Computational Biology Research Group, Embrapa Agricultural Informatics, Campinas, SP, Brazil.
Research Center on Biodiversity and Computing (BIOCOMP), Polytechnic School of the University of São Paulo (USP), São Paulo, Brazil.
PLoS One. 2020 Dec 30;15(12):e0244315. doi: 10.1371/journal.pone.0244315. eCollection 2020.
Secondary structure elements are generally found in almost all protein structures revealed so far. In general, there are more β-sheets than α helices found inside the protein structures. For example, considering the PDB, DSSP and Stride definitions for secondary structure elements and by using the consensus among those, we found 60,727 helices in 4,376 chains identified in all-α structures and 129,440 helices in 7,898 chains identified in all-α and α + β structures. For β-sheets, we identified 837,345 strands in 184,925 β-sheets located within 50,803 chains of all-β structures and 1,541,961 strands in 355,431 β-sheets located within 86,939 chains in all-β and α + β structures (data extracted on February 1, 2019). In this paper we would first like to address a full characterization of the nanoenvironment found at beta sheet locations and then compare those characteristics with the ones we already published for alpha helical secondary structure elements. For such characterization, we use here, as in our previous work about alpha helical nanoenvironments, set of STING protein structure descriptors. As in the previous work, we assume that we will be able to prove that there is a set of protein structure parameters/attributes/descriptors, which could fully describe the nanoenvironment around beta sheets and that appropriate statistically analysis will point out to significant changes in values for those parameters when compared for loci considered inside and outside defined secondary structure element. Clearly, while the univariate analysis is straightforward and intuitively understood, it is severely limited in coverage: it could be successfully applied at best in up to 25% of studied cases. The indication of the main descriptors for the specific secondary structure element (SSE) by means of the multivariate MANOVA test is the strong statistical tool for complete discrimination among the SSEs, and it revealed itself as the one with the highest coverage. The complete description of the nanoenvironment, by analogy, might be understood in terms of describing a key lock system, where all lock mini cylinders need to combine their elevation (controlled by a matching key) to open the lock. The main idea is as follows: a set of descriptors (cylinders in the key-lock example) must precisely combine their values (elevation) to form and maintain a specific secondary structure element nanoenvironment (a required condition for a key being able to open a lock).
二级结构元件通常存在于迄今为止发现的几乎所有蛋白质结构中。一般来说，蛋白质结构中发现的β-折叠比α-螺旋多。例如，考虑到 PDB、DSSP 和 Stride 对二级结构元件的定义，并使用这些定义的共识，我们在所有-α结构中鉴定的 4376 条链中发现了 60727 个螺旋，在所有-α和α+β结构中鉴定的 7898 条链中发现了 129440 个螺旋。对于β-折叠，我们在所有-β结构的 50803 条链中鉴定了 184925 个β-折叠的 837345 个链，在所有-β和α+β结构的 355431 个β-折叠中鉴定了 1541961 个链（数据于 2019 年 2 月 1 日提取）。在本文中，我们首先希望对β-折叠位置的纳米环境进行全面描述，然后将这些特征与我们已经发表的关于α-螺旋二级结构元件的特征进行比较。为此，我们在这里使用了与我们之前关于α-螺旋纳米环境的工作相同的 STING 蛋白质结构描述符集。与之前的工作一样，我们假设我们将能够证明存在一组蛋白质结构参数/属性/描述符，可以完全描述β-折叠周围的纳米环境，并且适当的统计分析将指出当比较定义的二级结构元件内部和外部的位置时，这些参数的值会发生显著变化。显然，虽然单变量分析是直接和直观的，但它的覆盖范围非常有限：它最多可以成功应用于研究案例的 25%。通过多元 MANOVA 检验对特定二级结构元件（SSE）的主要描述符的指示是在 SSE 之间进行完全区分的强大统计工具，并且它本身就是覆盖范围最高的工具。类似地，纳米环境的完整描述可以理解为描述一个关键锁系统，其中所有锁小圆柱需要结合它们的高度（由匹配的钥匙控制）来打开锁。主要思想如下：一组描述符（在钥匙锁示例中的小圆柱）必须精确地组合它们的值（高度），以形成和维持特定的二级结构元件纳米环境（钥匙能够打开锁的一个必要条件）。
