Automatic sequence design of major histocompatibility complex class I binding peptides impairing CD8+ T cell recognition

Deterministic Search Methods for Computational Protein Design

One main challenge in Computational Protein Design (CPD) lies in the exploration of the amino-acid sequence space, while considering, to some extent, side chain flexibility. The exorbitant size of the search space urges for the development of efficient exact deterministic search methods enabling identification of low-energy sequence-conformation models, corresponding either to the global minimum energy conformation (GMEC) or an ensemble of guaranteed near-optimal solutions. In contrast to stochastic local search methods that are not guaranteed to find the GMEC, exact deterministic approaches always identify the GMEC and prove its optimality in finite but exponential worst-case time. After a brief overview on these two classes of methods, we discuss the grounds and merits of four deterministic methods that have been applied to solve CPD problems. These approaches are based either on the Dead-End-Elimination theorem combined with A* algorithm (DEE/A*), on Cost Function Networks algorithms (CFN), on Integer Linear Programming solvers (ILP) or on Markov Random Fields solvers (MRF). The way two of these methods (DEE/A* and CFN) can be used in practice to identify low-energy sequence-conformation models starting from a pairwise decomposed energy matrix is detailed in this review.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Redesigning the stereospecificity of tyrosyl-tRNA synthetase

D-Amino acids are largely excluded from protein synthesis, yet they are of great interest in biotechnology. Unnatural amino acids have been introduced into proteins using engineered aminoacyl-tRNA synthetases (aaRSs), and this strategy might be applicable to D-amino acids. Several aaRSs can aminoacylate their tRNA with a D-amino acid; of these, tyrosyl-tRNA synthetase (TyrRS) has the weakest stereospecificity. We use computational protein design to suggest active site mutations in Escherichia coli TyrRS that could increase its D-Tyr binding further, relative to L-Tyr. The mutations selected all modify one or more sidechain charges in the Tyr binding pocket. We test their effect by probing the aminoacyl-adenylation reaction through pyrophosphate exchange experiments. We also perform extensive alchemical free energy simulations to obtain L-Tyr/D-Tyr binding free energy differences. Agreement with experiment is good, validating the structural models and detailed thermodynamic predictions the simulations provide. The TyrRS stereospecificity proves hard to engineer through charge-altering mutations in the first and second coordination shells of the Tyr ammonium group. Of six mutants tested, two are active towards D-Tyr; one of these has an inverted stereospecificity, with a large preference for D-Tyr. However, its activity is low. Evidently, the TyrRS stereospecificity is robust towards charge rearrangements near the ligand. Future design may have to consider more distant and/or electrically neutral target mutations, and possibly design for binding of the transition state, whose structure however can only be modeled.

Protein:Ligand binding free energies: A stringent test for computational protein design

A computational protein design method is extended to allow Monte Carlo simulations where two ligands are titrated into a protein binding pocket, yielding binding free energy differences. These provide a stringent test of the physical model, including the energy surface and sidechain rotamer definition. As a test, we consider tyrosyl-tRNA synthetase (TyrRS), which has been extensively redesigned experimentally. We consider its specificity for its substrate l-tyrosine (l-Tyr), compared to the analogs d-Tyr, p-acetyl-, and p-azido-phenylalanine (ac-Phe, az-Phe). We simulate l- and d-Tyr binding to TyrRS and six mutants, and compare the structures and binding free energies to a more rigorous "MD/GBSA" procedure: molecular dynamics with explicit solvent for structures and a Generalized Born + Surface Area model for binding free energies. Next, we consider l-Tyr, ac- and az-Phe binding to six other TyrRS variants. The titration results are sensitive to the precise rotamer definition, which involves a short energy minimization for each sidechain pair to help relax bad contacts induced by the discrete rotamer set. However, when designed mutant structures are rescored with a standard GBSA energy model, results agree well with the more rigorous MD/GBSA. As a third test, we redesign three amino acid positions in the substrate coordination sphere, with either l-Tyr or d-Tyr as the ligand. For two, we obtain good agreement with experiment, recovering the wildtype residue when l-Tyr is the ligand and a d-Tyr specific mutant when d-Tyr is the ligand. For the third, we recover His with either ligand, instead of wildtype Gln.

