Prospecting for pH sensors in host and pathogen systems

Human cells contain several types of membrane-enclosed compartments. Such compartmentalisation gives metabolic advantages, isolating regions (organelles) and processes within the cytoplasm of a cell interior, for example genome organisation, energy generation, and protein degradation. Intracellular organelles also allow unequal distributions of small molecules and ions to be set up by pumps made of proteins residing in the membranes.

Notably, gradients of pH (differences in the concentration of hydrogen ions) exist between several organelle interiors and the cytoplasm. Functioning of a cell depends on control of pH, as does its relationship, across the outer cell membrane, with the external environment. Two areas in which human health intersects with an understanding of pH are cancer growth and viral infection.

Metabolic changes in tumour cells lead to alteration in the balance of pH between cell interior and exterior. Separately, many viruses (including SARS-CoV-2) make use of a low pH organelle to release their genome into a host cell. In both of these diseases modern techniques are being used for large-scale data acquisition.

As a tumour grows, mutations accrue (called somatic mutations) that enable adaptation to the altered pH environment, and these are recorded (10,000s) and deposited in databases. Biochemical characterisation of SARS-CoV-2 is occurring at pace, including structural analysis with newly-developed techniques that are particularly suited to large assemblies such as viruses.

Our work addresses the question of how to make a model that predicts the key elements of pH response in biology, i.e. molecular pH sensors. For viruses this would allow prediction of which viruses use low the pH infection route, where their pH sensors are located, and potentially lead to new antiviral strategies. In cancer adaptation to pH, somatic mutations are known, but which directly interact with pH-relevant pathways is mostly unknown.

We aim to map mutation to atomic structure, and use the model to predict sites of pH sensing. Early work indicates that such a pipeline will reveal insights into metabolic changes in tumour growth, again with the potential to consider new therapeutic avenues. Predictions will be tested experimentally with a small number of exemplar systems.

The method is a synthesis of mostly available tools for making structural models and predicting pH-dependence from structure. Modellers have access to a hierarchy of techniques, based on their level of detail and computational resource required. Our model will be based in the so-called coarse-grained part of this hierarchy, where the level of detail is scaled back to allow for calculations that are fast enough to provide a quick turnaround, essential for a web application.

In this context, pH sensors are suited since they are focussed in a subset of amino acids in proteins. Our aim, through model development, benchmarking with known data, and testing in our own laboratory, is to enable any user, world-wide, to predict pH sensors in systems of interest to them. This approach will enable generation of testable hypotheses from the wealth of genomic and biochemical data being collected.