A comparison between internal protein nanoenvironments of α-helices and β-sheets

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).

SCOT: Rethinking the classification of secondary structure elements

Motivation Secondary structure classification is one of the most important issues in structure-based analyses due to its impact on secondary structure prediction, structural alignment and protein visualization. There are still open challenges concerning helix and sheet assignments which are currently not addressed by a single multi-purpose software. Results We introduce SCOT (Secondary structure Classification On Turns) as a novel secondary structure element assignment software which supports the assignment of turns, right-handed α-, 310- and π-helices, left-handed α- and 310-helices, 2.27- and polyproline II helices, β-sheets and kinks. We demonstrate that the introduction of helix Purity values enables a clear differentiation between helix classes. SCOT’s unique strengths are highlighted by comparing it to six state-of-the-art methods (DSSP, STRIDE, ASSP, SEGNO, DISICL and SHAFT). The assignment approaches were compared concerning geometric consistency, protein structure quality and flexibility dependency and their impact on secondary structure element-based structural alignments. We show that only SCOT’s combination of hydrogen bonds, geometric criteria and dihedral angles enables robust assignments independent of the structure quality and flexibility. We demonstrate that this combination and the elaborate kink detection lead to SCOT’s clear superiority for protein alignments. As the resulting helices and strands are provided in a PDB conform output format, they can immediately be used for structure alignment algorithms. Taken together, the application of our new method and the straight-forward visualization using the accompanying PyMOL scripts enable the comprehensive analysis of regular backbone geometries in proteins. Availability and implementation https://this-group.rocks Supplementary information Supplementary data are available at Bioinformatics online.

ACCURATE DISCRIMINATION OF OUTER MEMBRANE PROTEINS USING SECONDARY STRUCTURE ELEMENT ALIGNMENT AND SUPPORT VECTOR MACHINE

Outer membrane proteins (OMPs) play critical roles in many cellular processes and discriminating OMPs from other types of proteins is very important for OMPs identification in bacterial genomic proteins. In this study, a method SSEA_SVM is developed using secondary structure element alignment and support vector machine. Moreover, a novel kernel function is designed to utilize secondary structure information in the support vector machine classifier. A benchmark dataset, which consists of 208 OMPs, 673 globular proteins, and 206 α-helical membrane proteins, is used to evaluate the performance of SSEA_SVM. A high accuracy of 97.7% with 0.926 MCC is achieved while SSEA_SVM is applied to discriminating OMPs and non-OMPs. In comparison with existing methods in the literature, SSEA_SVM is also highly competitive. We suggest that SSEA_SVM is a much more promising method to identify OMPs in genomic proteins. A web server that implements SSEA_SVM is freely available at http://bioinfo.tmmu.edu.cn/SSEA_SVM/ .