A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.     

不同培养条件下酿酒酵母菌的转录组差异分析

从组学水平分析富硒条件下酿酒酵母菌(Saccharomyces cerevisiae)内在分子机制,为酿酒酵母菌富硒研究及富硒基因的挖掘利用提供理论依据。该研究以不加硒培养的酿酒酵母菌作为对照组Kb,以加20μg/mL硒培养的酿酒酵母菌为实验组Se,利用Illumina高通量测序平台对两组进行转录组测序,通过生物信息学方法对数据进行分析处理。结果表明,转录组测序共获得6 445个Unigenes,分别有1 401个(21.74%)、3 665个(56.87%)、5 630个(87.35%)、6 112个(94.83%)、6 077个(94.29%)、5 059个(78.49%) Unigenes被注释到GO、KEGG、COG、NR、Swiss Prot和Pfam数据库,共有6 150个(95.42%) Unigenes得到注释。在GO功能注释中,共得到41个GO功能小类,在KEGG代谢通路分析时,获得了113条KEGG通路。该转录组测序数据质量高,结果覆盖面广,为酿酒酵母菌富硒基因挖掘和研究提供了一定的理论参考。     

不同基因型丙型肝炎病毒E1、E2蛋白生物信息学分析

目的对不同基因型的丙型肝炎病毒（HCV）的E1、E2蛋白进行生物信息学比较分析,以找出重要的生物信息学数据。方法从Gene Bank中获取不同基因型HCV的E1、E2蛋白核苷酸与氨基酸序列,运用DNA Star,ClustalX,Bio Edit等国际通用的软件进行氨基酸和核苷酸的序列比对,计算核苷酸和氨基酸同源性。在线软件TMHMM v2.0分析E1、E2蛋白跨膜区。AntheProt5.0软件分析二级结构。结果 E1氨基酸起始于aa 193—aa196和终止于aa 382—aa 383,E2蛋白氨基酸起始于aa 384和终止于aa 744—aa 754。不同基因型间E1、E2蛋白基因核苷酸同源性为59.7%-77.0%,氨基酸同源性为60.6%-82.8%。E1、E2蛋白存在3个跨膜区：E1存在2个跨膜区,位于aa 273—aa 293和aa 363—aa 383;E2蛋白存在1个跨膜区,位于aa 723—aa 744。二级结构分析发现不同型HCV E1、E2蛋白富含α螺旋（21%-30%）,β折叠（26%-36%）和卷曲结构（43%-48%）。结论HCV E1、E2核苷酸和氨基酸序列表现为较大的异质性,其蛋白跨膜区富含α螺旋,胞外区以β折叠为主。     

AlphaFold2: A Role for Disordered Protein/Region Prediction?

The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs).