CAPRI rounds 3-5 reveal promising successes and future challenges for RosettaDock

Bilirubin is a new ligand for nuclear receptor Liver Receptor Homolog-1

The nuclear receptor Liver Receptor Homolog-1 (LRH-1, NR5A2 ) binds to phospholipids that regulate important LRH-1 functions in the liver. A recent compound screen unexpectedly identified bilirubin, the product of liver heme metabolism, as a possible ligand for LRH-1. Here, we show unconjugated bilirubin directly binds LRH-1 with apparent K d =9.3uM, altering LRH-1 interaction with all transcriptional coregulator peptides tested. Bilirubin decreased LRH-1 protease sensitivity, consistent with MD simulations predicting bilirubin stably binds LRH-1 within the canonical ligand binding site. Bilirubin activated a luciferase reporter specific for LRH-1, dependent on co-expression with the bilirubin membrane transporter SLCO1B1 , but bilirubin failed to activate ligand-binding genetic mutants of LRH-1. Gene profiling in HepG2 cells shows bilirubin selectively regulated transcripts from endogenous LRH-1 ChIP-seq target genes, which was significantly attenuated by either genetic knockdown of LRH-1, or by a specific chemical competitor of LRH-1. Gene set enrichment suggests bilirubin and LRH-1 share roles in cholesterol metabolism and lipid efflux, thus we propose a new role for LRH-1 in directly sensing intracellular levels of bilirubin.

Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.

Molecular docking and molecular dynamics simulation studies on the adsorption/desorption behavior of bone morphogenetic protein-7 on the β-tricalcium phosphate surface

The adsorption/desorption behavior, and conformational and orientational changes of proteins on the surface of biomaterials are significant parameters for understanding how biomaterials perform their biological functions. In this study, for the first time, the interactions between BMP-7 and β-TCP (001) surface models with different ion-rich terminations (Ca-rich and P-rich) were investigated by molecular dynamics simulation (MD) and steered molecular dynamics simulation (SMD). The results indicated that BMP-7 preferentially interacts with both Ca-rich and P-rich β-TCP (001) surfaces at its wrist epitope residues with certain conformational changes, which led to more exposure of BMP-7 knuckle epitope residues to the environment and facilitation for binding to the type II receptor. Compared to the P-rich surface, it is speculated that the Ca-rich surface was more conducive to BMP-7 signal transduction since the upright orientation of the protein adsorption would lead to smaller hindrance for receptor binding. This study provided more atomistic and molecular information for better understanding the process of Ca-P surfaces affecting BMP-7 biological properties and further interpreted the osteoinductive mechanism from the perspective of growth factor adsorption. Moreover, the docking screening method adopted in this study is of guiding significance to the design and development of bioactive materials.

Efficient flexible backbone protein-protein docking for challenging targets

Motivation

Binding-induced conformational changes challenge 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 flexibility, these methods struggle with efficiency and high false positive rates.

Results

Here, we develop and benchmark RosettaDock 4.0, which 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 near-native candidate structures in the coarse-grained phase compared to RosettaDock 3.2. It adaptively samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20-80%. In local docking of a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate (defined as cases with ≥50% chance of achieving 3 near-native structures in the 5 top-ranked ones) 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, the method successfully docks the complex.

Availability and implementation

As a part of the Rosetta software suite, RosettaDock 4.0 is available at https://www.rosettacommons.org to all non-commercial users for free and to commercial users for a fee.

Supplementary information

Supplementary data are available at Bioinformatics online.

Implementation of an experimental and computational tool set to study protein-mAb interactions

This work focused on the development of a combined experimental and computational tool set to study protein-mAb interactions. A model protein library was first screened using cross interaction chromatography to identify proteins with the strongest retention. Fluorescence polarization was then employed to study the interactions and thermodynamics of the selected proteins-lactoferrin, pyruvate kinase, and ribonuclease B with the mAb. Binding affinities of lactoferrin and pyruvate kinase to the mAb were seen to be relatively salt insensitive in the range examined. Further, a strong entropic contribution was observed, suggesting the importance of hydrophobic interactions. On the other hand, ribonuclease B-mAb binding was seen to be enthalpically driven and salt sensitive, indicating the importance of electrostatic interactions. Protein-protein docking was then carried out and the results identified the CDR region on the mAb as an important binding site for all three proteins. The binding interfaces identified for the mAb-lactoferrin and mAb-pyruvate kinase systems were found to contain complementary hydrophobic and oppositely charged clusters on the interacting regions which were indicative of both hydrophobic and electrostatic interactions. On the other hand, the binding site on ribonuclease B was predominantly positively charged with minimal hydrophobicity. This resulted in an alignment with negatively charged clusters on the mAb, supporting the contention that these interactions were primarily electrostatic in nature. Importantly, these computational results were found to be consistent with the fluorescence polarization data and this combined approach may have utility in examining mAb-HCP interactions which can often complicate the downstream processing of biologics. © 2019 American Institute of Chemical Engineers.

