Analysis of anisotropic side-chain packing in proteins and application to high-resolution structure prediction

Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database

The charge-transfer coupling is an important component in tight-binding methods. Because of the highly complex chemical structure of biomolecules, the anisotropic feature of charge-transfer couplings in realistic proteins cannot be ignored. In this work, we have performed the first large-scale quantitative assessment of charge-transfer preference by calculating the charge-transfer couplings in all 20 × 20 possible amino acid side-chain combinations, which are extracted from available high-quality structures of thousands of protein complexes. The charge-transfer database quantitatively shows distinct features of charge-transfer couplings among millions of amino acid side-chain combinations. The overall distribution of charge-transfer couplings reveals that only one average or representative structure cannot be regarded as the typical charge-transfer preference in realistic proteins. This work provides us an alternative route to comprehensively understand the charge-transfer couplings for the overall distribution of realistic proteins in the foreseen big data scenario.

Holistic Approach to Partial Covalent Interactions in Protein Structure Prediction and Design with Rosetta

Partial covalent interactions (PCIs) in proteins, which include hydrogen bonds, salt bridges, cation-π, and π-π interactions, contribute to thermodynamic stability and facilitate interactions with other biomolecules. Several score functions have been developed within the Rosetta protein modeling framework that identify and evaluate these PCIs through analyzing the geometry between participating atoms. However, we hypothesize that PCIs can be unified through a simplified electron orbital representation. To test this hypothesis, we have introduced orbital based chemical descriptors for PCIs into Rosetta, called the PCI score function. Optimal geometries for the PCIs are derived from a statistical analysis of high-quality protein structures obtained from the Protein Data Bank (PDB), and the relative orientation of electron deficient hydrogen atoms and electron-rich lone pair or π orbitals are evaluated. We demonstrate that nativelike geometries of hydrogen bonds, salt bridges, cation-π, and π-π interactions are recapitulated during minimization of protein conformation. The packing density of tested protein structures increased from the standard score function from 0.62 to 0.64, closer to the native value of 0.70. Overall, rotamer recovery improved when using the PCI score function (75%) as compared to the standard Rosetta score function (74%). The PCI score function represents an improvement over the standard Rosetta score function for protein model scoring; in addition, it provides a platform for future directions in the analysis of small molecule to protein interactions, which depend on partial covalent interactions.

A Comprehensive Analysis of Anion-Quadrupole Interactions in Protein Structures

The edgewise interactions of anions with phenylalanine (Phe) aromatic rings in proteins, known as anion-quadrupole interactions, have been well studied. However, the anion-quadrupole interactions of the tyrosine (Tyr) and tryptophan (Trp) rings have been less well studied, probably because these have been considered weaker than interactions of anions hydrogen bonded to Trp/Tyr side chains. Distinguishing such hydrogen bonding interactions, we comprehensively surveyed the edgewise interactions of certain anions (aspartate, glutamate, and phosphate) with Trp, Tyr, and Phe rings in high-resolution, nonredundant protein single chains and interfaces (protein-protein, DNA/RNA-protein, and membrane-protein). Trp/Tyr anion-quadrupole interactions are common, with Trp showing the highest propensity and average interaction energy for this type of interaction. The energy of an anion-quadrupole interaction (-15.0 to 0.0 kcal/mol, based on quantum mechanical calculations) depends not only on the interaction geometry but also on the ring atom. The phosphate anions at DNA/RNA-protein interfaces interact with aromatic residues with energies comparable to that of aspartate/glutamate anion-quadrupole interactions. At DNA-protein interfaces, the frequency of aromatic ring participation in anion-quadrupole interactions is comparable to that of positive charge participation in salt bridges, suggesting an underappreciated role for anion-quadrupole interactions at DNA-protein (or membrane-protein) interfaces. Although less frequent than salt bridges in single-chain proteins, we observed highly conserved anion-quadrupole interactions in the structures of remote homologues, and evolutionary covariance-based residue contact score predictions suggest that conserved anion-quadrupole interacting pairs, like salt bridges, contribute to polypeptide folding, stability, and recognition.

ChannelsDB: database of biomacromolecular tunnels and pores

ChannelsDB (http://ncbr.muni.cz/ChannelsDB) is a database providing information about the positions, geometry and physicochemical properties of channels (pores and tunnels) found within biomacromolecular structures deposited in the Protein Data Bank. Channels were deposited from two sources; from literature using manual deposition and from a software tool automatically detecting tunnels leading to the enzymatic active sites and selected cofactors, and transmembrane pores. The database stores information about geometrical features (e.g. length and radius profile along a channel) and physicochemical properties involving polarity, hydrophobicity, hydropathy, charge and mutability. The stored data are interlinked with available UniProt annotation data mapping known mutation effects to channel-lining residues. All structures with channels are displayed in a clear interactive manner, further facilitating data manipulation and interpretation. As such, ChannelsDB provides an invaluable resource for research related to deciphering the biological function of biomacromolecular channels.

Flexibility and Design: Conformational Heterogeneity along the Evolutionary Trajectory of a Redesigned Ubiquitin

Although protein design has been used to introduce new functions, designed variants generally only function as well as natural proteins after rounds of laboratory evolution. One possibility for this pattern is that designed mutants frequently sample nonfunctional conformations. To test this idea, we exploited advances in multiconformer modeling of room-temperature X-ray data collection on redesigned ubiquitin variants selected for increasing binding affinity to the deubiquitinase USP7. Initial core mutations disrupt natural packing and lead to increased flexibility. Additional, experimentally selected mutations quenched conformational heterogeneity through new stabilizing interactions. Stabilizing interactions, such as cation-pi stacking and ordered waters, which are not included in standard protein design energy functions, can create specific interactions that have long-range effects on flexibility across the protein. Our results suggest that increasing flexibility may be a useful strategy to escape local minima during initial directed evolution and protein design steps when creating new functions.

A distance- and orientation-dependent energy function of amino acid key blocks

Blocks are the selected portions of amino acids. They have been used effectively to represent amino acids in distinguishing the native conformation from the decoys. Although many statistical energy functions exist, most of them rely on the distances between two or more amino acids. In this study, the authors have developed a pairwise energy function "DOKB" that is both distance and orientation dependent, and it is based on the key blocks that bias the distal ends of side chains. The results suggest that both the distance and the orientation are needed to distinguish the fine details of the packing geometry. DOKB appears to perform well in recognizing native conformations when compared with six other energy functions. Highly packed clusters play important roles in stabilizing the structure. The investigation about the highly packed clusters at the residue level suggests that certain residue pairs in a low-energy region have lower probability to appear in the highly packed clusters than in the entire protein. The cluster energy term appears to significantly improve the recognition of the native conformations in ig_structal decoy set, in which more highly packed clusters are contained than in other decoy sets.

Representative Amino Acid Side Chain Interactions in Proteins. A Comparison of Highly Accurate Correlated ab Initio Quantum Chemical and Empirical Potential Procedures

Interactions between amino acid side chains play a crucial role both within a folded protein and between the interacting protein molecules. Here we have selected a representative set of 24 of the 400 (20 × 20) possible interacting side chain pairs based on data from Atlas of Protein Side-Chain Interactions. For each pair, we obtained its most favorable interaction geometry from the structural data and computed the interaction energy in the gas phase using several different, commonly used, ab initio and force field methods, namely Møller-Plesset perturbation theory (MP2), density functional theory combined with symmetry-adapted perturbation theory (DFT-SAPT), density functional theory empirically augmented with an empirical dispersion term (DFT-D), and empirical potentials using the OPLS-AA/L and Amber03 force fields. All the methods were compared against a reference method taken to be the CCSD(T) level of theory extrapolated to the complete basis set limit. We found a high degree of agreement between the different methods, even though the range of binding energies obtained was extremely large. The most computationally intensive methods yielded the best results. Among the less computationally time-consuming methods, the DFT-D method as well as parm03 force field provided consistently good results when compared to the reference values. We also tested how representative the chosen geometries of the side chains were and investigated the effect on the binding energies of the dielectric constant of the surrounding medium.

The L(M196)H mutation in Rhodobacter sphaeroides reaction center results in new electrostatic interactions

New histidine residue was introduced in M196 position in the reaction center of Rhodobacter sphaeroides in order to alter polarity of the BChl dimer's protein environment and to study how it affects properties and structure of the primary electron donor P. It was shown that in the absorption spectrum of the mutant RC the 6 nm red shift of the Q Y P band was observed together with considerable decrease of its amplitude. The mid-point potential of P/P (+) in the mutant RC was increased by +65 (±15) mV as compared to the E m P/P (+) value in the wild-type RC suggesting that the mutation resulted in new pigment-protein interactions. Crystal structure of RC L(M196)H determined at 2.4 Å resolution implies that BChl Р В and introduced histidine-M196 organize new electrostatic contact that may be specified either as π-π staking or as hydrogen-π interaction. Besides, the structure of the mutants RC shows that His-M196 apparently became involved in hydrogen bond network existing in BChl Р В vicinity that may favor stability of the mutant RC.

