Biology subjects

Gray, J. J.

Publications and source records attributed to Gray, J. J..

48 records · Page 3Linked to original sources

bioRxiv2020

Robustification of RosettaAntibody and Rosetta SnugDock

In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence-structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock--methods for antibody structure prediction and antibody-antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody-antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.

biophysics↗

bioRxiv2020

Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures

Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody. When evaluated on the Rosetta Antibody Benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same, and worse structures (measured by root-mean-squared distance [RMSD] from the experimental CDR H3 loop structure) than the standard Rosetta energy function for 30, 13, and 6 targets, respectively, and improved the average RMSD of predictions by 21.3% (0.48 [A]). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loop structures.

biophysics↗

bioRxiv2019

Protein Docking and Steered Molecular Dynamics Reveal Alternative Regulatory Sites on the SERCA Calcium Transporter

The transport activity of the calcium ATPase SERCA is modulated by an inhibitory interaction with a 52-residue transmembrane peptide, phospholamban (PLB). Biochemical and structural studies have revealed the primary inhibitory site on SERCA, but PLB has been hypothesized to interact with alternative sites on SERCA that are distinct from the inhibitory site. The present study was undertaken to test these hypotheses and explore structural determinants of SERCA regulation by PLB. Steered molecular dynamics (SMD) and membrane protein-protein docking experiments were performed to investigate the apparent affinity of PLB interactions with candidate sites on SERCA. We modeled the relative binding of PLB to several different conformations of SERCA, representing different enzymatic states sampled during the calcium transport catalytic cycle. Overall, the SMD and docking experiments suggest that the canonical binding site is preferred, but also provide evidence for alternative sites that are favorable for certain conformational states of SERCA.

biophysics↗

bioRxiv2019

Affinity-engineered human antibodies detect celiac disease gluten pMHC complexes and inhibit T-cell activation

Antibodies specific for antigenic peptides bound to major histocompatibility complex (MHC) molecules are valuable tools for studies of antigen presentation. Such T-cell receptor (TCR)-like antibodies may also have therapeutic potential in human disease due to their ability to target disease-associated antigens with high specificity. We previously generated celiac disease (CeD) relevant TCR-like antibodies that recognize the prevalent gluten epitope DQ2.5-glia-1a in complex with HLA-DQ2.5. Here, we report on second-generation high-affinity antibodies towards this epitope as well as a panel of novel TCR-like antibodies to another immunodominant gliadin epitope, DQ2.5-glia-2. The strategy for affinity engineering was based on Rosetta modeling combined with pIX phage display and is applicable to similar protein engineering efforts. We isolated picomolar affinity binders and validated them in Fab and IgG format. Flow cytometry experiments with CeD biopsy material confirm the unique disease specificity of these TCR-like antibodies and reinforce the notion that B cells and plasma cells have a dominant role in gluten antigen presentation in the inflamed CeD gut. Further, the lead candidate 3.C11 potently inhibited CD4+ T-cell activation and proliferation in vitro in an HLA and epitope specific manner, pointing to a potential for targeted disease interception without compromising systemic immunity. Significance StatementConsumption of gluten-containing food drives celiac disease in genetically predisposed individuals. The underlying disease mechanism is not fully understood, but it is strictly dependent on activation of pathogenic T cells. We have engineered high-affinity human antibodies recognizing the T-cell target HLA-DQ2.5 in complex with gluten epitopes and studied cell-specific antigen presentation in patients, which shows that plasma cells and not dendritic cells dominate the inflamed tissue. The only available treatment is lifelong adherence to a gluten-free diet, which is difficult and not effective in all cases. We show that at least one of our antibodies can specifically inhibit activation of pathogenic T-cells in vitro and therefore shows promise for therapy.

immunology↗

bioRxiv2019

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37-45

CAPRI Rounds 37 through 45 introduced larger complexes, new macromolecules, and multi-stage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since Round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pre-generated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in Round 41. While the results were broadly encouraging, they also highlighted the pressing need to invest in (1) flexible docking algorithms with the ability to model loop and linker motions and in (2) new sampling and scoring methods for oligosaccharide-protein interactions.

biophysics↗

bioRxiv2019

Toward computational design of protein crystals with improved resolution

Substantial advances have been made in the computational design of protein interfaces over the last 20 years. However, the interfaces targeted by design have typically been stable and high affinity. Here, we report the development of a generic computational design method to stabilize the weak interactions at crystallographic interfaces. Initially, we analyzed structures reported in the Protein Data Bank (PDB) to determine whether crystals with more stable interfaces result in higher resolution structures. We found that, for twenty-two variants of a single protein crystallized by a single individual, Rosetta score correlates with resolution. We next developed and tested a computational design protocol, seeking to identify point mutations that would improve resolution on a highly stable variant of staphylococcal nuclease (SNase {Delta}+PHS). Only one of eleven initial designs crystallized, forcing us to re-evaluate our strategy and base our designs on an ensemble of protein backbones. Using this strategy, four of the five designed proteins crystallized. Collecting diffraction data for multiple crystals per design and solving crystal structures, we found that designed crystals improved resolution modestly and in unpredictable ways, including altering crystal space group. Post-hoc, in silico analysis showed that crystal space groups could have been predicted for four of six variants (including WT), but that resolution did not correlate with interface stability, as it did in the preliminary results. Our results show that single point mutations can have significant effects on crystal resolution and space group, and that it is possible to computationally identify such mutations, suggesting a potential design strategy to generate high-resolution protein crystals from poorly diffracting ones.

biophysics↗

bioRxiv2019

Protein structure prediction and design in a biologically-realistic implicit membrane

