Abstract: Ubiquitination plays an important role in many cellular processes and is implicated in many diseases. Experimental identification of ubiquitination sites is challenging due to rapid turnover of ubiquitinated proteins and the large size of the ubiquitin modifier. We identified 141 new ubiquitination sites using a combination of liquid chromatography, mass spectrometry, and mutant yeast strains. Investigation of the sequence biases and structural preferences around known ubiquitination sites indicated that their properties were similar to those of intrinsically disordered protein regions. Using a combined set of new and previously known ubiquitination sites, we developed a random forest predictor of ubiquitination sites, UbPred. The class-balanced accuracy of UbPred reached 72%, with the area under the ROC curve at 80%. The application of UbPred showed that high confidence Rsp5 ubiquitin ligase substrates and proteins with very short half-lives were significantly enriched in the number of predicted ubiquitination sites. Proteome-wide prediction of ubiquitination sites in Saccharomyces cerevisiae indicated that highly ubiquitinated substrates were prevalent among transcription/enzyme regulators and proteins involved in cell cycle control. In the human proteome, cytoskeletal, cell cycle, regulatory, and cancer-associated proteins display higher extent of ubiquitination than proteins from other functional categories. We show that gain and loss of predicted ubiquitination sites may likely represent a molecular mechanism behind a number of disease-associatedmutations. UbPred is available at http://www.ubpred.org.

Abstract: We updated our protein–protein docking benchmark to include complexes that became available since our previous release. As before, we only considered high-resolution complex structures that are nonredundant at the family–family pair level, for which the X-ray or NMR unbound structures of the constituent proteins are also available. Benchmark 4.0 adds 52 new complexes to the 124 cases of Benchmark 3.0, representing an increase of 42%. Thus, benchmark 4.0 provides 176 unbound–unbound cases that can be used for protein–protein docking method development and assessment. Seventeen of the newly added cases are enzyme-inhibitor complexes, and we found no new antigen-antibody complexes. Classifying the new cases according to expected difficulty for protein–protein docking algorithms gives 33 rigid body cases, 11 cases of medium difficulty, and 8 cases that are difficult. Benchmark 4.0 listings and processed structure files are publicly accessible at http://zlab.umassmed.edu/benchmark/ Proteins 2010. © 2010 Wiley-Liss, Inc.

Abstract: A wide range of regulatory processes in the cell are mediated by flexible peptides that fold upon binding to globular proteins. Computational efforts to model these interactions are hindered by the large number of rotatable bonds in flexible peptides relative to typical ligand molecules, and the fact that different peptides assume different backbone conformations within the same binding site. In this study, we present Rosetta FlexPepDock, a novel tool for refining coarse peptide-protein models that allows significant changes in both peptide backbone and side chains. We obtain high resolution models, often of sub-angstrom backbone quality, over an extensive and general benchmark that is based on a large nonredundant dataset of 89 peptide-protein interactions. Importantly, side chains of known binding motifs are modeled particularly well, typically with atomic accuracy. In addition, our protocol has improved modeling quality for the important application of cross docking to PDZ domains. We anticipate that the ability to create high resolution models for a wide range of peptide-protein complexes will have significant impact on structure-based functional characterization, controlled manipulation of peptide interactions, and on peptide-based drug design.

Abstract: Biological macromolecules evolved to perform their function in specific cellular environment (subcellular compartments or tissues); therefore, they should be adapted to the biophysical characteristics of the corresponding environment, one of them being the characteristic pH. Many macromolecular properties are pH dependent, such as activity and stability. However, only activity is biologically important, while stability may not be crucial for the corresponding reaction. Here we show that the pH-optimum of activity (the pH of maximal activity) is correlated with the pHoptimum of stability (the pH of maximal stability) on a set of 310 proteins with available experimental data. We speculate that such a correlation is needed to allow the corresponding macromolecules to tolerate small pH fluctuations that are inevitable with cellular function. Our findings rationalize the efforts of correlating the pH of maximal stability and the characteristic pH of subcellular compartments, since only pH of activity is subject of evolutionary pressure. In addition, our analysis confirmed the previous observation that pH-optimum of activity and stability are not correlated with the isoelectric point, pI, or with the optimal temperature.