Probabilistic models for capturing more physicochemical properties on protein-protein interface

Protein-protein interactions play a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. It is of great interest to understand how proteins interact with each other. The general approach is to explore all possible poses and identify near-native ones with the energy function. The key issue here is to design an effective energy function, based on various physicochemical properties. In this paper, we first identify two new features, the coupled dihedral angles on the interfaces and the geometrical information on π-π interactions. We study these two features through statistical methods: a mixture of bivariate von Mises distributions is used to model the correlation of the coupled dihedral angles, while a mixture of bivariate normal distributions is used to model the orientation of the aromatic rings on π-π interactions. Using 6438 complexes, we parametrize the joint distribution of each new feature. Then, we propose a novel method to construct the energy function for protein-protein interface prediction, which includes the new features as well as the existing energy items such as dDFIRE energy, side-chain energy, atom contact energy, and amino acid energy. Experiments show that our method outperforms the state-of-the-art methods, ZRANK and ClusPro. We use the CAPRI evaluation criteria, Irmsd value, and Fnat value. On Benchmark v4.0, our method has an average Irmsd value of 3.39 Å and Fnat value of 62%, which improves upon the average Irmsd value of 3.89 Å and Fnat value of 49% for ZRANK, and the average Irmsd value of 3.99 Å and Fnat value of 46% for ClusPro. On the CAPRI targets, our method has an average Irmsd value of 3.56 Å and Fnat value of 42%, which improves upon the average Irmsd value of 4.27 Å and Fnat value of 39% for ZRANK, the average Irmsd value of 5.15 Å and Fnat value of 30% for ClusPro.

Recognition of platinum-DNA adducts by HMGB1a

Cisplatin (CP) and oxaliplatin (OX), platinum-based drugs used widely in chemotherapy, form adducts on intrastrand guanines (5'GG) in genomic DNA. DNA damage recognition proteins, transcription factors, mismatch repair proteins, and DNA polymerases discriminate between CP- and OX-GG DNA adducts, which could partly account for differences in the efficacy, toxicity, and mutagenicity of CP and OX. In addition, differential recognition of CP- and OX-GG adducts is highly dependent on the sequence context of the Pt-GG adduct. In particular, DNA binding protein domain HMGB1a binds to CP-GG DNA adducts with up to 53-fold greater affinity than to OX-GG adducts in the TGGA sequence context but shows much smaller differences in binding in the AGGC or TGGT sequence contexts. Here, simulations of the HMGB1a-Pt-DNA complex in the three sequence contexts revealed a higher number of interface contacts for the CP-DNA complex in the TGGA sequence context than in the OX-DNA complex. However, the number of interface contacts was similar in the TGGT and AGGC sequence contexts. The higher number of interface contacts in the CP-TGGA sequence context corresponded to a larger roll of the Pt-GG base pair step. Furthermore, geometric analysis of stacking of phenylalanine 37 in HMGB1a (Phe37) with the platinated guanines revealed more favorable stacking modes correlated with a larger roll of the Pt-GG base pair step in the TGGA sequence context. These data are consistent with our previous molecular dynamics simulations showing that the CP-TGGA complex was able to sample larger roll angles than the OX-TGGA complex or either CP- or OX-DNA complexes in the AGGC or TGGT sequences. We infer that the high binding affinity of HMGB1a for CP-TGGA is due to the greater flexibility of CP-TGGA compared to OX-TGGA and other Pt-DNA adducts. This increased flexibility is reflected in the ability of CP-TGGA to sample larger roll angles, which allows for a higher number of interface contacts between the Pt-DNA adduct and HMGB1a.

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.

Fullerene sorting proteins

Proteins bind fullerenes. Hydrophobic pockets can accommodate a carbon cage either in full or in part. However, the identification of proteins able to discriminate between different cages is an open issue. Prediction of candidates able to perform this function is desirable and is achieved with an inverse docking procedure that accurately accounts for (i) van der Waals interactions between the cage and the protein surface, (ii) desolvation free energy, (iii) shape complementarity, and (iv) minimization of the number of steric clashes through conformational variations. A set of more than 1000 protein structures is divided into four categories that either select C(60) or C(70) (p-C(60) or p-C(70)) and either accommodate the cages in the same pocket (homosaccic proteins, from σακκoζ meaning pocket) or in different pockets (heterosaccic proteins). In agreement with the experiments, the KcsA Potassium Channel is predicted to have one of the best performances for both cages. Possible ways to exploit the results and efficiently separate the two cages with proteins are also discussed.

Mapping the distribution of packing topologies within protein interiors shows predominant preference for specific packing motifs

Background

Mapping protein primary sequences to their three dimensional folds referred to as the 'second genetic code' remains an unsolved scientific problem. A crucial part of the problem concerns the geometrical specificity in side chain association leading to densely packed protein cores, a hallmark of correctly folded native structures. Thus, any model of packing within proteins should constitute an indispensable component of protein folding and design.

Results

In this study an attempt has been made to find, characterize and classify recurring patterns in the packing of side chain atoms within a protein which sustains its native fold. The interaction of side chain atoms within the protein core has been represented as a contact network based on the surface complementarity and overlap between associating side chain surfaces. Some network topologies definitely appear to be preferred and they have been termed 'packing motifs', analogous to super secondary structures in proteins. Study of the distribution of these motifs reveals the ubiquitous presence of typical smaller graphs, which appear to get linked or coalesce to give larger graphs, reminiscent of the nucleation-condensation model in protein folding. One such frequently occurring motif, also envisaged as the unit of clustering, the three residue clique was invariably found in regions of dense packing. Finally, topological measures based on surface contact networks appeared to be effective in discriminating sequences native to a specific fold amongst a set of decoys.

Conclusions

Out of innumerable topological possibilities, only a finite number of specific packing motifs are actually realized in proteins. This small number of motifs could serve as a basis set in the construction of larger networks. Of these, the triplet clique exhibits distinct preference both in terms of composition and geometry.

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions

Background

Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.

Results

Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance preferences were extracted from high-resolution protein structures and were used as a basis for a knowledge-based potential, called Hunter. We demonstrate that 4-distance description of side chain interactions can be used reliably to discriminate the native structure from a set of decoys. Hunter ranked the native structure as the top one in 217 out of 220 high-resolution decoy sets, in 25 out of 28 "Decoys 'R' Us" decoy sets and in 24 out of 27 high-resolution CASP7/8 decoy sets. The same concept was applied to side chain modelling in protein structures. On a set of very high-resolution protein structures the average RMSD was 1.47 Å for all residues and 0.73 Å for buried residues, which is in the range of attainable accuracy for a model. Finally, we show that Hunter performs as good or better than other top methods in homology modelling based on results from the CASP7 experiment. The supporting web site http://bioinfo.weizmann.ac.il/hunter/ was developed to enable the use of Hunter and for visualization and interactive exploration of 4-distance distributions.

Conclusions

Our results suggest that Hunter can be used as a tool for evaluation and for accurate modelling of residue-residue interactions in protein structures. The same methodology is applicable to other areas involving high-resolution modelling of biomolecules.

Baiting proteins with C60

About 20 proteins are known to modify their activity upon interaction with C60. Their structures are present in a database that includes more than 1200 protein structures selected as possible targets for drugs and to represent the entire Protein Data Bank. The set was examined with an algorithm that appraises quantitatively the interaction of C60 and the surface of each protein. The redundancy of the set allows to establish the predictive power of the approach that finds explicitly the most probable site where C60 docks on each protein. About 80% of the known fullerene binding proteins fall in the top 10% of scorers. The close match between the model and experiments vouches for the accuracy of the model and validates its predictions. The sites of docking are shown and discussed in view of the existing experimental data available for protein-C60 interaction. A closer exam of the 10 top scorers is discussed in detail. New proteins that can interact with C60 are identified and discussed for possible future applications as drug targets and fullerene derivatives bioconjugate materials.

Explicit orientation dependence in empirical potentials and its significance to side-chain modeling

Protein structure modeling and prediction have important applications throughout the biological sciences, from the design of pharmaceuticals to the elucidation of enzyme mechanisms. At the core of most protein modeling is an energy function, the minimum of which represents the free energy "cost" for forming a correct protein structure. The most commonly used energy functions are knowledge-based statistical potential functions; that is, they are empirically derived from statistical analysis of a set of high-resolution protein structures. When that kind of potential function is constructed, the anisotropic orientation dependence between the interacting groups is a critical component for accurately representing key molecular interactions, such as those involved in protein side-chain packing. In the literature, however, many potential functions are limited in their ability to describe orientation dependence. In all-atom potentials, they typically ignore heterogeneous chemical-bond connectivity. In coarse-grained potentials, such as (semi)-residue-based potentials, the simplified representation of residues often reduces the sensitivity of the potential to side-chain orientation. Recently, in an effort to maximally capture the orientation dependence in side-chain interactions, a new type of all-atom statistical potential was developed: OPUS-PSP (potential derived from side-chain packing). The key feature of this potential is its explicit description of orientation dependence in molecular interactions, which is achieved with a basis set of 19 rigid-body blocks extracted from the chemical structures of 20 amino acid residues. This basis set is specifically designed to maximally capture the essential elements of orientation dependence in molecular packing interactions. The potential is constructed from the orientation-specific packing statistics of pairs of those blocks in a nonredundant structural database. On decoy set tests, OPUS-PSP significantly outperforms most of the existing knowledge-based potentials in terms of both its ability to recognize native structures and its consistency in achieving high Z scores across decoy sets. The application of OPUS-PSP to conformational modeling of side chains has led to another method, called OPUS-Rota. In terms of combined speed and accuracy, OPUS-Rota outperforms all of the other methods in modeling side-chain conformation. In this Account, we briefly outline the basic scheme of the OPUS-PSP potential and its application to side-chain modeling via OPUS-Rota. Future perspectives on the modeling of orientation dependence are also discussed. The computer programs for OPUS-PSP and OPUS-Rota can be downloaded at http://sigler.bioch.bcm.tmc.edu/MaLab . They are free for academic users.