Identification of a ubiquitin-binding interface using Rosetta and DEER

ExoU is a type III-secreted cytotoxin expressing A₂ phospholipase activity when injected into eukaryotic target cells by the bacterium Pseudomonas aeruginosa The enzymatic activity of ExoU is undetectable in vitro unless ubiquitin, a required cofactor, is added to the reaction. The role of ubiquitin in facilitating ExoU enzymatic activity is poorly understood but of significance for designing inhibitors to prevent tissue injury during infections with strains of P. aeruginosa producing this toxin. Most ubiquitin-binding proteins, including ExoU, demonstrate a low (micromolar) affinity for monoubiquitin (monoUb). Additionally, ExoU is a large and dynamic protein, limiting the applicability of traditional structural techniques such as NMR and X-ray crystallography to define this protein-protein interaction. Recent advancements in computational methods, however, have allowed high-resolution protein modeling using sparse data. In this study, we combine double electron-electron resonance (DEER) spectroscopy and Rosetta modeling to identify potential binding interfaces of ExoU and monoUb. The lowest-energy scoring model was tested using biochemical, biophysical, and biological techniques. To verify the binding interface, Rosetta was used to design a panel of mutations to modulate binding, including one variant with enhanced binding affinity. Our analyses show the utility of computational modeling when combined with sensitive biological assays and biophysical approaches that are exquisitely suited for large dynamic proteins.

Balance of Anti-CD123 Chimeric Antigen Receptor Binding Affinity and Density for the Targeting of Acute Myeloid Leukemia

Chimeric antigen receptor (CAR)-redirected T lymphocytes are a promising immunotherapeutic approach and object of pre-clinical evaluation for the treatment of acute myeloid leukemia (AML). We developed a CAR against CD123, overexpressed on AML blasts and leukemic stem cells. However, potential recognition of low CD123-positive healthy tissues, through the on-target, off-tumor effect, limits safe clinical employment of CAR-redirected T cells. Therefore, we evaluated the effect of context-dependent variables capable of modulating CAR T cell functional profiles, such as CAR binding affinity, CAR expression, and target antigen density. Computational structural biology tools allowed for the design of rational mutations in the anti-CD123 CAR antigen binding domain that altered CAR expression and CAR binding affinity without affecting the overall CAR design. We defined both lytic and activation antigen thresholds, with early cytotoxic activity unaffected by either CAR expression or CAR affinity tuning but later effector functions impaired by low CAR expression. Moreover, the anti-CD123 CAR safety profile was confirmed by lowering CAR binding affinity, corroborating CD123 is a good therapeutic target antigen. Overall, full dissection of these variables offers suitable anti-CD123 CAR design optimization for the treatment of AML.

Microsecond Molecular Dynamics Simulations of Influenza Neuraminidase Suggest a Mechanism for the Increased Virulence of Stalk-Deletion Mutants

Deletions in the stalk of the influenza neuraminidase (NA) surface protein are associated with increased virulence, but the mechanisms responsible for this enhanced virulence are unclear. Here we use microsecond molecular dynamics simulations to explore the effect of stalk deletion on enzymatic activity, contrasting NA proteins from the A/swine/Shandong/N1/2009 strain both with and without a stalk deletion. By modeling and simulating neuraminidase apo glycoproteins embedded in complex-mixture lipid bilayers, we show that the geometry and dynamics of the neuraminidase enzymatic pocket may differ depending on stalk length, with possible repercussions on the binding of the endogenous sialylated-oligosaccharide receptors. We also use these simulations to predict previously unrecognized druggable “hotspots” on the neuraminidase surface that may prove useful for future efforts aimed at structure-based drug design.

Discovery of small molecule inhibitors of MyD88-dependent signaling pathways using a computational screen

In this study, we used high-throughput computational screening to discover drug-like inhibitors of the host MyD88 protein-protein signaling interaction implicated in the potentially lethal immune response associated with Staphylococcal enterotoxins. We built a protein-protein dimeric docking model of the Toll-interleukin receptor (TIR)-domain of MyD88 and identified a binding site for docking small molecules. Computational screening of 5 million drug-like compounds led to testing of 30 small molecules; one of these molecules inhibits the TIR-TIR domain interaction and attenuates pro-inflammatory cytokine production in human primary cell cultures. Compounds chemically similar to this hit from the PubChem database were observed to be more potent with improved drug-like properties. Most of these 2^nd generation compounds inhibit Staphylococcal enterotoxin B (SEB)-induced TNF-α, IFN-γ, IL-6, and IL-1β production at 2–10 μM in human primary cells. Biochemical analysis and a cell-based reporter assay revealed that the most promising compound, T6167923, disrupts MyD88 homodimeric formation, which is critical for its signaling function. Furthermore, we observed that administration of a single dose of T6167923 completely protects mice from lethal SEB-induced toxic shock. In summary, our in silico approach has identified anti-inflammatory inhibitors against in vitro and in vivo toxin exposure with promise to treat other MyD88-related pro-inflammatory diseases.