Pairwise decomposition of an MMGBSA energy function for computational protein design

Computational protein design (CPD) aims at predicting new proteins or modifying existing ones. The computational challenge is huge as it requires exploring an enormous sequence and conformation space. The difficulty can be reduced by considering a fixed backbone and a discrete set of sidechain conformations. Another common strategy consists in precalculating a pairwise energy matrix, from which the energy of any sequence/conformation can be quickly obtained. In this work, we examine the pairwise decomposition of protein MMGBSA energy functions from a general theoretical perspective, and an implementation proposed earlier for CPD. It includes a Generalized Born term, whose many-body character is overcome using an effective dielectric environment, and a Surface Area term, for which we present an improved pairwise decomposition. A detailed evaluation of the error introduced by the decomposition on the different energy components is performed. We show that the error remains reasonable, compared to other uncertainties.

Computational protein design with electrostatic focusing: experimental characterization of a conditionally folded helical domain with a reduced amino acid alphabet

Automated methodologies to design synthetic proteins from first principles use energy computations to estimate the ability of the sequences to adopt a targeted structure. This approach is still far from systematically producing native-like sequences, due, most likely, to inaccuracies when modeling the interactions between the protein and its aqueous environment. This is particularly challenging when engineering small protein domains (with less polar pair interactions than with the solvent). We have re-designed a three-helix bundle, domain B, using a fixed backbone and a four amino acid alphabet. We have enlarged the rotamer library with conformers that increase the weight of electrostatic interactions within the design process without altering the energy function used to compute the folding free energy. Our synthetic sequences show less than 15% similarity to any Swissprot sequence. We have characterized our sequences in different solvents using circular dichroism and nuclear magnetic resonance. The targeted structure achieved is dependent on the solvent used. This method can be readily extended to larger domains. Our method will be useful for the engineering of proteins that become active only in a given solvent and for designing proteins in the context of hydrophobic solvents, an important fraction of the situations in the cell.

Computational approaches for rational design of proteins with novel functionalities

Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes.

Computational protein design with a generalized Born solvent model: application to Asparaginyl-tRNA synthetase

Computational Protein Design (CPD) is a promising method for high throughput protein and ligand mutagenesis. Recently, we developed a CPD method that used a polar-hydrogen energy function for protein interactions and a Coulomb/Accessible Surface Area (CASA) model for solvent effects. We applied this method to engineer aspartyl-adenylate (AspAMP) specificity into Asparaginyl-tRNA synthetase (AsnRS), whose substrate is asparaginyl-adenylate (AsnAMP). Here, we implement a more accurate function, with an all-atom energy for protein interactions and a residue-pairwise generalized Born model for solvent effects. As a first test, we compute aminoacid affinities for several point mutants of Aspartyl-tRNA synthetase (AspRS) and Tyrosyl-tRNA synthetase and stability changes for three helical peptides and compare with experiment. As a second test, we readdress the problem of AsnRS aminoacid engineering. We compare three design criteria, which optimize the folding free-energy, the absolute AspAMP affinity, and the relative (AspAMP-AsnAMP) affinity. The sequences and conformations are improved with respect to our previous, polar-hydrogen/CASA study: For several designed complexes, the AspAMP carboxylate forms three interactions with a conserved arginine and a designed lysine, as in the active site of the AspRS:AspAMP complex. The conformations and interactions are well maintained in molecular dynamics simulations and the sequences have an inverted specificity, favoring AspAMP over AsnAMP. The method is not fully successful, since experimental measurements with the seven most promising sequences show that they do not catalyze at a detectable level the adenylation of Asp (or Asn) with ATP. This may be due to weak AspAMP binding and/or disruption of transition-state stabilization.

Pushing the limits of automatic computational protein design: design, expression, and characterization of a large synthetic protein based on a fungal laccase scaffold

The de novo engineering of new proteins will allow the design of complex systems in synthetic biology. But the design of large proteins is very challenging due to the large combinatorial sequence space to be explored and the lack of a suitable selection system to guide the evolution and optimization. One way to approach this challenge is to use computational design methods based on the current crystallographic data and on molecular mechanics. We have used a laccase protein fold as a scaffold to design a new protein sequence that would adopt a 3D conformation in solution similar to a wild-type protein, the Trametes versicolor (TvL) fungal laccase. Laccases are multi-copper oxidases that find utility in a variety of industrial applications. The laccases with highest activity and redox potential are generally secreted fungal glycoproteins. Prokaryotic laccases have been identified with some desirable features, but they often exhibit low redox potentials. The designed sequence (DLac) shares a 50% sequence identity to the original TvL protein. The new DLac gene was overexpressed in E. coli and the majority of the protein was found in inclusion bodies. Both soluble protein and refolded insoluble protein were purified, and their identity was verified by mass spectrometry. Neither protein exhibited the characteristic T1 copper absorbance, neither bound copper by atomic absorption, and neither was active using a variety of laccase substrates over a range of pH values. Circular dichroism spectroscopy studies suggest that the DLac protein adopts a molten globule structure that is similar to the denatured and refolded native fungal TvL protein, which is significantly different from the natively secreted fungal protein. Taken together, these results indicate that the computationally designed DLac expressed in E. coli is unable to utilize the same folding pathway that is used in the expression of the parent TvL protein or the prokaryotic laccases. This sequence can be used going forward to help elucidate the sequence requirements needed for prokaryotic multi-copper oxidase expression.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-011-9080-9) contains supplementary material, which is available to authorized users.