Calculation of proteins' total side-chain torsional entropy and its influence on protein-ligand interactions

Despite the high density within a typical protein fold, the ensemble of sterically permissible side-chain repackings is vast. Here, we examine the extent of this variability that survives energetic biases due to van der Waals interactions, hydrogen bonding, salt bridges, and solvation. Monte Carlo simulations of an atomistic model exhibit thermal fluctuations among a diverse set of side-chain arrangements, even with the peptide backbone fixed in its crystallographic conformation. We have quantified the torsional entropy of this native-state ensemble, relative to that of a noninteracting reference system, for 12 small proteins. The reduction in entropy per rotatable bond due to each kind of interaction is remarkably consistent across this set of molecules. To assess the biophysical importance of these fluctuations, we have estimated side-chain entropy contributions to the binding affinity of several peptide ligands with calmodulin. Calculations for our fixed-backbone model correlate very well with experimentally determined binding entropies over a range spanning more than 80 kJ/(mol x 308 K).

The SV40 capsid is stabilized by a conserved pentapeptide hinge of the major capsid protein VP1

The simian virus 40 (SV40) outer shell is composed of 72 pentamers of VP1. The core of the VP1 monomer is a beta-barrel with jelly-roll topology and extending N- and C-terminal arms. A pentapeptide hinge, KNPYP, tethers the C-arm to the VP1 beta-barrel core. The five C-arms that extend from each pentamer insert into the neighbouring pentamers, tying them together through different types of interactions. In the mature virion, this element adopts either of six conformations according to their location in the capsid. We found that the hinge is conserved among 16 members of the Polyomaviridae, attesting to its importance in capsid assembly and/or structure. We have used site-directed mutagenesis to gain an understanding into the structural requirements of this element: Y299 was changed to A, F, and T, and P300 to A and G. The mutants showed reduction in viability to varying degrees. Unexpectedly, assembly was reduced only to a small extent. However, the data showed that the mutants were highly unstable. The largest effect was observed for mutations of P300, indicating a role of the proline in the virion structure. P300G was more unstable than P300A, indicating a requirement for rigidity of the pentapeptide hinge. Y299T and Y299A were more defective in viability than Y299F, highlighting the importance of an aromatic ring at this position. Structural inspection showed that this aromatic ring contacts C-arms of neighbouring pentamers. Computational modelling predicted loss of stability of the Y mutants in concordance with the experimental results. This study provides insights into the structural details of the pentapeptide hinge that are responsible for capsid stability.

Distance dependent centroid to centroid force fields using high resolution decoys

Simplified force fields play an important role in protein structure prediction and de novo protein design by requiring less computational effort than detailed atomistic potentials. A side chain centroid based, distance dependent pairwise interaction potential has been developed. A linear programming based formulation was used in which non-native "decoy" conformers are forced to take a higher energy compared with the corresponding native structure. This model was trained on an enhanced and diverse protein set. High quality decoy structures were generated for approximately 1400 nonhomologous proteins using torsion angle dynamics along with restricted variations of the hydrophobic cores of the native structure. The resulting decoy set was used to train the model yielding two different side chain centroid based force fields that differ in the way distance dependence has been used to calculate energy parameters. These force fields were tested on an independent set of 148 test proteins with 500 decoy structures for each protein. The side chain centroid force fields were successful in correctly identifying approximately 86% native structures. The Z-scores produced by the proposed centroid-centroid distance dependent force fields improved compared with other distance dependent C(alpha)-C(alpha) or side chain based force fields.

