A physical reference state unifies the structure-derived potential of mean force for protein folding and binding

Rational engineering of L-asparaginase reveals importance of dual activity for cancer cell toxicity

Using proteins in a therapeutic context often requires engineering to modify functionality and enhance efficacy. We have previously reported that the therapeutic antileukemic protein macromolecule Escherichia coli L-asparaginase is degraded by leukemic lysosomal cysteine proteases. In the present study, we successfully engineered L-asparaginase to resist proteolytic cleavage and at the same time improve activity. We employed a novel combination of mutant sampling using a genetic algorithm in tandem with flexibility studies using molecular dynamics to investigate the impact of lid-loop and mutations on drug activity. Applying these methods, we successfully predicted the more active L-asparaginase mutants N24T and N24A. For the latter, a unique hydrogen bond network contributes to higher activity. Furthermore, interface mutations controlling secondary glutaminase activity demonstrated the importance of this enzymatic activity for drug cytotoxicity. All selected mutants were expressed, purified, and tested for activity and for their ability to form the active tetrameric form. By introducing the N24A and N24A R195S mutations to the drug L-asparaginase, we are a step closer to individualized drug design.

Structural and functional similarities between osmotin from Nicotiana tabacum seeds and human adiponectin

Osmotin, a plant protein, specifically binds a seven transmembrane domain receptor-like protein to exert its biological activity via a RAS2/cAMP signaling pathway. The receptor protein is encoded in the gene ORE20/PHO36 and the mammalian homolog of PHO36 is a receptor for the human hormone adiponectin (ADIPOR1). Moreover it is known that the osmotin domain I can be overlapped to the β-barrel domain of adiponectin. Therefore, these observations and some already existing structural and biological data open a window on a possible use of the osmotin or of its derivative as adiponectin agonist. We have modelled the three-dimensional structure of the adiponectin trimer (ADIPOQ), and two ADIPOR1 and PHO36 receptors. Moreover, we have also modelled the following complexes: ADIPOQ/ADIPOR1, osmotin/PHO36 and osmotin/ADIPOR1. We have then shown the structural determinants of these interactions and their physico-chemical features and analyzed the related interaction residues involved in the formation of the complexes. The stability of the modelled structures and their complexes was always evaluated and controlled by molecular dynamics. On the basis of these results a 9 residues osmotin peptide was selected and its interaction with ADIPOR1 and PHO36 was modelled and analysed in term of energetic stability by molecular dynamics. To confirm in vivo the molecular modelling data, osmotin has been purified from nicotiana tabacum seeds and its nine residues peptide synthesized. We have used cultured human synovial fibroblasts that respond to adiponectin by increasing the expression of IL-6, TNF-alpha and IL-1beta via ADIPOR1. The biological effect on fibroblasts of osmotin and its peptide derivative has been found similar to that of adiponectin confirming the results found in silico.

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

Designing coarse grained-and atom based-potentials for protein-protein docking

BACKGROUND

Protein-protein docking is a challenging computational problem in functional genomics, particularly when one or both proteins undergo conformational change(s) upon binding. The major challenge is to define a scoring function soft enough to tolerate these changes and specific enough to distinguish between near-native and "misdocked" conformations.

RESULTS

Using a linear programming (LP) technique, we developed two types of potentials: (i) Side chain-based and (ii) Heavy atom-based. To achieve this we considered a set of 161 transient complexes and generated a large set of putative docked structures (decoys), based on a shape complementarity criterion, for each complex. The demand on the potentials was to yield, for the native (correctly docked) structure, a potential energy lower than those of any of the non-native (misdocked) structures. We show that the heavy atom-based potentials were able to comply with this requirement but not the side chain-based one. Thus, despite the smaller number of parameters, the capability of heavy atom-based potentials to discriminate between native and "misdocked" conformations is improved relative to those of the side chain-based potentials. The performance of the atom-based potentials was evaluated by a jackknife test on a set of 50 complexes taken from the Zdock2.3 decoys set.

CONCLUSIONS

Our results show that, using the LP approach, we were able to train our potentials using a dataset of transient complexes only the newly developed potentials outperform three other known potentials in this test.

Recovering physical potentials from a model protein databank

Knowledge-based approaches frequently employ empirical relations to determine effective potentials for coarse-grained protein models directly from protein databank structures. Although these approaches have enjoyed considerable success and widespread popularity in computational protein science, their fundamental basis has been widely questioned. It is well established that conventional knowledge-based approaches do not correctly treat many-body correlations between amino acids. Moreover, the physical significance of potentials determined by using structural statistics from different proteins has remained obscure. In the present work, we address both of these concerns by introducing and demonstrating a theory for calculating transferable potentials directly from a databank of protein structures. This approach assumes that the databank structures correspond to representative configurations sampled from equilibrium solution ensembles for different proteins. Given this assumption, this physics-based theory exactly treats many-body structural correlations and directly determines the transferable potentials that provide a variationally optimized approximation to the free energy landscape for each protein. We illustrate this approach by first constructing a databank of protein structures using a model potential and then quantitatively recovering this potential from the structure databank. The proposed framework will clarify the assumptions and physical significance of knowledge-based potentials, allow for their systematic improvement, and provide new insight into many-body correlations and cooperativity in folded proteins.

Dimerization of the AtoC response regulator and modelling of its binding to DNA

Bacterial signal transduction systems can be viewed as an entity of multi-sensory and output domains, whereas the functions of response regulators play a pivotal role in the complex network interactions. One crucial property among response regulators functions is their oligomerization and subsequent binding to DNA. The AtoS-AtoC two component system, functionally modulated by various agents, influences fundamental cellular processes such as short-chain fatty acid catabolism and poly-(R)-3-hydroxybutyrate biosynthesis in Escherichia coli. Among the already reported characteristic properties, AtoC binds to a specific site, a palindromic repeat of 20 nucleotides within the atoDAEB promoter. Since experimental structures of AtoC or its complex with DNA are not yet available, an almost complete homology model of AtoC and of its putative entity as a dimer is constructed for this study, as well as a model of its binding to its target DNA sequence. The latter is associated with large conformational changes, as shown by molecular dynamics simulations. Subsequent biochemical study, including cross-linking via chemical agents, revealed the ability of AtoC to form oligomers in vitro.

Reference state for the generalized Yvon-Born-Green theory: application for coarse-grained model of hydrophobic hydration

Coarse-grained (CG) models provide a computationally efficient means for investigating phenomena that remain beyond the scope of atomically detailed models. Although CG models are often parametrized to reproduce the results of atomistic simulations, it is highly desirable to determine accurate CG models from experimental data. Recently, we have introduced a generalized Yvon-Born-Green (g-YBG) theory for directly (i.e., noniteratively) determining variationally optimized CG potentials from structural correlation functions. In principle, these correlation functions can be determined from experiment. In the present work, we introduce a reference state potential into the g-YBG framework. The reference state defines a fixed contribution to the CG potential. The remaining terms in the potential are then determined, such that the combined potential provides an optimal approximation to the many-body potential of mean force. By specifying a fixed contribution to the potential, the reference state significantly reduces the computational complexity and structural information necessary for determining the remaining potentials. We also validate the quantitative accuracy of the proposed method and numerically demonstrate that the reference state provides a convenient framework for transferring CG potentials from neat liquids to more complex systems. The resulting CG model provides a surprisingly accurate description of the two- and three-particle solvation structures of a hydrophobic solute in methanol. This work represents a significant step in developing the g-YBG theory as a useful computational framework for determining accurate CG models from limited experimental data.

Stacking and energetic contribution of aromatic islands at the binding interface of antibody proteins

Background

The enrichment and importance of some aromatic residues, such as Tyr and Trp, have been widely noticed at the binding interfaces of antibodies from many experimental and statistical results, some of which were even identified as “hot spots” contributing significantly greater to the binding affinity than other amino acids. However, how these aromatic residues influence the immune binding still deserves further investigation. A large-scale examination was done regarding the local spatial environment around the interfacial Tyr or Trp residues. Energetic contribution of these Tyr and Trp residues to the binding affinity was then studied regarding 82 representative antibody interfaces covering 509 immune complexes from the PDB database and IMGT/3Dstructure-DB.

Results

The connectivity analysis of interfacial residues showed that Tyr and Trp tended to cluster into the spatial Aromatic Islands (AI) rather than being distributed randomly at the antibody interfaces. Out of 82 antibody-antigen complexes, 72% (59) interfaces were found to contain AI with more than 3 aromatic residues. The statistical test against an empirical distribution indicated that the existence of AI was significant in about 60% representative antibody interfaces. Secondly, the loss of solvent accessible surface area (SASA) for side chains of aromatic residues between actually crowded state and independent state was nicely correlated with the AI size increasing in a linearly positive way which indicated that the aromatic side chains in AI tended to take a compact and ordered stacking conformation at the interfaces. Interestingly, the SASA loss of AI was also correlated roughly with the averaged gap of binding free energy between the theoretical and experimental data for immune complexes.

Conclusions

The results of our study revealed the wide existence and statistical significance of “Aromatic Island” (AI) composed of the spatially clustered Tyr and Trp residues at the antibody interfaces. The regular arrangement and stacking of aromatic side chains in AI could probably produce extra cooperative effects to the binding affinity which was firstly observed through the large-scale data analysis. The finding in this work not only provides insights into the functional role of aromatic residues in the antibody-antigen interaction, but also may facilitate the antibody engineering and potential clinical applications.

Information-theoretic analysis of the reference state in contact potentials used for protein structure prediction

Using information-theoretic concepts, we examine the role of the reference state, a crucial component of empirical potential functions, in protein fold recognition. We derive an information-based connection between the probability distribution functions of the reference state and those that characterize the decoy set used in threading. In examining commonly used contact reference states, we find that the quasi-chemical approximation is informatically superior to other variant models designed to include characteristics of real protein chains, such as finite length and variable amino acid composition from protein to protein. We observe that in these variant models, the total divergence, the operative function that quantifies discrimination, decreases along with threading performance. We find that any amount of nativeness encoded in the reference state model does not significantly improve threading performance. A promising avenue for the development of better potentials is suggested by our information-theoretic analysis of the action of contact potentials on individual protein sequences. Our results show that contact potentials perform better when the compositional properties of the data set used to derive the score function probabilities are similar to the properties of the sequence of interest. Results also suggest to use only sequences of similar composition in deriving contact potentials, to tailor the contact potential specifically for a test sequence.

Docking study of the precursor peptide of mastoparan onto its putative processing enzyme, dipeptidyl peptidase IV: a revisit to molecular ticketing

Stepwise-cleavage process of promastoparans to reach maturity was investigated theoretically by combining ab initio folding and unbounded docking. The comparison between the structures of the promastoparans both before and after docking were examined along with the hydrogen bonding interaction pattern between the dipetidyl peptidase IV (DPPIV) and promastoparans to reveal how the endpoint of this stepwise cleavage is recognized among these promastoparans with highly resemble amino acid sequences. The current approach of folding and docking study provides structural insight on the stepwise cleavage process.

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

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

PIE-efficient filters and coarse grained potentials for unbound protein-protein docking

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.