Computational design of protein-ligand binding: modifying the specificity of asparaginyl-tRNA synthetase

A method for computational design of protein-ligand interactions is implemented and tested on the asparaginyl- and aspartyl-tRNA synthetase enzymes (AsnRS, AspRS). The substrate specificity of these enzymes is crucial for the accurate translation of the genetic code. The method relies on a molecular mechanics energy function and a simple, continuum electrostatic, implicit solvent model. As test calculations, we first compute AspRS-substrate binding free energy changes due to nine point mutations, for which experimental data are available; we also perform large-scale redesign of the entire active site of each enzyme (40 amino acids) and compare to experimental sequences. We then apply the method to engineer an increased binding of aspartyl-adenylate (AspAMP) into AsnRS. Mutants are obtained using several directed evolution protocols, where four or five amino acid positions in the active site are randomized. Promising mutants are subjected to molecular dynamics simulations; Poisson-Boltzmann calculations provide an estimate of the corresponding, AspAMP, binding free energy changes, relative to the native AsnRS. Several of the mutants are predicted to have an inverted binding specificity, preferring to bind AspAMP rather than the natural substrate, AsnAMP. The computed binding affinities are significantly weaker than the native, AsnRS:AsnAMP affinity, and in most cases, the active site structure is significantly changed, compared to the native complex. This almost certainly precludes catalytic activity. One of the designed sequences has a higher affinity and more native-like structure and may represent a valid candidate for Asp activity.

Challenges in the computational design of proteins

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

Incorporating receptor flexibility in the molecular design of protein interfaces

The success of antibody-based pharmaceuticals has led to a resurgence in interest in computational structure-based design. Most efforts to date have been on the redesign of existing interfaces. These efforts have mostly neglected the inherent flexibility of the receptor that is critical for binding. In this work, we extend on a previous study to perform a series of designs of protein binding interfaces by incorporating receptor flexibility using an ensemble of conformers collected from explicit-solvent molecular dynamics (MD) simulations. All designer complexes are subjected to 30 ns of MD in explicit solvent to assess for stability for a total of 480 ns of dynamics. This is followed by end-point free energy calculations whereby intermolecular potential energy, polar and non-polar solvation energy and entropy of ligand and receptor are subtracted from that of the complex and averaged over 320 snapshots collected from each of the 30 ns MD simulations. Our initial effort consisted of redesigning the interface of three well-studied complexes, namely barnase-barstar, lysozyme-antibody D1.3 and trypsin-BPTI. The design was performed with flexible backbone approach. MD simulations revealed that all three complexes remained stable. Interestingly, the redesigned trypsin-BPTI complex was significantly more favorable than the native complex. This was attributed to the favorable electrostatics and entropy that complemented the already favorable non-polar component. Another aspect of this work consisted of grafting the surface of three proteins, namely tenascin, CheY and MBP1 to bind to barnase, trypsin and lysozyme. The process was initially performed using fixed backbone, and more than 300 ns of the explicit-solvent MD simulation revealed some of the complexes to dissociate over the course of the trajectories, whereas others remained stable. Free energy calculations confirmed that the non-polar component of the free energy as computed by summing the van der Waals energy and the non-polar solvation energy was a strong predictor of stability. Four complexes (two stable and two unstable) were selected, and redesigned using multiple conformers collected from the MD simulation. The resulting designer systems were then immersed in explicit solvent and 30 ns of MD was carried out on each. Interestingly, those complexes that were initially stable remained stable, whereas one of the unstable complexes became stable following redesign with flexible backbone. Free energy calculations showed significant improvements in the affinity for most complexes, revealing that the use of multiple conformers in protein design may significantly enhance such efforts.

