Modeling structurally variable regions in homologous proteins with rosetta

Differential laboratory passaging of SARS-CoV-2 viral stocks impacts the in vitro assessment of neutralizing antibodies

Viral populations in natural infections can have a high degree of sequence diversity, which can directly impact immune escape. However, antibody potency is often tested in vitro with a relatively clonal viral populations, such as laboratory virus or pseudotyped virus stocks, which may not accurately represent the genetic diversity of circulating viral genotypes. This can affect the validity of viral phenotype assays, such as antibody neutralization assays. To address this issue, we tested whether recombinant virus carrying SARS-CoV-2 spike (VSV-SARS-CoV-2-S) stocks could be made more genetically diverse by passage, and if a stock passaged under selective pressure was more capable of escaping monoclonal antibody (mAb) neutralization than unpassaged stock or than viral stock passaged without selective pressures. We passaged VSV-SARS-CoV-2-S four times concurrently in three cell lines and then six times with or without polyclonal antiserum selection pressure. All three of the monoclonal antibodies tested neutralized the viral population present in the unpassaged stock. The viral inoculum derived from serial passage without antiserum selection pressure was neutralized by two of the three mAbs. However, the viral inoculum derived from serial passage under antiserum selection pressure escaped neutralization by all three mAbs. Deep sequencing revealed the rapid acquisition of multiple mutations associated with antibody escape in the VSV-SARS-CoV-2-S that had been passaged in the presence of antiserum, including key mutations present in currently circulating Omicron subvariants. These data indicate that viral stock that was generated under polyclonal antiserum selection pressure better reflects the natural environment of the circulating virus and may yield more biologically relevant outcomes in phenotypic assays. Thus, mAb assessment assays that utilize a more genetically diverse, biologically relevant, virus stock may yield data that are relevant for prediction of mAb efficacy and for enhancing biosurveillance.

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3, but also revealed how quickly viral escape can curtail effective options4,5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including Evusheld™ and its constituent, cilgavimab4,6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protection in vivo against the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.

Modeling of protein conformational changes with Rosetta guided by limited experimental data

Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.

Computational and experimental approaches to probe GPCR activation and signaling

G protein-coupled receptors (GPCRs) regulate different physiological functions, e.g., sensation, growth, digestion, reproductivity, nervous and immune systems response, and many others. In eukaryotes, they are also responsible for intercellular communication in response to pathogens. The major primary messengers binding to these cell-surface receptors constitute small-molecule or peptide hormones and neurotransmitters, nucleotides, lipids as well as small proteins. The simplicity of the way how GPCR signaling can be regulated by their endogenous agonists prompted the usage of GPCRs as major drug targets in modern pharmacology. Drugs targeting GPCRs inhibit pathological processes at the very beginning. This enables to significantly reduce the occurrence of morphological changes caused by diseases. Until recently, X-ray crystallography was the method of the first choice to obtain high-resolution structural information about GPCRs. Following X-ray crystallography, cryo-EM gained attention in GPCR studies as a quick and low-cost alternative. FRET microscopy is also widely used for GPCRs in the analysis of protein-protein interactions (PPIs) in intact cells as well as for screening purposes. Regarding computational methods, molecular dynamics (MD) for many years has proven its usefulness in studying the GPCR activation. MODELLER and Rosetta were widely used to generate preliminary homology models of GPCRs for MD simulation systems. Apart from the conventional all-atom approach with explicitly defined solvent, also other techniques have been applied to GPCRs, e.g., MARTINI or hybrid methods involving the coarse-grained representation, less demanding regarding computational resources, and thus offering much larger simulation timescales.

Novel Allosteric Mechanism of Dual p53/MDM2 and p53/MDM4 Inhibition by a Small Molecule

Restoration of the p53 tumor suppressor for personalised cancer therapy is a promising treatment strategy. However, several high-affinity MDM2 inhibitors have shown substantial side effects in clinical trials. Thus, elucidation of the molecular mechanisms of action of p53 reactivating molecules with alternative functional principle is of the utmost importance. Here, we report a discovery of a novel allosteric mechanism of p53 reactivation through targeting the p53 N-terminus which promotes inhibition of both p53/MDM2 (murine double minute 2) and p53/MDM4 interactions. Using biochemical assays and molecular docking, we identified the binding site of two p53 reactivating molecules, RITA (reactivation of p53 and induction of tumor cell apoptosis) and protoporphyrin IX (PpIX). Ion mobility-mass spectrometry revealed that the binding of RITA to serine 33 and serine 37 is responsible for inducing the allosteric shift in p53, which shields the MDM2 binding residues of p53 and prevents its interactions with MDM2 and MDM4. Our results point to an alternative mechanism of blocking p53 interaction with MDM2 and MDM4 and may pave the way for the development of novel allosteric inhibitors of p53/MDM2 and p53/MDM4 interactions.

Structural Prediction of Peptide-MHC Binding Modes

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8⁺ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.

