PathFams: statistical detection of pathogen-associated protein domains

Background

A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal.

Results

To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors.

Conclusions

We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at https://pathfams.uwaterloo.ca.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07982-8.

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Discovery of novel bacterial toxins by genomics and computational biology

Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes.

Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.

Common Amino Acid Subsequences in a Universal Proteome--Relevance for Food Science

A common subsequence is a fragment of the amino acid chain that occurs in more than one protein. Common subsequences may be an object of interest for food scientists as biologically active peptides, epitopes, and/or protein markers that are used in comparative proteomics. An individual bioactive fragment, in particular the shortest fragment containing two or three amino acid residues, may occur in many protein sequences. An individual linear epitope may also be present in multiple sequences of precursor proteins. Although recent recommendations for prediction of allergenicity and cross-reactivity include not only sequence identity, but also similarities in secondary and tertiary structures surrounding the common fragment, local sequence identity may be used to screen protein sequence databases for potential allergens in silico. The main weakness of the screening process is that it overlooks allergens and cross-reactivity cases without identical fragments corresponding to linear epitopes. A single peptide may also serve as a marker of a group of allergens that belong to the same family and, possibly, reveal cross-reactivity. This review article discusses the benefits for food scientists that follow from the common subsequences concept.