Improved prediction of protein side-chain conformations with SCWRL4

Improving ranking of models for protein complexes with side chain modeling and atomic potentials

An atomically detailed potential for docking pairs of proteins is derived using mathematical programming. A refinement algorithm that builds atomically detailed models of the complex and combines coarse grained and atomic scoring is introduced. The refinement step consists of remodeling the interface side chains of the top scoring decoys from rigid docking followed by a short energy minimization. The refined models are then re-ranked using a combination of coarse grained and atomic potentials. The docking algorithm including the refinement and re-ranking, is compared favorably to other leading docking packages like ZDOCK, Cluspro, and PATCHDOCK, on the ZLAB 3.0 Benchmark and a test set of 30 novel complexes. A detailed analysis shows that coarse grained potentials perform better than atomic potentials for realistic unbound docking (where the exact structures of the individual bound proteins are unknown), probably because atomic potentials are more sensitive to local errors. Nevertheless, the atomic potential captures a different signal from the residue potential and as a result a combination of the two scores provides a significantly better prediction than each of the approaches alone.

BCL::Score--knowledge based energy potentials for ranking protein models represented by idealized secondary structure elements

The topology of most experimentally determined protein domains is defined by the relative arrangement of secondary structure elements, i.e. α-helices and β-strands, which make up 50–70% of the sequence. Pairing of β-strands defines the topology of β-sheets. The packing of side chains between α-helices and β-sheets defines the majority of the protein core. Often, limited experimental datasets restrain the position of secondary structure elements while lacking detail with respect to loop or side chain conformation. At the same time the regular structure and reduced flexibility of secondary structure elements make these interactions more predictable when compared to flexible loops and side chains. To determine the topology of the protein in such settings, we introduce a tailored knowledge-based energy function that evaluates arrangement of secondary structure elements only. Based on the amino acid C_β atom coordinates within secondary structure elements, potentials for amino acid pair distance, amino acid environment, secondary structure element packing, β-strand pairing, loop length, radius of gyration, contact order and secondary structure prediction agreement are defined. Separate penalty functions exclude conformations with clashes between amino acids or secondary structure elements and loops that cannot be closed. Each individual term discriminates for native-like protein structures. The composite potential significantly enriches for native-like models in three different databases of 10,000–12,000 protein models in 80–94% of the cases. The corresponding application, “BCL::ScoreProtein,” is available at www.meilerlab.org.

Structural, thermodynamical, and dynamical properties of oligomers formed by the amyloid NNQQ peptide: insights from coarse-grained simulations

Characterizing the early formed oligomeric intermediates of amyloid peptides is of particular interest due to their links with neurodegenerative diseases. Here we study the NNQQ peptide, known to display parallel β-strands in amyloid fibrils by x-ray microcrystallography, and investigate the structural, thermodynamical, and dynamical properties of 20 NNQQ peptides using molecular dynamics and replica exchange molecular dynamics simulations coupled to a coarse-grained force field. All simulations are initiated from randomized and fully dispersed monomeric conformations. Our simulations reveal that the phase transition is characterized by a change in the oligomer and β-sheet size distributions and the percentage of mixed parallel/antiparallel β-strands when the sheets are formed. At all temperatures, however, the fraction of parallel β-strands remains low, though there are many association/fragmentation events. This work and a growing body of computational studies provide strong evidence that the critical nucleus goes beyond 20 chains and reordering of the β-strands occurs in larger oligomers.

Structure refinement of protein model decoys requires accurate side-chain placement

In this study, the application of temperature-based replica-exchange (T-ReX) simulations for structure refinement of decoys taken from the I-TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self-guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T-ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T-ReX simulation model is provided. Additionally, the effect of side-chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near-native backbone conformations among the starting decoys, the determinant of their refinement is side-chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T-ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I-TASSER decoy sets and a 25% reduction in values of C(α) root-mean-square deviation. The hybrid model succeeded in obtaining a sharper funnel to low-energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near-native packing of side chains, the T-ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem.

AMPLE: a cluster-and-truncate approach to solve the crystal structures of small proteins using rapidly computed ab initio models

Protein ab initio models predicted from sequence data alone can enable the elucidation of crystal structures by molecular replacement. However, the calculation of such ab initio models is typically computationally expensive. Here, a computational pipeline based on the clustering and truncation of cheaply obtained ab initio models for the preparation of structure ensembles is described. Clustering is used to select models and to quantitatively predict their local accuracy, allowing rational truncation of predicted inaccurate regions. The resulting ensembles, with or without rapidly added side chains, solved 43% of all test cases, with an 80% success rate for all-α proteins. A program implementing this approach, AMPLE, is included in the CCP4 suite of programs. It only requires the input of a FASTA sequence file and a diffraction data file. It carries out the modelling using locally installed Rosetta, creates search ensembles and automatically performs molecular replacement and model rebuilding.

Identification and characterization of carprofen as a multitarget fatty acid amide hydrolase/cyclooxygenase inhibitor

Pain and inflammation are major therapeutic areas for drug discovery. Current drugs for these pathologies have limited efficacy, however, and often cause a number of unwanted side effects. In the present study, we identify the nonsteroidal anti-inflammatory drug carprofen as a multitarget-directed ligand that simultaneously inhibits cyclooxygenase-1 (COX-1), COX-2, and fatty acid amide hydrolase (FAAH). Additionally, we synthesized and tested several derivatives of carprofen, sharing this multitarget activity. This may result in improved analgesic efficacy and reduced side effects (Naidu et al. J. Pharmacol. Exp. Ther.2009, 329, 48-56; Fowler, C. J.; et al. J. Enzyme Inhib. Med. Chem.2012, in press; Sasso et al. Pharmacol. Res.2012, 65, 553). The new compounds are among the most potent multitarget FAAH/COX inhibitors reported so far in the literature and thus may represent promising starting points for the discovery of new analgesic and anti-inflammatory drugs.