Correlated conformational dynamics of the human GluN1-GluN2A type N-methyl-D-aspartate (NMDA) receptor

N-Methyl-D-aspartate receptors (NMDARs) are glutamate-gated ion channels found in the nerve cell membranes. As a result of overexcitation of NMDARs, neuronal death occurs and may lead to diseases such as epilepsy, stroke, Alzheimer's disease, and Parkinson's disease. In this study, human GluN1- GluN2A type NMDAR structure is modeled based on the X-ray structure of the Xenopus laevis template and missing loops are added by ab-initio loop modeling. The final structure is chosen according to two different model assessment scores. To be able to observe the structural changes upon ligand binding, glycine and glutamate molecules are docked into the corresponding binding sites of the receptor. Subsequently, molecular dynamics simulations of 1.3 μs are performed for both apo and ligand-bound structures. Structural parameters, which have been considered to show functionally important changes in previous NMDAR studies, are monitored as conformational rulers to understand the dynamics of the conformational changes. Moreover, principal component analysis (PCA) is performed for the equilibrated part of the simulations. From these analyses, the differences in between apo and ligand-bound simulations can be summarized as the following: The girdle right at the beginning of the pore loop, which connects M2 and M3 helices of the ion channel, partially opens. Ligands act like an adhesive for the ligand-binding domain (LBD) by keeping the bi-lobed structure together and consequently this is reflected to the overall dynamics of the protein as an increased correlation of the LBD with especially the amino-terminal domain (ATD) of the protein.

Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. These methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore the conformational space more broadly. Based on the insights of this analysis, we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Characterization of the ExoU activation mechanism using EPR and integrative modeling

ExoU, a type III secreted phospholipase effector of Pseudomonas aeruginosa, serves as a prototype to model large, dynamic, membrane-associated proteins. ExoU is synergistically activated by interactions with membrane lipids and ubiquitin. To dissect the activation mechanism, structural homology was used to identify an unstructured loop of approximately 20 residues in the ExoU amino acid sequence. Mutational analyses indicate the importance of specific loop amino acid residues in mediating catalytic activity. Engineered disulfide cross-links show that loop movement is required for activation. Site directed spin labeling EPR and DEER (double electron-electron resonance) studies of apo and holo states demonstrate local conformational changes at specific sites within the loop and a conformational shift of the loop during activation. These data are consistent with the formation of a substrate-binding pocket providing access to the catalytic site. DEER distance distributions were used as constraints in RosettaDEER to construct ensemble models of the loop in both apo and holo states, significantly extending the range for modeling a conformationally dynamic loop.

Structural modelling of the lumenal domain of human GPAA1, the metallo-peptide synthetase subunit of the transamidase complex, reveals zinc-binding mode and two flaps surrounding the active site

Background

The transamidase complex is a molecular machine in the endoplasmic reticulum of eukaryotes that attaches a glycosylphosphatidylinositol (GPI) lipid anchor to substrate proteins after cleaving a C-terminal propeptide with a defined sequence signal. Its five subunits are very hydrophobic; thus, solubility, heterologous expression and complex reconstruction are difficult. Therefore, theoretical approaches are currently the main source of insight into details of 3D structure and of the catalytic process.

Results

In this work, we generated model 3D structures of the lumenal domain of human GPAA1, the M28-type metallo-peptide-synthetase subunit of the transamidase, including zinc ion and model substrate positions. In comparative molecular dynamics (MD) simulations of M28-type structures and our GPAA1 models, we estimated the metal ion binding energies with evolutionary conserved amino acid residues in the catalytic cleft. We find that canonical zinc binding sites 2 and 3 are strongest binders for Zn1 and, where a second zinc is available, sites 2 and 4 for Zn2. Zinc interaction of site 5 with Zn1 enhances upon substrate binding in structures with only one zinc. Whereas a previously studied glutaminyl cyclase structure, the best known homologue to GPAA1, binds only one zinc ion at the catalytic site, GPAA1 can sterically accommodate two. The M28-type metallopeptidases segregate into two independent branches with regard to one/two zinc ion binding modality in a phylogenetic tree where the GPAA1 family is closer to the joint origin of both groups. For GPAA1 models, MD studies revealed two large loops (flaps) surrounding the active site being involved in an anti-correlated, breathing-like dynamics.

Conclusions

In the light of combined sequence-analytic and phylogenetic arguments as well as 3D structural modelling results, GPAA1 is most likely a single zinc ion metallopeptidase. Two large flaps environ the catalytic site restricting access to large substrates.

Reviewers

This article was reviewed by Thomas Dandekar (MD) and Michael Gromiha.

Global phosphoproteomic analysis of Ebola virions reveals a novel role for VP35 phosphorylation-dependent regulation of genome transcription

Ebola virus (EBOV) causes severe human disease with a high case fatality rate. The balance of evidence implies that the virus circulates in bats. The molecular basis for host-viral interactions, including the role for phosphorylation during infections, is largely undescribed. To address this, and to better understand the biology of EBOV, the phosphorylation of EBOV proteins was analyzed in virions purified from infected monkey Vero-E6 cells and bat EpoNi/22.1 cells using high-resolution mass spectrometry. All EBOV structural proteins were detected with high coverage, along with phosphopeptides. Phosphorylation sites were identified in all viral structural proteins. Comparison of EBOV protein phosphorylation in monkey and bat cells showed only partial overlap of phosphorylation sites, with shared sites found in NP, VP35, and VP24 proteins, and no common sites in the other proteins. Three-dimensional structural models were built for NP, VP35, VP40, GP, VP30 and VP24 proteins using available crystal structures or by de novo structure prediction to elucidate the potential role of the phosphorylation sites. Phosphorylation of one of the identified sites in VP35, Thr-210, was demonstrated to govern the transcriptional activity of the EBOV polymerase complex. Thr-210 phosphorylation was also shown to be important for VP35 interaction with NP. This is the first study to compare phosphorylation of all EBOV virion proteins produced in primate versus bat cells, and to demonstrate the role of VP35 phosphorylation in the viral life cycle. The results uncover a novel mechanism of EBOV transcription and identify novel targets for antiviral drug development.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Humanization of yeast genes with multiple human orthologs reveals functional divergence between paralogs