Computational approaches to study the effects of small genomic variations

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

De novo peptide design and experimental validation of histone methyltransferase inhibitors

Histones are small proteins critical to the efficient packaging of DNA in the nucleus. DNA–protein complexes, known as nucleosomes, are formed when the DNA winds itself around the surface of the histones. The methylation of histone residues by enhancer of zeste homolog 2 (EZH2) maintains gene repression over successive cell generations. Overexpression of EZH2 can silence important tumor suppressor genes leading to increased invasiveness of many types of cancers. This makes the inhibition of EZH2 an important target in the development of cancer therapeutics. We employed a three-stage computational de novo peptide design method to design inhibitory peptides of EZH2. The method consists of a sequence selection stage and two validation stages for fold specificity and approximate binding affinity. The sequence selection stage consists of an integer linear optimization model that was solved to produce a rank-ordered list of amino acid sequences with increased stability in the bound peptide-EZH2 structure. These sequences were validated through the calculation of the fold specificity and approximate binding affinity of the designed peptides. Here we report the discovery of novel EZH2 inhibitory peptides using the de novo peptide design method. The computationally discovered peptides were experimentally validated in vitro using dose titrations and mechanism of action enzymatic assays. The peptide with the highest in vitro response, SQ037, was validated in nucleo using quantitative mass spectrometry-based proteomics. This peptide had an IC₅₀ of 13.5 M, demonstrated greater potency as an inhibitor when compared to the native and K27A mutant control peptides, and demonstrated competitive inhibition versus the peptide substrate. Additionally, this peptide demonstrated high specificity to the EZH2 target in comparison to other histone methyltransferases. The validated peptides are the first computationally designed peptides that directly inhibit EZH2. These inhibitors should prove useful for further chromatin biology investigations.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

Protein WISDOM: a workbench for in silico de novo design of biomolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Molecular dynamics in drug design: new generations of compstatin analogs

We report the computational and rational design of new generations of potential peptide-based inhibitors of the complement protein C3 from the compstatin family. The binding efficacy of the peptides is tested by extensive molecular dynamics-based structural and physicochemical analysis, using 32 atomic detail trajectories in explicit water for 22 peptides bound to human, rat or mouse target protein C3, with a total of 257 ns. The criteria for the new design are: (i) optimization for C3 affinity and for the balance between hydrophobicity and polarity to improve solubility compared to known compstatin analogs; and (ii) development of dual specificity, human-rat/mouse C3 inhibitors, which could be used in animal disease models. Three of the new analogs are analyzed in more detail as they possess strong and novel binding characteristics and are promising candidates for further optimization. This work paves the way for the development of an improved therapeutic for age-related macular degeneration, and other complement system-mediated diseases, compared to known compstatin variants.

Benchmarking and analysis of protein docking performance in Rosetta v3.2

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.

Anchored design of protein-protein interfaces

Background

Few existing protein-protein interface design methods allow for extensive backbone rearrangements during the design process. There is also a dichotomy between redesign methods, which take advantage of the native interface, and de novo methods, which produce novel binders.

Methodology

Here, we propose a new method for designing novel protein reagents that combines advantages of redesign and de novo methods and allows for extensive backbone motion. This method requires a bound structure of a target and one of its natural binding partners. A key interaction in this interface, the anchor, is computationally grafted out of the partner and into a surface loop on the design scaffold. The design scaffold's surface is then redesigned with backbone flexibility to create a new binding partner for the target. Careful choice of a scaffold will bring experimentally desirable characteristics into the new complex. The use of an anchor both expedites the design process and ensures that binding proceeds against a known location on the target. The use of surface loops on the scaffold allows for flexible-backbone redesign to properly search conformational space.

Conclusions and Significance

This protocol was implemented within the Rosetta3 software suite. To demonstrate and evaluate this protocol, we have developed a benchmarking set of structures from the PDB with loop-mediated interfaces. This protocol can recover the correct loop-mediated interface in 15 out of 16 tested structures, using only a single residue as an anchor.

Discovery of entry inhibitors for HIV-1 via a new de novo protein design framework

A new (to our knowledge) de novo design framework with a ranking metric based on approximate binding affinity calculations is introduced and applied to the discovery of what we believe are novel HIV-1 entry inhibitors. The framework consists of two stages: a sequence selection stage and a validation stage. The sequence selection stage produces a rank-ordered list of amino-acid sequences by solving an integer programming sequence selection model. The validation stage consists of fold specificity and approximate binding affinity calculations. The designed peptidic inhibitors are 12-amino-acids-long and target the hydrophobic core of gp41. A number of the best-predicted sequences were synthesized and their inhibition of HIV-1 was tested in cell culture. All peptides examined showed inhibitory activity when compared with no drug present, and the novel peptide sequences outperformed the native template sequence used for the design. The best sequence showed micromolar inhibition, which is a 3-15-fold improvement over the native sequence, depending on the donor. In addition, the best sequence equally inhibited wild-type and Enfuvirtide-resistant virus strains.

A generalized approach to sampling backbone conformations with RosettaDock for CAPRI rounds 13-19

In CAPRI rounds 13-19, the most native-like structure predicted by RosettaDock resulted in two high, one medium, and one acceptable accuracy model out of 13 targets. The current rounds of CAPRI were especially challenging with many unbound and homology modeled starting structures. Novel docking methods, including EnsembleDock and SnugDock, allowed backbone conformational sampling during docking and enabled the creation of more accurate models. For Target 32, α-amylase/subtilisin inhibitor-subtilisin savinase, we sampled different backbone conformations at an interfacial loop to produce five high-quality models including the most accurate structure submitted in the challenge (2.1 Å ligand rmsd, 0.52 Å interface rmsd). For Target 41, colicin-immunity protein, we used EnsembleDock to sample the ensemble of nuclear magnetic resonance (NMR) models of the immunity protein to generate a medium accuracy structure. Experimental data identifying the catalytic residues at the binding interface for Target 40 (trypsin-inhibitor) were used to filter RosettaDock global rigid body docking decoys to determine high accuracy predictions for the two distinct binding sites in which the inhibitor interacts with trypsin. We discuss our generalized approach to selecting appropriate methods for different types of docking problems. The current toolset provides some robustness to errors in homology models, but significant challenges remain in accommodating larger backbone uncertainties and in sampling adequately for global searches.