Membrane bending is critical for the stability of voltage sensor segments in the membrane

The interaction between membrane proteins and the surrounding membrane is becoming increasingly appreciated for its role in regulating protein function, protein localization, and membrane morphology. In particular, recent studies have suggested that membrane deformation is needed to stably accommodate proteins harboring charged amino acids in their transmembrane (TM) region, as it is energetically prohibitive to bury charge in the hydrophobic core of the bilayer. Unfortunately, current computational methods are poorly equipped for describing such deformations, as atomistic simulations are often too short to observe large-scale membrane reorganization and most continuum approaches assume a flat membrane. Previously, we developed a method that overcomes these shortcomings by using elasticity theory to characterize equilibrium membrane distortions in the presence of a TM protein, while using traditional continuum electrostatic and nonpolar energy models to determine the energy of the protein in the membrane. Here, we linked the elastostatics, electrostatics, and nonpolar numeric solvers to permit the calculation of energies for nontrivial membrane deformations. We then coupled this procedure to a robust search algorithm that identifies optimal membrane shapes for a TM protein of arbitrary chemical composition. This advance now permits us to explore a host of biological phenomena that were beyond the scope of our original method. We show that the energy required to embed charged residues in the membrane can be highly nonadditive, and our model provides a simple mechanical explanation for this nonadditivity. Our results also predict that isolated voltage sensor segments do not insert into rigid membranes, but membrane bending dramatically stabilizes these proteins in the bilayer despite their high charge content. Additionally, we use the model to explore hydrophobic mismatch with regard to nonpolar peptides and mechanosensitive channels. Our method is in quantitative agreement with molecular dynamics simulations at a tiny fraction of the computational cost.

Tackling the challenges posed by target flexibility in drug design

IMPORTANCE OF THE FIELD

Current computational docking methods are often effective in predicting accurate drug-binding geometries in cases of relatively rigid target structures. However, binding of drug-like ligands to protein receptor molecules frequently involves or even requires conformational adaptation. Realistic prediction of ligand-receptor binding geometries and complex stability needs in many cases an appropriate inclusion of conformational changes, not only for the ligand, but also for the receptor molecule.

AREAS COVERED IN THIS REVIEW

Recent approaches to efficiently account for target receptor flexibility during docking simulations are reviewed.

WHAT THE READER WILL GAIN

The reader will gain insights into methods to efficiently treat protein side-chain flexibility and approaches for continuous adaptation of backbone conformations in pre-calculated essential or soft collective degrees of freedom. In addition, molecular dynamics or Monte Carlo based methods providing simultaneous inclusion of receptor and ligand flexibility are discussed as well as promising new developments to generate conformationally diverse ensembles of a protein structure. The large variety of possible conformational changes in proteins on ligand binding is illustrated for the enzyme reverse transcriptase of HIV-1, which is an important drug target.

TAKE HOME MESSAGE

If the backbone conformation of the binding site does not change, current docking programs can perform well by taking side-chain reorientations into account only. Future progress to account for full target flexibility in docking requires both accurate prediction of the essential modes of backbone motion and improvements in scoring to enhance selectivity. Thus, the scoring function should realistically cover energetic and particularly entropic contributions to binding, which would allow more realistic estimates of binding free energies.

Contrast-matched small-angle X-ray scattering from a heavy-atom-labeled protein in structure determination: application to a lead-substituted calmodulin-peptide complex

The information content in 1-D solution X-ray scattering profiles is generally restricted to low-resolution shape and size information that, on its own, cannot lead to unique 3-D structures of biological macromolecules comparable to all-atom models derived from X-ray crystallography or NMR spectroscopy. Here we show that contrast-matched X-ray scattering data collected on a protein incorporating specific heavy-atom labels in 65% aqueous sucrose buffer can dramatically enhance the power of conventional small- and wide-angle X-ray scattering (SAXS/WAXS) measurements. Under contrast-matching conditions the protein is effectively invisible and the main contribution to the X-ray scattering intensity arises from the heavy atoms, allowing direct extraction of pairwise distances between them. In combination with conventional aqueous SAXS/WAXS data, supplemented by NMR-derived residual dipolar couplings (RDCs) measured in a weakly aligning medium, we show that it is possible to position protein domains relative to one another within a precision of 1 Å. We demonstrate this approach with respect to the determination of domain positions in a complex between calmodulin, in which the four Ca(2+) ions have been substituted by Pb(2+), and a target peptide. The uniqueness of the resulting solution is established by an exhaustive search over all models compatible with the experimental data, and could not have been achieved using aqueous SAXS and RDC data alone. Moreover, we show that the correct structural solution can be recovered using only contrast-matched SAXS and aqueous SAXS/WAXS data.

An amino acid substitution in Fasciola hepatica P-glycoprotein from triclabendazole-resistant and triclabendazole-susceptible populations

Control of fasciolosis is threatened by the development of anthelmintic resistance. Enhanced triclabendazole (TCBZ) efflux by ABC transporters such as P-glycoprotein (Pgp) has been implicated in this process. A putative full length cDNA coding for a Pgp expressed in adult Fasciola hepatica has been constructed and used to design a primer set capable of amplifying a region encoding part of the second nucleotide binding domain of Pgp when genomic DNA was used as a template. Application of this primer set to genomic DNA from TCBZ-resistant and -susceptible field populations has shown a significant difference in the alleles present. Analysis of an allele occurring at a three-fold higher frequency in the "resistant" population revealed that it was characterised by a serine to arginine substitution at residue 1144. Homology modelling studies have been used to locate this site in the Pgp structure and hence assess its potential to modify functional activity.