Despite over a billion years of evolutionary divergence, several thousand human genes possess clearly identifiable orthologs in yeast, and many have undergone lineage-specific duplications in one or both lineages. These duplicated genes may have been free to diverge in function since their expansion, and it is unclear how or at what rate ancestral functions are retained or partitioned among co-orthologs between species and within gene families. Thus, in order to investigate how ancestral functions are retained or lost post-duplication, we systematically replaced hundreds of essential yeast genes with their human orthologs from gene families that have undergone lineage-specific duplications, including those with single duplications (1 yeast gene to 2 human genes, 1:2) or higher-order expansions (1:>2) in the human lineage. We observe a variable pattern of replaceability across different ortholog classes, with an obvious trend toward differential replaceability inside gene families, and rarely observe replaceability by all members of a family. We quantify the ability of various properties of the orthologs to predict replaceability, showing that in the case of 1:2 orthologs, replaceability is predicted largely by the divergence and tissue-specific expression of the human co-orthologs, i.e., the human proteins that are less diverged from their yeast counterpart and more ubiquitously expressed across human tissues more often replace their single yeast ortholog. These trends were consistent with in silico simulations demonstrating that when only one ortholog can replace its corresponding yeast equivalent, it tends to be the least diverged of the pair. Replaceability of yeast genes having more than 2 human co-orthologs was marked by retention of orthologous interactions in functional or protein networks as well as by more ancestral subcellular localization. Overall, we performed >400 human gene replaceability assays, revealing 50 new human-yeast complementation pairs, thus opening up avenues to further functionally characterize these human genes in a simplified organismal context.

Structure of a rabies virus polymerase complex from electron cryo-microscopy

Rabies virus (RABV) and other viruses with single-segment, negative-sense, RNA genomes have a multi-functional polymerase protein (L) that carries out the various reactions required for transcription and replication. Many of these viruses are serious human pathogens, and L is a potential target for antiviral therapeutics. Drugs that inhibit polymerases of HCV and HIV-1 provide successful precedents. The structure described here of the RABV L protein in complex with its P-protein cofactor shows a conformation poised for initiation of transcription or replication. Channels in the molecule and the relative positions of catalytic sites suggest that L couples a distinctive capping reaction with priming and initiation of transcription, and that replication and transcription have different priming configurations and different product exit sites.

Nonsegmented negative-stranded (NNS) RNA viruses, among them the virus that causes rabies (RABV), include many deadly human pathogens. The large polymerase (L) proteins of NNS RNA viruses carry all of the enzymatic functions required for viral messenger RNA (mRNA) transcription and replication: RNA polymerization, mRNA capping, and cap methylation. We describe here a complete structure of RABV L bound with its phosphoprotein cofactor (P), determined by electron cryo-microscopy at 3.3 Å resolution. The complex closely resembles the vesicular stomatitis virus (VSV) L-P, the one other known full-length NNS-RNA L-protein structure, with key local differences (e.g., in L-P interactions). Like the VSV L-P structure, the RABV complex analyzed here represents a preinitiation conformation. Comparison with the likely elongation state, seen in two structures of pneumovirus L-P complexes, suggests differences between priming/initiation and elongation complexes. Analysis of internal cavities within RABV L suggests distinct template and product entry and exit pathways during transcription and replication.

Computational reconstruction of atomistic protein structures from coarse-grained models

Three-dimensional protein structures, whether determined experimentally or theoretically, are often too low resolution. In this mini-review, we outline the computational methods for protein structure reconstruction from incomplete coarse-grained to all atomistic models. Typical reconstruction schemes can be divided into four major steps. Usually, the first step is reconstruction of the protein backbone chain starting from the C-alpha trace. This is followed by side-chains rebuilding based on protein backbone geometry. Subsequently, hydrogen atoms can be reconstructed. Finally, the resulting all-atom models may require structure optimization. Many methods are available to perform each of these tasks. We discuss the available tools and their potential applications in integrative modeling pipelines that can transfer coarse-grained information from computational predictions, or experiment, to all atomistic structures.

Toward rational antibody design: recent advancements in molecular dynamics simulations

Because antibodies have become an important therapeutic tool, rational antibody design is a challenging issue involving various science and technology fields. From the computational aspect, many types of design-assist methods have been developed, but their accuracy is not fully satisfactory. Because of recent advancements in computational power, molecular dynamics (MD) simulation has become a helpful tool to trace the motion of proteins and to characterize their properties. Thus, MD simulation has been applied to various systems involving antigen-antibody complexes and has been shown to provide accurate insight into antigen-antibody interactions and dynamics at an atomic resolution. Therefore, it is highly possible that MD simulation will play several roles complementing the conventional antibody design. In this review, we address several important features of MD simulation in the context of rational antibody design.