Abstract: Detection of pockets on protein surfaces is an important step toward finding the binding sites of small molecules. In a previous study, we defined a pocket as a space into which a small spherical probe can enter, but a large probe cannot. The radius of the large probes corresponds to the shallowness of pockets. We showed that each type of binding molecule has a characteristic shallowness distribution. In this study, we introduced fundamental changes to our previous algorithm by using a 3D grid representation of proteins and probes, and the theory of mathematical morphology. We invented an efficient algorithm for calculating deep and shallow pockets (multiscale pockets) simultaneously, using several different sizes of spherical probes (multiscale probes). We implemented our algorithm as a new program, ghecom (grid-based HECOMi finder). The statistics of calculated pockets for the structural dataset showed that our program had a higher performance of detecting binding pockets, than four other popular pocket-finding programs proposed previously. The ghecom also calculates the shallowness of binding ligands, R(inaccess) (minimum radius of inaccessible spherical probes) that can be obtained from the multiscale molecular volume. We showed that each part of the binding molecule had a bias toward a specific range of shallowness. These findings will be useful for predicting the types of molecules that will be most likely to bind putative binding pockets, as well as the configurations of binding molecules. The program ghecom is available through the Web server (http://biunit.naist.jp/ghecom).

Abstract: Ten years of experience with molecular class-specific information systems (MCSIS) such as with the hand-curated G protein-coupled receptor database (GPCRDB) or the semiautomatically generated nuclear receptor database has made clear that a wide variety of questions can be answered when protein-related data from many different origins can be flexibly combined. MCSISes revolve around a multiple sequence alignment (MSA) that includes "all" available sequences from the entire superfamily, and it has been shown at many occasions that the quality of these alignments is the most crucial aspect of the MCSIS approach. We describe here a system called 3DM that can automatically build an entire MCSIS. 3DM bases the MSA on a multiple structure alignment, which implies that the availability of a large number of superfamily members with a known three-dimensional structure is a requirement for 3DM to succeed well. Thirteen MCSISes were constructed and placed on the Internet for examination. These systems have been instrumental in a large series of research projects related to enzyme activity or the understanding and engineering of specificity, protein stability engineering, DNA-diagnostics, drug design, and so forth.

Abstract: Sulfide:quinone oxidoreductases (SQR) are ubiquitous membrane-bound flavoproteins involved in sulfide detoxification, in sulfide-dependent energy conservation processes and potenatially in the homeostasis of the neurotransmitter sulfide. The first 2 structures of SQRs from the bacterium Aquifex aeolicus (Marcia et al., Proc Nad Acad Sci USA 2009; 106:96259630) and the archaeon Acidianus ambivalens (Brito et al., Biochemistry 2009; 48:5613-5622) were determined recently by Xray crystallography revealing unexpected differences in the active sites and in flavin adenine dinucleotide binding. Besides the reciprocal differences, they show a different conformation of the active site compared with another sulfide oxidizing enzyme, the flavocytochrome c-sulfide dehydrogenase (FCSD) from Allochromatium vinosum (protein data bank id: 1FCD). In addition to the new structural data, the number of available SQR-like protein sequences is continuously increasing (Pham et aL, Microbiology 2008; 154:3112-3121) and the SQR activity of new members of this protein family was recently proven too (Chan et al., J Bacteriol 2009; 191:1026-1034). In the light of the new data, here we revisit the previously proposed contradictory SQR classification and we define new structure-based sequence fingerprints that support a subdivision of the SQR family into six groups. Our report summarizes the state-of-art knowledge about SQRs and highlights the questions that still remain unanswered. Despite two decades of work already done on these enzymes, new and most exciting discoveries can be expected in the future.