PROTDES: CHARMM toolbox for computational protein design

We present an open-source software able to automatically mutate any residue positions and find the best aminoacids in an arbitrary protein structure without requiring pairwise approximations. Our software, PROTDES, is based on CHARMM and it searches automatically for mutations optimizing a protein folding free energy. PROTDES allows the integration of molecular dynamics within the protein design. We have implemented an heuristic optimization algorithm that iteratively searches the best aminoacids and their conformations for an arbitrary set of positions within a structure. Our software allows CHARMM users to perform protein design calculations and to create their own procedures for protein design using their own energy functions. We show this by implementing three different energy functions based on different solvent treatments: surface area accessibility, generalized Born using molecular volume and an effective energy function. PROTDES, a tutorial, parameter sets, configuration tools and examples are freely available at http://soft.synth-bio.org/protdes.html.

Computational protein design with side-chain conformational entropy

Recent advances in modeling protein structures at the atomic level have made it possible to tackle "de novo" computational protein design. Most procedures are based on combinatorial optimization using a scoring function that estimates the folding free energy of a protein sequence on a given main-chain structure. However, the computation of the conformational entropy in the folded state is generally an intractable problem, and its contribution to the free energy is not properly evaluated. In this article, we propose a new automated protein design methodology that incorporates such conformational entropy based on statistical mechanics principles. We define the free energy of a protein sequence by the corresponding partition function over rotamer states. The free energy is written in variational form in a pairwise approximation and minimized using the Belief Propagation algorithm. In this way, a free energy is associated to each amino acid sequence: we use this insight to rescore the results obtained with a standard minimization method, with the energy as the cost function. Then, we set up a design method that directly uses the free energy as a cost function in combination with a stochastic search in the sequence space. We validate the methods on the design of three superficial sites of a small SH3 domain, and then apply them to the complete redesign of 27 proteins. Our results indicate that accounting for entropic contribution in the score function affects the outcome in a highly nontrivial way, and might improve current computational design techniques based on protein stability.

Characterization of T-cell immunogenicity of two PE/PPE proteins of Mycobacterium tuberculosis

The PE and PPE proteins of Mycobacterium tuberculosis form a source of antigenic variation among different strains of this bacterium. Two of the PE_PGRS protein-encoding genes, rv3812 and rv3018c, are expressed in pathogenic mycobacteria and are implicated, respectively, in the persistence of the organism in macrophages and in virulence. Peptides derived from these proteins have been predicted to bind major histocompatibility complex (MHC) class I with high affinity on the basis of immunoinformatics analysis, suggesting a possible role for these proteins in antimycobacterial immunity. In the present work, using DNA constructs containing the rv3812 and rv3018c genes of M. tuberculosis, the immunogenicity of these proteins was demonstrated in BALB/c mice. Immunization with either DNA construct induced a significant number of CD8+-type T cells and a strong Th1-type response, with high gamma interferon (IFN-gamma) and low interleukin-4 responses. Three nonameric peptides of Rv3812 and two of Rv3018c elicited a strong T-cell response in an MHC-restricted manner. An epitope-specific response was demonstrated by the lysis of peptide-pulsed antigen-presenting cells, release of perforin and IFN-gamma production. Experimentally, these peptides bound with high affinity to MHC H-2Kd and showed low dissociation rates of peptide-MHC complexes. This study suggests that the identified T-cell epitopes may contribute to immunity against tuberculosis if included in a vaccine.

Modeling backbone flexibility to achieve sequence diversity: the design of novel alpha-helical ligands for Bcl-xL

Computational protein design can be used to select sequences that are compatible with a fixed-backbone template. This strategy has been used in numerous instances to engineer novel proteins. However, the fixed-backbone assumption severely restricts the sequence space that is accessible via design. For challenging problems, such as the design of functional proteins, this may not be acceptable. Here, we present a method for introducing backbone flexibility into protein design calculations and apply it to the design of diverse helical BH3 ligands that bind to the anti-apoptotic protein Bcl-xL, a member of the Bcl-2 protein family. We demonstrate how normal mode analysis can be used to sample different BH3 backbones, and show that this leads to a larger and more diverse set of low-energy solutions than can be achieved using a native high-resolution Bcl-xL complex crystal structure as a template. We tested several of the designed solutions experimentally and found that this approach worked well when normal mode calculations were used to deform a native BH3 helix structure, but less well when they were used to deform an idealized helix. A subsequent round of design and testing identified a likely source of the problem as inadequate sampling of the helix pitch. In all, we tested 17 designed BH3 peptide sequences, including several point mutants. Of these, eight bound well to Bcl-xL and four others showed weak but detectable binding. The successful designs showed a diversity of sequences that would have been difficult or impossible to achieve using only a fixed backbone. Thus, introducing backbone flexibility via normal mode analysis effectively broadened the set of sequences identified by computational design, and provided insight into positions important for binding Bcl-xL.