The exonuclease Xrn1 activates transcription and translation of mRNAs encoding membrane proteins

The highly conserved 5’–3’ exonuclease Xrn1 regulates gene expression in eukaryotes by coupling nuclear DNA transcription to cytosolic mRNA decay. By integrating transcriptome-wide analyses of translation with biochemical and functional studies, we demonstrate an unanticipated regulatory role of Xrn1 in protein synthesis. Xrn1 promotes translation of a specific group of transcripts encoding membrane proteins. Xrn1-dependence for translation is linked to poor structural RNA contexts for translation initiation, is mediated by interactions with components of the translation initiation machinery and correlates with an Xrn1-dependence for mRNA localization at the endoplasmic reticulum, the translation compartment of membrane proteins. Importantly, for this group of mRNAs, Xrn1 stimulates transcription, mRNA translation and decay. Our results uncover a crosstalk between the three major stages of gene expression coordinated by Xrn1 to maintain appropriate levels of membrane proteins.

The exonuclease Xrn1 mediates crosstalk between transcription and mRNA decay in yeast. Here the authors demonstrate that Xrn1 promotes translation of mRNAs encoding membrane proteins, coupling transcription, translation, and mRNA decay.

Interactions between RAMP2 and CRF receptors: The effect of receptor subtypes, splice variants and cell context

Corticotrophin releasing factor (CRF) acts via two family B G-protein-coupled receptors, CRFR1 and CRFR2. Additional subtypes exist due to alternative splicing. CRFR1α is the most widely expressed subtype and lacks a 29-residue insert in the first intracellular loop that is present in CRFR1β. It has been shown previously that co-expression of CRFR1β with receptor activity modifying protein 2 (RAMP2) in HEK 293S cells increased the cell-surface expression of both proteins suggesting a physical interaction as seen with RAMPs and calcitonin receptor-like receptor (CLR). This study investigated the ability of CRFR1α, CRFR1β and CRFR2β to promote cell-surface expression of FLAG-tagged RAMP2. Four different cell-lines were utilised to investigate the effect of varying cellular context; COS-7, HEK 293T, HEK 293S and [ΔCTR]HEK 293 (which lacks endogenous calcitonin receptor). In all cell-lines, CRFR1α and CRFR1β enhanced RAMP2 cell-surface expression. The magnitude of the effect on RAMP2 was dependent on the cell-line ([ΔCTR]HEK 293 > COS-7 > HEK 293T > HEK 293S). RT-PCR indicated this variation may relate to differences in endogenous RAMP expression between cell types. Furthermore, pre-treatment with CRF resulted in a loss of cell-surface FLAG-RAMP2 when it was co-expressed with CRFR1 subtypes. CRFR2β co-expression had no effect on RAMP2 in any cell-line. Molecular modelling suggests that the potential contact interface between the extracellular domains of RAMP2 and CRF receptor subtypes is smaller than that of RAMP2 and CRL, the canonical receptor:RAMP pairing, assuming a physical interaction. Furthermore, a specific residue difference between CRFR1 subtypes (glutamate) and CRFR2β (histidine) in this interface region may impair CRFR2β:RAMP2 interaction by electrostatic repulsion.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

The Role of the Active Site Flap in Streptavidin/Biotin Complex Formation

Obtaining a detailed description of how active site flap motion affects substrate or ligand binding will advance structure-based drug design (SBDD) efforts on systems including the kinases, HSP90, HIV protease, ureases, etc. Through this understanding, we will be able to design better inhibitors and better proteins that have desired functions. Herein we address this issue by generating the relevant configurational states of a protein flap on the molecular energy landscape using an approach we call MT_Flex-b and then following this with a procedure to estimate the free energy associated with the motion of the flap region. To illustrate our overall workflow, we explored the free energy changes in the streptavidin/biotin system upon introducing conformational flexibility in loop_3-4 in the biotin unbound ( apo) and bound ( holo) state. The free energy surfaces were created using the Movable Type free energy method, and for further validation, we compared them to potential of mean force (PMF) generated free energy surfaces using MD simulations employing the FF99SBILDN and FF14SB force fields. We also estimated the free energy thermodynamic cycle using an ensemble of closed-like and open-like end states for the ligand unbound and bound states and estimated the binding free energy to be approximately -16.2 kcal/mol (experimental -18.3 kcal/mol). The good agreement between MT_Flex-b in combination with the MT method with experiment and MD simulations supports the effectiveness of our strategy in obtaining unique insights into the motions in proteins that can then be used in a range of biological and biomedical applications.

Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction

Motivation

Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction.

Results

We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed.

Availability and Implementation

Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx.

Supplementary information

Supplementary data are available at Bioinformatics online.

Studying Kv Channels Function using Computational Methods

In recent years, molecular modeling techniques, combined with MD simulations, provided significant insights on voltage-gated (Kv) potassium channels intrinsic properties. Among the success stories are the highlight of molecular level details of the effects of mutations, the unraveling of several metastable intermediate states, and the influence of a particular lipid, PIP₂, in the stability and the modulation of Kv channel function. These computational studies offered a detailed view that could not have been reached through experimental studies alone. With the increase of cross disciplinary studies, numerous experiments provided validation of these computational results, which endows an increase in the reliability of molecular modeling for the study of Kv channels. This chapter offers a description of the main techniques used to model Kv channels at the atomistic level.

Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations

An increasing number of G protein-coupled receptor (GPCR) crystal structures provide important-albeit static-pictures of how small molecules or peptides interact with their receptors. These high-resolution structures represent a tremendous opportunity to apply molecular dynamics (MD) simulations to capture atomic-level dynamical information that is not easy to obtain experimentally. Understanding ligand binding and unbinding processes, as well as the related responses of the receptor, is crucial to the design of better drugs targeting GPCRs. Here, we discuss possible ways to study the dynamics involved in the binding of small molecules to GPCRs, using long timescale MD simulations or metadynamics-based approaches.

Ensemblator v3: Robust atom-level comparative analyses and classification of protein structure ensembles

Ensembles of protein structures are increasingly used to represent the conformational variation of a protein as determined by experiment and/or by molecular simulations, as well as uncertainties that may be associated with structure determinations or predictions. Making the best use of such information requires the ability to quantitatively compare entire ensembles. For this reason, we recently introduced the Ensemblator (Clark et al., Protein Sci 2015; 24:1528), a novel approach to compare user-defined groups of models, in residue level detail. Here we describe Ensemblator v3, an open-source program that employs the same basic ensemble comparison strategy but includes major advances that make it more robust, powerful, and user-friendly. Ensemblator v3 carries out multiple sequence alignments to facilitate the generation of ensembles from non-identical input structures, automatically optimizes the key global overlay parameter, optionally performs "ensemble clustering" to classify the models into subgroups, and calculates a novel "discrimination index" that quantifies similarities and differences, at residue or atom level, between each pair of subgroups. The clustering and automatic options mean that no pre-knowledge about an ensemble is required for its analysis. After describing the novel features of Ensemblator v3, we demonstrate its utility using three case studies that illustrate the ease with which complex analyses are accomplished, and the kinds of insights derived from clustering into subgroups and from the detailed information that locates significant differences. The Ensemblator v3 enhances the structural biology toolbox by greatly expanding the kinds of problems to which this ensemble comparison strategy can be applied.

Molecular modelling studies of kdr mutations in voltage gated sodium channel revealed significant conformational variations contributing to insecticide resistance

Voltage gated sodium channels (VGSC) of mosquito vectors are the primary targets of dichlorodiphenyltrichloroethane (DDT) and other synthetic pyrethroids used in public health programmes. The knockdown resistant (kdr) mutations in VGSC are associated with the insecticide resistance especially in Anophelines. The present study is aimed to emphasize and demarcate the impact of three kdr-mutations such as L1014S, L1014F and L1014H on insecticide resistance. The membrane model of sodium transport domain of VGSC (STD-VGSC) was constructed using de novo approach based on domain and trans-membrane predictions. The comparative molecular modelling studies of wild type and mutant models of STD-VGSC revealed that L1014F mutant was observed to be near native to the wild type model in all the respects, but, L1014S and L1014H mutations showed drastic variations in the energy levels, root mean square fluctuations (RMSF) that resulted in conformational variations. The predicted binding sites also showed variable cavity volumes and RMSF in L1014S and L1014H mutants. Further, DDT also found be bound in near native manner to wild type in L1014F mutant and with variable orientation and affinities in L1014S and L1014H mutants. The variations and fluctuations observed in mutant structures explained that each mutation has its specific impact on the conformation of VGSC and its binding with DDT. The study provides new insights into the structure-function-correlations of mutant STD-VGSC structures and demonstrates the role and effects of kdr mutations on insecticide resistance in mosquito vectors.

Fast de novo discovery of low-energy protein loop conformations

In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All-atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side-chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near-native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402-1412. © 2017 Wiley Periodicals, Inc.

Antibody H3 Structure Prediction

Antibodies are proteins of the immune system that are able to bind to a huge variety of different substances, making them attractive candidates for therapeutic applications. Antibody structures have the potential to be useful during drug development, allowing the implementation of rational design procedures. The most challenging part of the antibody structure to experimentally determine or model is the H3 loop, which in addition is often the most important region in an antibody's binding site. This review summarises the approaches used so far in the pursuit of accurate computational H3 structure prediction.

Critical Features of Fragment Libraries for Protein Structure Prediction

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint

Ab structure prediction has made great strides, but accurately modeling CDR H3 loops remains elusive. Unlike the other five CDR loops, CDR H3 does not adopt canonical conformations and must be modeled de novo. During Antibody Modeling Assessment II, we found that biasing simulations toward kinked conformations enables generating low-root mean square deviation models (Weitzner et al. 2014. Proteins 82: 1611-1623), and since then, we have presented new geometric parameters defining the kink conformation (Weitzner et al. 2015. Structure 23: 302-311). In this study, we use these parameters to develop a new biasing constraint. When applied to a benchmark set of high-quality CDR H3 loops, the average minimum root mean square deviation sampled is 0.93 Å, compared with 1.34 Å without the constraint. We then test the performance of the constrained de novo method for homology modeling and rigid-body docking and present the results for 1) the Antibody Modeling Assessment II targets, 2) the 2009 RosettaAntibody benchmark set, and 3) the high-quality set.