Abstract: The structures and mechanism of action of many terpene cyclases are known, but no structures of diterpene cyclases have yet been reported. Here, we propose structural models based on bioinformatics, site-directed mutagenesis, domain swapping, enzyme inhibition, and spectroscopy that help explain the nature of diterpene cyclase structure, function, and evolution. Bacterial diterpene cyclases contain approximately 20 alpha-helices and the same conserved "QW" and DxDD motifs as in triterpene cyclases, indicating the presence of a betagamma barrel structure. Plant diterpene cyclases have a similar catalytic motif and betagamma-domain structure together with a third, alpha-domain, forming an alphabetagamma structure, and in H(+)-initiated cyclases, there is an EDxxD-like Mg(2+)/diphosphate binding motif located in the gamma-domain. The results support a new view of terpene cyclase structure and function and suggest evolution from ancient (betagamma) bacterial triterpene cyclases to (betagamma) bacterial and thence to (alphabetagamma) plant diterpene cyclases.

Abstract: We extend PRIME, an intermediate-resolution protein model previously used in simulations of the aggregation of polyalanine and polyglutamine, to the description of the geometry and energetics of peptides containing all twenty amino acid residues. The 20 amino acid side chains are classified into 14 groups according to their hydrophobicity, polarity, size, charge and potential for side chain hydrogen bonding. The parameters for extended PRIME, called PRIME 20, include hydrogen-bonding energies, side-chain interaction range and energy, and excluded volume. The parameters are obtained by applying a perceptron- learning algorithm and a modified stochastic learning algorithm that optimizes the energy gap between 711 known native states from the PDB and decoy structures generated by gapless threading. The number of independent pair-interaction parameters is chosen to be small enough to be physically meaningful yet large enough to give reasonably accurate results in discriminating decoys from native structures. The most physically meaningful results are obtained with 19 energy parameters.

Abstract: The effective interactions and phase behavior of protein solutions under strong electrostatic coupling conditions are difficult to understand due to the complex charge pattern and irregular geometry of protein surfaces. This distinguishes them from related systems such as DNA or conventional colloids. In this work, we discuss the question of universality of the reentrant condensation (RC) of proteins in solution induced by multivalent counterions, i.e., redissolution on adding further salts after phase separation, as recently discovered (Zhang et al., Phys Rev Lett 2008; 101:148101). The discussion is based on a systematic investigation of five different proteins with different charge patterns and five different multivalent counterions. Zeta potential measurements confirm the effective charge inversion of proteins in the reentrant regime via binding of multivalent counterions, which is supported by Monte Carlo simulations. Charge inversion by trivalent cations requires an overall negative net charge of the protein. Statistical analysis of a representative set of protein sequences reveals that, in theory, this effect could be possible for about half of all proteins. Our results can be exploited for the control of the phase behavior of proteins, in particular facilitating protein crystallization. Proteins 2010. © 2010 Wiley-Liss, Inc.

Abstract: Protein-RNA interactions are essential in living organisms and they are involved in very different and important cellular processes. Thus, understanding protein-RNA recognition at molecular level is a key goal not only from a basic biological point of view but also for biotechnological and therapeutic purposes. On basis of the most updated available set of nonredundant X-ray structures of protein-RNA complexes, we have computed protein-RNA interface propensities for ribonucleotides and aminoacid residues. The results show several protein residues with high tendency to bind RNA, such as arginine, lysine, and histidine. However, we could not observe any clear preferences for protein binding among the different ribonucleotides. We applied these propensity values to predict RNA-binding areas on proteins, using an ad hoc algorithm called OPRA (Optimal Protein-RNA Area). First, for each protein residue, we derived a predictive score from its corresponding protein-RNA interface propensity weighed by its accessible surface area (ASA). Then, optimal patch energy scores were computed for each residue by adding up the individual scores of the neighboring surface residues. The resulting patch scores correlate well with the known RNA-binding sites on protein surfaces. The OPRA method has been benchmarked on a test set of 30 unbound proteins involved in protein-RNA complexes of known structure, where it is able to successfully predict RNA-binding sites on protein surfaces with around 80% positive predictive value. This can be useful for identifying potential RNA-binding sites on proteins, and can help to model protein-RNA interactions of biological and therapeutic interest. Proteins 2010. © 2009 Wiley-Liss, Inc.

Abstract: There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse-grain model generation and evaluation at the Cα or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full-atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root-mean-square deviation of the best models from the native structures is 4.28 A, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community-wide experiment for protein structure prediction CASP8.

Abstract: LDL cholesterol (LDL-C) is cleared from plasma via cellular uptake and internalization processes that are largely mediated by the low-density lipoprotein cholesterol receptor (LDL-R). LDL-R is targeted for lysosomal degradation by association with proprotein convertase subtilisin-kexin type 9 (PCSK9). Gain of function mutations in PCSK9 can result in excessive loss of receptors and dyslipidemia. On the other hand, receptor-sparing phenomena, including loss-of-function mutations or inhibition of PCSK9, can lead to enhanced clearance of plasma lipids. We hypothesize that desolvation and resolvation processes, in many cases, constitute rate-determining steps for protein-ligand association and dissociation, respectively. To test this hypothesis, we analyzed and compared the predicted desolvation properties of wild-type versus gain-of-function mutant Asp374Tyr PCSK9 using WaterMap, a new in silico method for predicting the preferred locations and thermodynamic properties of water solvating proteins ("hydration sites"). We compared these results with binding kinetics data for PCSK9, full-length LDL-R ectodomain, and isolated EGF-A repeat. We propose that the fast k(on) and entropically driven thermodynamics observed for PCSK9-EGF-A binding stem from the functional replacement of water occupying stable PCSK9 hydration sites (i.e., exchange of PCSK9 H-bonds from water to polar EGF-A groups). We further propose that the relatively fast k(off) observed for EGF-A unbinding stems from the limited displacement of solvent occupying unstable hydration sites. Conversely, the slower k(off) observed for EGF-A and LDL-R unbinding from Asp374Tyr PCSK9 stems from the destabilizing effects of this mutation on PCSK9 hydration sites, with a concomitant increase in the persistence of the bound complex.

Abstract: A hierarchical approach has been developed for protein-protein docking. In the first step, a Fast Fourier Transform (FFT)-based docking algorithm is used to globally sample all putative binding modes, in which the protein is represented by a reduced model, that is, each side chain on the protein surface is represented by its center of mass. Compared to conventional FFT docking with all-atom models, the FFT docking method with a reduced model is expected to generate more hits because it allows larger side-chain flexibility. Next, the filtered binding modes (normally several thousands) are refined by an iteratively derived knowledge-based scoring function ITScorePP and by considering backbone/loop flexibility using an ensemble docking algorithm. The distance-dependent potentials of ITScorePP were extracted by a physics-based iterative method, which circumvents the long-standing reference state problem in the knowledge-based approaches. With this hierarchical protocol, we have participated in the CAPRI experiments for Rounds 15-19 of 11 targets (T32-T42). In the predictor experiments, we achieved correct binding modes for six targets: three are with high accuracy (T40 for both distinct binding modes, T41, and T42), two are with medium accuracy (T34 and T37), and one is acceptable (T32). In the scorer experiments, of the seven target complexes that contain at least one acceptable mode submitted by the CAPRI predictor groups, we obtained correct binding modes for four targets: three are with high accuracy (T37, T40, and T41) and one is with medium accuracy (T34), suggesting good accuracy and robustness of ITScorePP.

Abstract: Although it is clear that amyloid beta (Aβ) peptides play a pivotal role in the development of Alzheimer's disease, the precise molecular model of action remains unclear. Aβ peptide forms assemble both in aqueous solution and in lipid membranes. It has been proposed that deleterious effects occur when the peptides interact with membranes, possibly by forming Ca(2+) permeant ion channels. In the accompanying manuscript, we propose models in which the C-terminus third of six Aβ42 peptides forms a six-stranded β-barrel in highly toxic soluble oligomers. Here we extend this hypothesis to membrane-bound assemblies. In these Aβ models, the hydrophobic β-barrel of a hexamer may either reside on the surface of the bilayer, or span the bilayer. Transmembrane pores are proposed to form between several hexamers. Once the β-barrels of six hexamers have spanned the bilayer, they may merge to form a more stable 36-stranded β-barrel. We favor models in which parallel β-barrels formed by N-terminus segments comprise the lining of the pores. These types of models explain why the channels are selective for cations and how metal ions, such as Zn(2+) , synthetic peptides that contain histidines, and some small organic cations may block channels or inhibit formation of channels. Our models were developed to be consistent with microscopy studies of Aβ assemblies in membranes, one of which is presented here for the first time.

Abstract: The ATTRACT protein-protein docking program combined with a coarse-grained protein model has been used to predict protein-protein complex structures in CAPRI rounds 13-19. For six targets acceptable or better quality solutions have been submitted (high quality predictions for targets 32, 40, 41, and 42). The improved performance compared to previous rounds can be attributed in part to the inclusion of conformational flexibility during systematic searches and an optimized scoring function. In addition, a recently developed method for the prediction of putative protein binding sites based on the electrostatic penalty to place neutral low dielectric probes on the protein surface was applied to the most recent targets. The approach resulted in useful predictions of putative binding sites that can help to limit the systematic docking searches. Possible improvements of the docking approach in particular at the scoring and refinement steps are discussed.

Abstract: The authors acknowledge access to ESRF beamlines ID23-1, ID29, and BM16, and local support by A.M. Gonc¸alves (ID23-1), X. Thibault (ID29), and G.C. Fox (BM16). H. Rodri´guez is a recipient of an I3P predoctoral fellowship from the CSIC

Abstract: Predicting the phenotypes of missense mutations uncovered by large-scale sequencing projects is an important goal in computational biology. High-confidence predictions can be an aid in focusing experimental and association studies on those mutations most likely to be associated with causative relationships between mutation and disease. As an aid in developing these methods further, we have derived a set of random mutations of the enzymatic domains of human cystathionine beta synthase. This enzyme is a dimeric protein that catalyzes the condensation of serine and homocysteine to produce cystathionine. Yeast missing this enzyme cannot grow on medium lacking a source of cysteine, while transfection of functional human CBS into yeast strains missing endogenous enzyme can successfully complement for the missing gene. We used PCR mutagenesis with error-prone Taq polymerase to produce 948 colonies and compared cell growth in the presence or absence of a cysteine source as a measure of CBS function. We were able to infer the phenotypes of 204 single-site mutants, 79 of them deleterious and 125 neutral. This set was used to test the accuracy of six publicly available prediction methods for phenotype prediction of missense mutations: SIFT, PolyPhen, PMut, SNPs3D, PhD-SNP, and nsSNPAnalyzer. The top methods are PolyPhen, SIFT, and nsSNPAnalyzer, which have similar performance. Using kernel discriminant functions, we found that the difference in position-specific scoring matrix values is more predictive than the wild-type PSSM score alone, and that the relative surface area in the biologically relevant complex is more predictive than that of the monomeric proteins.

Abstract: Analysis of trajectories from our rigid-body dynamics simulation package, BioSimz, is used to find regions on the surface of unbound proteins that form frequent and tenacious encounter complexes with their binding partner. Binding partners are significantly more likely to sojourn around true binding regions than around the remainder of the protein surface. This information is used to restrict the search space for flexible protein-protein docking using our SwarmDock algorithm, reducing the computational expense of docking, and improving or matching the ranking of successfully docked poses for all but four of 26 test cases. Running the simulations with external crowder proteins, at near physiological concentration, further enhances the binding region, compared to simulations without external crowders. Information gleaned from these simulations can give mechanistic insights into binding events. The application of these techniques to CAPRI targets 32 and 38-40 is discussed.

