New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data

Structural analysis of TrkA mutations in patients with congenital insensitivity to pain reveals PLCγ as an analgesic drug target

Chronic pain is a major health issue, and the search for new analgesics has become increasingly important because of the addictive properties and unwanted side effects of opioids. To explore potentially new drug targets, we investigated mutations in the NTRK1 gene found in individuals with congenital insensitivity to pain with anhidrosis (CIPA). NTRK1 encodes tropomyosin receptor kinase A (TrkA), the receptor for nerve growth factor (NGF) and that contributes to nociception. Molecular modeling and biochemical analysis identified mutations that decreased the interaction between TrkA and one of its substrates and signaling effectors, phospholipase Cγ (PLCγ). We developed a cell-permeable phosphopeptide derived from TrkA (TAT-pQYP) that bound the Src homology domain 2 (SH2) of PLCγ. In HEK-293T cells, TAT-pQYP inhibited the binding of heterologously expressed TrkA to PLCγ and decreased NGF-induced, TrkA-mediated PLCγ activation and signaling. In mice, intraplantar administration of TAT-pQYP decreased mechanical sensitivity in an inflammatory pain model, suggesting that targeting this interaction may be analgesic. The findings demonstrate a strategy to identify new targets for pain relief by analyzing the signaling pathways that are perturbed in CIPA.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.