New compstatin variants through two de novo protein design frameworks

Two de novo protein design frameworks are applied to the discovery of new compstatin variants. One is based on sequence selection and fold specificity, whereas the other approach is based on sequence selection and approximate binding affinity calculations. The proposed frameworks were applied to a complex of C3c with compstatin variant E1 and new variants with improved binding affinities are predicted and experimentally validated. The computational studies elucidated key positions in the sequence of compstatin that greatly affect the binding affinity. Positions 4 and 13 were found to favor Trp, whereas positions 1, 9, and 10 are dominated by Asn, and position 11 consists mainly of Gln. A structural analysis of the C3c-bound peptide analogs is presented.

Structure-based design of peptides against G3BP with cytotoxicity on tumor cells

Herein, we report a successful application of molecular modeling techniques to design two novel peptides with cytotoxicity on tumor cells. First, the interactions between the nuclear transport factor 2 (NTF2)-like domain of G3BP and the SH3 domain of RasGAP were studied by a well-designed protocol, which combines homology modeling, protein/protein docking, molecular dynamics simulations, molecular mechanics/generalized born surface area (MM/GBSA) free energy calculations, and MM/GBSA free energy decomposition analysis together. Then, based on the theoretical predictions, two novel peptides were designed and synthesized for biological assays, and they showed an obvious sensitizing effect on cis-platin. Furthermore, the designed peptides had no significant effects on normal cells, while cis-platin did. Our results demonstrate that it is feasible to use the peptides to enhance the efficacy of clinical drugs and to kill cancer cells selectively. We believe that our work should be very useful for finding new therapies for cancers.

Refining near-native protein-protein docking decoys by local resampling and energy minimization

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 A or more and only 7% increased by 0.5 A or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.

Rapid structural characterization of human antibody-antigen complexes through experimentally validated computational docking

If we understand the structural rules governing antibody (Ab)-antigen (Ag) interactions in a given virus, then we have the molecular basis to attempt to design and synthesize new epitopes to be used as vaccines or optimize the antibodies themselves for passive immunization. Comparing the binding of several different antibodies to related Ags should also further our understanding of general principles of recognition. To obtain and compare the three-dimensional structure of a large number of different complexes, however, we need a faster method than traditional experimental techniques. While biocomputational docking is fast, its results might not be accurate. Combining experimental validation with computational prediction may be a solution. As a proof of concept, here we isolated a monoclonal Ab from the blood of a human donor recovered from dengue virus infection, characterized its immunological properties, and identified its epitope on domain III of dengue virus E protein through simple and rapid NMR chemical shift mapping experiments. We then obtained the three-dimensional structure of the Ab/Ag complex by computational docking, using the NMR data to drive and validate the results. In an attempt to represent the multiple conformations available to flexible Ab loops, we docked several different starting models and present the result as an ensemble of models equally agreeing with the experimental data. The Ab was shown to bind a region accessible only in part on the viral surface, explaining why it cannot effectively neutralize the virus.

Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors.

Reconstitution and engineering of apoptotic protein interactions on the bacterial cell surface

The interactions between pro- and anti-apoptotic members of the Bcl-2 class of proteins control whether a cell lives or dies, and the study of these protein-protein interactions has been an area of intense research. In this report, we describe a new tool for the study and engineering of apoptotic protein interactions that is based on the flow cytometric detection of these interactions on the surface of Escherichia coli. After validation of the assay with the well-studied interaction between the Bak(72-87) peptide and the anti-apoptotic protein Bcl-x(L), the effect of both increasing and decreasing Bak peptide length on Bcl-x(L) binding was investigated. Previous work demonstrated that the Bak(72-87) peptide also binds to the anti-apoptotic protein Bcl-2, albeit with lower binding affinity compared to Bcl-x(L). Here, we demonstrate that a slightly longer Bak peptide corresponding to amino acids 72-89 of Bak binds Bcl-x(L) and Bcl-2 equally well. Approximate binding affinity calculations on these peptide-protein complexes confirm the experimental observations. The flow cytometric assay was also used to screen a saturation mutagenesis library of Bak(72-87) variants for improved affinity to Bcl-x(L). The best variants obtained from this library exhibit an apparent K(d) to Bcl-x(L) 4-fold lower than that of wild-type Bak(72-87).