Standardized evaluation of protein stability

We compare mean force potential values of a large series of PDB models of proteins and peptides and find that, either as monomers or polymers, proteins longer than 200-250 residues have equivalent MFP values that are averaged to -65+/-3 kcal/aa. This value is named the standard or stability value. The standard value is reached irrespective of sequences and 3D folds. Peptides are too short to follow the rule and frequently exist as populations of conformers; one exception is peptides in amyloid fibrils. Fibrils surpass the standard value in accordance with their uppermost stability. In parallel, we calculate median MFP values of amino acids in stably folded PDB models of proteins: median values vary from -25 for Gly to -115 kcal/aa for Trp. These median values are used to score primary sequences of proteins: all sequences converge to a mean value of -63.5+/-2.5 kcal/aa, i.e., only 1.5 kcal less than the folded model standard. Sequences from unfolded proteins have lower values. This supports the conclusion that sequences carry in an important message and more specifically that diversity of amino acids in sequences is mandatory for stability. We also use the median amino acid MFP to score residue stability in 3D folds. This demonstrates that 3D folds are compromises between fragments of high and fragments of low scores and that functional residues are often but not always in the extreme score values. The approach opens to possibilities of evaluating any 3D model and of detecting functional residues and should help in conducting mutation assays.

The subunit interfaces of weakly associated homodimeric proteins

We analyzed subunit interfaces in 315 homodimers with an X-ray structure in the Protein Data Bank, validated by checking the literature for data that indicate that the proteins are dimeric in solution and that, in the case of the "weak" dimers, the homodimer is in equilibrium with the monomer. The interfaces of the 42 weak dimers, which are smaller by a factor of 2.4 on average than in the remainder of the set, are comparable in size with antibody-antigen or protease-inhibitor interfaces. Nevertheless, they are more hydrophobic than in the average transient protein-protein complex and similar in amino acid composition to the other homodimer interfaces. The mean numbers of interface hydrogen bonds and hydration water molecules per unit area are also similar in homodimers and transient complexes. Parameters related to the atomic packing suggest that many of the weak dimer interfaces are loosely packed, and we suggest that this contributes to their low stability. To evaluate the evolutionary selection pressure on interface residues, we calculated the Shannon entropy of homologous amino acid sequences at 60% sequence identity. In 93% of the homodimers, the interface residues are better conserved than the residues on the protein surface. The weak dimers display the same high degree of interface conservation as other homodimers, but their homologs may be heterodimers as well as homodimers. Their interfaces may be good models in terms of their size, composition, and evolutionary conservation for the labile subunit contacts that allow protein assemblies to share and exchange components, allosteric proteins to undergo quaternary structure transitions, and molecular machines to operate in the cell.

Exploring the linkage dependence of polyubiquitin conformations using molecular modeling

Posttranslational modification of proteins by covalent attachment of a small protein ubiquitin (Ub) or a polymeric chain of Ub molecules (called polyubiquitin) is involved in controlling a vast variety of processes in eukaryotic cells. The question of how different polyubiquitin signals are recognized is central to understanding the specificity of various types of polyubiquitination. In polyubiquitin, monomers are linked to each other via an isopeptide bond between the C-terminal glycine of one Ub and a lysine of the other. The functional outcome of polyubiquitination depends on the particular lysine involved in chain formation and appears to rely on linkage-dependent conformation of polyubiquitin. Thus, K48-linked chains, a universal signal for proteasomal degradation, under physiological conditions adopt a closed conformation where functionally important residues L8, I44, and V70 are sequestered at the interface between two adjacent Ub monomers. By contrast, K63-linked chains, which act as a nonproteolytic regulatory signal, adopt an extended conformation that lacks hydrophobic interubiquitin contact. Little is known about the functional roles of the so-called "noncanonical" chains (linked via K6, K11, K27, K29, or K33, or linked head-to-tail), and no structural information on these chains is available, except for information on the crystal structure of the head-to-tail-linked diubiquitin (Ub(2)). In this study, we use molecular modeling to examine whether any of the noncanonical chains can adopt a closed conformation similar to that in K48-linked polyubiquitin. Our results show that the eight possible Ub(2) chains can be divided into two groups: chains linked via K6, K11, K27, or K48 are predicted to form a closed conformation, whereas chains linked via K29, K33, or K63, or linked head-to-tail are unable to form such a contact due to steric occlusion. These predictions are validated by the known structures of K48-, K63-, and head-to-tail-linked chains. Our study also predicts structural models for Ub(2) chains linked via K6, K11, or K27. The implications of these findings for linkage-selective recognition of noncanonical polyubiquitin signals by various receptors are discussed.

Structural effects of clinically observed mutations in JAK2 exons 13-15: comparison with V617F and exon 12 mutations

Background

The functional relevance of many of the recently detected JAK2 mutations, except V617F and exon 12 mutants, in patients with chronic myeloproliferative neoplasia (MPN) has been significantly overlooked. To explore atomic-level explanations of the possible mutational effects from those overlooked mutants, we performed a set of molecular dynamics simulations on clinically observed mutants, including newly discovered mutations (K539L, R564L, L579F, H587N, S591L, H606Q, V617I, V617F, C618R, L624P, whole exon 14-deletion) and control mutants (V617C, V617Y, K603Q/N667K).

Results

Simulation results are consistent with all currently available clinical/experimental evidence. The simulation-derived putative interface, not possibly obtained from static models, between the kinase (JH1) and pseudokinase (JH2) domains of JAK2 provides a platform able to explain the mutational effect for all mutants, including presumably benign control mutants, at the atomic level.

Conclusion

The results and analysis provide structural bases for mutational mechanisms of JAK2, may advance the understanding of JAK2 auto-regulation, and have the potential to lead to therapeutic approaches. Together with recent mutation profiling results demonstrating the breadth of clinically observed JAK2 mutations, our findings suggest that molecular testing/diagnostics of JAK2 should extend beyond V617F and exon 12 mutations, and perhaps should encompass most of the pseudo-kinase domain-coding region.

An all-atom knowledge-based energy function for protein-DNA threading, docking decoy discrimination, and prediction of transcription-factor binding profiles

How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a statistical potential based on the distance-scaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcription-factor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks.informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes.

Four distances between pairs of amino acids provide a precise description of their interaction

The three-dimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a four-dimensional grid and compared to a random model, from which the preference for specific four-distances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the four-distance data have higher information content than classical bond descriptions, we were able to identify many unique inter-residue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures.

A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys

Near-native selections from docking decoys have proved challenging especially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked structures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein-protein docking selection by introducing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys.

Another look at the conditions for the extraction of protein knowledge-based potentials

Protein knowledge-based potentials are effective free energies obtained from databases of known protein structures. They are used to parameterize coarse-grained protein models in many folding simulation and structure prediction methods. Two common approaches are used in the derivation of knowledge-based potentials. One assumes that the energy parameters optimize the native structure stability. The other assumes that interaction events are related to their energies according to the Boltzmann distribution, and that they are distributed independently of other events, that is, the quasi-chemical approximation. Here, these assumptions are systematically tested by extracting contact energies from artificial databases of lattice proteins with predefined pairwise contact energies. Databases of protein sequences are designed to either satisfy the Boltzmann distribution at high or low temperatures, or to simultaneously optimize the native stability and folding kinetics. It is found that the quasi-chemical approximation, with the ideal reference state, accurately reproduce the true energies for high temperature Boltzmann distributed sequences (weakly interacting residues), but less accurately at low temperatures, where the sequences correspond to energy minima and the residues are strongly interacting. To overcome this problem, an iterative procedure for Boltzmann distributed sequences is introduced, which accounts for interacting residue correlations and eliminates the need for the quasi-chemical approximation. In this case, the energies are accurately reproduced at any ensemble temperature. However, when the database of sequences designed for optimal stability and kinetics is used, the energy correlation is less than optimal using either method, exhibiting random and systematic deviations from linearity. Therefore, the assumption that native structures are maximally stable or that sequences are determined according to the Boltzmann distribution seems to be inadequate for obtaining accurate energies. The limited number of sequences in the database and the inhomogeneous concentration of amino acids from one structure to another do not seem to be major obstacles for improving the quality of the extracted pairwise energies, with the exception of repulsive interactions.

Predictive bioinformatic identification of minor receptor group human rhinoviruses

Major group HRVs bind intercellular adhesion molecule 1 and minor group HRVs bind members of the low-density lipoprotein receptor (LDLR) family for cell entry. Whereas the former share common sequence motives in their viral capsid proteins (VPs), in the latter only a lysine residue within the binding epitope in VP1 is conserved; this lysine is also present in "K-type" major group HRVs that fail to use LDLR for infection. By using the available sequences three-dimensional models of VP1 of all HRVs were built and binding energies, with respect to module 3 of the very-low-density lipoprotein receptor, were calculated. Based on the predicted affinities K-type HRVs and minor group HRVs were correctly classified.

Structure-based design of a T-cell receptor leads to nearly 100-fold improvement in binding affinity for pepMHC

T-cell receptors (TCRs) are proteins that recognize peptides from foreign proteins bound to the major histocompatibility complex (MHC) on the surface of an antigen-presenting cell. This interaction enables the T cells to initiate a cell-mediated immune response to terminate cells displaying the foreign peptide on their MHC. Naturally occurring TCRs have high specificity but low affinity toward the peptide-MHC (pepMHC) complex. This prevents the usage of solubilized TCRs for diagnosis and treatment of viral infections or cancers. Efforts to enhance the binding affinity of several TCRs have been reported in recent years, through randomized libraries and in vitro selection. However, there have been no reported efforts to enhance the affinity via structure-based design, which allows more control and understanding of the mechanism of improvement. Here, we have applied structure-based design to a human TCR to improve its pepMHC binding. Our design method evolved based on iterative steps of prediction, testing, and generating more predictions based on the new data. The final design function, named ZAFFI, has a correlation of 0.77 and average error of 0.35 kcal/mol with the binding free energies of 26 point mutations for this system that we measured by surface plasmon resonance (SPR). Applying the filter that we developed to remove nonbinding predictions, this correlation increases to 0.85, and the average error decreases to 0.3 kcal/mol. Using this algorithm, we predicted and tested several point mutations that improved binding, with one giving over sixfold binding improvement. Four of the point mutations that improved binding were then combined to give a mutant TCR that binds the pepMHC 99 times more strongly than the wild-type TCR.

Structure-based prediction of transcription factor binding sites using a protein-DNA docking approach

Accurate identification of transcription factor binding sites is critical to our understanding of transcriptional regulatory networks. To overcome the issue of high false-positive predictions that trouble the sequence-based prediction techniques, we have developed a structure-based prediction method that takes into consideration of interactions between the amino acids of a transcription factor and the nucleotides of its DNA binding sequence at structural level, along with an efficient protein-DNA docking algorithm. The docked structures between a protein and a DNA are evaluated using a knowledge-based energy function, in conjunction with van der Waals energy. Our docking algorithm supports quasi-flexible docking, overcoming a number of limiting issues faced by similar docking algorithms. Our rigid-body docking algorithm is tested on a dataset of 141 nonredundant transcription factor-DNA complex structures. The test results show that 63.1% of the 141 complex structures are reconstructed with accuracies better than 1.0 A RMSDs (root mean square deviation) and 79.4% of the complexes are predicted with accuracies better than 3.0 A RMSDs when using the native DNA structures. Our quasi-flexible docking algorithm, assuming that the DNA structures are not known, is tested on a separate set of 45 transcription factor-DNA complexes, of which 57.8% of the docked complex conformations achieve better than 1.0 A RMSDs while 71.1% of the complexes have RMSDs less than 3.0 A. We have also applied our method to predict the binding motifs of the ferric uptake regulator in E. coli and showed that most of the experimentally identified sites can be predicted accurately.

Probabilistic models and machine learning in structural bioinformatics

Structural bioinformatics is concerned with the molecular structure of biomacromolecules on a genomic scale, using computational methods. Classic problems in structural bioinformatics include the prediction of protein and RNA structure from sequence, the design of artificial proteins or enzymes, and the automated analysis and comparison of biomacromolecules in atomic detail. The determination of macromolecular structure from experimental data (for example coming from nuclear magnetic resonance, X-ray crystallography or small angle X-ray scattering) has close ties with the field of structural bioinformatics. Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis and experimental determination of macromolecular structure that are based on such methods. These developments include generative models of protein structure, the estimation of the parameters of energy functions that are used in structure prediction, the superposition of macromolecules and structure determination methods that are based on inference. Although this review is not exhaustive, I believe the selected topics give a good impression of the exciting new, probabilistic road the field of structural bioinformatics is taking.

Molecular modelling of co-receptor CD8 alpha alpha and its complex with MHC class I and T-cell receptor in sea bream (Sparus aurata)

T-cells are the main actors of cell-mediated immune defence; they recognize and respond to peptide antigens associated with MHC class I and class II molecules. In this paper, we investigated by molecular modelling methods in the teleost sea bream (Sparus aurata) the interaction among the molecules of the tertiary complex CD8/MHC-I/TCR, which determines the T-cell-mediated immunological response to foreign molecules. First, we predicted the three-dimensional structure of CD8 alpha alpha dimer and MHC-I, and, successively, we simulated the CD8 alpha alpha/MHC-I complex. Finally, the 3D structure of the CD8/MHC-I/TCR complex was simulated in order to investigate the possible changes that can influence TCR signalling events.

Human sirt-1: molecular modeling and structure-function relationships of an unordered protein

Background

Sirt-1 is a NAD+-dependent nuclear deacetylase of 747 residues that in mammals is involved in various important metabolic pathways, such as glucose metabolism and insulin secretion, and often works on many different metabolic substrates as a multifunctional protein. Sirt-1 down-regulates p53 activity, rising lifespan, and cell survival; it also deacetylases peroxisome proliferator-activated receptor-gamma (PPAR-γ) and its coactivator 1 alpha (PGC-1α), promoting lipid mobilization, positively regulating insulin secretion, and increasing mitochondrial dimension and number. Therefore, it has been implicated in diseases such as diabetes and the metabolic syndrome and, also, in the mechanisms of longevity induced by calorie restriction. Its whole structure is not yet experimentally determined and the structural features of its allosteric site are unknown, and no information is known about the structural changes determined by the binding of its allosteric effectors.

Methodology

In this study, we modelled the whole three-dimensional structure of Sirt-1 and that of its endogenous activator, the nuclear protein AROS. Moreover, we modelled the Sirt-1/AROS complex in order to study the structural basis of its activation and regulation.

Conclusions

Amazingly, the structural data show that Sirt-1 is an unordered protein with a globular core and two large unordered structural regions at both termini, which play an important role in the protein-protein interaction. Moreover, we have found on Sirt-1 a conserved pharmacophore pocket of which we have discussed the implication.

Computational structural analysis: multiple proteins bound to DNA

Background

With increasing numbers of crystal structures of protein∶DNA and protein∶protein∶DNA complexes publically available, it is now possible to extract sufficient structural, physical-chemical and thermodynamic parameters to make general observations and predictions about their interactions. In particular, the properties of macromolecular assemblies of multiple proteins bound to DNA have not previously been investigated in detail.

Methodology/Principal Findings

We have performed computational structural analyses on macromolecular assemblies of multiple proteins bound to DNA using a variety of different computational tools: PISA; PROMOTIF; X3DNA; ReadOut; DDNA and DCOMPLEX. Additionally, we have developed and employed an algorithm for approximate collision detection and overlapping volume estimation of two macromolecules. An implementation of this algorithm is available at http://promoterplot.fmi.ch/Collision1/. The results obtained are compared with structural, physical-chemical and thermodynamic parameters from protein∶protein and single protein∶DNA complexes. Many of interface properties of multiple protein∶DNA complexes were found to be very similar to those observed in binary protein∶DNA and protein∶protein complexes. However, the conformational change of the DNA upon protein binding is significantly higher when multiple proteins bind to it than is observed when single proteins bind. The water mediated contacts are less important (found in less quantity) between the interfaces of components in ternary (protein∶protein∶DNA) complexes than in those of binary complexes (protein∶protein and protein∶DNA).The thermodynamic stability of ternary complexes is also higher than in the binary interactions. Greater specificity and affinity of multiple proteins binding to DNA in comparison with binary protein-DNA interactions were observed. However, protein-protein binding affinities are stronger in complexes without the presence of DNA.

Conclusions/Significance

Our results indicate that the interface properties: interface area; number of interface residues/atoms and hydrogen bonds; and the distribution of interface residues, hydrogen bonds, van der Walls contacts and secondary structure motifs are independent of whether or not a protein is in a binary or ternary complex with DNA. However, changes in the shape of the DNA reduce the off-rate of the proteins which greatly enhances the stability and specificity of ternary complexes compared to binary ones.

DARS (Decoys As the Reference State) potentials for protein-protein docking

Decoys As the Reference State (DARS) is a simple and natural approach to the construction of structure-based intermolecular potentials. The idea is generating a large set of docked conformations with good shape complementarity but without accounting for atom types, and using the frequency of interactions extracted from these decoys as the reference state. In principle, the resulting potential is ideal for finding near-native conformations among structures obtained by docking, and can be combined with other energy terms to be used directly in docking calculations. We investigated the performance of various DARS versions for docking enzyme-inhibitor, antigen-antibody, and other type of complexes. For enzyme-inhibitor pairs, DARS provides both excellent discrimination and docking results, even with very small decoy sets. For antigen-antibody complexes, DARS is slightly better than a number of interaction potentials tested, but results are worse than for enzyme-inhibitor complexes. With a few exceptions, the DARS docking results are also good for the other complexes, despite poor discrimination, and we show that the latter is not a correct test for docking accuracy. The analysis of interactions in antigen-antibody pairs reveals that, in constructing pairwise potentials for such complexes, one should account for the asymmetry of hydrophobic patches on the two sides of the interface. Similar asymmetry does occur in the few other complexes with poor DARS docking results.

A CD4 homologue in sea bass (Dicentrarchus labrax): molecular characterization and structural analysis

CD4 is a transmembrane glycoprotein fundamental for cell-mediated immunity. Its action as a T cell co-receptor increases the avidity of association between a T cell and an antigen-presenting cell by interacting with portions of the complex between MHC class II and TR molecules. In this paper we report the cDNA cloning, expression and structural analysis of a CD4 homologue from sea bass (Dicentrarchus labrax). The sea bass CD4 cDNA consists of 2071 bp that translates in one reading frame to give the entire molecule containing 480 amino acids. The analysis of the sequence shows the presence of four putative Ig-like domains and that some fundamental structural features, like a disulphide bond in domain D2 and the CXC signalling motif in the cytoplasmic tail, are conserved from sea bass to mammals. Real-time PCR analysis showed that very high levels of CD4 mRNA transcripts are present in thymus, followed by gut and gills. In vitro stimulation of head kidney leukocytes with LPS and PHA-L gave an increase of CD4 mRNA levels after 4h and a decrease after 24h. Homology modelling has been applied to create a 3D model of sea bass CD4 and to investigate its interaction with sea bass MHC-II. The analysis of the 3D complex between sea bass CD4 and sea bass MHC-II suggests that the absence of a disulfide bond in the CD4 D1 domain could make this molecule more flexible, inducing a different conformation and affecting the binding and the way of interaction between CD4 and MHC-II. Our results will add new insights into the sea bass T cell immune responses and will help in the identification of T cell subsets in teleost fishes to better understand the evolution of cell-mediated immunity from fish to mammals.

A knowledge-based forcefield for protein-protein interface design

A distance-dependent knowledge-based potential for protein-protein interactions is derived and tested for application in protein design. Information on residue type specific C(alpha) and C(beta) pair distances is extracted from complex crystal structures in the Protein Data Bank and used in the form of radial distribution functions. The use of only backbone and C(beta) position information allows generation of relative protein-protein orientation poses with minimal sidechain information. Further coarse-graining can be done simply in the same theoretical framework to give potentials for residues of known type interacting with unknown type, as in a one-sided interface design problem. Both interface design via pose generation followed by sidechain repacking and localized protein-protein docking tests are performed on 39 nonredundant antibody-antigen complexes for which crystal structures are available. As reference, Lennard-Jones potentials, unspecific for residue type and biasing toward varying degrees of residue pair separation are used as controls. For interface design, the knowledge-based potentials give the best combination of consistently designable poses, low RMSD to the known structure, and more tightly bound interfaces with no added computational cost. 77% of the poses could be designed to give complexes with negative free energies of binding. Generally, larger interface separation promotes designability, but weakens the binding of the resulting designs. A localized docking test shows that the knowledge-based nature of the potentials improves performance and compares respectably with more sophisticated all-atoms potentials.

Interfacial atom pair analysis

The relations of the binding free energies in a dataset of 69 protein complexes with the numbers of interfacial atom pairs, as well as with the atomic distances of the pairs, are analyzed. It is found that the interfacial main-chain atom pairs contribute more to the correlation than the interfacial side chain atom pairs do, and the polar atom pairs contribute more than the non-polar atom pairs do. Interfacial atom pairs with atomic distance in the range of 6-12 A are the most important to explain the differences in binding free energies in the datasets.

Docking with PIPER and refinement with SDU in rounds 6-11 of CAPRI

Our approach to protein-protein docking includes three main steps. First we run PIPER, a new rigid body docking program. PIPER is based on the Fast Fourier Transform (FFT) correlation approach that has been extended to use pairwise interactions potentials, thereby substantially increasing the number of near-native structures generated. The interaction potential is also new, based on the DARS (Decoys As the Reference State) principle. In the second step, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the conformations are refined by a new medium-range optimization method SDU (Semi-Definite programming based Underestimation). SDU has been developed to locate global minima within regions of the conformational space in which the energy function is funnel-like. The method constructs a convex quadratic underestimator function based on a set of local energy minima, and uses this function to guide future sampling. The combined method performed reliably without the direct use of biological information in most CAPRI problems that did not require homology modeling, providing acceptable predictions for targets 21, and medium quality predictions for targets 25 and 26.

Molecular analysis of thymopentin binding to HLA-DR molecules

Thymopentin (TP5) triggers an immune response by contacting with T cells; however the molecular basis of how TP5 achieves this process remains incompletely understood. According to the main idea of immunomodulation, we suppose that it would be necessary for TP5 to form complex with human class II major histocompatibility complex DR molecules (HLA-DR) before TP5 interacts with T cells. The uptake of TP5 by EBV-transformed B cells expressing HLA-DR molecules and the histogram of fluorescence intensities were observed by using fluorescent- labeled TP5, testifying the direct binding of TP5 to HLA-DR. The binding specificity was confirmed by the inhibition with unlabeled TP5, suggesting the recognition of TP5 by HLA-DR. To confirm the interaction between TP5 and HLA-DR, the complex formation was predicted by using various modeling strategies including six groups of trials with different parameters, alanine substitutions of TP5, and the mutants of HLA-DR. The results demonstrated that TP5 and its alanine substitutions assumed distinct conformations when they bound to HLA-DR. The observation further showed that there was flexibility in how the peptide bound within the binding cleft. Also, the molecular analysis supplemented a newly important discovery to the effect of Val anchor on TP5 binding HLA-DR, and revealed the important effects of Glu11 and Asn62 on the recognition of TP5. These results demonstrated the capability of TP5 to associate with HLA-DR in living antigen presenting cells (APC), thereby providing a new and promising strategy to understand the immunomodulation mechanism induced by TP5 and to design potential immunoregulatory polypeptides.