High selectivity of the γ-aminobutyric acid transporter 2 (GAT-2, SLC6A13) revealed by structure-based approach

The solute carrier 6 (SLC6) is a family of ion-dependent transporters that mediate uptake into the cell of osmolytes such as neurotransmitters and amino acids. Four SLC6 members transport GABA, a key neurotransmitter that triggers inhibitory signaling pathways via various receptors (e.g., GABA(A)). The GABA transporters (GATs) regulate the concentration of GABA available for signaling and are thus targeted by a variety of anticonvulsant and relaxant drugs. Here, we characterize GAT-2, a transporter that plays a role in peripheral GABAergic mechanisms, by constructing comparative structural models based on crystallographic structures of the leucine transporter LeuT. Models of GAT-2 in two different conformations were constructed and experimentally validated, using site-directed mutagenesis. Computational screening of 594,166 compounds including drugs, metabolites, and fragment-like molecules from the ZINC database revealed distinct ligands for the two GAT-2 models. 31 small molecules, including high scoring compounds and molecules chemically related to known and predicted GAT-2 ligands, were experimentally tested in inhibition assays. Twelve ligands were found, six of which were chemically novel (e.g., homotaurine). Our results suggest that GAT-2 is a high selectivity/low affinity transporter that is resistant to inhibition by typical GABAergic inhibitors. Finally, we compared the binding site of GAT-2 with those of other SLC6 members, including the norepinephrine transporter and other GATs, to identify ligand specificity determinants for this family. Our combined approach may be useful for characterizing interactions between small molecules and other membrane proteins, as well as for describing substrate specificities in other protein families.

Design and characterization of novel cell-penetrating peptides from pituitary adenylate cyclase-activating polypeptide

The discovery of cell-penetrating peptide opened up new promising avenues for the non-invasive delivery of non-permeable biomolecules within the intracellular compartment. However, some setbacks such as possible toxic effects or unexpected immunological responses have limited their use in clinic. To overcome these obstacles, we investigated the use of novel cell-penetrating peptides (CPPs) derived from the endogenous neuropeptide Pituitary adenylate cyclase-activating polypeptide (PACAP). First, we demonstrated the propensity of native PACAP isoforms (PACAP27 and PACAP38) to efficiently deliver a large and non-permeable molecule, i.e. streptavidin, into cells. An inactive modified fragment of PACAP38, i.e. [Arg(17)]PACAP(11-38), with preserved cell-penetrating physico-chemical properties, was also synthesized and successfully use for the intracellular delivery of various cargoes such as small molecules, peptides, proteins, and polynucleotides. Especially, its effectiveness as a transfection agent was comparable to Lipofectamine 2000 while being non-toxic for cells. Uptake mechanism studies demonstrated that direct translocation, caveolae-dependent endocytosis and macropinocytosis were involved in the internalization of [Arg(17)]PACAP(11-38). This study not only opened up a new aspect in the usefulness of PACAP and its derivatives for therapeutic application but also contributed to the identification of new members of the CPP family. As such, inactive PACAP-related analogs could represent excellent vectors for in vitro and in vivo applications.

Predicting HLA class I non-permissive amino acid residues substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.

Structural informatics, modeling, and design with an open-source Molecular Software Library (MSL)

We present the Molecular Software Library (MSL), a C++ library for molecular modeling. MSL is a set of tools that supports a large variety of algorithms for the design, modeling, and analysis of macromolecules. Among the main features supported by the library are methods for applying geometric transformations and alignments, the implementation of a rich set of energy functions, side chain optimization, backbone manipulation, calculation of solvent accessible surface area, and other tools. MSL has a number of unique features, such as the ability of storing alternative atomic coordinates (for modeling) and multiple amino acid identities at the same backbone position (for design). It has a straightforward mechanism for extending its energy functions and can work with any type of molecules. Although the code base is large, MSL was created with ease of developing in mind. It allows the rapid implementation of simple tasks while fully supporting the creation of complex applications. Some of the potentialities of the software are demonstrated here with examples that show how to program complex and essential modeling tasks with few lines of code. MSL is an ongoing and evolving project, with new features and improvements being introduced regularly, but it is mature and suitable for production and has been used in numerous protein modeling and design projects. MSL is open-source software, freely downloadable at http://msl-libraries.org. We propose it as a common platform for the development of new molecular algorithms and to promote the distribution, sharing, and reutilization of computational methods.

Evolution of specific protein-protein interaction sites following gene duplication

Gene duplication is a common evolutionary process that leads to the expansion and functional diversification of protein subfamilies. The evolutionary events that cause paralogous proteins to bind different protein ligands (functionally diverged interfaces) are investigated and compared to paralogous proteins that bind the same protein ligand (functionally preserved interfaces). We find that functionally diverged interfaces possess more subfamily-specific residues than functionally preserved interfaces. These subfamily-specific residues are usually partially buried at the interface rim and achieve specific binding through optimized hydrogen bond geometries. In addition to optimized hydrogen bond geometries, side-chain modeling experiments suggest that steric effects are also important for binding specificity. Residues that are completely buried at the interface hub are also less conserved in functionally diverged interfaces than in functionally preserved interfaces. Consistent with this finding, hub residues contribute less to free energy of binding in functionally diverged interfaces than in functionally preserved interfaces. Therefore, we propose that protein binding is a delicate balance between binding affinity that primarily occurs at the interface hub and binding specificity that primarily occurs at the interface rim.

An energy-based conformer library for side chain optimization: improved prediction and adjustable sampling

Side chain optimization is a fundamental component of protein modeling applications such as docking, structural prediction, and design. In these applications side chain flexibility is often provided by rotamer or conformer libraries, which are collections of representative side chain conformations. Here we demonstrate that the sampling provided by the library can be substantially improved by adding an energetic criterion to its creation. The result of the new procedure is the Energy-Based library, a conformer library selected according to the propensity of its elements to fit energetically into natural protein environments. The new library performs outstandingly well in side chain optimization, producing structures with significantly lower energies and resulting in improved side chain conformation prediction. In addition, because the library was created as an ordered list, its size can be adjusted to any desired level. This feature provides unprecedented versatility in tuning sampling. It allows to precisely balance the number of conformers required by each amino acid type, equalizing their chances to fit into structural environments. It also allows to scale the amount of sampling to the specific requirement of any given side optimization problem. A rotameric version of the library was also produced with the same method to support applications that require a dihedral-only description of side chain conformation. The libraries are available at http://seneslab.org/EBL.

