Regeneration and plasticity after spinal cord injury are limited by inhibitory proteoglycans; here, modulation of a receptor for proteoglycans in rats is shown to lead to functional recovery after injury. Functional recovery following severe spinal cord injury is often modest due to inhibited neural and axonal regeneration at the scar location. Glia-produced extracellular matrix components may be responsible for this reduced regeneration potential. Using an in vitro model of the inhibitory extracellular matrix that forms after spinal cord injury, Jerry Silver and colleagues identify a component on axons — protein tyrosine phosphatase σ (PTPσ) — that permanently adheres axonal growth cones to these inhibitory extracellular matrix molecules, rendering the axons unable to regrow. In rodent model studies, a peptide mimic of PTPσ can block the proteoglycan sites in the matrix, allowing axons to pass through the injured area and regrow, leading to functional recovery in both the locomotor and urinary systems. Contusive spinal cord injury leads to a variety of disabilities owing to limited neuronal regeneration and functional plasticity. It is well established that an upregulation of glial-derived chondroitin sulphate proteoglycans (CSPGs) within the glial scar and perineuronal net creates a barrier to axonal regrowth and sprouting1,2,3,4,5. Protein tyrosine phosphatase σ (PTPσ), along with its sister phosphatase leukocyte common antigen-related (LAR) and the nogo receptors 1 and 3 (NgR), have recently been identified as receptors for the inhibitory glycosylated side chains of CSPGs6,7,8. Here we find in rats that PTPσ has a critical role in converting growth cones into a dystrophic state by tightly stabilizing them within CSPG-rich substrates. We generated a membrane-permeable peptide mimetic of the PTPσ wedge domain that binds to PTPσ and relieves CSPG-mediated inhibition. Systemic delivery of this peptide over weeks restored substantial serotonergic innervation to the spinal cord below the level of injury and facilitated functional recovery of both locomotor and urinary systems. Our results add a new layer of understanding to the critical role of PTPσ in mediating the growth-inhibited state of neurons due to CSPGs within the injured adult spinal cord.

Purpose: The third-generation EGFR inhibitor, osimertinib, is the first mutant-selective inhibitor that has received regulatory approval for the treatment of patients with EGFR-mutant lung cancer. Despite the development of highly selective third-generation inhibitors, acquired resistance remains a significant clinical challenge. Recently, we and others have identified a novel osimertinib resistance mutation, G724S, which was not predicted in in vitro screens. Here, we investigate how G724S confers resistance to osimertinib. Experimental Design: We combine structure-based predictive modeling of G724S in combination with the 2 most common EGFR-activating mutations, exon 19 deletion (Ex19Del) and L858R, with in vitro drug-response models and patient genomic profiling. Results: Our simulations suggest that the G724S mutation selectively reduces osimertinib-binding affinity in the context of Ex19Del. Consistent with our simulations, cell lines transduced with Ex19Del/G724S demonstrate resistance to osimertinib, whereas cells transduced with L858R/G724S are sensitive to osimertinib. Subsequent clinical genomic profiling data further suggest G724S occurs with Ex19Del but not L858R. Furthermore, we demonstrate that Ex19Del/G724S retains sensitivity to afatinib, but not to erlotinib, suggesting a possible therapy for patients at the time of disease relapse. Conclusions: Altogether, these data suggest that G724S is an allele-specific resistance mutation emerging in the context of Ex19Del but not L858R. Our results fundamentally reframe the problem of targeted therapy resistance from one focused on the “drug–resistance mutation” pair to one focused on the “activating mutation–drug–resistance mutation” trio. This has broad implications across clinical oncology.

Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, computational and experimental methods capable of quantifying the energetic determinants of these equilibria are limited to systems of modest size. Recently, it has been demonstrated that the artificial intelligence system AlphaFold2 can be manipulated to produce structurally valid protein conformational ensembles. Here, we extend these studies and explore the extent to which AlphaFold2 contact distance distributions can approximate projections of the conformational Boltzmann distributions. For this purpose, we examine the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Our studies suggest that AlphaFold2 normalized contact distance distributions can correlate with conformation probabilities obtained with other methods but that they suffer from peak broadening. We also find that the AlphaFold2 contact distance distributions can be sensitive to point mutations. Overall, we anticipate that our findings will be valuable as the community seeks to model the thermodynamics of conformational changes in large biomolecular systems.

The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure–activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein–ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on http://meilerlab.org/.