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Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

Mutations in RAS isoforms (KRAS, NRAS, and HRAS) are among the most frequent oncogenic alterations in many cancers, making these proteins high priority therapeutic targets. Effectively targeting RAS isoforms requires an exact understanding of their active, inactive, and druggable conformations. However, there is no structural catalog of RAS conformations to guide therapeutic targeting or examining the structural impact of RAS mutations. Here we present an expanded classification of RAS conformations based on analyses of the catalytic switch 1 (SW1) and switch 2 (SW2) loops. From 721 human KRAS, NRAS, and HRAS structures available in the Protein Data Bank (206 RAS–protein cocomplexes, 190 inhibitor-bound, and 325 unbound, including 204 WT and 517 mutated structures), we created a broad conformational classification based on the spatial positions of Y32 in SW1 and Y71 in SW2. Clustering all well-modeled SW1 and SW2 loops using a density-based machine learning algorithm defined additional conformational subsets, some previously undescribed. Three SW1 conformations and nine SW2 conformations were identified, each associated with different nucleotide states (GTP-bound, nucleotide-free, and GDP-bound) and specific bound proteins or inhibitor sites. The GTP-bound SW1 conformation could be further subdivided on the basis of the hydrogen bond type made between Y32 and the GTP γ-phosphate. Further analysis clarified the catalytic impact of G12D and G12V mutations and the inhibitor chemistries that bind to each druggable RAS conformation. Overall, this study has expanded our understanding of RAS structural biology, which could facilitate future RAS drug discovery. Significance: Analysis of >700 RAS structures helps define an expanded landscape of active, inactive, and druggable RAS conformations, the structural impact of common RAS mutations, and previously uncharacterized RAS inhibitor–binding modes.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94 respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines were shown to recall the physiological dimers with significantly higher accuracy than the non-physiological set, lending support for the pertinence of our benchmark dataset. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Amplification and/or overexpression of the PAK1 gene is common in several malignancies, and inhibition of PAK1 by small molecules has been shown to impede the growth and survival of such cells. Potent inhibitors of PAK1 and its close relatives, PAK2, and PAK3, have been described, but clinical development has been hindered by recent findings that PAK2 function is required for normal cardiovascular function in adult mice. A unique allosteric PAK1-selective inhibitor, NVS-PAK1-1, provides a potential path forward, but has relatively modest potency in cells. Here, we report the development of BJG-05-039, a PAK1-seletive degrader consisting of the allosteric PAK1 inhibitor NVS-PAK1-1 conjugated to lenalidomide, a recruiter of the E3 ubiquitin ligase substrate adaptor Cereblon (CRBN). BJG-05-039 induced degradation of PAK1, but not PAK2, and displayed enhanced anti-proliferative effects relative to its parent compound in PAK1-dependent, but not PAK2-dependent, cell lines. Notably, BJG-05-039 promoted sustained PAK1 degradation and inhibition of downstream signaling effects at ten-fold lower dosage than NVS-PAK1-1. Our findings suggest that selective PAK1 degradation may confer more potent pharmacological effects compared with catalytic inhibition and highlight the potential advantages of PAK1-targeted degradation.

While Ras/mitogen-activated protein kinase (MAPK) signaling is activated in most human cancers, attempts to target this pathway using kinase active site inhibitors have not typically led to durable, clinical benefit. To address this shortcoming, we sought to test the feasibility of an alternative targeting strategy, focused on the ERK2 substrate binding domains, D and DBP. We found that disabling the ERK2-DBP domain in mice caused baseline erythrocytosis. Consequently, we investigated the role of the ERK2-D and -DBP domains in disease, using a JAK2-dependent model of polycythemia vera (PV). Importantly, inactivation of the ERK2-DBP domain promoted the progression of disease from PV to myelofibrosis (MF), suggesting that the ERK2-DBP domain normally opposes progression. ERK2-DBP inactivation also prevented oncogenic JAK2 kinase (JAK2V617F) from promoting oncogene-induced senescence (OIS) in vitro. The ERK2-DBP mutation attenuated JAK2-mediated OIS by preventing the physical interaction of ERK2 with the transcription factor, Egr1. Because inactivation of the ERK2-DBP created a functional ERK2 kinase limited to binding substrates through its D-domain, these data suggested that the D-domain substrates were responsible for promoting oncogene-induced progenitor growth and tumor progression, and that pharmacologic targeting of the ERK2-D domain might attenuate cancer cell growth. Indeed, pharmacologic agents targeting the ERK2-D domain were effective in attenuating the growth of JAK2-dependent myeloproliferative neoplasm cell lines. Taken together, these data indicate that the ERK-D and -DBP domains can play distinct roles in the progression of neoplasms and that the D-domain has the potential to be potent therapeutic target in Ras/MAPK dependent cancers.