Kemp elimination catalysts by computational enzyme design

The design of new enzymes for reactions not catalysed by naturally occurring biocatalysts is a challenge for protein engineering and is a critical test of our understanding of enzyme catalysis. Here we describe the computational design of eight enzymes that use two different catalytic motifs to catalyse the Kemp elimination-a model reaction for proton transfer from carbon-with measured rate enhancements of up to 10(5) and multiple turnovers. Mutational analysis confirms that catalysis depends on the computationally designed active sites, and a high-resolution crystal structure suggests that the designs have close to atomic accuracy. Application of in vitro evolution to enhance the computational designs produced a >200-fold increase in k(cat)/K(m) (k(cat)/K(m) of 2,600 M(-1)s(-1) and k(cat)/k(uncat) of >10(6)). These results demonstrate the power of combining computational protein design with directed evolution for creating new enzymes, and we anticipate the creation of a wide range of useful new catalysts in the future.

OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing

Here we report an orientation-dependent statistical all-atom potential derived from side-chain packing, named OPUS-PSP. It features a basis set of 19 rigid-body blocks extracted from the chemical structures of all 20 amino acid residues. The potential is generated from the orientation-specific packing statistics of pairs of those blocks in a non-redundant structural database. The purpose of such an approach is to capture the essential elements of orientation dependence in molecular packing interactions. Tests of OPUS-PSP on commonly used decoy sets demonstrate that it significantly outperforms most of the existing knowledge-based potentials in terms of both its ability to recognize native structures and consistency in achieving high Z-scores across decoy sets. As OPUS-PSP excludes interactions among main-chain atoms, its success highlights the crucial importance of side-chain packing in forming native protein structures. Moreover, OPUS-PSP does not explicitly include solvation terms, and thus the potential should perform well when the solvation effect is difficult to determine, such as in membrane proteins. Overall, OPUS-PSP is a generally applicable potential for protein structure modeling, especially for handling side-chain conformations, one of the most difficult steps in high-accuracy protein structure prediction and refinement.

A mixed-α,β miniprotein stereochemically reprogrammed to high-binding affinity for acetylcholine

The protein-structure space is limited to L configuration in the asymmetric alpha-amino acid structures; the function space, on other hand, seems limitless because of the chemical diversity in the amino acid side chain structures. The chemical diversity in side chain structure may be multiplied beneficially with the stereochemical diversity in main chain structure; thus, de novo protein design may be explored for customizing molecular structures stereochemically and molecular functions chemically. Illustrating de novo design in the structure space of L and D alphabet, canonical all-beta folds of poly-L structure were reprogrammed as bracelet, boat, and canoe-shaped molecules-the "boat" as a receptor-like pocket and the "canoe" as a metal-ion receptor-simply by mutating specific L-amino acid residues to the corresponding D stereochemical structure. Demonstrating customization of molecular shape stereochemically and function chemically, a 15-residue mixed-alpha, beta miniprotein of canonical poly-L structure is now reprogrammed stereochemically as a cup-shaped receptor for acetylcholine via cation-pi interaction with a triad of aromatic side chains placed in mimicry of the acetylcholine-receptor sites both natural and artificial. Evidence from CD, fluorescence, NMR, DSC, ITC, MD, and molecular-docking studies is presented to show that a rationally designed 15-residue mixed-L, D peptide is a cooperatively ordered molecular fold in the stereochemically specified molecular morphology, submicromolar in affinity of acetylcholine and thus an acetylcholine receptor exceptionally small and simple. .

FireDock: fast interaction refinement in molecular docking

Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.

Relating destabilizing regions to known functional sites in proteins

BACKGROUND

Most methods for predicting functional sites in protein 3D structures, rely on information on related proteins and cannot be applied to proteins with no known relatives. Another limitation of these methods is the lack of a well annotated set of functional sites to use as benchmark for validating their predictions. Experimental findings and theoretical considerations suggest that residues involved in function often contribute unfavorably to the native state stability. We examine the possibility of systematically exploiting this intrinsic property to identify functional sites using an original procedure that detects destabilizing regions in protein structures. In addition, to relate destabilizing regions to known functional sites, a novel benchmark consisting of a diverse set of hand-curated protein functional sites is derived.

RESULTS

A procedure for detecting clusters of destabilizing residues in protein structures is presented. Individual residue contributions to protein stability are evaluated using detailed atomic models and a force-field successfully applied in computational protein design. The most destabilizing residues, and some of their closest neighbours, are clustered into destabilizing regions following a rigorous protocol. Our procedure is applied to high quality apo-structures of 63 unrelated proteins. The biologically relevant binding sites of these proteins were annotated using all available information, including structural data and literature curation, resulting in the largest hand-curated dataset of binding sites in proteins available to date. Comparing the destabilizing regions with the annotated binding sites in these proteins, we find that the overlap is on average limited, but significantly better than random. Results depend on the type of bound ligand. Significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for most nucleic acid binding sites. These differences are rationalised in terms of the geometry and energetics of the binding site.