Beyond basins: φ,ψ preferences of a residue depend heavily on the φ,ψ values of its neighbors

The Ramachandran plot distributions of nonglycine residues from experimentally determined structures are routinely described as grouping into one of six major basins: β, PII , α, αL , ξ and γ'. Recent work describing the most common conformations adopted by pairs of residues in folded proteins [i.e., (φ,ψ)2 -motifs] showed that commonly described major basins are not true single thermodynamic basins, but are composed of distinct subregions that are associated with various conformations of either the preceding or following neighbor residue. Here, as documentation of the extent to which the conformational preferences of a central residue are influenced by the conformations of its two neighbors, we present a set of φ,ψ-plots that are delimited simultaneously by the φ,ψ-angles of its neighboring residues on both sides. The level of influence seen here is typically greater than the influence associated with considering the identities of neighboring residues, implying that the use of this heretofore untapped information can improve the accuracy of structure prediction algorithms and low resolution protein structure refinement.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

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.

Designing peptide sequences in flexible chain conformations to bind RNA: a search algorithm combining Monte Carlo, self-consistent mean field and concerted rotation techniques

A search algorithm combining Monte Carlo, self-consistent mean field, and concerted rotation techniques was developed to discover peptide sequences that are reasonable HIV drug candidates due to their exceptional binding to human tRNAUUU(Lys3), the primer of HIV replication. The search algorithm allows for iteration between sequence mutations and conformation changes during sequence evolution. Searches conducted for different classes of peptides identified several potential peptide candidates. Analysis of the energy revealed that the asparagine and cysteine at residues 11 and 12 play important roles in "recognizing" tRNA(Lys3) via van der Waals interactions, contributing to binding specificity. Arginines preferentially attract the phosphate linkage via charge-charge interaction, contributing to binding affinity. Evaluation of the RNA/peptide complex's structure revealed that adding conformation changes to the search algorithm yields peptides with better binding affinity and specificity to tRNA(Lys3) than a previous mutation-only algorithm.

Improving Loop Modeling of the Antibody Complementarity-Determining Region 3 Using Knowledge-Based Restraints

Structural restrictions are present even in the most sequence diverse portions of antibodies, the complementary determining region (CDR) loops. Previous studies identified robust rules that define canonical structures for five of the six CDR loops, however the heavy chain CDR 3 (HCDR3) defies standard classification attempts. The HCDR3 loop can be subdivided into two domains referred to as the “torso” and the “head” domains and two major families of canonical torso structures have been identified; the more prevalent “bulged” and less frequent “non-bulged” torsos. In the present study, we found that Rosetta loop modeling of 28 benchmark bulged HCDR3 loops is improved with knowledge-based structural restraints developed from available antibody crystal structures in the PDB. These restraints restrict the sampling space Rosetta searches in the torso domain, limiting the φ and ψ angles of these residues to conformations that have been experimentally observed. The application of these restraints in Rosetta result in more native-like structure sampling and improved score-based differentiation of native-like HCDR3 models, significantly improving our ability to model antibody HCDR3 loops.

The Dynamic Process of Drug-GPCR Binding at Either Orthosteric or Allosteric Sites Evaluated by Metadynamics

Major advances in G Protein-Coupled Receptor (GPCR) structural biology over the past few years have yielded a significant number of high-resolution crystal structures for several different receptor subtypes. This dramatic increase in GPCR structural information has underscored the use of automated docking algorithms for the discovery of novel ligands that can eventually be developed into improved therapeutics. However, these algorithms are often unable to discriminate between different, yet energetically similar, poses because of their relatively simple scoring functions. Here, we describe a metadynamics-based approach to study the dynamic process of ligand binding to/unbinding from GPCRs with a higher level of accuracy and yet satisfying efficiency.

Long antibody HCDR3s from HIV-naïve donors presented on a PG9 neutralizing antibody background mediate HIV neutralization

Development of broadly neutralizing antibodies (bnAbs) against HIV-1 usually requires prolonged infection and induction of Abs with unusual features, such as long heavy-chain complementarity-determining region 3 (HCDR3) loops. Here we sought to determine whether the repertoires of HIV-1-naïve individuals contain Abs with long HCDR3 loops that could mediate HIV-1 neutralization. We interrogated at massive scale the structural properties of long Ab HCDR3 loops in HIV-1-naïve donors, searching for structured HCDR3s similar to those of the HIV-1 bnAb PG9. We determined the nucleotide sequences encoding 2.3 × 10(7)unique HCDR3 amino acid regions from 70 different HIV-1-naïve donors. Of the 26,917 HCDR3 loops with 30-amino acid length identified, we tested 30 for further study that were predicted to have PG9-like structure when chimerized onto PG9. Three of these 30 PG9 chimeras bound to the HIV-1 gp120 monomer, and two were neutralizing. In addition, we found 14 naturally occurring HCDR3 sequences that acquired the ability to bind to the HIV-1 gp120 monomer when adding 2- to 7-amino acid mutations via computational design. Of those 14 designed Abs, 8 neutralized HIV-1, with IC50values ranging from 0.7 to 98 µg/mL. These data suggest that the repertoire of HIV-1-naïve individuals contains rare B cells that encode HCDR3 loops that bind or neutralize HIV-1 when presented on a PG9 background with relatively few or no additional mutations. Long HCDR3 sequences are present in the HIV-naïve B-cell repertoire, suggesting that this class of bnAbs is a favorable target for rationally designed preventative vaccine efforts.