For many human cancers and tumor-associated diseases, mutations in the RAS isoforms (KRAS, NRAS, and HRAS) are the most common oncogenic alterations, making these proteins high-priority therapeutic targets. Effectively targeting the RAS isoforms requires an exact understanding of their active, inactive, and druggable conformations. However, there is no structure-guided catalogue of RAS conformations to guide therapeutic targeting or examining the structural impact of RAS mutations. We present an expanded classification of RAS conformations based on analyzing their catalytic switch 1 (SW1) and switch 2 (SW2) loops. From all 721 available human KRAS, NRAS, and HRAS structures in the Protein Data Bank (PDB) (206 RAS-protein complexes, 190 inhibitor-bound, and 325 unbound, including 204 WT and 517 mutated structures), we created a broad conformational classification based on the spatial positions of residue Y32 in SW1 and residue Y71 in SW2. Subsequently, we defined additional conformational subsets (some previously undescribed) by clustering all well modeled SW1 and SW2 loops using a density-based machine learning algorithm with a backbone dihedral-based distance metric. In all, we identified three SW1 conformations and nine SW2 conformations, each which are associated with different nucleotide states (GTP-bound, nucleotide-free, and GDP-bound) and specific bound proteins or inhibitor sites. The GTP-bound SW1 conformation can be further subdivided based on the hydrogen (H)-bond type made between residue Y32 and the GTP γ-phosphate: water-mediated, direct, or no H-bond. Further analyzing these structures clarified the catalytic impact of the G12D and G12V RAS mutations, and the inhibitor chemistries that bind to each druggable RAS conformation. To facilitate future RAS structural analyses, we have created a web database, called Rascore, presenting an updated and searchable dataset of human KRAS, NRAS, and HRAS structures in the PDB, and which includes a page for analyzing user uploaded RAS structures by our algorithm (http://dunbrack.fccc.edu/rascore/).SignificanceAnalyzing >700 experimentally determined RAS structures helped define an expanded landscape of active, inactive and druggable RAS conformations, the structural impact of common RAS mutations, and previously uncharacterized RAS-inhibitor binding modes.

224 Background: Driver mutations in the exonuclease domain (ExoD) of DNA polymerase epsilon ( POLE) have been associated with high tumor mutation burden (TMB). High TMB is an important marker for immunotherapy, and thus understanding the mechanisms that lead to high TMB is an area of active investigation. Many tumors with POLE ExoD driver mutations have additional POLE variants of uncertain significance (VUS). Some of these VUS lie within the ExoD, while most lie outside the ExoD. This study investigates the role of such POLE VUS on TMB and POLE functionality of ExoD-mutated tumors. Methods: Retrospective colorectal cancer (CRC) genomic profiles from Caris Life Sciences (n=1,870) were separated into groups based on the presence of POLE ExoD mutations and the TMB status. Groups were defined as: ‘TMB-high (TMB-H) POLE ExoD driver without additional POLE VUS’, ‘TMB-H POLE ExoD driver plus POLE VUS’, and ‘TMB-low (TMB-L) POLE variant(s) without ExoD driver’. An additional group with ‘TMB-H POLE variant(s) without ExoD driver’ was identified as potentially novel drivers for future study. The microsatellite instability status (MSI or MSS) was also considered. The level of TMB, the presence of polymerase proofreading-associated mutational signatures and POLE stability/functionality analyzed using AlphaFold2 models and Rosetta Molecular Modeling Suite, were evaluated. Results: POLE variants were identified in 4.9% of the CRCs studied (92/1870). In all, 52.2% (48/92) of those tumors were TMB-H, and 38.0% (35/92) of them contained POLE ExoD drivers. The highest median TMB (mTMB) was found in the ‘TMB-H POLE ExoD driver plus POLE VUS’ group, and this was significantly different from the ‘TMB-H POLE ExoD driver without additional POLE VUS’ and the ‘TMB-L POLE variant without ExoD driver’ group, even when MSI tumors were excluded (p

Computational methods for predicting protein-ligand binding free energy continue to be popular as a potential cost-cutting method in the drug discovery process. However, accurate predictions are often difficult to make as estimates must be made for certain electronic and entropic terms in conventional force field based scoring functions. Mixed quantum mechanics/molecular mechanics (QM/MM) methods allow electronic effects for a small region of the protein to be calculated, treating the remaining atoms as a fixed charge background for the active site. Such a semi-empirical QM/MM scoring function has been implemented in AMBER using DivCon and tested on a set of 23 metalloprotein-ligand complexes, where QM/MM methods provide a particular advantage in the modeling of the metal ion. The binding affinity of this set of proteins can be calculated with an R(2) of 0.64 and a standard deviation of 1.88 kcal/mol without fitting and 0.71 and a standard deviation of 1.69 kcal/mol with fitted weighting of the individual scoring terms. In this study we explore using various methods to calculate terms in the binding free energy equation, including entropy estimates and minimization standards. From these studies we found that using the rotational bond estimate to ligand entropy results in a reasonable R(2) of 0.63 without fitting. We also found that using the ESCF energy of the proteins without minimization resulted in an R(2) of 0.57, when using the rotatable bond entropy estimate.