Bluues server: electrostatic properties of wild-type and mutated protein structures

MOTIVATION

Electrostatic calculations are an important tool for deciphering many functional mechanisms in proteins. Generalized Born (GB) models offer a fast and convenient computational approximation over other implicit solvent-based electrostatic models. Here we present a novel GB-based web server, using the program Bluues, to calculate numerous electrostatic features including pKa-values and surface potentials. The output is organized allowing both experts and beginners to rapidly sift the data. A novel feature of the Bluues server is that it explicitly allows to find electrostatic differences between wild-type and mutant structures.

AVAILABILITY

The Bluues server, examples and extensive help files are available for non-commercial use at URL: http://protein.bio.unipd.it/bluues/.

Monte Carlo simulations of peptide-membrane interactions with the MCPep web server

The MCPep server (http://bental.tau.ac.il/MCPep/) is designed for non-experts wishing to perform Monte Carlo (MC) simulations of helical peptides in association with lipid membranes. MCPep is a web implementation of a previously developed MC simulation model. The model has been tested on a variety of peptides and protein fragments. The simulations successfully reproduced available empirical data and provided new molecular insights, such as the preferred locations of peptides in the membrane and the contribution of individual amino acids to membrane association. MCPep simulates the peptide in the aqueous phase and membrane environments, both described implicitly. In the former, the peptide is subjected solely to internal conformational changes, and in the latter, each MC cycle includes additional external rigid body rotational and translational motions to allow the peptide to change its location in the membrane. The server can explore the interaction of helical peptides of any amino-acid composition with membranes of various lipid compositions. Given the peptide’s sequence or structure and the natural width and surface charge of the membrane, MCPep reports the main determinants of peptide–membrane interactions, e.g. average location and orientation in the membrane, free energy of membrane association and the peptide’s helical content. Snapshots of example simulations are also provided.

Self-complementarity within proteins: bridging the gap between binding and folding

Complementarity, in terms of both shape and electrostatic potential, has been quantitatively estimated at protein-protein interfaces and used extensively to predict the specific geometry of association between interacting proteins. In this work, we attempted to place both binding and folding on a common conceptual platform based on complementarity. To that end, we estimated (for the first time to our knowledge) electrostatic complementarity (Em) for residues buried within proteins. Em measures the correlation of surface electrostatic potential at protein interiors. The results show fairly uniform and significant values for all amino acids. Interestingly, hydrophobic side chains also attain appreciable complementarity primarily due to the trajectory of the main chain. Previous work from our laboratory characterized the surface (or shape) complementarity (Sm) of interior residues, and both of these measures have now been combined to derive two scoring functions to identify the native fold amid a set of decoys. These scoring functions are somewhat similar to functions that discriminate among multiple solutions in a protein-protein docking exercise. The performances of both of these functions on state-of-the-art databases were comparable if not better than most currently available scoring functions. Thus, analogously to interfacial residues of protein chains associated (docked) with specific geometry, amino acids found in the native interior have to satisfy fairly stringent constraints in terms of both Sm and Em. The functions were also found to be useful for correctly identifying the same fold for two sequences with low sequence identity. Finally, inspired by the Ramachandran plot, we developed a plot of Sm versus Em (referred to as the complementarity plot) that identifies residues with suboptimal packing and electrostatics which appear to be correlated to coordinate errors.

Ligand-specific homology modeling of human cannabinoid (CB1) receptor

Cannabinoid (CB1) receptor is a therapeutic drug target, and its structure and conformational changes after ligand binding are of great interest. To study the protein conformations in ligand bound state and assist in drug discovery, CB1 receptor homology models are needed for computer-based ligand screening. The known CB1 ligands are highly diverse structurally, so CB1 receptor may undergo considerable conformational changes to accept different ligands, which is challenging for molecular docking methods. To account for the flexibility of CB1 receptor, we constructed four CB1 receptor models based on four structurally distinct ligands, HU-210, ACEA, WIN55212-2 and SR141716A, using the newest X-ray crystal structures of human β₂ adrenergic receptor and adenosine A(2A) receptor as templates. The conformations of these four CB1-ligand complexes were optimized by molecular dynamics (MD) simulations. The models revealed interactions between CB1 receptor and known binders suggested by experiments and could successfully discriminate known ligands and non-binders in our docking assays. MD simulations were used to study the most flexible ligand, ACEA, in its free and bound states to investigate structural mobility achieved by the rearrangement of the fatty acid chain. Our models may capture important conformational changes of CB1 receptor to help improve accuracy in future CB1 drug screening.

Large loop conformation sampling using the activation relaxation technique, ART-nouveau method

We present an adaptation of the ART-nouveau energy surface sampling method to the problem of loop structure prediction. This method, previously used to study protein folding pathways and peptide aggregation, is well suited to the problem of sampling the conformation space of large loops by targeting probable folding pathways instead of sampling exhaustively that space. The number of sampled conformations needed by ART nouveau to find the global energy minimum for a loop was found to scale linearly with the sequence length of the loop for loops between 8 and about 20 amino acids. Considering the linear scaling dependence of the computation cost on the loop sequence length for sampling new conformations, we estimate the total computational cost of sampling larger loops to scale quadratically compared to the exponential scaling of exhaustive search methods.

COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

We have developed a new COFACTOR webserver for automated structure-based protein function annotation. Starting from a structural model, given by either experimental determination or computational modeling, COFACTOR first identifies template proteins of similar folds and functional sites by threading the target structure through three representative template libraries that have known protein-ligand binding interactions, Enzyme Commission number or Gene Ontology terms. The biological function insights in these three aspects are then deduced from the functional templates, the confidence of which is evaluated by a scoring function that combines both global and local structural similarities. The algorithm has been extensively benchmarked by large-scale benchmarking tests and demonstrated significant advantages compared to traditional sequence-based methods. In the recent community-wide CASP9 experiment, COFACTOR was ranked as the best method for protein-ligand binding site predictions. The COFACTOR sever and the template libraries are freely available at http://zhanglab.ccmb.med.umich.edu/COFACTOR.

In silico and in vivo studies of an Arabidopsis thaliana gene, ACR2, putatively involved in arsenic accumulation in plants

Previously, our in silico analyses identified four candidate genes that might be involved in uptake and/or accumulation of arsenics in plants: arsenate reductase 2 (ACR2), phytochelatin synthase 1 (PCS1) and two multi-drug resistant proteins (MRP1 and MRP2) [Lund et al. (2010) J Biol Syst 18:223-224]. We also postulated that one of these four genes, ACR2, seems to play a central role in this process. To investigate further, we have constructed a 3D structure of the Arabidopsis thaliana ACR2 protein using the iterative implementation of the threading assembly refinement (I-TASSER) server. These analyses revealed that, for catalytic metabolism of arsenate, the arsenate binding-loop (AB-loop) and residues Phe-53, Phe-54, Cys-134, Cys-136, Cys-141, Cys-145, and Lys-135 are essential for reducing arsenate to arsenic intermediates (arsenylated enzyme-substrate intermediates) and arsenite in plants. Thus, functional predictions suggest that the ACR2 protein is involved in the conversion of arsenate to arsenite in plant cells. To validate the in silico results, we exposed a transfer-DNA (T-DNA)-tagged mutant of A. thaliana (mutation in the ACR2 gene) to various amounts of arsenic. Reverse transcriptase PCR revealed that the mutant exhibits significantly reduced expression of the ACR2 gene. Spectrophotometric analyses revealed that the amount of accumulated arsenic compounds in this mutant was approximately six times higher than that observed in control plants. The results obtained from in silico analyses are in complete agreement with those obtained in laboratory experiments.

ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction

The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.

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.

From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A

Atomic-level molecular dynamics simulations are widely used for the characterization of the structural dynamics of proteins; however, they are limited to shorter time scales than the duration of most of the relevant biological processes. Properly designed coarse-grained models that trade atomic resolution for efficient sampling allow access to much longer time-scales. In-depth understanding of the structural dynamics, however, must involve atomic details. In this study, we tested a method for the rapid reconstruction of all-atom models from α carbon atom positions in the application to convert a coarse-grained folding trajectory of a well described model system: the B domain of protein A. The results show that the method and the spatial resolution of the resulting coarse-grained models enable computationally inexpensive reconstruction of realistic all-atom models. Additionally, by means of structural clustering, we determined the most persistent ensembles of the key folding step, the transition state. Importantly, the analysis of the overall structural topologies suggests a dominant folding pathway. This, together with the all-atom characterization of the obtained ensembles, in the form of contact maps, matches the experimental results well.

The folding transition state of protein L is extensive with nonnative interactions (and not small and polarized)

Progress in understanding protein folding relies heavily upon an interplay between experiment and theory. In particular, readily interpretable experimental data that can be meaningfully compared to simulations are required. According to standard mutational ϕ analysis, the transition state for Protein L contains only a single hairpin. However, we demonstrate here using ψ analysis with engineered metal ion binding sites that the transition state is extensive, containing the entire four-stranded β sheet. Underreporting of the structural content of the transition state by ϕ analysis also occurs for acyl phosphatase [Pandit, A. D., Jha, A., Freed, K. F. & Sosnick, T. R., (2006). Small proteins fold through transition states with native-like topologies. J. Mol. Biol.361, 755-770], ubiquitin [Sosnick, T. R., Dothager, R. S. & Krantz, B. A., (2004). Differences in the folding transition state of ubiquitin indicated by ϕ and ψ analyses. Proc. Natl Acad. Sci. USA 101, 17377-17382] and BdpA [Baxa, M., Freed, K. F. & Sosnick, T. R., (2008). Quantifying the structural requirements of the folding transition state of protein A and other systems. J. Mol. Biol.381, 1362-1381]. The carboxy-terminal hairpin in the transition state of Protein L is found to be nonnative, a significant result that agrees with our Protein Data Bank-based backbone sampling and all-atom simulations. The nonnative character partially explains the failure of accepted experimental and native-centric computational approaches to adequately describe the transition state. Hence, caution is required even when an apparent agreement exists between experiment and theory, thus highlighting the importance of having alternative methods for characterizing transition states.

Expanding molecular modeling and design tools to non-natural sidechains

Protein-protein interactions encode the wiring diagram of cellular signaling pathways and their deregulations underlie a variety of diseases, such as cancer. Inhibiting protein-protein interactions with peptide derivatives is a promising way to develop new biological and therapeutic tools. Here, we develop a general framework to computationally handle hundreds of non-natural amino acid sidechains and predict the effect of inserting them into peptides or proteins. We first generate all structural files (pdb and mol2), as well as parameters and topologies for standard molecular mechanics software (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to increase peptide ligand binding affinity. Our results obtained on non-natural mutants of a BCL9 peptide targeting beta-catenin show very good correlation between predicted and experimental binding free-energies, indicating that such predictions can be used to design new inhibitors. Data generated in this work, as well as PyMOL and UCSF Chimera plug-ins for user-friendly visualization of non-natural sidechains, are all available at http://www.swisssidechain.ch. Our results enable researchers to rapidly and efficiently work with hundreds of non-natural sidechains.

