Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations

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.

Comparison of functional classification systems

In microbiome analysis, functional profiling is based on assigning reads or contigs to terms or nodes in a functional classification system. There are a number of large, general-purpose functional classifications that are in use, such as eggNOG, KEGG, InterPro and SEED. Smaller, special-purpose classifications include CARD, EC, MetaCyc and VFDB. Here, we compare the different classifications in terms of their overlap, redundancy, structure and assignment rates. We also provide mappings between main concepts in different classifications. For the large classifications, we find that eggNOG performs the best with respect to sequence redundancy and structure, SEED has the cleanest hierarchy, whereas KEGG and InterPro:BP might be more informative for medical applications. We illustrate the practical assignment rates for different classifications using a number of metagenomic samples.

Learning representations for gene ontology terms by jointly encoding graph structure and textual node descriptors

Measuring the semantic similarity between Gene Ontology (GO) terms is a fundamental step in numerous functional bioinformatics applications. To fully exploit the metadata of GO terms, word embedding-based methods have been proposed recently to map GO terms to low-dimensional feature vectors. However, these representation methods commonly overlook the key information hidden in the whole GO structure and the relationship between GO terms. In this paper, we propose a novel representation model for GO terms, named GT2Vec, which jointly considers the GO graph structure obtained by graph contrastive learning and the semantic description of GO terms based on BERT encoders. Our method is evaluated on a protein similarity task on a collection of benchmark datasets. The experimental results demonstrate the effectiveness of using a joint encoding graph structure and textual node descriptors to learn vector representations for GO terms.

SimExact – An Efficient Method to Compute Function Similarity Between Proteins Using Gene Ontology

Background:

The rapidly growing protein and annotation databases necessitate the development of efficient tools to process this valuable information. Biologists frequently need to find proteins similar to a given protein, for which BLAST tools are commonly used. With the development of biomedical ontologies, e.g. Gene Ontology, methods were designed to measure function (semantic) similarity between two proteins. These methods work well on protein pairs, but are not suitable for protein query processing.

Objective:

Our aim is to facilitate searching of similar proteins in an acceptable time.

Methods:

A novel method SimExact for high speed searching of functionally similar proteins has been proposed.

Results:

The experiments of this study show that SimExact gives correct results required for protein searching. A fully functional prototype of an online tool (www.datafurnish.com/protsem.php) has been provided that generates a ranked list of the proteins similar to a query protein, with a response time of less than 20 seconds in our setup. SimExact was used to search for protein pairs having high disparity between function similarity and sequence similarity.

Conclusion:

SimExact makes such searches practical, which would not be possible in a reasonable time otherwise.

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain

The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial. We present a collection of 21 benchmark data sets that aim at circumventing the difficulties in building benchmarks for large biomedical knowledge graphs by exploiting proxies for biomedical entity similarity. These data sets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, and explore proxy similarities calculated based on protein sequence similarity, protein family similarity, protein-protein interactions and phenotype-based gene similarity. Data sets have varying sizes and cover four different species at different levels of annotation completion. For each data set, we also provide semantic similarity computations with state-of-the-art representative measures. Database URL: https://github.com/liseda-lab/kgsim-benchmark.

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.