Abstract: The release of polysaccharide from the plant cell wall is a key process to release the stored energy from plant biomass. Within the ruminant digestive system, a host of commensal microorganisms speed the breakdown of plant cell matter releasing fermentable sugars. The presence of phenolic compounds, most notably ferulic acid (FA), esterified within the cell wall is thought to pose a significant impediment to the degradation of the plant cell wall. The structure of a FA esterase from the ruminant bacterium Butyrivibrio proteoclasticus has been determined in two different space groups, in both the apo-form, and the ligand bound form with FA located in the active site. The structure reveals a new lid domain that has no structural homologues in the PDB. The flexibility of the lid domain is evident by the presence of three different conformations adopted by different molecules in the crystals. In the FA-bound structures, these conformations show sequential binding and closing of the lid domain over the substrate. Enzymatic activity assays demonstrate a broad activity against plant-derived hemicellulose, releasing at least four aromatic compounds including FA, coumaric acid, coumarin-3-carboxylic acid, and cinnamic acid. The rumen is a complex ecosystem that efficiently degrades plant biomass and the genome of B. proteoclasticus contains greater than 130 enzymes, which are potentially involved in this process of which Est1E is the first to be well characterized. Proteins 2010. © 2009 Wiley-Liss, Inc.

Abstract: Structural models of macromolecular assemblies are instrumental for gaining a mechanistic understanding of cellular processes. Determining these structures is a major challenge for experimental techniques, such as X-ray crystallography, NMR spectroscopy and electron microscopy. Thus, computational modeling techniques, including molecular docking, are required. The development of most molecular docking methods has so far been focused on modeling of binary complexes. We have recently introduced the MultiFit method for modeling the structure of a multi-subunit complex by simultaneously optimizing the fit of the model into an electron microscopy density map of the entire complex and the shape complementarity between interacting subunits. Here, we report algorithmic advances of the MultiFit method that result in an efficient and accurate assembly of the input subunits into their density map. The successful predictions and the increasing number of complexes being characterized by electron microscopy suggests that the CAPRI challenge could be extended to include docking-based modeling of macromolecular assemblies guided by electron microscopy.

Abstract: Theoretical exploration of fundamental biological processes involving the forced unraveling of multimeric proteins, the sliding motion in protein fibers and the mechanical deformation of biomolecular assemblies under physiological force loads is challenging even for distributed computing systems. Using a C(α)-based coarse-grained self organized polymer (SOP) model, we implemented the Langevin simulations of proteins on graphics processing units (SOP-GPU program). We assessed the computational performance of an end-to-end application of the program, where all the steps of the algorithm are running on a GPU, by profiling the simulation time and memory usage for a number of test systems. The ∼90-fold computational speedup on a GPU, compared with an optimized central processing unit program, enabled us to follow the dynamics in the centisecond timescale, and to obtain the force-extension profiles using experimental pulling speeds (v(f) = 1-10 μm/s) employed in atomic force microscopy and in optical tweezers-based dynamic force spectroscopy. We found that the mechanical molecular response critically depends on the conditions of force application and that the kinetics and pathways for unfolding change drastically even upon a modest 10-fold increase in v(f). This implies that, to resolve accurately the free energy landscape and to relate the results of single-molecule experiments in vitro and in silico, molecular simulations should be carried out under the experimentally relevant force loads. This can be accomplished in reasonable wall-clock time for biomolecules of size as large as 10(5) residues using the SOP-GPU package.