Annexin A2/P11 interaction: new insights into annexin A2 tetramer structure by chemical crosslinking, high-resolution mass spectrometry, and computational modeling

During the past few years, the structural analysis of proteins and protein complexes by chemical crosslinking and mass spectrometry has enjoyed increasing popularity. With this approach we have investigated the quaternary structure of the complex between annexin A2 and p11, which is involved in numerous cellular processes. Although high-resolution data are available for both interaction partners as well as for the complex between two p11 subunits and two annexin A2 N-terminal peptides, the structure of the complete annexin A2/p11 heterotetramer has not yet been solved at high resolution. Thus, the quaternary structure of the biologically relevant, membrane-bound annexin A2/p11 complex is still under discussion, while the existence of a heterotetramer or a heterooctamer is the prevailing opinion. We gained further insight into the spatial organization of the annexin A2/p11 heterotetramer by employing chemical crosslinking combined with high-resolution mass spectrometry. Furthermore, tandem mass spectrometry served as a tool for an exact localization of crosslinked amino acid residues and for a confirmation of crosslinked product assignment. On the basis of distance constraints from the crosslinking data we derived structural models of the annexin A2/p11 heterotetramer by computational docking with Rosetta. We propose an octameric model for the annexin A2/p11 complex, which exerts annexin A2 function. The proposed structure of the annexin A2/p11 octamer differs from so far suggested models and sheds new light into annexin A2/p11 interaction.

Solution- and adsorbed-state structural ensembles predicted for the statherin-hydroxyapatite system

We have developed a multiscale structure prediction technique to study solution- and adsorbed-state ensembles of biomineralization proteins. The algorithm employs a Metropolis Monte Carlo-plus-minimization strategy that varies all torsional and rigid-body protein degrees of freedom. We applied the technique to fold statherin, starting from a fully extended peptide chain in solution, in the presence of hydroxyapatite (HAp) (001), (010), and (100) monoclinic crystals. Blind (unbiased) predictions capture experimentally observed macroscopic and high-resolution structural features and show minimal statherin structural change upon adsorption. The dominant structural difference between solution and adsorbed states is an experimentally observed folding event in statherin's helical binding domain. Whereas predicted statherin conformers vary slightly at three different HAp crystal faces, geometric and chemical similarities of the surfaces allow structurally promiscuous binding. Finally, we compare blind predictions with those obtained from simulation biased to satisfy all previously published solid-state NMR (ssNMR) distance and angle measurements (acquired from HAp-adsorbed statherin). Atomic clashes in these structures suggest a plausible, alternative interpretation of some ssNMR measurements as intermolecular rather than intramolecular. This work demonstrates that a combination of ssNMR and structure prediction could effectively determine high-resolution protein structures at biomineral interfaces.

Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking

High-resolution homology models are useful in structure-based protein engineering applications, especially when a crystallographic structure is unavailable. Here, we report the development and implementation of RosettaAntibody, a protocol for homology modeling of antibody variable regions. The protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of V(L)-V(H) rigid-body orientation and CDR backbone and side-chain conformations. The protocol was tested on a benchmark of 54 antibody crystal structures. The median root mean square deviation (rmsd) of the antigen binding pocket comprised of all the CDR residues was 1.5 A with 80% of the targets having an rmsd lower than 2.0 A. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6, 1.9, 2.4, 3.1, and 6.0 A for very short (4-6 residues), short (7-9), medium (10-11), long (12-14) and very long (17-22) loops, respectively. When the set of ten top-scoring antibody homology models are used in local ensemble docking to antigen, a moderate-to-high accuracy docking prediction was achieved in seven of fifteen targets. This success in computational docking with high-resolution homology models is encouraging, but challenges still remain in modeling antibody structures for sequences with long H3 loops. This first large-scale antibody-antigen docking study using homology models reveals the level of "functional accuracy" of these structural models toward protein engineering applications.

A combination of rescoring and refinement significantly improves protein docking performance

To determine the structures of protein-protein interactions, protein docking is a valuable tool that complements experimental methods to characterize protein complexes. Although protein docking can often produce a near-native solution within a set of global docking predictions, there are sometimes predictions that require refinement to elucidate correct contacts and conformation. Previously, we developed the ZRANK algorithm to rerank initial docking predictions from ZDOCK, a docking program developed by our lab. In this study, we have applied the ZRANK algorithm toward refinement of protein docking models in conjunction with the protein docking program RosettaDock. This was performed by reranking global docking predictions from ZDOCK, performing local side chain and rigid-body refinement using RosettaDock, and selecting the refined model based on ZRANK score. For comparison, we examined using RosettaDock score instead of ZRANK score, and a larger perturbation size for the RosettaDock search, and determined that the larger RosettaDock perturbation size with ZRANK scoring was optimal. This method was validated on a protein-protein docking benchmark. For refining docking benchmark predictions from the newest ZDOCK version, this led to improved structures of top-ranked hits in 20 of 27 cases, and an increase from 23 to 27 cases with hits in the top 20 predictions. Finally, we optimized the ZRANK energy function using refined models, which provides a significant improvement over the original ZRANK energy function. Using this optimized function and the refinement protocol, the numbers of cases with hits ranked at number one increased from 12 to 19 and from 7 to 15 for two different ZDOCK versions. This shows the effective combination of independently developed docking protocols (ZDOCK/ZRANK, and RosettaDock), indicating that using diverse search and scoring functions can improve protein docking results.