CONCLUSION

We find that although destabilizing regions as detected here can in general not be used to predict binding sites in protein structures, they can provide useful information, particularly on the location of functional sites that bind polysaccharides and small ligands. This information can be exploited in methods for predicting function in protein structures with no known relatives. Our publicly available benchmark of hand-curated functional sites in proteins should help other workers derive and validate new prediction methods.

Method To Assess Packing Quality of Transmembrane α-Helices in Proteins. 2. Validation by “Correct vs Misleading” Test

We describe a set of tests designed to check the ability of the new "membrane score" method (see the first paper of this series) to assess the packing quality of transmembrane (TM) alpha-helical domains in proteins. The following issues were addressed: (1) Whether there is a relation between the score (S(mem)) of a model and its closeness to the "nativelike" conformation? (2) Is it possible to recognize a correct model among misfolded and erroneous ones? (3) To what extent the score of a homology-built model is sensitive to errors in sequence alignment? To answer the first question, two test cases were considered: (i) Several models of bovine aquaporin-1 (target protein) were built on the structural templates provided by its homologs with known X-ray structure. (ii) Side chains in the spatial models of visual rhodopsin and cytochrome c oxidase were rebuilt based on the backbone scaffolds taken from their crystal structures, and the resulting models were iteratively fitted into the full-atom X-ray conformations. It was shown that the higher the S(mem) value of a model is, the lower its root-mean-square deviation is from the "correct" (crystal) structure of a target. Furthermore, the "membrane score" method successfully identifies the rhodopsin crystal structure in an ensemble of "rotamer-type" decoys, thus providing the way to optimize mutual orientations of alpha-helices in models of TM domains. Finally, being applied to a set of homology models of rhodopsin built on its crystal structure with systematically shifted alignment, the approach demonstrates a prominent ability to detect alignment errors. We therefore assume that the "membrane score" method will be helpful in optimization of in silico models of TM domains in proteins, especially those in GPCRs.

A tightly packed hydrophobic cluster directs the formation of an off-pathway sub-millisecond folding intermediate in the alpha subunit of tryptophan synthase, a TIM barrel protein

Protein misfolding is now recognized as playing a crucial role in both normal and pathogenic folding reactions. An interesting example of misfolding at the earliest state of a natural folding reaction is provided by the alpha-subunit of tryptophan synthase, a (beta/alpha)(8) TIM barrel protein. The molecular basis for the formation of this off-pathway misfolded intermediate, I(BP), and a subsequent on-pathway intermediate, I1, was probed by mutational analysis of 20 branched aliphatic side-chains distributed throughout the sequence. The elimination of I(BP) and the substantial destabilization of I1 by replacement of a selective set of the isoleucine, leucine or valine residues (ILV) with alanine in a large ILV cluster external-to-the-barrel and spanning the N and C termini (cluster 2) implies tight-packing at most sites in both intermediates. Differential effects on I(BP) and I1 for replacements in alpha3, beta4 and alpha8 at the boundaries of cluster 2 suggest that their incorporation into I1 but not I(BP) reflects non-native folds at the edges of the crucial (beta/alpha)(1-2)beta(3) core in I(BP). The retention of I(BP) and the smaller and consistent destabilization of both I(BP) and I1 by similar replacements in an internal-to-the-barrel ILV cluster (cluster 1) and a second external-to-the-barrel ILV cluster (cluster 3) imply molten globule-like packing. The tight packing inferred, in part, for I(BP) or for all of I1 in cluster 2, but not in clusters 1 and 3, may reflect the larger size of cluster 2 and/or the enhanced number of isoleucine, leucine and valine self-contacts in and between contiguous elements of secondary structure. Tightly packed ILV-dominated hydrophobic clusters could serve as an important driving force for the earliest events in the folding and misfolding of the TIM barrel and other members of the (beta/alpha)(n) class of proteins.

