Structural similarity and classification of protein interaction interfaces

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

An Insight into Oligopeptide Transporter 3 (OPT3) Family Proteins

BACKGROUND

OPT3s are involved in the transport of Fe from xylem to phloem, in loading Fe into phloem, and in the transmission of shoot-to-root iron signaling. Yet, apart from Arabidopsis, little is known about these transporters'functions in other plant species.

OBJECTIVE

OPT3 proteins of several plant species were characterized using bioinformatical tools. Also, a probable Fe chelating protein, GSH, was used in docking analyses to shed light on the interactions of ligand binding sites of OPT3s.

METHODS

The multiple sequence alignment (MSA) analysis, protein secondary and tertiary structure analyses, molecular phylogeny analysis, transcription factor binding site analyses, co-expression and docking analyses were performed using up-to-date bioinformatical tools.

RESULTS

All OPT3s in this study appear to be transmembrane proteins. They appear to have broad roles and substrate specificities in different metabolic processes. OPT3 gene structures were highly conserved. Promoter analysis showed that bZIP, WRKY, Dof and AT-Hook Transcription factors (TFs) may regulate the expression of OPT3 genes. Consequently, they seemed to be taking part in both biotic and abiotic stress responses as well as growth and developmental processes.

CONCLUSION

The results showed that OPT3 proteins are involved in ROS regulation, plant stress responses, and basal pathogen resistance. They have species-specific roles in biological processes. Lastly, the transport of iron through OPT3s may occur with GSH according to the binding affinity results of the docking analyses.

Sequence and Structure Properties Uncover the Natural Classification of Protein Complexes Formed by Intrinsically Disordered Proteins via Mutual Synergistic Folding

Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that take into account the particular biological and biophysical properties of MSF complexes. Here, we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current protein structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions

Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.

PBSword: a web server for searching similar protein-protein binding sites

PBSword is a web server designed for efficient and accurate comparisons and searches of geometrically similar protein–protein binding sites from a large-scale database. The basic idea of PBSword is that each protein binding site is first represented by a high-dimensional vector of ‘visual words’, which characterizes both the global and local shape features of the binding site. It then uses a scalable indexing technique to search for those binding sites whose visual words representations are similar to that of the query binding site. Our system is able to return ranked results of binding sites in short time from a database of 194 322 domain–domain binding sites. PBSword supports query by protein ID and by new structures uploaded by users. PBSword is a useful tool to investigate functional connections among proteins based on the local structures of binding site and has potential applications to protein–protein docking and drug discovery. The system is hosted at http://pbs.rnet.missouri.edu.

Fast protein binding site comparisons using visual words representation

MOTIVATION

Finding geometrically similar protein binding sites is crucial for understanding protein functions and can provide valuable information for protein-protein docking and drug discovery. As the number of known protein-protein interaction structures has dramatically increased, a high-throughput and accurate protein binding site comparison method is essential. Traditional alignment-based methods can provide accurate correspondence between the binding sites but are computationally expensive.

RESULTS

In this article, we present a novel method for the comparisons of protein binding sites using a 'visual words' representation (PBSword). We first extract geometric features of binding site surfaces and build a vocabulary of visual words by clustering a large set of feature descriptors. We then describe a binding site surface with a high-dimensional vector that encodes the frequency of visual words, enhanced by the spatial relationships among them. Finally, we measure the similarity of binding sites by utilizing metric space operations, which provide speedy comparisons between protein binding sites. Our experimental results show that PBSword achieves a comparable classification accuracy to an alignment-based method and improves accuracy of a feature-based method by 36% on a non-redundant dataset. PBSword also exhibits a significant efficiency improvement over an alignment-based method.

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.