Network Analytics: Evolutionary Compression, Diffusion and the Action Equation for Mutations
Authors: Lichtarge Olivier 
Published: June 2021

Data integration is essential to overcome the current logjam of high throughput data and publications. Here, we show an approach that twins networks with evolution. In malaria, in p53 biology and in head and neck cancer, early studies demonstrate drug target identification and disease stratification. More broadly, these studies shed new light on the genotype‐phenotype relationship and on the impact of mutations, essential steps towards personalized medicine.The first example shows that structured data from gene interaction networks in hundreds of species can be compressed efficiently by eliminating redundant evolutionary relationships. This enables novel functional predictions, including that the essential, but functionally uncharacterized, Plasmodium falciparum antigen EXP1 is a membrane glutathione S‐transferase. We find that EXP1 is a GST that efficiently degrades cytotoxic hematin and which is potently inhibited by artesunate. Thus, EXP1 is a possible molecular target to a frontline antimalarial drug.A second example focuses on unstructured data from the literature. A Knowledge Integration Toolkit (KnIT) automatically and scalably mines an entire body of literature to create interaction networks. As proof of principle, KnIT read 25 million public PubMed abstracts to identify new protein kinases that phosphorylate the tumor suppressor p53. Focusing on a top candidate of pharmaceutical interest, we found that this protein phosphorylates p53 at Ser315, in vitro and in vivo, and functionally inhibits p53. By identifying a novel p53 inhibitory kinase, this study demonstrates that automated reasoning over the entire literature generates molecular hypotheses beyond human reach and that accelerate scientific discovery.The third example turns to the issue of personalizing the networks discussed above by quantifying the impact of individual mutational variations. The difficulty is that this impact depends on the unique context of each mutation, which is complex and often cryptic. Modeling evolution as a continuous and differentiable mapping between genotype to phenotype, yields a formal equation for the Evolutionary Action (EA) of coding mutations on fitness, the terms of which are readily computable from phylogenetic sequence analysis. Mutational, clinical, and population genetic evidence show this Evolutionary Action equation predicts the effect of point mutations in vivo and in vitro in diverse proteins, correlates disease‐causing gene mutations with morbidity, and determines the frequency of human coding polymorphisms, respectively. In practice, this work explicitly bridges molecular evolution with population genetics with applications from protein redesign to the clinical assessment of human genetic variations.Together these early studies suggest a broad integrative network formalism that unifies structured and unstructured data, and which can be personalized to an individual's relevant mutational variations.Support or Funding InformationThe author gratefully acknowledges support from NIH GM066099, GM79656, DE025181 and NSF1356569 and DARPA N66001‐15‐C‐4042.