PURPOSE OF REVIEWTo summarize recent advances in the discovery of chemical inhibitors targeting the HIV capsid and research on their mechanisms of action.
RECENT FINDINGSHIV infection is critically ...dependent on functions of the viral capsid. Numerous studies have reported the identification of a variety of compounds that bind to the capsid protein; some of these inhibit reverse transcription and nuclear entry, steps required for infection. Other capsid-targeting compounds appear to act by perturbing capsid assembly, resulting in noninfectious progeny virions. Inhibitors may bind to several different positions on the capsid protein, including sites in both protein domains. However, the antiviral activity of many reported capsid-targeting inhibitors has not been definitively linked to capsid binding. Until recently, the low-to-moderate potency of reported capsid-targeting inhibitors has precluded their further clinical development. In 2017, GS-CA1, a highly potent capsid inhibitor, was described that holds promise for clinical development.
SUMMARYSmall molecules that bind to the viral capsid protein can be potent inhibitors of HIV infection. Capsid-targeting drugs are predicted to exhibit high barriers to viral resistance, and ongoing work in this area is contributing to an understanding of the molecular biology of HIV uncoating and maturation.
Activating mutations in HER2 (ERBB2) drive the growth of a subset of breast and other cancers and tend to co-occur with HER3 (ERBB3) missense mutations. The HER2 tyrosine kinase inhibitor neratinib ...has shown clinical activity against HER2-mutant tumors. To characterize the role of HER3 mutations in HER2-mutant tumors, we integrate computational structural modeling with biochemical and cell biological analyses. Computational modeling predicts that the frequent HER3E928G kinase domain mutation enhances the affinity of HER2/HER3 and reduces binding of HER2 to its inhibitor neratinib. Co-expression of mutant HER2/HER3 enhances HER2/HER3 co-immunoprecipitation and ligand-independent activation of HER2/HER3 and PI3K/AKT, resulting in enhanced growth, invasiveness, and resistance to HER2-targeted therapies, which can be reversed by combined treatment with PI3Kα inhibitors. Our results provide a mechanistic rationale for the evolutionary selection of co-occurring HER2/HER3 mutations and the recent clinical observations that HER3 mutations are associated with a poor response to neratinib in HER2-mutant cancers.
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•Co-occurring HER2/HER3 mutations promote oncogenesis and invasion via PI3K activation•HER3 mutations reduce sensitivity to HER2 inhibitors in HER2-mutant cancer cells•Tumors with HER2/HER3 mutations are sensitive to HER2 TKI + PI3Kα inhibitor
Hanker and Brown et al. demonstrate that co-occurring HER2 and HER3 mutations cooperatively activate HER2/HER3 and PI3K signaling in tumor cells, leading to enhanced growth, invasion, and resistance to HER2 inhibitors. HER2/HER3 double-mutant tumor models are sensitive to the combination of a HER2 TKI and a PI3Kα inhibitor.
The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research ...problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 Å. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 Å. In addition to de novo structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein−protein complexes, design altered specificity protein−protein and protein−DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem.
Bruton's tyrosine kinase (BTK) mediates B cell signaling and is also present in innate immune cells but not T cells. BTK propagates B cell receptor (BCR) responses to antigen-engagement as well as to ...stimulation via CD40, toll-like receptors (TLRs), Fc receptors (FCRs) and chemokine receptors. Importantly, BTK can modulate signaling, acting as a "rheostat" rather than an "on-off" switch; thus, overexpression leads to autoimmunity while decreased levels improve autoimmune disease outcomes. Autoreactive B cells depend upon BTK for survival to a greater degree than normal B cells, reflected as loss of autoantibodies with maintenance of total antibody levels when BTK is absent. This review describes contributions of BTK to immune tolerance, including studies testing BTK-inhibitors for treatment of autoimmune diseases.
Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials Kaufmann, K. W., et al. (2010) ...Biochemistry 49, 2987–2998. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.
Introduction
Supratotal resection (SpTR) of glioblastoma may be associated with improved survival, but published results have varied in part from lack of consensus on the definition and appropriate ...use of SpTR. A previous small survey of neurosurgical oncologists with expertise performing SpTR found resection 1–2 cm beyond contrast enhancement was an acceptable definition and glioblastoma involving the right frontal and bilateral anterior temporal lobes were considered most amenable to SpTR. The general neurosurgical oncology community has not yet confirmed the practicality of this definition.