A novel high resolution Calpha--Calpha distance dependent force field based on a high quality decoy set

This work presents a novel C(alpha)--C(alpha) distance dependent force field which is successful in selecting native structures from an ensemble of high resolution near-native conformers. An enhanced and diverse protein set, along with an improved decoy generation technique, contributes to the effectiveness of this potential. High quality decoys were generated for 1489 nonhomologous proteins and used to train an optimization based linear programming formulation. The goal in developing a set of high resolution decoys was to develop a simple, distance-dependent force field that yields the native structure as the lowest energy structure and assigns higher energies to decoy structures that are quite similar as well as those that are less similar. The model also includes a set of physical constraints that were based on experimentally observed physical behavior of the amino acids. The force field was tested on two sets of test decoys not in the training set and was found to excel on all the metrics that are widely used to measure the effectiveness of a force field. The high resolution force field was successful in correctly identifying 113 native structures out of 150 test cases and the average rank obtained for this test was 1.87. All the high resolution structures (training and testing) used for this work are available online and can be downloaded from http://titan.princeton.edu/HRDecoys.

On side-chain conformational entropy of proteins

The role of side-chain entropy (SCE) in protein folding has long been speculated about but is still not fully understood. Utilizing a newly developed Monte Carlo method, we conducted a systematic investigation of how the SCE relates to the size of the protein and how it differs among a protein's X-ray, NMR, and decoy structures. We estimated the SCE for a set of 675 nonhomologous proteins, and observed that there is a significant SCE for both exposed and buried residues for all these proteins-the contribution of buried residues approaches approximately 40% of the overall SCE. Furthermore, the SCE can be quite different for structures with similar compactness or even similar conformations. As a striking example, we found that proteins' X-ray structures appear to pack more "cleverly" than their NMR or decoy counterparts in the sense of retaining higher SCE while achieving comparable compactness, which suggests that the SCE plays an important role in favouring native protein structures. By including a SCE term in a simple free energy function, we can significantly improve the discrimination of native protein structures from decoys.

Development of novel statistical potentials describing cation-pi interactions in proteins and comparison with semiempirical and quantum chemistry approaches

Novel statistical potentials derived from known protein structures are presented. They are designed to describe cation-pi and amino-pi interactions between a positively charged amino acid or an amino acid carrying a partially charged amino group and an aromatic moiety. These potentials are based on the propensity of residue types to be separated by a certain spatial distance or to have a given relative orientation. Several such potentials, describing different kinds of correlations between residue types, distances, and orientations, are derived and combined in a way that maximizes their information content and minimizes their redundancy. To test the ability of these potentials to describe cation-pi and amino-pi systems, we compare their energies with those computed with the CHARMM molecular mechanics force field and with quantum chemistry calculations at the Hartree-Fock level (HF) and at the second order of the Møller-Plesset perturbation theory (MP2). The latter calculations are performed in the gas phase and in acetone, in order to mimic the average dielectric constant of protein environments. The energies computed with the best of our statistical potentials and with gas-phase HF or MP2 show correlation coefficients up to 0.96 when considering one side-chain degree of freedom in the statistical potentials and up to 0.94 when using a totally simplified model excluding all side-chain degrees of freedom. These potentials perform as well as, or better than, the CHARMM molecular mechanics force field that uses a much more detailed protein representation. The good performance of our cation-pi statistical potentials suggests their utility in protein structure and stability prediction and in protein design.

High-resolution protein-protein docking

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

Characterizing conserved structural contacts by pair-wise relative contacts and relative packing groups

To adequately deal with the inherent complexity of interactions between protein side-chains, we develop and describe here a novel method for characterizing protein packing within a fold family. Instead of approaching side-chain interactions absolutely from one residue to another, we instead consider the relative interactions of contacting residue pairs. The basic element, the pair-wise relative contact, is constructed from a sequence alignment and contact analysis of a set of structures and consists of a cluster of similarly oriented, interacting, side-chain pairs. To demonstrate this construct's usefulness in analyzing protein structure, we used the pair-wise relative contacts to analyze two sets of protein structures as defined by SCOP: the diverse globin-like superfamily (126 structures) and the more uniform heme binding globin family (a 94 structure subset of the globin-like superfamily). The superfamily structure set produced 1266 unique pair-wise relative contacts, whereas the family structure subset gave 1001 unique pair-wise relative contacts. For both sets, we show that these constructs can be used to accurately and automatically differentiate between fold classes. Furthermore, these pair-wise relative contacts correlate well with sequence identity and thus provide a direct relationship between changes in sequence and changes in structure. To capture the complexity of protein packing, these pair-wise relative contacts can be superimposed around a single residue to create a multi-body construct called a relative packing group. Construction of convex hulls around the individual packing groups provides a measure of the variation in packing around a residue and defines an approximate volume of space occupied by the groups interacting with a residue. We find that these relative packing groups are useful in understanding the structural quality of sequence or structure alignments. Moreover, they provide context to calculate a value for structural randomness, which is important in properly assessing the quality of a structural alignment. The results of this study provide the framework for future analysis for correlating sequence changes to specific structure changes.