We describe peptide mapping through Split Antibiotic Resistance Complementation (SpARC-map), a method to identify the probable interface between two interacting proteins. Our method is based on in vivo affinity selection inside a bacterial host and uses high-throughput DNA sequencing to infer probable protein–protein interaction (PPI) interfaces. SpARC-map uses only routine microbiology techniques, with no reliance on specialized instrumentation, dedicated reagents, or reconstituting protein complexes in vitro. SpARC-map can be tuned to detect PPIs over a broad range of affinities, multiplexed to probe multiple PPIs in parallel, and its nonspecific background can be precisely measured, enabling the sensitive detection of weak PPIs. Using SpARC-map, we recover known PPI interfaces in the p21–PCNA, p53–MDM2, and MYC–MAX complexes. We also use SpARC-map to probe the purinosome, the weakly bound complex of six purine biosynthetic enzymes, where no PPI interfaces are known. There, we identify interfaces that satisfy structural requirements for substrate channeling, as well as protein surfaces that participate in multiple distinct interactions, which we validate using site-specific photocrosslinking in live human cells. Finally, we show that SpARC-map results can impose stringent constraints on machine learning–based structure prediction.

We describe Structured Random Binding (SRB), a minimal model of protein-protein interactions rooted in the statistical physics of disordered systems. In this model, nonspecific binding is a generic consequence of the interaction between random proteins, exhibiting a phase transition from a high temperature state where nonspecific complexes are transient and lack well-defined interaction interfaces, to a low temperature state where the complex structure is frozen and a definite interaction interface is present. Numerically, weakly-bound nonspecific complexes can evolve into tightly-bound, highly specific complexes, but only if the structural correlation length along the peptide backbone is short; moreover, evolved tightly-bound homodimers favor the same interface structure that is predominant in real protein homodimers.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by the death of motor neurons leading to paralysis and death, generally 3–5 years post-symptom onset. The most frequent genetic cause of ALS is a hexanucleotide repeat expansion (HRE) in the chromosome 9 open reading frame 72 (C9orf72) gene, that has three major hypothesised pathological mechanisms including the production of dipeptide repeat proteins (DPRs). Our laboratory has previously identified purine metabolism dysfunction in induced neural progenitor cell-derived astrocytes (iAstrocytes) from C9orf72 ALS (C9-ALS) cases (C9-iAstrocytes), driven by loss of the enzyme adenosine deaminase (ADA). Here, we have demonstrated that loss of ADA along with changes to ecto-5′-nucleotidase and hypoxanthine-guanine phosphoribosyl transferase led to disruption in purine metabolite levels including purine dNTP output. These changes were recapitulated in patient CSF, whilst loss of ADA was recapitulated in patient white matter. Immunofluorescence also demonstrated purinosome formation dysfunction in C9-iAstrocytes. These changes are likely driven by DPRs as ADA loss was recapitulated in in vitro and in vivo DPR models. Finally, ADA levels could be recovered by reducing DPR levels either by inhibiting serine/arginine-rich splicing factor 1 or overexpressing RuvB-like 2. Our data demonstrate that DPR production negatively affects purine function in C9-ALS suggesting a potentially pivotal role for purine metabolism dysfunction in C9-ALS pathology.

We describe Structured Random Binding (SRB), a minimal model of protein-protein interactions rooted in the statistical physics of disordered systems. In this model, nonspecific binding is a generic consequence of the interaction between random proteins, exhibiting a phase transition from a high temperature state where nonspecific complexes are transient and lack well-defined interaction interfaces, to a low temperature state where the complex structure is frozen and a definite interaction interface is present. Numerically, weakly-bound nonspecific complexes can evolve into tightly-bound, highly specific complexes, but only if the structural correlation length along the peptide backbone is short; moreover, evolved tightly-bound homodimers favor the same interface structure that is predominant in real protein homodimers.

We describe peptide mapping through Sp lit A ntibiotic R esistance C omplementation (SpARC-map), a method to identify the probable interface between two interacting proteins. Our method is based on in vivo affinity selection inside a bacterial host, and uses high throughput DNA sequencing to infer the probable protein-protein interaction (PPI) interfaces. SpARC-map uses only routine microbiology techniques, with no reliance on specialized instrumentation, dedicated reagents, or reconstituting protein complexes in vitro. SpARC-map can be tuned to detect PPIs over a broad range of affinities, multiplexed to probe multiple PPIs in parallel, and its nonspecific background can be precisely measured, enabling the sensitive detection of weak PPIs. Using SpARC-map, we recover known PPI interfaces in the p21-PCNA, p53-MDM2, and MYC-MAX complexes. We also use SpARC-map to probe the purinosome, the weakly bound complex of six purine biosynthetic enzymes, where no PPI interfaces are known. There, we identify interfaces that satisfy structural requirements for substrate channeling, as well as protein surfaces that participate in multiple distinct interactions, which we validate using site-specific photocrosslinking in live human cells. Finally, we show that SpARC-map results can impose stringent constraints on machine learning based structure prediction. Significance Statement: Protein-protein interactions (PPIs) are vital to biological function, and the identity of the PPI interface can lend valuable insights on the structure/function of protein complexes. Identifying PPI interfaces using standard structural biology approaches, such as x-ray protein crystallography or cryo-electron microscopy, is technically challenging and require reconstituting protein complexes in vitro. As an alternative, SpARC-map is a sensitive and potentially high throughput method for identifying probable PPI interfaces, requiring only standard laboratory methods and straightforward bioinformatic analyses. Its accessibility gives SpARC-map the potential to be a tool of first resort for identifying PPIs and probable PPI interfaces, thereby facilitating scientific discovery across molecular and cellular biology.