Transamidation of wheat flour inhibits the response to gliadin of intestinal T cells in celiac disease

BACKGROUND & AIMS

Celiac disease is characterized by activation of HLA-DQ2/DQ8-restricted intestinal gluten-specific CD4(+) T cells. In particular, gluten becomes a better T-cell antigen following deamidation catalyzed by tissue transglutaminase. To date, the only available therapy is represented by adherence to a gluten-free diet. Here, we examined a new enzyme strategy to preventively abolish gluten activity.

METHODS

Enzyme modifications of the immunodominant alpha-gliadin peptide p56-68 were analyzed by mass spectrometry, and peptide binding to HLA-DQ2 was simulated by modeling studies. Wheat flour was treated with microbial transglutaminase and lysine methyl ester; gliadin was subsequently extracted, digested, and deamidated. Gliadin-specific intestinal T-cell lines (iTCLs) were generated from biopsy specimens from 12 adult patients with celiac disease and challenged in vitro with different antigen preparations.

RESULTS

Tissue transglutaminase-mediated transamidation with lysine or lysine methyl ester of p56-68 or gliadin in alkaline conditions inhibited the interferon gamma expression in iTCLs; also, binding to DQ2 was reduced but not abolished, as suggested by in silico analysis. Lysine methyl ester was particularly effective in abrogating the activity of gliadin. Notably, a block in the response was observed when iTCLs were challenged with gliadin extracted from flour pretreated with microbial transglutaminase and lysine methyl ester.

CONCLUSIONS

Transamidation of wheat flour with a food-grade enzyme and an appropriate amine donor can be used to block the T cell-mediated gliadin activity. Considering the crucial role of adaptive immunity in celiac disease, our findings highlight the potential of the proposed treatment to prevent cereal toxicity.

Complex-type-dependent scoring functions in protein–protein docking

A major challenge in the field of protein-protein docking is to discriminate between the many wrong and few near-native conformations, i.e. scoring. Here, we introduce combinatorial complex-type-dependent scoring functions for different types of protein-protein complexes, protease/inhibitor, antibody/antigen, enzyme/inhibitor and others. The scoring functions incorporate both physical and knowledge-based potentials, i.e. atomic contact energy (ACE), the residue pair potential (RP), electrostatic and van der Waals' interactions. For different type complexes, the weights of the scoring functions were optimized by the multiple linear regression method, in which only top 300 structures with ligand root mean square deviation (L_RMSD) less than 20 A from the bound (co-crystallized) docking of 57 complexes were used to construct a training set. We employed the bound docking studies to examine the quality of the scoring function, and also extend to the unbound (separately crystallized) docking studies and extra 8 protein-protein complexes. In bound docking of the 57 cases, the first hits of protease/inhibitor cases are all ranked in the top 5. For the cases of antibody/antigen, enzyme/inhibitor and others, there are 17/19, 5/6 and 13/15 cases with the first hits ranked in the top 10, respectively. In unbound docking studies, the first hits of 9/17 protease/inhibitor, 6/19 antibody/antigen, 1/6 enzyme/inhibitor and 6/15 others' complexes are ranked in the top 10. Additionally, for the extra 8 cases, the first hits of the two protease/inhibitor cases are ranked in the top for the bound and unbound test. For the two enzyme/inhibitor cases, the first hits are ranked 1st for bound test, and the 119th and 17th for the unbound test. For the others, the ranks of the first hits are the 1st for the bound test and the 12th for the 1WQ1 unbound test. To some extent, the results validated our divide-and-conquer strategy in the docking study, which might hopefully shed light on the prediction of protein-protein interactions.

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

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

Simulation of conformational changes occurring when a protein interacts with its receptor

In order to simulate the conformational changes occurring when a protein interacts with its receptor, we firstly evaluated the structural differences between the experimental unbound and bound conformations for selected proteins and created theoretical complexes by replacing, in each experimental complex, the protein-bound with the protein-unbound chain. The theoretical models were then subjected to additional modeling refinements to improve the side chain geometry. Comparing the theoretical and experimental complexes in term of structural and energetic factors is resulted that the refined theoretical complexes became more similar to the experimental ones. We applied the same procedure within an homology modeling experiment, using as templates the experimental structures of human interleukin-1beta (IL-1beta) unbound and bound with its receptor, to build models of the homologous proteins from mouse and trout in unbound and bound conformations and to simulate the interaction with the related receptors. Our results suggest that homology modeling techniques are sensitive to differences between bound and unbound conformations, and that modeling with accuracy the side chains in the complex improves the interaction and molecular recognition. Moreover, our refinement procedure could be used in protein-protein interaction studies and, also, applied in conjunction with rigid-body docking when is not available the protein-bound conformation.

A free-rotating and self-avoiding chain model for deriving statistical potentials based on protein structures

Statistical potentials have been widely used in protein studies despite the much-debated theoretical basis. In this work, we have applied two physical reference states for deriving the statistical potentials based on protein structure features to achieve zero interaction and orthogonalization. The free-rotating chain-based potential applies a local free-rotating chain reference state, which could theoretically be described by the Gaussian distribution. The self-avoiding chain-based potential applies a reference state derived from a database of artificial self-avoiding backbones generated by Monte Carlo simulation. These physical reference states are independent of known protein structures and are based solely on the analytical formulation or simulation method. The new potentials performed better and yielded higher Z-scores and success rates compared to other statistical potentials. The end-to-end distance distribution produced by the self-avoiding chain model was similar to the distance distribution of protein atoms in structure database. This fact may partly explain the basis of the reference states that depend on the atom pair frequency observed in the protein database. The current study showed that a more physical reference model improved the performance of statistical potentials in protein fold recognition, which could also be extended to other types of applications.

