ECM constructs, we developed a species transport model that includes diffusion and matrix-binding components to predict retention of G3 fusion proteins in glycan-rich ECM. By iteratively comparing our computational model to experimental results, we are able to determine a reasonable range of parameters for a robust computational model of G3 transport. We anticipate this overall approach to building a data-driven model is translatable to other ECM-targeting therapeutic strategies.
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DOI：
10.1074/jbc.c112.358002
发表时间：
2012-06-22
期刊：
The Journal of biological chemistry
影响因子：
--
作者：
Lepur A;Salomonsson E;Nilsson UJ;Leffler H
通讯作者：
Leffler H
影响因子：
3
作者：
Ochieng, J;Furtak, V;Lukyanov, P
通讯作者：
Lukyanov, P
影响因子：
1.2
作者：
Burkel, Brian;Morris, Brett A.;Keely, Patricia J.
通讯作者：
Keely, Patricia J.
影响因子：
5.1
作者：
Elsayed, Y.;Lekakou, C.;Tomlins, P.
通讯作者：
Tomlins, P.
影响因子：
4.3
作者：
Boecker, Sophia;Elling, Lothar
通讯作者：
Elling, Lothar