Cell contact-dependent outer membrane exchange in myxobacteria: genetic determinants and mechanism

Biofilms are dense microbial communities. Although widely distributed and medically important, how biofilm cells interact with one another is poorly understood. Recently, we described a novel process whereby myxobacterial biofilm cells exchange their outer membrane (OM) lipoproteins. For the first time we report here the identification of two host proteins, TraAB, required for transfer. These proteins are predicted to localize in the cell envelope; and TraA encodes a distant PA14 lectin-like domain, a cysteine-rich tandem repeat region, and a putative C-terminal protein sorting tag named MYXO-CTERM, while TraB encodes an OmpA-like domain. Importantly, TraAB are required in donors and recipients, suggesting bidirectional transfer. By use of a lipophilic fluorescent dye, we also discovered that OM lipids are exchanged. Similar to lipoproteins, dye transfer requires TraAB function, gliding motility and a structured biofilm. Importantly, OM exchange was found to regulate swarming and development behaviors, suggesting a new role in cell–cell communication. A working model proposes TraA is a cell surface receptor that mediates cell–cell adhesion for OM fusion, in which lipoproteins/lipids are transferred by lateral diffusion. We further hypothesize that cell contact–dependent exchange helps myxobacteria to coordinate their social behaviors.

All cells interact with their environment, including other cells, to elicit cellular responses. Cell–cell interactions between eukaryotic cells are widely appreciated as large multicellular organisms coordinate cell behaviors for tissue and organ functions. In bacteria cell–cell interactions are not widely appreciated, as these organisms are relatively simple and are often depicted as single-cell entities. However, over the past decade, the concept of bacteria living in microbial communities or biofilms has received broad acceptance as a major lifestyle. As biofilm cells are packed in tight physical contact, there is an opportunity for cell–cell signaling to provide spatial and physiological clues of neighboring cells to elicit cellular responses. Although much has been learned about diffusible signals through quorum sensing, little is known about cell contact–dependent signaling in bacteria. In this report we describe a new mechanism where bacterial cells within structured biofilms form contacts that allow cellular material to be exchanged. This exchange elicits phenotypic changes, including in cell movements and development. We hypothesize that OM exchange involves kin recognition that bestows social benefits to myxobacterial populations.

Structural modelling and dynamics of proteins for insights into drug interactions

Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.

Distinct dimerization for various alloforms of the amyloid-beta protein: Aβ(1-40), Aβ(1-42), and Aβ(1-40)(D23N)

The Amyloid-beta protein is related to Alzheimer's disease, and various experiments have shown that oligomers as small as the dimer are cytotoxic. Two alloforms are mainly produced: Aβ(1-40) and Aβ(1-42). They have very different oligomer distributions, and it was recently suggested, from experimental studies, that this variation may originate from structural differences in their dimer structures. Little structural information is available on the Aβ dimer, however, and to complement experimental observations, we simulated the folding of the wild-type Aβ(1-40) and Aβ(1-42) dimers as well as the mutated Aβ(1-40)(D23N) dimer using an accurate coarse-grained force field coupled to Hamiltonian-temperature replica exchange molecular dynamics. The D23N variant impedes the salt-bridge formation between D23 and K28 seen in the wild-type Aβ, leading to very different fibrillation properties and final amyloid fibrils. Our results show that the Aβ(1-42) dimer has a higher propensity than the Aβ(1-40) dimer to form β-strands at the central hydrophobic core (residues 17-21) and at the C-terminal (residues 30-42), which are two segments crucial to the oligomerization of Aβ. The free energy landscape of the Aβ(1-42) dimer is also broader and more complex than that of the Aβ(1-40) dimer. Interestingly, D23N also impacts the free energy landscape by increasing the population of configurations with higher β-strand propensities when compared against Aβ(40). In addition, while Aβ(1-40)(D23N) displays a higher β-strand propensity at the C-terminal, its solvent accessibility does not change with respect to the wild-type sequence. Overall, our results show the strong impact of the two amino acids Ile41-Ala42 and the salt-bridge D23-K28 on the folding of the Aβ dimer.

Practical structure solution with ARCIMBOLDO

ARCIMBOLDO combines the location of small fragments with Phaser and density modification with SHELXE of all possible Phaser solutions. Its uses are explained and illustrated through practical test cases.

Since its release in September 2009, the structure-solution program ARCIMBOLDO, based on the combination of locating small model fragments such as polyalanine α-helices with density modification with the program SHELXE in a multisolution frame, has evolved to incorporate other sources of stereochemical or experimental information. Fragments that are more sophisticated than the ubiquitous main-chain α-­helix can be proposed by modelling side chains onto the main chain or extracted from low-homology models, as locally their structure may be similar enough to the unknown one even if the conventional molecular-replacement approach has been unsuccessful. In such cases, the program may test a set of alternative models in parallel against a specified figure of merit and proceed with the selected one(s). Experimental information can be incorporated in three ways: searching within ARCIMBOLDO for an anomalous fragment against anomalous differences or MAD data or finding model fragments when an anomalous substructure has been determined with another program such as SHELXD or is subsequently located in the anomalous Fourier map calculated from the partial fragment phases. Both sources of information may be combined in the expansion process. In all these cases the key is to control the workflow to maximize the chances of success whilst avoiding the creation of an intractable number of parallel processes. A GUI has been implemented to aid the setup of suitable strategies within the various typical scenarios. In the present work, the practical application of ARCIMBOLDO within each of these scenarios is described through the distributed test cases.

