Protein super-secondary structure and quaternary structure topology: theoretical description and application

Prediction of the Stability of Protein Substructures Using AI/ML Techniques

This chapter explores the innovative application of machine learning techniques to understand and predict the stability of protein substructures. Accurately identifying stable substructures within proteins necessitates incorporating the local context, crucial for elucidating the roles of supersecondary structures. This approach emphasizes the importance of contextual information in understanding the stability and functionality of protein regions, thereby providing a more comprehensive view of protein mechanics and interactions. The chapter focuses on our findings regarding the DnaK Hsp70 chaperone protein, utilizing it as a case study. This research highlights how context-dependent physico-chemical features derived from protein sequences can accurately classify residues into stable and unstable substructures by leveraging logistic regression, random forest, and support vector machine methods. The findings represent a pivotal step towards the rational design of proteins with tailored properties, offering new insights into protein engineering and the fundamental principles underpinning protein supersecondary structures.

Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences enjoys high levels of use and finds numerous applications in the development of a broad range of other bioinformatics tools. Numerous sequence-based predictors of SS and SSS were developed and published in recent years. We survey and analyze 45 SS predictors that were released since 2018, focusing on their inputs, predictive models, scope of their prediction, and availability. We also review 32 sequence-based SSS predictors, which primarily focus on predicting coiled coils and beta-hairpins and which include five methods that were published since 2018. Substantial majority of these predictive tools rely on machine learning models, including a variety of deep neural network architectures. They also frequently use evolutionary sequence profiles. We discuss details of several modern SS and SSS predictors that are currently available to the users and which were published in higher impact venues.

How protein topology controls allosteric regulations

Allostery is an important regulatory mechanism of protein functions. Among allosteric proteins, certain protein structure types are more observed. However, how allosteric regulation depends on protein topology remains elusive. In this study, we extracted protein topology graphs at the fold level and found that known allosteric proteins mainly contain multiple domains or subunits and allosteric sites reside more often between two or more domains of the same fold type. Only a small fraction of fold-fold combinations are observed in allosteric proteins, and homo-fold-fold combinations dominate. These analyses imply that the locations of allosteric sites including cryptic ones depend on protein topology. We further developed TopoAlloSite, a novel method that uses the kernel support vector machine to predict the location of allosteric sites on the overall protein topology based on the subgraph-matching kernel. TopoAlloSite successfully predicted known cryptic allosteric sites in several allosteric proteins like phosphopantothenoylcysteine synthetase, spermidine synthase, and sirtuin 6, demonstrating its power in identifying cryptic allosteric sites without performing long molecular dynamics simulations or large-scale experimental screening. Our study demonstrates that protein topology largely determines how its function can be allosterically regulated, which can be used to find new druggable targets and locate potential binding sites for rational allosteric drug design.

Circuit topology predicts pathogenicity of missense mutations

The contact topology of a protein determines important aspects of the folding process. The topological measure of contact order has been shown to be predictive of the rate of folding. Circuit topology is emerging as another fundamental descriptor of biomolecular structure, with predicted effects on the folding rate. We analyze the residue‐based circuit topological environments of 21 K mutations labeled as pathogenic or benign. Multiple statistical lines of reasoning support the conclusion that the number of contacts in two specific circuit topological arrangements, namely inverse parallel and cross relations, with contacts involving the mutated residue have discriminatory value in determining the pathogenicity of human variants. We investigate how results vary with residue type and according to whether the gene is essential. We further explore the relationship to a number of structural features and find that circuit topology provides nonredundant information on protein structures and pathogenicity of mutations. Results may have implications for the polymer physics of protein folding and suggest that “local” topological information, including residue‐based circuit topology and residue contact order, could be useful in improving state‐of‐the‐art machine learning algorithms for pathogenicity prediction.

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.

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

Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.