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, {Delta}{Delta}G calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.\n\nSignificance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

biophysics↗

bioRxiv2018

Flexible backbone assembly and refinement of symmetrical homomeric complexes

Symmetrical homomeric proteins are ubiquitous in every domain of life, and information about their structure is essential to decipher function. The size of these complexes often makes them intractable to high-resolution structure determination experiments. Computational docking algorithms offer a promising alternative for modeling large complexes with arbitrary symmetry. Accuracy of existing algorithms, however, is limited by backbone inaccuracies when using homology-modeled monomers. Here, we present Rosetta SymDock2 with a broad search of symmetrical conformational space using a six-dimensional coarse-grained score function followed by an all-atom flexible-backbone refinement, which we demonstrate to be essential for physically-realistic modeling of tightly packed complexes. In global docking of a benchmark set of complexes of different point symmetries -- staring from homology-modeled monomers -- we successfully dock (defined as predicting three near-native structures in the five top-scoring models) 19 out of 31 cyclic complexes and 5 out of 12 dihedral complexes.\n\nHighlightsO_LISymDock2 is an algorithm to assemble symmetric protein structures from monomers\nC_LIO_LICoarse-grained score function discriminates near-native conformations\nC_LIO_LIFlexible backbone refinement is necessary to create realistic all-atom models\nC_LIO_LIResults improve six-fold and outperform other symmetric docking algorithms\nC_LI\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/409730_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (64K):\norg.highwire.dtl.DTLVardef@1e4834borg.highwire.dtl.DTLVardef@167bd2eorg.highwire.dtl.DTLVardef@1b52510org.highwire.dtl.DTLVardef@1945a02_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2017

Repertoire analysis of antibody CDR-H3 loops suggests affinity maturation does not typically result in rigidification

Antibodies can rapidly evolve in specific response to antigens. Affinity maturation drives this evolution through cycles of mutation and selection leading to enhanced antibody specificity and affinity. Elucidating the biophysical mechanisms that underlie affinity maturation is fundamental to understanding B-cell immunity. An emergent hypothesis is that affinity maturation reduces the conformational flexibility of the antibodys antigen-binding paratope to minimize entropic losses incurred upon binding. In recent years, computational and experimental approaches have tested this hypothesis on a small number of antibodies, often observing a decrease in the flexibility of the Complementarity Determining Region (CDR) loops that typically comprise the paratope and in particular the CDR-H3 loop, which contributes a plurality of antigen contacts. However, there were a few exceptions, and previous studies were limited to a small handful of cases. Here, we determined the structural flexibility of the CDR-H3 loop for thousands of recently-determined homology models of the human peripheral blood cell antibody repertoire using rigidity theory. We found no clear delineation in the flexibility of naive and antigen-experienced antibodies. To account for possible sources of error, we additionally analyzed hundreds of human and mouse antibodies in the Protein Data Bank through both rigidity theory and B-factor analysis. By both metrics, we observed only a slight decrease in the CDR-H3 loop flexibility when comparing affinity-matured antibodies to naive antibodies, and the decrease was not as drastic as previously reported. Further analysis, incorporating molecular dynamics (MD) simulations, revealed a spectrum of changes in flexibility. Our results suggest that rigidification may be just one of many biophysical mechanisms for increasing affinity.

biophysics↗

bioRxiv2017

Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets

Computational prediction of protein-protein complex structures facilitates a fundamental understanding of biological mechanisms and enables therapeutics design. Binding-induced conformational changes challenge all current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased protein flexibility, these methods struggle with efficiency and high false positive rates. Here, we develop and benchmark a method that efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of nearnative candidate structures in the coarse-grained phase compared to a previous method. Further, the method adapts to the diversity of backbone conformations in the ensemble by modulating sampling rates. It samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes, and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, RosettaDock 4.0 can dock the complex successfully.\n\nSignificancePredicting binding-induced conformational plasticity in protein backbones remains a principal challenge in computational protein-protein docking. To date, there are no methods that can reliably dock proteins that undergo more than 1 [A] root-mean-squared-deviation of the backbones of the interface residues upon binding. Here, we present a method that samples backbone motions and scores conformations rapidly, obtaining-for the first time-successful docking of nearly 50% of flexible target complexes with backbone conformational change up to 2.2 [A] RMSD. This method will be applicable to a broader range of protein docking problems, which in turn will help us understand biomolecular assembly and protein function.

biophysics↗

bioRxiv2017

Comparison Of NMR And Crystal Structures Of Membrane Proteins And Computational Refinement To Improve Model Quality

Membrane proteins are challenging to study and restraints for structure determination are typically sparse or of low resolution because the membrane environment that surrounds them leads to a variety of experimental challenges. When membrane protein structures are determined by different techniques in different environments, a natural question is \"which structure is most biologically relevant?\" Towards answering this question, we compiled a dataset of membrane proteins with known structures determined by both solution NMR and X-ray crystallography. By investigating differences between the structures, we found that RMSDs between crystal and NMR structures are below 5 [A] in the membrane region, NMR ensembles have a higher convergence in the membrane region, crystal structures typically have a straighter transmembrane region, have higher stereo-chemical correctness, and are more tightly packed. After quantifying these differences, we used high-resolution refinement of the NMR structures to mitigate them, which paves the way for identifying and improving the structural quality of membrane proteins.

biophysics↗

bioRxiv2017

The Rosetta all-atom energy function for macromolecular modeling and design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosettas success is the energy function: amodel parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, beta_nov15. Applying these concepts,we explain how to use Rosetta energies to identify and analyze the features of biomolecular models.Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, carbohydrates, nucleic acids, and other macromolecules.

biophysics↗