Algorithms for systematic identification of small subgraphs

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Introduction

Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation

In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement

Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology

We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results

We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion

In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.

Identifying Myc interactors

In this chapter, we discuss in detail two essential methods used to evaluate the interaction of Myc with another protein of interest: co-immunoprecipitation (Co-IP) and in vitro pull-down assays. Co-IP is a method that, by immunoaffinity, allows the identification of protein-protein interactions within cells. We provide methods to conduct Co-IPs from whole-cell extracts as well as cytoplasmic and nuclear-enriched fractions. By contrast, the pull-down assay evaluates whether a bait protein that is bound to a solid support can specifically interact with a prey protein that is in solution. We provide methods to conduct in vitro pull-downs and further detail how to use this assay to distinguish whether a protein-protein interaction is direct or indirect. We also discuss methods used to screen for Myc interactors and provide an in silico strategy to help prioritize hits for further validation using the described Co-IP and in vitro pull-down assays.

Large Scale Chemical Cross-linking Mass Spectrometry Perspectives

The spectacular heterogeneity of a complex protein mixture from biological samples becomes even more difficult to tackle when one’s attention is shifted towards different protein complex topologies, transient interactions, or localization of PPIs. Meticulous protein-by-protein affinity pull-downs and yeast-two-hybrid screens are the two approaches currently used to decipher proteome-wide interaction networks. Another method is to employ chemical cross-linking, which gives not only identities of interactors, but could also provide information on the sites of interactions and interaction interfaces. Despite significant advances in mass spectrometry instrumentation over the last decade, mapping Protein-Protein Interactions (PPIs) using chemical cross-linking remains time consuming and requires substantial expertise, even in the simplest of systems. While robust methodologies and software exist for the analysis of binary PPIs and also for the single protein structure refinement using cross-linking-derived constraints, undertaking a proteome-wide cross-linking study is highly complex. Difficulties include i) identifying cross-linkers of the right length and selectivity that could capture interactions of interest; ii) enrichment of the cross-linked species; iii) identification and validation of the cross-linked peptides and cross-linked sites.

In this review we examine existing literature aimed at the large-scale protein cross-linking and discuss possible paths for improvement. We also discuss short-length cross-linkers of broad specificity such as formaldehyde and diazirine-based photo-cross-linkers. These cross-linkers could potentially capture many types of interactions, without strict requirement for a particular amino-acid to be present at a given protein-protein interface. How these shortlength, broad specificity cross-linkers be applied to proteome-wide studies? We will suggest specific advances in methodology, instrumentation and software that are needed to make such a leap.