Elucidating Common Structural Features of Human Pathogenic Variations Using Large-Scale Atomic-Resolution Protein Networks

Abstract

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface “hot-spots,” but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

Comprehensive assessment of cancer missense mutation clustering in protein structures

“…They may occur preferentially at key residues in the 3D core of proteins, destabilizing them (14). Others may abolish specific molecular interactions and would tend to cluster at protein interaction interfaces (38,78). Indeed, our analyses identified enrichment of missense mutations in interaction interfaces of known tumor suppressors with their substrates (e.g., in PTEN, FBXW7, SPOP, STK11, VHL), with essential cocomplex partners (e.g., in PPP2R1A, PIK3R1, VHL) and with DNA (e.g., in TP53).…”

SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

“…The first release has been used as a basis for many further studies, including the development of energy functions [48], [46] which were subsequently implemented in the CCharPPI web server for characterising protein-protein interactions [49], as well as being used for ranking docked poses [45], [58], [6], [50]. SKEMPI has also been used to study human disease [56], [16], [55], assessing the role of dynamics on binding [69], exploring the conservation of binding regions [28], evaluating experimental affinity measurement methods [22], as well serving as a data source for models which predict dissociation rate changes upon mutation [1], pathological mutations [23], hotspot residues (e.g. [30], [44], [42], [66]) and changes in binding energy (e.g.…”

Docking features for predicting binding loss due to protein mutation

“…A recent structural SNPs survey by Das et al (2014) [42] found that variants at interaction interfaces tend to disrupt interactions of greater biophysical strength, compared to variants outside the interface. However, variants at interaction interfaces do not fall upon more highly conserved residues, compared to those outside.…”

“…The finding that greater buried surface area (positive ∆∆BSA) was characteristic of non-binders, yet is typically associated with higher binding affinity in experimental findings [42] also warrants deeper investigation (Figure 4). ∆∆BSA also contained the fewest outliers of any feature in the model (Figure 4, red plus marks), suggesting that binding redistribution is consistently different for the two classes (although p=.06).…”