De novo design of helical peptides to inhibit tumor necrosis factor-α by disrupting its trimer formation

Helical peptide TNFα inhibitors were designed by targeting their dimer structure.

Assessment of Detection and Refinement Strategies for de novo Protein Structures Using Force Field and Statistical Potentials

De novo predictions of protein structures at high resolution are plagued by the problem of detecting the native conformation from false energy minima. In this work, we provide an assessment of various detection and refinement protocols on a small subset of the second-generation all-atom Rosetta decoy set (Tsai et al. Proteins 2003, 53, 76-87) using two potentials:  the all-atom CHARMM PARAM22 force field combined with generalized Born/surface-area (GB-SA) implicit solvation and the DFIRE-AA statistical potential. Detection schemes included DFIRE-AA conformational scoring and energy minimization followed by scoring with both GB-SA and DFIRE-AA potentials. Refinement methods included short-time (1-ps) molecular dynamics simulations, temperature-based replica exchange molecular dynamics, and a new computational unfold/refold procedure. Refinement methods include temperature-based replica exchange molecular dynamics and a new computational unfold/refold procedure. Our results indicate that simple detection with only minimization is the best protocol for finding the most nativelike structures in the decoy set. The refinement techniques that we tested are generally unsuccessful in improving detection; however, they provide marginal improvements to some of the decoy structures. Future directions in the development of refinement techniques are discussed in the context of the limitations of the protocols evaluated in this study.

CASP10-BCL::Fold efficiently samples topologies of large proteins

During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template-based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native-like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE-only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native-like assembly of SSEs for further refinement and submission. It was also observed that for some β-strand proteins model refinement failed as β-strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non-natural topologies that require loop regions to pass through the center of the protein.

Structure-based pKa prediction provides a thermodynamic basis for the role of histidines in pH-induced conformational transitions in dengue virus

pH-induced conformational changes in dengue virus (DENV) are critical to its ability to infect host cells. The envelope protein heterodimers that make up the viral envelope shift from a dimer to a trimer conformation at low-pH during membrane fusion. Previous studies have suggested that the ionization of histidine residues at low-pH is central to this pH-induced conformational change. We sought out to use molecular modeling with structure-based pKa prediction to provide a quantitative basis for the role of histidines in pH-induced conformational changes and identify which histidine residues were primarily responsible for this transition. We combined existing crystallographic and cryo-electron microscopy data to construct templates of the dimer and trimer conformations for the mature and immature virus. We then generated homology models for the four DENV serotypes and carried out structure-based pKa prediction using Rosetta. Our results showed that the pKa values of a subset of conserved histidines in DENV successfully capture the thermodynamics necessary to drive pH-induced conformational changes during fusion. Here, we identified the structural determinants underlying these pKa values and compare our findings with previous experimental results.

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Structure-based pKa prediction for histidines in dengue virus was carried out.

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Conserved histidine residues drive pH-dependent conformation changes.

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The mature dimer form of the envelop protein is destabilized at low-pH.

Effects of HCM cTnI mutation R145G on troponin structure and modulation by PKA phosphorylation elucidated by molecular dynamics simulations

Cardiac troponin (cTn) is a key molecule in the regulation of human cardiac muscle contraction. The N-terminal cardiac-specific peptide of the inhibitory subunit of troponin, cTnI (cTnI(1-39)), is a target for phosphorylation by protein kinase A (PKA) during β-adrenergic stimulation. We recently presented evidence indicating that this peptide interacts with the inhibitory peptide (cTnl(137-147)) when S23 and S24 are phosphorylated. The inhibitory peptide is also the target of the point mutation cTnI-R145G, which is associated with hypertrophic cardiomyopathy (HCM), a disease associated with sudden death in apparently healthy young adults. It has been shown that both phosphorylation and this mutation alter the cTnC-cTnI (C-I) interaction, which plays a crucial role in modulating contractile activation. However, little is known about the molecular-level events underlying this modulation. Here, we computationally investigated the effects of the cTnI-R145G mutation on the dynamics of cTn, cTnC Ca(2+) handling, and the C-I interaction. Comparisons were made with the cTnI-R145G/S23D/S24D phosphomimic mutation, which has been used both experimentally and computationally to study the cTnI N-terminal specific effects of PKA phosphorylation. Additional comparisons between the phosphomimic mutations and the real phosphorylations were made. For this purpose, we ran triplicate 150 ns molecular dynamics simulations of cTnI-R145G Ca(2+)-bound cTnC(1-161)-cTnI(1-172)-cTnT(236-285), cTnI-R145G/S23D/S24D Ca(2+)-bound cTnC(1-161)-cTnI(1-172)-cTnT(236-285), and cTnI-R145G/PS23/PS24 Ca(2+)-bound cTnC(1-161)-cTnI(1-172)-cTnT(236-285), respectively. We found that the cTnI-R145G mutation did not impact the overall dynamics of cTn, but stabilized crucial Ca(2+)-coordinating interactions. However, the phosphomimic mutations increased overall cTn fluctuations and destabilized Ca(2+) coordination. Interestingly, cTnI-R145G blunted the intrasubunit interactions between the cTnI N-terminal extension and the cTnI inhibitory peptide, which have been suggested to play a crucial role in modulating troponin function during β-adrenergic stimulation. These findings offer a molecular-level explanation for how the HCM mutation cTnI-R145G reduces the modulation of cTn by phosphorylation of S23/S24 during β-adrenergic stimulation.

