Host-Pathogen Interaction Prediction via Multi-Stage Filtering
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Solution Overview
Problem
Current methods for predicting host-pathogen protein-protein interactions (HPIs) suffer from high false-positive rates, limited experimental data, and the difficulty in handling low-complexity regions of pathogen proteins, such as those from Plasmodium falciparum, making it challenging to identify effective therapeutic targets.
Innovation Solution
A system and method utilizing a database-driven approach with a biological knowledge-based filter, domain-based statistical filter, and an extreme gradient boosting (XGBoost) model to predict HPIs, which includes a positive dataset of known interactions and a negative dataset of non-interacting proteins, significantly reducing false positives by using sequence composition and domain information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large-scale experimental HPI detection methods like tandem affinity purification and yeast two-hybrid experiments are used, then a larger picture of host-pathogen interactions can be obtained, but false-negative and false-positive error rates increase significantly along with high cost and time consumption
Solution Approach 1:
The patent uses domain information as an intermediary to bridge the gap between limited experimental HPI data and comprehensive interaction prediction. By analyzing domain-domain interaction patterns from known HPIs and applying them to predict unknown interactions, the system achieves high accuracy without requiring extensive experimental validation of each individual interaction.
Solution Approach 2:
The patent creates a computational model that copies the interaction patterns observed in known HPIs. By training machine learning algorithms on experimental HPI data and domain composition information, the system replicates the underlying biological interaction rules to predict new HPIs, avoiding the need for direct experimental testing of every possible interaction pair.
2Productivity
If computational approaches use limited experimentally known HPIs or homology-based approaches to generate models, then predictions can be made with scarce data, but false positives increase due to reliance on intra-species PPIs and domain information alone
Solution Approach 1:
The patent applies local quality by focusing analysis on specific domain regions rather than treating entire proteins uniformly. By examining domain-domain interaction patterns and domain composition at the local level, the system identifies specific interaction motifs that are characteristic of true HPIs, thereby improving prediction accuracy and reducing false positives from homology-based approaches.
Solution Approach 2:
The patent combines multiple types of information (experimentally known HPIs, domain composition data, domain-domain interaction patterns, and sequence features) to create a composite predictive model. This multi-faceted approach leverages the strengths of different data sources while compensating for their individual weaknesses, achieving high reliability even with limited experimental data.
Data Source
AI summary
Pathogens invade and infect humans. Understanding the infection mechanism is essential for determining targets for new therapeutics. Existing methods provide too many false positive results. A method and system for predicting protein-protein interaction between a host and a pathogen has been provided. The disclosure provides a pipeline for predicting HPIs, which is a combination of biological knowledge-based filters, domain-based filter and sequence-based predictions. Biologically feasible interactions are only possible when both the proteins share common localization and overlapping expression profiles. This observation was used as the first filter to remove biologically irrelevant HPIs. Proteins interact with each other through domains. Both interacting and non-interacting protein pairs provide valuable information about the probability of protein-protein interactions and hence both were used to derive statistical inferences to remove improbable HPIs. Finally, sequence composition of known interacting pairs of HPIs were used to train an XGBoost model and filter the most probable HPIs.