Incorporation of noncanonical amino acids into Rosetta and use in computational protein-peptide interface design

Noncanonical amino acids (NCAAs) can be used in a variety of protein design contexts. For example, they can be used in place of the canonical amino acids (CAAs) to improve the biophysical properties of peptides that target protein interfaces. We describe the incorporation of 114 NCAAs into the protein-modeling suite Rosetta. We describe our methods for building backbone dependent rotamer libraries and the parameterization and construction of a scoring function that can be used to score NCAA containing peptides and proteins. We validate these additions to Rosetta and our NCAA-rotamer libraries by showing that we can improve the binding of a calpastatin derived peptides to calpain-1 by substituting NCAAs for native amino acids using Rosetta. Rosetta (executables and source), auxiliary scripts and code, and documentation can be found at (http://www.rosettacommons.org/).

On the applicability of elastic network normal modes in small-molecule docking

Incorporating backbone flexibility into protein-ligand docking is still a challenging problem. In protein-protein docking, normal mode analysis (NMA) has become increasingly popular as it can be used to describe the collective motions of a biological system, but the question of whether NMA can also be useful in predicting the conformational changes observed upon small-molecule binding has only been addressed in a few case studies. Here, we describe a large-scale study on the applicability of NMA for protein-ligand docking using 433 apo/holo pairs of the Astex data sets. On the basis of sets of the first normal modes from the apo structure, we first generated for each paired holo structure a set of conformations that optimally reproduce its C(α) trace with respect to the underlying normal mode subspace. Using AutoDock, GOLD, and FlexX we then docked the original ligands into these conformations to assess how the docking performance depends on the number of modes used to reproduce the holo structure. The results of our study indicate that, even for such a best-case scenario, the use of normal mode analysis in small-molecule docking is restricted and that a general rule on how many modes to use does not seem to exist or at least is not easy to find.

Structures of Aβ17-42 trimers in isolation and with five small-molecule drugs using a hierarchical computational procedure

The amyloid-β protein (Aβ) oligomers are believed to be the main culprits in the cytoxicity of Alzheimer's disease (AD) and p3 peptides (Aβ17-42 fragments) are present in AD amyloid plaques. Many small-molecule or peptide-based inhibitors are known to slow down Aβ aggregation and reduce the toxicity in vitro, but their exact modes of action remain to be determined since there has been no atomic level of Aβ(p3)-drug oligomers. In this study, we have determined the structure of Aβ17-42 trimers both in aqueous solution and in the presence of five small-molecule inhibitors using a multiscale computational study. These inhibitors include 2002-H20, curcumin, EGCG, Nqtrp, and resveratrol. First, we used replica exchange molecular dynamics simulations coupled to the coarse-grained (CG) OPEP force field. These CG simulations reveal that the conformational ensemble of Aβ17-42 trimer can be described by 14 clusters with each peptide essentially adopting turn/random coil configurations, although the most populated cluster is characterized by one peptide with a β-hairpin at Phe19-Leu31. Second, these 14 dominant clusters and the less-frequent fibril-like state with parallel register of the peptides were subjected to atomistic Autodock simulations. Our analysis reveals that the drugs have multiple binding modes with different binding affinities for trimeric Aβ17-42 although they interact preferentially with the CHC region (residues 17-21). The compounds 2002-H20 and Nqtrp are found to be the worst and best binders, respectively, suggesting that the drugs may interfere at different stages of Aβ oligomerization. Finally, explicit solvent molecular dynamics of two predicted Nqtrp-Aβ17-42 conformations describe at atomic level some possible modes of action for Nqtrp.

Factor inhibiting HIF (FIH) recognizes distinct molecular features within hypoxia-inducible factor-α (HIF-α) versus ankyrin repeat substrates

Factor Inhibiting HIF (FIH) catalyzes the β-hydroxylation of asparagine residues in HIF-α transcription factors as well as ankyrin repeat domain (ARD) proteins such as Notch and Gankyrin. Although FIH-mediated hydroxylation of HIF-α is well characterized, ARDs were only recently identified as substrates, and less is known about their recognition and hydroxylation by FIH. We investigated the molecular determinants of FIH substrate recognition, with a focus on differences between HIF and ARD substrates. We show that for ARD proteins, structural context is an important determinant of FIH-recognition, but analyses of chimeric substrate proteins indicate that the ankyrin fold alone is not sufficient to explain the distinct substrate properties of the ARDs compared with HIF. For both substrates the kinetic parameters of hydroxylation are influenced by the amino acids proximal to the target asparagine. Although FIH tolerates a variety of chemically disparate residues proximal to the asparagine, we demonstrate that certain combinations of amino acids are not permissive to hydroxylation. Finally, we characterize a conserved RLL motif in HIF and demonstrate that it mediates a high affinity interaction with FIH in the presence of cell lysate or macromolecular crowding agents. Collectively, our data highlight the importance of residues proximal to the asparagine in determining hydroxylation, and identify additional substrate-specific elements that contribute to distinct properties of HIF and ARD proteins as substrates for FIH. These distinct features are likely to influence FIH substrate choice in vivo and, therefore, have important consequences for HIF regulation.

Rigid core and flexible terminus: structure of solubilized light-harvesting chlorophyll a/b complex (LHCII) measured by EPR

The structure of the major light-harvesting chlorophyll a/b complex (LHCII) was analyzed by pulsed EPR measurements and compared with the crystal structure. Site-specific spin labeling of the recombinant protein allowed the measurement of distance distributions over several intra- and intermolecular distances in monomeric and trimeric LHCII, yielding information on the protein structure and its local flexibility. A spin label rotamer library based on a molecular dynamics simulation was used to take the local mobility of spin labels into account. The core of LHCII in solution adopts a structure very similar or identical to the one seen in crystallized LHCII trimers with little motional freedom as indicated by narrow distance distributions along and between α helices. However, distances comprising the lumenal loop domain show broader distance distributions, indicating some mobility of this loop structure. Positions in the hydrophilic N-terminal domain, upstream of the first trans-membrane α helix, exhibit more and more mobility the closer they are to the N terminus. The nine amino acids at the very N terminus that have not been resolved in any of the crystal structure analyses give rise to very broad and possibly bimodal distance distributions, which may represent two families of preferred conformations.