A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design

The development and validation of computational macromolecular modeling and design methods depend on suitable benchmark datasets and informative metrics for comparing protocols. In addition, if a method is intended to be adopted broadly in diverse biological applications, there needs to be information on appropriate parameters for each protocol, as well as metrics describing the expected accuracy compared to experimental data. In certain disciplines, there exist established benchmarks and public resources where experts in a particular methodology are encouraged to supply their most efficient implementation of each particular benchmark. We aim to provide such a resource for protocols in macromolecular modeling and design. We present a freely accessible web resource (https://kortemmelab.ucsf.edu/benchmarks) to guide the development of protocols for protein modeling and design. The site provides benchmark datasets and metrics to compare the performance of a variety of modeling protocols using different computational sampling methods and energy functions, providing a "best practice" set of parameters for each method. Each benchmark has an associated downloadable benchmark capture archive containing the input files, analysis scripts, and tutorials for running the benchmark. The captures may be run with any suitable modeling method; we supply command lines for running the benchmarks using the Rosetta software suite. We have compiled initial benchmarks for the resource spanning three key areas: prediction of energetic effects of mutations, protein design, and protein structure prediction, each with associated state-of-the-art modeling protocols. With the help of the wider macromolecular modeling community, we hope to expand the variety of benchmarks included on the website and continue to evaluate new iterations of current methods as they become available.

Broadening the Spectrum of Ehlers Danlos Syndrome in Patients With Congenital Adrenal Hyperplasia

CONTEXT

The contiguous gene deletion syndrome (CAH-X) was described in a subset (7%) of congenital adrenal hyperplasia (CAH) patients with a TNXA/TNXB chimera, resulting in deletions of CYP21A2, encoding 21-hydroxylase necessary for cortisol biosynthesis, and TNXB, encoding the extracellular matrix glycoprotein tenascin-X (TNX). This TNXA/TNXB chimera is characterized by a 120-bp deletion in exon 35 and results in TNXB haploinsufficiency, disrupted TGF-β signaling, and an Ehlers Danlos syndrome phenotype.

OBJECTIVE

The objective of the study was to determine the genetic status of TNXB and resulting protein defects in CAH patients with a CAH-X phenotype but not the previously described TNXA/TNXB chimera. Design, Settings, Participants, and Intervention: A total of 246 unrelated CAH patients were screened for TNXB defects. Genetic defects were investigated by Southern blotting, multiplex ligation-dependent probe amplification, Sanger, and next-generation sequencing. Dermal fibroblasts and tissue were used for immunoblotting, immunohistochemical, and coimmunoprecipitation experiments.

MAIN OUTCOME MEASURES

The genetic and protein status of tenascin-X in phenotypic CAH-X patients was measured.

RESULTS

Seven families harbor a novel TNXB missense variant c.12174C>G (p.C4058W) and a clinical phenotype consistent with hypermobility-type Ehlers Danlos syndrome. Fourteen CAH probands carry previously described TNXA/TNXB chimeras, and seven unrelated patients carry the novel TNXB variant, resulting in a CAH-X prevalence of 8.5%. This highly conserved pseudogene-derived variant in the TNX fibrinogen-like domain is predicted to be deleterious and disulfide bonded, results in reduced dermal elastin and fibrillin-1 staining and altered TGF-β1 binding, and represents a novel TNXA/TNXB chimera. Tenascin-X protein expression was normal in dermal fibroblasts, suggesting a dominant-negative effect.

CONCLUSIONS

CAH-X syndrome is commonly found in CAH due to 21-hydroxylase deficiency and may result from various etiological mechanisms.

Advancements in therapeutically targeting orphan GPCRs

G-protein coupled receptors (GPCRs) are popular biological targets for drug discovery and development. To date there are more than 140 orphan GPCRs, i.e., receptors whose endogenous ligands are unknown. Traditionally orphan GPCRs have been difficult to study and the development of therapeutic compounds targeting these receptors has been extremely slow although these GPCRs are considered important targets based on their distribution and behavioral phenotype as revealed by animals lacking the receptor. Recent advances in several methods used to study orphan receptors, including protein crystallography and homology modeling are likely to be useful in the identification of therapeutics targeting these receptors. In the past 13 years, over a dozen different Class A GPCRs have been crystallized; this trend is exciting, since homology modeling of GPCRs has previously been limited by the availability of solved structures. As the number of solved GPCR structures continues to grow so does the number of templates that can be used to generate increasingly accurate models of phylogenetically related orphan GPCRs. The availability of solved structures along with the advances in using multiple templates to build models (in combination with molecular dynamics simulations that reveal structural information not provided by crystallographic data and methods for modeling hard-to-predict flexible loop regions) have improved the quality of GPCR homology models. This, in turn, has improved the success rates of virtual ligand screens that use homology models to identify potential receptor binding compounds. Experimental testing of the predicted hits and validation using traditional GPCR pharmacological approaches can be used to drive ligand-based efforts to probe orphan receptor biology as well as to define the chemotypes and chemical scaffolds important for binding. As a result of these advances, orphan GPCRs are emerging from relative obscurity as a new class of drug targets.