Abstract: Hsp90 is an important cellular chaperone and attractive target for therapeutics against both cancer and infectious organisms. The Hsp90 protein from the parasite Plasmodium falciparum, the causative agent of malaria, is critical for this organism's survival; the anti-Hsp90 drug geldanamycin is toxic to P. falciparum growth. We have solved the structure of the N-terminal ATP-binding domain of P. falciparum Hsp90, which contains a principal drug-binding pocket, in both apo and ADP-bound states at 2.3 A resolution. The structure shows that P. falciparum Hsp90 is highly similar to human Hsp90, and likely binds agents such as geldanamycin in an identical manner. Our results should aid in the structural understanding of Hsp90-drug interactions in P. falciparum, and provide a scaffold for future drug-discovery efforts.

Abstract: Identifying correct binding modes in a large set of models is an important step in protein-protein docking. We identified protein docking filter based on overlap area that significantly reduces the number of candidate structures that require detailed examination. We also developed potentials based on residue contacts and overlap areas using a comprehensive learning set of 640 two chain protein complexes with mathematical programming. Our potential showed substantially better recognition capacity compared to other publicly accessible protein docking potentials in discriminating between native and nonnative binding modes on a large test set of 84 complexes independent of our training set. We were able to rank a near native model on the top in 43 cases and within top 10 in 51 cases. We also report an atomic potential that ranks a near native model on the top in 46 cases and within top 10 in 58 cases. Our filter + potential is well suited for selecting a small set of models to be refined to atomic resolution.

Abstract: Antiandrogen flutamide, an antagonist of the wild-type androgen receptor (AR), is used in the clinics for treating metastatic prostate cancer. However, the T877A mutated AR is paradoxically activated by hydroxyflutamide, an active form of flutamide. Despite of crystallographic studies, how the T877A mutation results in antagonist-agonist conversion of hydroxyflutamide remains a puzzle. Here, started from a structural model of the apo form of AR ligand-binding domain (AR-LBD), we have investigated the impact of the T877A mutation on ligand-induced helix-12 positioning by replica-exchange molecular dynamics (REMD) simulations with an unique protocol, which is capable of simulating the H12 dynamics and keeping the main body of AR-LBD unchanged. Specifically, (i) we have computationally demonstrated that on the binding of hydroxyflutamide, the apo form of H12 rearranges into the agonistic form in the T877A mutant, but into the antagonistic forms in the wild-type receptor, shedding light on hydroxyflutamide agonism/antagonism; (ii) By REMD simulations, we have predicted antiandrogen SC184 is a non-agonist of the T877A mutant. This was confirmed by luciferase assays; and (iii) on the basis of the binding modes of hydroxyflutamide and SC184 from the simulations, we designed a novel flutamide derivative called SC333, which was subsequently predicted to be a pure antagonist of the T877A mutant. We then synthesized and experimentally confirmed SC333 is a pan-antiandrogen effective against the wild-type and the T877A and W741C mutated ARs, showing low micromolar cytotoxicity in LNCaP cells. Importantly, we demonstrated that distribution of the H12 conformations from REMD simulations is correlated with ligand agonist/antagonist activity. Proteins 2010. © 2009 Wiley-Liss, Inc.

Abstract: One of the most important requirements in computer-aided drug design is the ability to reliably evaluate the binding free energies. However, the process of ligand binding is very complex because of the intricacy of the interrelated processes that are difficult to predict and quantify. In fact, the deeper understanding of the origin of the observed binding free energies requires the ability to decompose these free energies to their contributions from different interactions. Furthermore, it is important to evaluate the relative entropic and enthalpic contributions to the overall free energy. Such an evaluation is useful for assessing temperature effects and exploring specialized options in enzyme design. Unfortunately, calculations of binding entropies have been much more challenging than calculations of binding free energies. This work is probably the first to present microscopic evaluation of all of the relevant components to the binding entropy, namely configurational, polar solvation, and hydrophobic entropies. All of these contributions are evaluated by the restraint release approach. The calculated results shed an interesting light on major compensation effects in both the solvation and hydrophobic effect and, despite some overestimate, can provide very useful insight. This study also helps in analyzing some problems with the widely used molecular mechanics/Poisson-Boltzmann surface area approach.