Computational sidechain placement and protein mutagenesis with implicit solvent models

Structure prediction and computational protein design should benefit from accurate solvent models. We have applied implicit solvent models to two problems that are central to this area. First, we performed sidechain placement for 29 proteins, using a solvent model that combines a screened Coulomb term with an Accessible Surface Area term (CASA model). With optimized parameters, the prediction quality is comparable with earlier work that omitted electrostatics and solvation altogether. Second, we computed the stability changes associated with point mutations involving ionized sidechains. For over 1000 mutations, including many fully or partly buried positions, we compared CASA and two generalized Born models (GB) with a more accurate model, which solves the Poisson equation of continuum electrostatics numerically. CASA predicts the correct sign and order of magnitude of the stability change for 81% of the mutations, compared to 97% with the best GB. We also considered 140 mutations for which experimental data are available. Comparing to experiment requires additional assumptions about the unfolded protein structure, protein relaxation in response to the mutations, and contributions from the hydrophobic effect. With a simple, commonly-used unfolded state model, the mean unsigned error is 2.1 kcal/mol with both CASA and the best GB. Overall, the electrostatic model is not important for sidechain placement; CASA and GB are equivalent for surface mutations, while GB is far superior for fully or partly buried positions. Thus, for problems like protein design that involve all these aspects, the most recent GB models represent an important step forward. Along with the recent discovery of efficient, pairwise implementations of GB, this will open new possibilities for the computational engineering of proteins.

Evaluation of T-cell responses to peptides with MHC class I-binding motifs derived from PE_PGRS 33 protein of Mycobacterium tuberculosis

The PE and PPE proteins of Mycobacterium tuberculosis form a source of antigenic variation among different strains of M. tuberculosis. One of the PE_PGRS proteins, Rv1818c, plays a role in the pathogenesis of mycobacterial infection and specifically influences host-cell responses to tuberculosis infection. Although little is known about these two classes of protein, an immunoinformatics approach has indicated the possibility of their participation in eliciting a major histocompatibility complex (MHC) class I-mediated immune response against tuberculosis, as peptides derived from Rv1818c are predicted to bind to MHC class I molecules with high affinity. In the present work, a DNA vaccine was constructed encoding the full-length Rv1818c protein of M. tuberculosis and its immunogenicity was analysed in BALB/c mice. Immunization with Rv1818c DNA induced a strong CD8+ cytotoxic lymphocyte and Th1-type response, with high levels of gamma interferon (IFN-gamma) and low levels of interleukin-4. Two nonameric peptides (Peptide(6-14) and Peptide(385-393)) from Rv1818c were identified by their ability to induce the production of IFN-gamma by CD8+ T cells in mice immunized with Rv1818c DNA. An epitope-specific response was demonstrated by the lysis of peptide-pulsed antigen-presenting cells, release of cytotoxic granules and IFN-gamma production. These peptides bound with high affinity to MHC H-2K(d) and showed low dissociation rates of peptide-MHC complexes. These results could form the basis for testing the identified T-cell epitopes of PE_PGRS proteins in the induction of protective immunity against M. tuberculosis challenge in the mouse model.

A Monte Carlo sampling method of amino acid sequences adaptable to given main-chain atoms in the proteins

We have developed a computational method of protein design to detect amino acid sequences that are adaptable to given main-chain coordinates of a protein. In this method, the selection of amino acid types employs a Metropolis Monte Carlo method with a scoring function in conjunction with the approximation of free energies computed from 3D structures. To compute the scoring function, a side-chain prediction using another Metropolis Monte Carlo method was performed to select structurally suitable side-chain conformations from a side-chain library. In total, two layers of Monte Carlo procedures were performed, first to select amino acid types (1st layer Monte Carlo) and then to predict side-chain conformations (2nd layers Monte Carlo). We applied this method to sequence design for the entire sequence on the SH3 domain, Protein G, and BPTI. The predicted sequences were similar to those of the wild-type proteins. We compared the results of the predictions with and without the 2nd layer Monte Carlo method. The results revealed that the two-layer Monte Carlo method produced better sequence similarity to the wild-type proteins than the one-layer method. Finally, we applied this method to neuraminidase of influenza virus. The results were consistent with the sequences identified from the isolated viruses.

