Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction

PSSNet-An Accurate Super-Secondary Structure for Protein Segmentation

A super-secondary structure (SSS) is a spatially unique ensemble of secondary structural elements that determine the three-dimensional shape of a protein and its function, rendering SSSs attractive as folding cores. Understanding known types of SSSs is important for developing a deeper understanding of the mechanisms of protein folding. Here, we propose a universal PSSNet machine-learning method for SSS recognition and segmentation. For various types of SSS segmentation, this method uses key characteristics of SSS geometry, including the lengths of secondary structural elements and the distances between them, torsion angles, spatial positions of Cα atoms, and primary sequences. Using four types of SSSs (βαβ-unit, α-hairpin, β-hairpin, αα-corner), we showed that extensive SSS sets could be reliably selected from the Protein Data Bank and AlphaFold 2.0 database of protein structures.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Using bioinformatic protein sequence similarity to investigate if SARS CoV-2 infection could cause an ocular autoimmune inflammatory reactions?

Although severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection have emerged globally, findings related to ocular involvement and reported cases are quite limited. Immune reactions against viral infections are closely related to viral and host proteins sequence similarity. Molecular Mimicry has been described for many different viruses; sequence similarities of viral and human tissue proteins may trigger autoimmune reactions after viral infections due to similarities between viral and human structures. With this study, we aimed to investigate the protein sequence similarity of SARS CoV-2 with retinal proteins and retinal pigment epithelium (RPE) surface proteins. Retinal proteins involved in autoimmune retinopathy and retinal pigment epithelium surface transport proteins were analyzed in order to infer their structural similarity to surface glycoprotein (S), nucleocapsid phosphoprotein (N), membrane glycoprotein (M), envelope protein (E), ORF1ab polyprotein (orf1ab) proteins of SARS CoV-2. Protein similarity comparisons, 3D protein structure prediction, T cell epitopes-MHC binding prediction, B cell epitopes-MHC binding prediction and the evaluation of the antigenicity of peptides assessments were performed. The protein sequence analysis was made using the Pairwise Sequence Alignment and the LALIGN program. 3D protein structure estimates were made using Swiss Model with default settings and analyzed with TM-align web server. T-cell epitope identification was performed using the Immune Epitope Database and Analysis (IEDB) resource Tepitool. B cell epitopes based on sequence characteristics of the antigen was performed using amino acid scales and HMMs with the BepiPred 2.0 web server. The predicted peptides/epitopes in terms of antigenicity were examined using the default settings with the VaxiJen v2.0 server. Analyses showed that, there is a meaningful similarities between 6 retinal pigment epithelium surface transport proteins (MRP-4, MRP-5, RFC1, SNAT7, TAUT and MATE) and the SARS CoV-2 E protein. Immunoreactive epitopic sites of these proteins which are similar to protein E epitope can create an immune stimulation on T cytotoxic and T helper cells and 6 of these 9 epitopic sites are also vaxiJen. These result imply that autoimmune cross-reaction is likely between the studied RPE proteins and SARS CoV-2 E protein. The structure of SARS CoV-2, its proteins and immunologic reactions against these proteins remain largely unknown. Understanding the structure of SARS CoV-2 proteins and demonstration of similarity with human proteins are crucial to predict an autoimmune response associated with immunity against host proteins and its clinical manifestations as well as possible adverse effects of vaccination.

RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites

Malonylation, which has recently emerged as an important lysine modification, regulates diverse biological activities and has been implicated in several pervasive disorders, including cardiovascular disease and cancer. However, conventional global proteomics analysis using tandem mass spectrometry can be time-consuming, expensive and technically challenging. Therefore, to complement and extend existing experimental methods for malonylation site identification, we developed two novel computational methods for malonylation site prediction based on random forest and deep learning machine learning algorithms, RF-MaloSite and DL-MaloSite, respectively. DL-MaloSite requires the primary amino acid sequence as an input and RF-MaloSite utilizes a diverse set of biochemical, physiochemical and sequence-based features. While systematic assessment of performance metrics suggests that both ‘RF-MaloSite’ and ‘DL-MaloSite’ perform well in all metrics tested, our methods perform particularly well in the areas of accuracy, sensitivity and overall method performance (assessed by the Matthew’s Correlation Coefficient). For instance, RF-MaloSite exhibited MCC scores of 0.42 and 0.40 using 10-fold cross-validation and an independent test set, respectively. Meanwhile, DL-MaloSite was characterized by MCC scores of 0.51 and 0.49 based on 10-fold cross-validation and an independent set, respectively. Importantly, both methods exhibited efficiency scores that were on par or better than those achieved by existing malonylation site prediction methods. The identification of these sites may also provide important insights into the mechanisms of crosstalk between malonylation and other lysine modifications, such as acetylation, glutarylation and succinylation. To facilitate their use, both methods have been made freely available to the research community at https://github.com/dukkakc/DL-MaloSite-and-RF-MaloSite.

In silico Prediction of Human Secretory Proteins in Plasma Based on Discrete Firefly Optimization and Application to Cancer Biomarkers Identification

The early control and prevention of cancer contributes effectively interventions and cancer therapies. Secretory protein, one of the richest biomarkers, is proved important as molecular signposts of the physiological state of a cell. In this work, we aim to propose a proteomic high-throughput technology platform to facilitate detection of early cancer by means of biomarkers that secreted into the bloodstream. We compile a new benchmark dataset of human secretory proteins in plasma. A series of sequence-derived features, which have been proved involved in the structure and function of the secretory proteins, are collected to mathematically encode these proteins. Considering the influence of potential irrelevant or redundant features, we introduce discrete firefly optimization algorithm to perform feature selection. We evaluate and compare the proposed method SCRIP (Secretory proteins in plasma) with state-of-the-art approaches on benchmark datasets and independent testing datasets. SCRIP achieves the average AUC values of 0.876 and 0.844 in five-fold the cross-validation and independent test, respectively. Besides that, we also test SCRIP on proteins in four types of cancer tissues and successfully detect 66∼77% potential cancer biomarkers.