Dissecting NGF interactions with TrkA and p75 receptors by structural and functional studies of an anti-NGF neutralizing antibody

The anti-nerve growth factor (NGF) monoclonal antibody alphaD11 is a potent antagonist that neutralizes the biological functions of its antigen in vivo. NGF antagonism is expected to be a highly effective and safe therapeutic approach in many pain states. A comprehensive functional and structural analysis of alphaD11 monoclonal antibody was carried out, showing its ability to neutralize NGF binding to either tropomyosine receptor kinase A (TrkA) or p75 receptors. The 3-D structure of the alphaD11 Fab fragment was solved at 1.7 A resolution. A computational docking model of the alphaD11 Fab-NGF complex, based on epitope mapping using a pool of 44 NGF mutants and experimentally validated by small-angle X-ray scattering, provided the structural basis for identifying the residues involved in alphaD11 Fab binding. The present study pinpoints loop II of NGF to be an important structural determinant for NGF biological activity mediated by TrkA receptor.

Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles

Accommodating backbone flexibility continues to be the most difficult challenge in computational docking of protein-protein complexes. Towards that end, we simulate four distinct biophysical models of protein binding in RosettaDock, a multiscale Monte-Carlo-based algorithm that uses a quasi-kinetic search process to emulate the diffusional encounter of two proteins and to identify low-energy complexes. The four binding models are as follows: (1) key-lock (KL) model, using rigid-backbone docking; (2) conformer selection (CS) model, using a novel ensemble docking algorithm; (3) induced fit (IF) model, using energy-gradient-based backbone minimization; and (4) combined conformer selection/induced fit (CS/IF) model. Backbone flexibility was limited to the smaller partner of the complex, structural ensembles were generated using Rosetta refinement methods, and docking consisted of local perturbations around the complexed conformation using unbound component crystal structures for a set of 21 target complexes. The lowest-energy structure contained >30% of the native residue-residue contacts for 9, 13, 13, and 14 targets for KL, CS, IF, and CS/IF docking, respectively. When applied to 15 targets using nuclear magnetic resonance ensembles of the smaller protein, the lowest-energy structure recovered at least 30% native residue contacts in 3, 8, 4, and 8 targets for KL, CS, IF, and CS/IF docking, respectively. CS/IF docking of the nuclear magnetic resonance ensemble performed equally well or better than KL docking with the unbound crystal structure in 10 of 15 cases. The marked success of CS and CS/IF docking shows that ensemble docking can be a versatile and effective method for accommodating conformational plasticity in docking and serves as a demonstration for the CS theory--that binding-competent conformers exist in the unbound ensemble and can be selected based on their favorable binding energies.

The RosettaDock server for local protein-protein docking

The RosettaDock server (http://rosettadock.graylab.jhu.edu) identifies low-energy conformations of a protein–protein interaction near a given starting configuration by optimizing rigid-body orientation and side-chain conformations. The server requires two protein structures as inputs and a starting location for the search. RosettaDock generates 1000 independent structures, and the server returns pictures, coordinate files and detailed scoring information for the 10 top-scoring models. A plot of the total energy of each of the 1000 models created shows the presence or absence of an energetic binding funnel. RosettaDock has been validated on the docking benchmark set and through the Critical Assessment of PRedicted Interactions blind prediction challenge.

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Protein binding sites undergo ligand specific conformational changes upon ligand binding. However, most docking protocols rely on a fixed conformation of the receptor, or on the prior knowledge of multiple conformations representing the variation of the pocket, or on a known bounding box for the ligand. Here we described a general induced fit docking protocol that requires only one initial pocket conformation and identifies most of the correct ligand positions as the lowest score. We expanded a previously used diverse "cross-docking" benchmark to thirty ligand-protein pairs extracted from different crystal structures. The algorithm systematically scans pairs of neighbouring side chains, replaces them by alanines, and docks the ligand to each 'gapped' version of the pocket. All docked positions are scored, refined with original side chains and flexible backbone and re-scored. In the optimal version of the protocol pairs of residues were replaced by alanines and only one best scoring conformation was selected from each 'gapped' pocket for refinement. The optimal SCARE (SCan Alanines and REfine) protocol identifies a near native conformation (under 2 angstroms RMSD) as the lowest rank for 80% of pairs if the docking bounding box is defined by the predicted pocket envelope, and for as many as 90% of the pairs if the bounding box is derived from the known answer with approximately 5 angstroms margin as used in most previous publications. The presented fully automated algorithm takes about 2 h per pose of a single processor time, requires only one pocket structure and no prior knowledge about the binding site location. Furthermore, the results for conformationally conserved pockets do not deteriorate due to substantial increase of the pocket variability.