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.

Anchor profiles of HLA-specific peptides: analysis by a novel affinity scoring method and experimental validation

The study of intermolecular interactions is a fundamental research subject in biology. Here we report on the development of a quantitative structure-based affinity scoring method for peptide-protein complexes, named PepScope. The method operates on the basis of a highly specific force field function (CHARMM) that is applied to all-atom structural representations of peptide-receptor complexes. Peptide side-chain contributions to total affinity are scored after detailed rotameric sampling followed by controlled energy refinement. A de novo approach to estimate dehydration energies was developed, based on the simulation of individual amino acids in a solvent box filled with explicit water molecules. Transferability of the method was demonstrated by its application to the hydrophobic HLA-A2 and -A24 receptors, the polar HLA-A1, and the sterically ruled HLA-B7 receptor. A combined theoretical and experimental study on 39 anchor substitutions in FxSKQYMTx/HLA-A2 and -A24 complexes indicated a prediction accuracy of about two thirds of a log-unit in Kd. Analysis of free energy contributions identified a great role of desolvation and conformational strain effects in establishing a given specificity profile. Interestingly, the method rightly predicted that most anchor profiles are less specific than so far assumed. This suggests that many potential T-cell epitopes could be missed with current prediction methods. The results presented in this work may therefore significantly affect T-cell epitope discovery programs applied in the field of peptide vaccine development.

"De novo" design of peptides with specific lipid-binding properties

In this study, we describe an in silico method to design peptides that can be made of non-natural amino acids and elicit specific membrane-interacting properties. The originality of the method holds in the capacities developed to design peptides from any non-natural amino acids as easily as from natural ones, and to test the structure stability by an angular dynamics rather than the currently-used molecular dynamics. The goal of this study was to design a non-natural tilted peptide. Tilted peptides are short protein fragments able to destabilize lipid membranes and characterized by an asymmetric distribution of hydrophobic residues along their helix structure axis. The method is based on the random generation of peptides and their selection on three main criteria: mean hydrophobicity and the presence of at least one polar residue; tilted insertion at the level of the acyl chains of lipids of a membrane; and conformational stability in that hydrophobic phase. From 10,000,000 randomly-generated peptides, four met all the criteria. One was synthesized and tested for its lipid-destabilizing properties. Biophysical assays showed that the "de novo" peptide made of non-natural amino acids is helical either in solution or into lipids as tested by Fourier transform infrared spectroscopy and is able to induce liposome fusion. These results are in agreement with the calculations and validate the theoretical approach.

Reversal of some viral IL-6 electrostatic properties compared to IL-6 contributes to a loss of alpha receptor component recruitment

Human interleukin-6 (hIL-6) is a pleiotropic mediator of activation and proliferation across a large number of different cell types. Human herpesvirus-8 (HHV-8) has been associated with classical and AIDS-related Kaposi's sarcoma (KS). HHV-8 encodes viral IL-6 (vIL-6), a functional homolog of human interleukin-6, that promotes the growth of KS and of some lymphoma cells. Signaling induced by human IL-6 requires recruitment of the glycoprotein gp130, which acts as the signal transducing chain, and of IL-6Ralpha, which is necessary for cognate recognition and high affinity receptor complex formation. In contrast, the formation of a functional complex between vIL-6 and gp130 does not require the presence of IL-6Ralpha. The physico-chemical properties of vIL-6 have been analyzed and compared to those of hIL-6 and of the receptor chains, gp130 and IL-6Ralpha. Interaction sites on vIL-6 involve more hydrophobic residues than those of hIL-6. The electrostatic fields induced by vIL-6 and IL-6Ralpha are repulsive and prevent interaction between vIL-6 and IL-6Ralpha, whereas the electrostatic field induced by hIL-6 steers the complex formation with IL-6Ralpha. Subsequently, electrostatic binding free energy in the vIL-6/IL-6Ralpha complex is destabilizing, whereas it is stabilizing in the complex comprising hIL-6. These properties result from charge reversals between viral and human IL-6, an unusual phenomenon of amino acid substitutions within a homologous protein family. This suggests a selection pressure for vIL-6 to by-pass the IL-6Ralpha control of host defense against virus infection. This selection pressure has yielded the reversal of electrostatic properties of vIL-6 when compared to hIL-6.