Optimization of van der Waals energy for protein side-chain placement and design

Computational determination of optimal side-chain conformations in protein structures has been a long-standing and challenging problem. Solving this problem is important for many applications including homology modeling, protein docking, and for placing small molecule ligands on protein-binding sites. Programs available as of this writing are very fast and reasonably accurate, as measured by deviations of side-chain dihedral angles; however, often due to multiple atomic clashes, they produce structures with high positive energies. This is problematic in applications where the energy values are important, for example when placing small molecules in docking applications; the relatively small binding energy of the small molecule is drowned by the large energy due to atomic clashes that hampers finding the lowest energy state of the docked ligand. To address this we have developed an algorithm for generating a set of side-chain conformations that is dense enough that at least one of its members would have a root mean-square deviation of no more than R Å from any possible side-chain conformation of the amino acid. We call such a set a side-chain cover set of order R for the amino acid. The size of the set is constrained by the energy of the interaction of the side chain to the backbone atoms. Then, side-chain cover sets are used to optimize the conformation of the side chains given the coordinates of the backbone of a protein. The method we use is based on a variety of dead-end elimination methods and the recently discovered dynamic programming algorithm for this problem. This was implemented in a computer program called Octopus where we use side-chain cover sets with very small values for R, such as 0.1 Å, which ensures that for each amino-acid side chain the set contains a conformation with a root mean-square deviation of, at most, R from the optimal conformation. The side-chain dihedral-angle accuracy of the program is comparable to other implementations; however, it has the important advantage that the structures produced by the program have negative energies that are very close to the energies of the crystal structure for all tested proteins.

Site-directed mutations and the polymorphic variant Ala160Thr in the human thromboxane receptor uncover a structural role for transmembrane helix 4

The human thromboxane A2 receptor (TP), belongs to the prostanoid subfamily of Class A GPCRs and mediates vasoconstriction and promotes thrombosis on binding to thromboxane (TXA2). In Class A GPCRs, transmembrane (TM) helix 4 appears to be a hot spot for non-synonymous single nucleotide polymorphic (nsSNP) variants. Interestingly, A160T is a novel nsSNP variant with unknown structure and function. Additionally, within this helix in TP, Ala160(4.53) is highly conserved as is Gly164(4.57). Here we target Ala160(4.53) and Gly164(4.57) in the TP for detailed structure-function analysis. Amino acid replacements with smaller residues, A160S and G164A mutants, were tolerated, while bulkier beta-branched replacements, A160T and A160V showed a significant decrease in receptor expression (Bmax). The nsSNP variant A160T displayed significant agonist-independent activity (constitutive activity). Guided by molecular modeling, a series of compensatory mutations were made on TM3, in order to accommodate the bulkier replacements on TM4. The A160V/F115A double mutant showed a moderate increase in expression level compared to either A160V or F115A single mutants. Thermal activity assays showed decrease in receptor stability in the order, wild type>A160S>A160V>A160T>G164A, with G164A being the least stable. Our study reveals that Ala160(4.53) and Gly164(4.57) in the TP play critical structural roles in packing of TM3 and TM4 helices. Naturally occurring mutations in conjunction with site-directed replacements can serve as powerful tools in assessing the importance of regional helix-helix interactions.

Modeling large regions in proteins: applications to loops, termini, and folding

Template-based methods for predicting protein structure provide models for a significant portion of the protein but often contain insertions or chain ends (InsEnds) of indeterminate conformation. The local structure prediction "problem" entails modeling the InsEnds onto the rest of the protein. A well-known limit involves predicting loops of ≤12 residues in crystal structures. However, InsEnds may contain as many as ~50 amino acids, and the template-based model of the protein itself may be imperfect. To address these challenges, we present a free modeling method for predicting the local structure of loops and large InsEnds in both crystal structures and template-based models. The approach uses single amino acid torsional angle "pivot" moves of the protein backbone with a C(β) level representation. Nevertheless, our accuracy for loops is comparable to existing methods. We also apply a more stringent test, the blind structure prediction and refinement categories of the CASP9 tournament, where we improve the quality of several homology based models by modeling InsEnds as long as 45 amino acids, sizes generally inaccessible to existing loop prediction methods. Our approach ranks as one of the best in the CASP9 refinement category that involves improving template-based models so that they can function as molecular replacement models to solve the phase problem for crystallographic structure determination.

Homology modeling of cannabinoid receptors: discovery of cannabinoid analogues for therapeutic use

Cannabinoids represent a promising class of compounds for developing novel therapeutic agents. Since the isolation and identification of the major psychoactive component Δ(9)-THC in Cannabis sativa in the 1960s, numerous analogues of the classical plant cannabinoids have been synthesized and tested for their biological activity. These compounds primarily target the cannabinoid receptors 1 (CB1) and Cannabinoid receptors 2 (CB2). This chapter focuses on CB1. Despite the lack of crystal structures for CB1, protein-based homology modeling approaches and molecular docking methods can be used in the design and discovery of cannabinoid analogues. Efficient synthetic approaches for therapeutically interesting cannabinoid analogues have been developed to further facilitate the drug discovery process.

Force fields for homology modeling

Accurate all-atom energy functions are crucial for successful high-resolution protein structure prediction. In this chapter, we review both physics-based force fields and knowledge-based potentials used in protein modeling. Because it is important to calculate the energy as accurately as possible given the limitations imposed by sampling convergence, different components of the energy, and force fields representing them to varying degrees of detail and complexity are discussed. Force fields using Cartesian as well as torsion angle representations of protein geometry are covered. Since solvent is important for protein energetics, different aqueous and membrane solvation models for protein simulations are also described. Finally, we summarize recent progress in protein structure refinement using new force fields.