Modeling the structure of mAb 14B7 bound to the anthrax protective antigen

The anthrax protective antigen (PA) is a key component of the tripartite anthrax toxin. Monoclonal antibody (mAb) 14B7 and its engineered, affinity-matured variants have been shown to be effective in blocking PA binding to cellular receptors and mitigating anthrax toxicity. Here, we perform computational structural modeling of the mAb 14B7-PA interaction. Our objectives are to determine the structure of the 14B7-PA complex, to deduce a structural explanation for the affinity maturation from the docking models, and to study the effect of inaccuracies in the antibody homology model on docking. We used the RosettaDock program to dock PA with the mAb 14B7 crystal structure or homology model. Our simulations generate two distinct binding orientations consistent with experimental residue mutations that diminish 14B7-PA binding. Furthermore, the models suggest new site-directed mutations to positively identify one of these two solutions as the correct 14B7-PA docking orientation. The models indicate that PA regions 648-660 and 712-720 may be important for 14B7 binding in addition to the known PA epitope, and the binding interfaces are similar to that seen in the PA complex with cellular receptor CMG2. Antibody residues involved in affinity maturation do not contact the antigen in the docking models, suggesting that affinity maturation in the 14B7 family does not result from direct enhancements of antibody-antigen contacts. Docking the homology model produces low-resolution representations of the crystal structure docking orientations, but homology model docking is frustrated by antibody H3 loop conformation errors. This work demonstrates the usefulness and limitations of computational structure prediction for the development of antibody therapeutics, and reemphasizes the need for flexible backbone docking algorithms to achieve high-resolution docking using homology models.

Incorporating biochemical information and backbone flexibility in RosettaDock for CAPRI rounds 6-12

In CAPRI rounds 6-12, RosettaDock successfully predicted 2 of 5 unbound-unbound targets to medium accuracy. Improvement over the previous method was achieved with computational mutagenesis to select decoys that match the energetics of experimentally determined hot spots. In the case of Target 21, Orc1/Sir1, this resulted in a successful docking prediction where RosettaDock alone or with simple site constraints failed. Experimental information also helped limit the interacting region of TolB/Pal, producing a successful prediction of Target 26. In addition, we docked multiple loop conformations for Target 20, and we developed a novel flexible docking algorithm to simultaneously optimize backbone conformation and rigid-body orientation to generate a wide diversity of conformations for Target 24. Continued challenges included docking of homology targets that differ substantially from their template (sequence identity <50%) and accounting for large conformational changes upon binding. Despite a larger number of unbound-unbound and homology model binding targets, Rounds 6-12 reinforced that RosettaDock is a powerful algorithm for predicting bound complex structures, especially when combined with experimental data.

Protein-protein docking: progress in CAPRI rounds 6-12 using a combination of methods: the introduction of steered solvated molecular dynamics

In recent rounds of CAPRI, the Bii group has employed a combination of techniques for the prediction of the structure of protein-protein complexes. We currently use third-party software for rigid-body and semiflexible docking (MolFit, 3D-Dock, RosettaDock), and our own steered molecular dynamics (SMD) technique for flexible refinement. SMD has also been found to be useful for discriminating near-native from false positive docking decoys. In addition to this, a variety of sources of information, including multiple descriptors of interface quality combined with a QSAR-like technique, published biological information, and continuum electrostatics calculations, are also used in the assessment of candidate complexes. We shall concentrate on results for CAPRI rounds 9-11 (targets 24-27). In these rounds, the Bii group has been successful in submitting a medium quality model for each of CAPRI targets 25 and 26, and a model of acceptable quality for target 27.

Using the natural evolution of a rotavirus-specific human monoclonal antibody to predict the complex topography of a viral antigenic site

BACKGROUND

Understanding the interaction between viral proteins and neutralizing antibodies at atomic resolution is hindered by a lack of experimentally solved complexes. Progress in computational docking has led to the prediction of increasingly high-quality model antibody-antigen complexes. The accuracy of atomic-level docking predictions is improved when integrated with experimental information and expert knowledge.

METHODS

Binding affinity data associated with somatic mutations of a rotavirus-specific human adult antibody (RV6-26) are used to filter potential docking orientations of an antibody homology model with respect to the rotavirus VP6 crystal structure. The antibody structure is used to probe the VP6 trimer for candidate interface residues.

RESULTS

Three conformational epitopes are proposed. These epitopes are candidate antigenic regions for site-directed mutagenesis of VP6, which will help further elucidate antigenic function. A pseudo-atomic resolution RV6-26 antibody-VP6 complex is proposed consistent with current experimental information.

CONCLUSION

The use of mutagenesis constraints in docking calculations allows for the identification of a small number of alternative arrangements of the antigen-antibody interface. The mutagenesis information from the natural evolution of a neutralizing antibody can be used to discriminate between residue-scale models and create distance constraints for atomic-resolution docking. The integration of binding affinity data or other information with computation may be an advantageous approach to assist peptide engineering or therapeutic antibody design.

FastContact: a free energy scoring tool for protein-protein complex structures

‘FastContact’ is a server that estimates the direct electrostatic and desolvation interaction free energy between two proteins in units of kcal/mol. Users submit two proteins in PDB format, and the output is emailed back to the user in three files: one output file, and the two processed proteins. Besides the electrostatic and desolvation free energy, the server reports residue contact free energies that rapidly highlight the hotspots of the interaction and evaluates the van der Waals interaction using CHARMm. Response time is ∼1 min. The server has been successfully tested and validated, scoring refined complex structures and blind sets of docking decoys, as well as proven useful predicting protein interactions. ‘FastContact’ offers unique capabilities from biophysical insights to scoring and identifying important contacts.