Abstract: The rice class I chitinase OsChia1b, also referred to as RCC2 or Cht-2, is composed of an N-terminal chitin-binding domain (ChBD) and a C-terminal catalytic domain (CatD), which are connected by a proline- and threonine-rich linker peptide. Because of the ability to inhibit fungal growth, the OsChia1b gene has been used to produce transgenic plants with enhanced disease resistance. As an initial step toward elucidating the mechanism of hydrolytic action and antifungal activity, the full-length structure of OsChia1b was analyzed by X-ray crystallography and small-angle X-ray scattering (SAXS). We determined the crystal structure of full-length OsChia1b at 2.00-A resolution, but there are two possibilities for a biological molecule with and without interdomain contacts. The SAXS data showed an extended structure of OsChia1b in solution compared to that in the crystal form. This extension could be caused by the conformational flexibility of the linker. A docking simulation of ChBD with tri-N-acetylchitotriose exhibited a similar binding mode to the one observed in the crystal structure of a two-domain plant lectin complexed with a chitooligosaccharide. A hypothetical model based on the binding mode suggested that ChBD is unsuitable for binding to crystalline α-chitin, which is a major component of fungal cell walls because of its collisions with the chitin chains on the flat surface of α-chitin. This model also indicates the difference in the binding specificity of plant and bacterial ChBDs of GH19 chitinases, which contribute to antifungal activity. Proteins 2010. © 2010 Wiley-Liss,Inc.

Abstract: The sequence KLVFFAE (A beta 16-22) in Alzheimer's beta-amyloid is thought to be a core beta-structure that could act as a template for folding other parts of the polypeptide or molecules into fibrillar assemblies rich in beta-sheet. To elucidate the mechanism of the initial folding process, we undertook combined X-ray fiber/powder diffraction and infrared (IR) spectroscopy to analyze lyophilized A beta 16-22 and solubilized/dried peptide containing nitrile probes at F19 and/or F20. Solubilized/dried wild-type (WT) A beta 16-22 and the peptide containing cyanophenylalanine at F19 (19CN) or at F20 (20CN) gave fiber patterns consistent with slab-like beta-crystallites that were cylindrically averaged around the axis parallel to the polypeptide chain direction. The WT and 19CN assemblies showed 30-A period arrays arising from the stacking of the slabs along the peptide chain direction, whereas the 20CN assemblies lacked any such stacking. The electron density projection along the peptide chain direction indicated similar side-chain dispositions for WT and 20CN, but not for 19CN. These X-ray results and modeling imply that in the assembly of WT A beta 16-22 the F19 side chain is localized within the intersheet space and is involved in hydrophobic contact with amino acids across the intersheet space, whereas the F20 side chain localized near the slab surface is less important for the intersheet interaction, but involved in slab stacking. IR observations for the same peptides in dilute solution showed a greater degree of hydrogen bonding for the nitrile groups in 20CN than in 19CN, supporting this interpretation.

Abstract: The properties of the amyloid-beta peptide that lead to aggregation associated with Alzheimer's disease are not fully understood. This study aims at identifying conformational differences among four variants of full-length Abeta42 that are known to display very different aggregation properties. By extensive all-atom Monte Carlo simulations, we find that a variety of beta-sheet structures with distinct turns are readily accessible for full-length Abeta42. In the simulations, wild type (WT) Abeta42 preferentially populates two major classes of conformations, either extended with high beta-sheet content or more compact with lower beta-sheet content. The three mutations studied alter the balance between these classes. Strong mutational effects are observed in a region centered at residues 23-26, where WT Abeta42 tends to form a turn. The aggregation-accelerating E22G mutation associated with early onset of Alzheimer's disease makes this turn region conformationally more diverse, whereas the aggregation-decelerating F20E mutation has the reverse effect, and the E22G/I31E mutation reduces the turn population. Comparing results for the four Abeta42 variants, we identify specific conformational properties of residues 23-26 that might play a key role in aggregation.