Figueiredo Neto Manoel, Figueiredo Marxa L
Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, 625 Harrison St, West Lafayette, IN 47904, United States.
Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, 625 Harrison St, West Lafayette, IN 47904, United States.
J Theor Biol. 2016 Nov 21;409:11-17. doi: 10.1016/j.jtbi.2016.08.036. Epub 2016 Aug 27.
We have utilized hidden Markov models using HMMER software to predict and generate putative strong secretory signal peptide sequences for directing efficient secretion of cytokines from skeletal muscle for therapeutic applications. The results show that this approach can analyze signal sequences of a skeletal muscle secretome dataset and classify them, emitting new sequences that are strong candidate skeletal muscle-enriched signal peptides. The emitted signal peptides also were analyzed for their hydropathy and secondary structure profiles as compared to native signal peptides. The emitted signal peptides had a higher degree of hydropathy and helical composition relative to native sequences, which may suggest that these new sequences may hold promize for promoting enhanced secretion of proteins including cytokines or propeptides from skeletal muscle.
我们利用HMMER软件中的隐马尔可夫模型来预测并生成假定的强分泌信号肽序列，以指导细胞因子从骨骼肌中高效分泌用于治疗应用。结果表明，这种方法能够分析骨骼肌分泌蛋白质组数据集的信号序列并对其进行分类，同时生成新的序列，这些新序列是富含骨骼肌的信号肽的有力候选者。与天然信号肽相比，还对生成的信号肽的亲水性和二级结构特征进行了分析。相对于天然序列，生成的信号肽具有更高程度的亲水性和螺旋结构，这可能表明这些新序列在促进包括细胞因子或前肽在内的蛋白质从骨骼肌中增强分泌方面具有潜力。