A new protein-protein docking scoring function based on interface residue properties

MOTIVATION

Protein-protein complexes are known to play key roles in many cellular processes. However, they are often not accessible to experimental study because of their low stability and difficulty to produce the proteins and assemble them in native conformation. Thus, docking algorithms have been developed to provide an in silico approach of the problem. A protein-protein docking procedure traditionally consists of two successive tasks: a search algorithm generates a large number of candidate solutions, and then a scoring function is used to rank them.

RESULTS

To address the second step, we developed a scoring function based on a Voronoï tessellation of the protein three-dimensional structure. We showed that the Voronoï representation may be used to describe in a simplified but useful manner, the geometric and physico-chemical complementarities of two molecular surfaces. We measured a set of parameters on native protein-protein complexes and on decoys, and used them as attributes in several statistical learning procedures: a logistic function, Support Vector Machines (SVM), and a genetic algorithm. For the later, we used ROGER, a genetic algorithm designed to optimize the area under the receiver operating characteristics curve. To further test the scores derived with ROGER, we ranked models generated by two different docking algorithms on targets of a blind prediction experiment, improving in almost all cases the rank of native-like solutions.

AVAILABILITY

http://genomics.eu.org/spip/-Bioinformatics-tools-

GRAMM-X public web server for protein-protein docking

Protein docking software GRAMM-X and its web interface () extend the original GRAMM Fast Fourier Transformation methodology by employing smoothed potentials, refinement stage, and knowledge-based scoring. The web server frees users from complex installation of database-dependent parallel software and maintaining large hardware resources needed for protein docking simulations. Docking problems submitted to GRAMM-X server are processed by a 320 processor Linux cluster. The server was extensively tested by benchmarking, several months of public use, and participation in the CAPRI server track.

Structural model of the mAb 806-EGFR complex using computational docking followed by computational and experimental mutagenesis

In this work, we combined computational protein-protein docking with computational and experimental mutagenesis to predict the structure of the complex formed by monoclonal antibody 806 (mAb 806) and the epidermal growth factor receptor (EGFR). We docked mAb 806, an antitumor antibody, to its epitope of EGFR residues 287-302. Potential mAb 806-EGFR orientations were generated, and computational mutagenesis was used to filter them according to their agreement with experimental mutagenesis data. Further computational mutagenesis suggested additional mutations, which were tested to arrive at a final structure that was most consistent with experimental mutagenesis data. We propose that this is the EGFR-mAb 806 structure, in which mAb 806 binds to an untethered form of the receptor, consistent with published experimental results. The steric hindrance created by the antibody near the EGFR dimer interface interferes with receptor dimerization, and we postulate this as the structural origin for the antitumor effect of mAb 806.

Flexible protein–protein docking

Predicting the structure of protein-protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Flexibility can be addressed at several levels: implicitly, by smoothing the protein surfaces or allowing some degree of interpenetration (soft docking) or by performing multiple docking runs from various conformations (cross or ensemble docking); or explicitly, by allowing sidechain and/or backbone flexibility. Although significant improvements have been achieved in the modeling of sidechains, methods for the explicit inclusion of backbone flexibility in docking are still being developed. A few novel approaches have emerged involving collective degrees of motion, multicopy representations and multibody docking, which should allow larger conformational changes to be modeled.

High-resolution protein-protein docking

The high-resolution prediction of protein-protein docking can now create structures with atomic-level accuracy. This progress arises from both improvements in the rapid sampling of conformations and increased accuracy of binding free energy calculations. Consequently, the quality of models submitted to the blind prediction challenge CAPRI (Critical Assessment of PRedicted Interactions) has steadily increased, including complexes predicted from homology structures of one binding partner and complexes with atomic accuracy at the interface. By exploiting experimental information, docking has created model structures for real applications, even when confronted with challenges such as moving backbones and uncertain monomer structures. Work remains to be done in docking large or flexible proteins, ranking models consistently, and producing models accurate enough to allow computational design of higher affinities or specificities.

Scoring a diverse set of high-quality docked conformations: A metascore based on electrostatic and desolvation interactions

Predicting protein-protein interactions involves sampling and scoring docked conformations. Barring some large structural rearrangement, rapidly sampling the space of docked conformations is now a real possibility, and the limiting step for the successful prediction of protein interactions is the scoring function used to reduce the space of conformations from billions to a few, and eventually one high affinity complex. An atomic level free-energy scoring function that estimates in units of kcal/mol both electrostatic and desolvation interactions (plus van der Waals if appropriate) of protein-protein docked conformations is used to rerank the blind predictions (860 in total) submitted for six targets to the community-wide Critical Assessment of PRediction of Interactions (CAPRI; http://capri.ebi.ac.uk). We found that native-like models often have varying intermolecular contacts and atom clashes, making unlikely that one can construct a universal function that would rank all these models as native-like. Nevertheless, our scoring function is able to consistently identify the native-like complexes as those with the lowest free energy for the individual models of 16 (out of 17) human predictors for five of the targets, while at the same time the modelers failed to do so in more than half of the cases. The scoring of high-quality models developed by a wide variety of methods and force fields confirms that electrostatic and desolvation forces are the dominant interactions determining the bound structure. The CAPRI experiment has shown that modelers can predict valuable models of protein-protein complexes, and improvements in scoring functions should soon solve the docking problem for complexes whose backbones do not change much upon binding. A scoring server and programs are available at http://structure.pitt.edu.