Hierarchical mechanochemical switches in angiostatin

We wish to propose a novel mechanism by which the triggering of a biochemical signal can be controlled by the hierarchical coupling between a protein redox equilibrium and an external mechanical force. We have characterized this mechanochemical mechanism in angiostatin, and we have evidence that it can switch the access to partially unfolded structures of this protein. We have identified a metastable intermediate that is specifically accessible under thioredoxin-rich reducing conditions, like those met by angiostatin on the surface of a tumor cell. The structure of the same intermediate accounts for the unexplained antiangiogenic activity of angiostatin. These findings demonstrate a new link between redox biology and mechanically regulated processes.

PIPER: an FFT-based protein docking program with pairwise potentials

The Fast Fourier Transform (FFT) correlation approach to protein-protein docking can evaluate the energies of billions of docked conformations on a grid if the energy is described in the form of a correlation function. Here, this restriction is removed, and the approach is efficiently used with pairwise interaction potentials that substantially improve the docking results. The basic idea is approximating the interaction matrix by its eigenvectors corresponding to the few dominant eigenvalues, resulting in an energy expression written as the sum of a few correlation functions, and solving the problem by repeated FFT calculations. In addition to describing how the method is implemented, we present a novel class of structure-based pairwise intermolecular potentials. The DARS (Decoys As the Reference State) potentials are extracted from structures of protein-protein complexes and use large sets of docked conformations as decoys to derive atom pair distributions in the reference state. The current version of the DARS potential works well for enzyme-inhibitor complexes. With the new FFT-based program, DARS provides much better docking results than the earlier approaches, in many cases generating 50% more near-native docked conformations. Although the potential is far from optimal for antibody-antigen pairs, the results are still slightly better than those given by an earlier FFT method. The docking program PIPER is freely available for noncommercial applications.

Protein binding site prediction using an empirical scoring function

Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method called PINUP for binding site prediction of monomeric proteins. With only two weight parameters to optimize, PINUP produces not only 42.2% coverage of actual interfaces (percentage of correctly predicted interface residues in actual interface residues) but also 44.5% accuracy in predicted interfaces (percentage of correctly predicted interface residues in the predicted interface residues) in a cross validation using a 57-protein dataset. By comparison, the expected accuracy via random prediction (percentage of actual interface residues in surface residues) is only 15%. The binding sites of the 57-protein set are found to be easier to predict than that of an independent test set of 68 proteins. The average coverage and accuracy for this independent test set are 30.5 and 29.4%, respectively. The significant gain of PINUP over expected random prediction is attributed to (i) effective residue-energy score and accessible-surface-area-dependent interface-propensity, (ii) isolation of functional constraints contained in the conservation score from the structural constraints through the combination of residue-energy score (for structural constraints) and conservation score and (iii) a consensus region built on top-ranked initial patches.

Insights into protein-protein interfaces using a Bayesian network prediction method

Identifying the interface between two interacting proteins provides important clues to the function of a protein, and is becoming increasing relevant to drug discovery. Here, surface patch analysis was combined with a Bayesian network to predict protein-protein binding sites with a success rate of 82% on a benchmark dataset of 180 proteins, improving by 6% on previous work and well above the 36% that would be achieved by a random method. A comparable success rate was achieved even when evolutionary information was missing, a further improvement on our previous method which was unable to handle incomplete data automatically. In a case study of the Mog1p family, we showed that our Bayesian network method can aid the prediction of previously uncharacterised binding sites and provide important clues to protein function. On Mog1p itself a putative binding site involved in the SLN1-SKN7 signal transduction pathway was detected, as was a Ran binding site, previously characterized solely by conservation studies, even though our automated method operated without using homologous proteins. On the remaining members of the family (two structural genomics targets, and a protein involved in the photosystem II complex in higher plants) we identified novel binding sites with little correspondence to those on Mog1p. These results suggest that members of the Mog1p family bind to different proteins and probably have different functions despite sharing the same overall fold. We also demonstrated the applicability of our method to drug discovery efforts by successfully locating a number of binding sites involved in the protein-protein interaction network of papilloma virus infection. In a separate study, we attempted to distinguish between the two types of binding site, obligate and non-obligate, within our dataset using a second Bayesian network. This proved difficult although some separation was achieved on the basis of patch size, electrostatic potential and conservation. Such was the similarity between the two interacting patch types, we were able to use obligate binding site properties to predict the location of non-obligate binding sites and vice versa.

Atomically detailed potentials to recognize native and approximate protein structures

Atomically detailed potentials for recognition of protein folds are presented. The potentials consist of pair interactions between atoms. One or three distance steps are used to describe the range of interactions between a pair. Training is carried out with the mathematical programming approach on the decoy sets of Baker, Levitt, and some of our own design. Recognition is required not only for decoy-native structural pairs but also for pairs of decoy and homologous structures. Performance is tested on the targets of CASP5 using templates from the Protein Data Bank, on two test ab initio decoy sets from Skolnick's laboratory, and on decoy sets from Moult's laboratory. We conclude that the newly derived potentials have significant recognition capacity, comparable to the best models derived from other techniques. The new potentials require a significantly smaller number of parameters. The enhanced recognition capacity extends primarily to the identification of structures generated by ab initio simulation and less to the recognition of approximate shapes created by homology.

Docking prediction using biological information, ZDOCK sampling technique, and clustering guided by the DFIRE statistical energy function

We entered the CAPRI experiment during the middle of Round 4 and have submitted predictions for all 6 targets released since then. We used the following procedures for docking prediction: (1) the identification of possible binding region(s) of a target based on known biological information, (2) rigid-body sampling around the binding region(s) by using the docking program ZDOCK, (3) ranking of the sampled complex conformations by employing the DFIRE-based statistical energy function, (4) clustering based on pairwise root-mean-square distance and the DFIRE energy, and (5) manual inspection and relaxation of the side-chain conformations of the top-ranked structures by geometric constraint. Reasonable predictions were made for 4 of the 6 targets. The best fraction of native contacts within the top 10 models are 89.1% for Target 12, 54.3% for Target 13, 29.3% for Target 14, and 94.1% for Target 18. The origin of successes and failures is discussed. .