Methods
Seventy-six neurosurgical oncology members of the AANS/CNS Tumor Section were surveyed, representing 34.0% of the 223 members who were administered the survey. Participants were presented with 11 definitions of SpTR and rated each definition’s appropriateness. Participants additionally reviewed magnetic resonance imaging for 10 anatomically distinct glioblastomas and assessed the tumor location’s eloquence, perceived equipoise of enrolling patients in a randomized trial comparing gross total to SpTR, and their personal treatment plans.
Results
Most neurosurgeons surveyed agree that gross total plus resection of some non-contrast enhancement (n = 57, 80.3%) or resection 1–2 cm beyond contrast enhancement (n = 52, 73.2%) are appropriate definitions for SpTR. Cases were divided into three anatomically distinct groups by perceived equipoise between gross total and SpTR. The best clinical trial candidates were thought to be right anterior temporal (n = 58, 76.3%) and right frontal (n = 55, 73.3%) glioblastomas.
Conclusion
Support exists among neurosurgical oncologists with varying familiarity performing SpTR to adopt the proposed consensus definition of SpTR of glioblastoma and to potentially investigate the utility of SpTR to treat right anterior temporal and right frontal glioblastomas in a clinical trial. A smaller proportion of general neurosurgical oncologists than SpTR experts would personally treat a left anterior temporal glioblastoma with SpTR.
Structure-based drug design is frequently used to accelerate the development of small-molecule therapeutics. Although substantial progress has been made in X-ray crystallography and nuclear magnetic ...resonance (NMR) spectroscopy, the availability of high-resolution structures is limited owing to the frequent inability to crystallize or obtain sufficient NMR restraints for large or flexible proteins. Computational methods can be used to both predict unknown protein structures and model ligand interactions when experimental data are unavailable. This paper describes a comprehensive and detailed protocol using the Rosetta modeling suite to dock small-molecule ligands into comparative models. In the protocol presented here, we review the comparative modeling process, including sequence alignment, threading and loop building. Next, we cover docking a small-molecule ligand into the protein comparative model. In addition, we discuss criteria that can improve ligand docking into comparative models. Finally, and importantly, we present a strategy for assessing model quality. The entire protocol is presented on a single example selected solely for didactic purposes. The results are therefore not representative and do not replace benchmarks published elsewhere. We also provide an additional tutorial so that the user can gain hands-on experience in using Rosetta. The protocol should take 5-7 h, with additional time allocated for computer generation of models.
Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the ...spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease.
To illustrate this approach in a clinical application, we analyzed 13 candidate missense variants in regulator of telomere elongation helicase 1 (RTEL1) identified in patients with Familial Interstitial Pneumonia (FIP). We curated pathogenic and neutral RTEL1 variants from the literature and public databases. We then used homology modeling to construct a 3D structural model of RTEL1 and mapped known variants into this structure. We next developed a pathogenicity prediction algorithm based on proximity to known disease causing and neutral variants and evaluated its performance with leave-one-out cross-validation. We further validated our predictions with segregation analyses, telomere lengths, and mutagenesis data from the homologous XPD protein. Our algorithm for classifying RTEL1 VUS based on spatial proximity to pathogenic and neutral variation accurately distinguished 7 known pathogenic from 29 neutral variants (ROC AUC = 0.85) in the N-terminal domains of RTEL1. Pathogenic proximity scores were also significantly correlated with effects on ATPase activity (Pearson r = -0.65, p = 0.0004) in XPD, a related helicase. Applying the algorithm to 13 VUS identified from sequencing of RTEL1 from patients predicted five out of six disease-segregating VUS to be pathogenic. We provide structural hypotheses regarding how these mutations may disrupt RTEL1 ATPase and helicase function.
Spatial analysis of missense variation accurately classified candidate VUS in RTEL1 and suggests how such variants cause disease. Incorporating spatial proximity analyses into other pathogenicity prediction tools may improve accuracy for other genes and genetic diseases.
Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved ...computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.
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