New surface contacts formed upon reductive lysine methylation: Improving the probability of protein crystallization

Surface lysine methylation (SLM) is a technique for improving the rate of success of protein crystallization by chemically methylating lysine residues. The exact mechanism by which SLM enhances crystallization is still not clear. To study these mechanisms, and to analyze the conditions where SLM will provide the optimal benefits for rescuing failed crystallization experiments, we compared 40 protein structures containing N,N-dimethyl-lysine (dmLys) to a nonredundant set of 18,972 nonmethylated structures from the PDB. By measuring the relative frequency of intermolecular contacts (where contacts are defined as interactions between the residues in proximity with a distance of 3.5 Å or less) of basic residues in the methylated versus nonmethylated sets, dmLys-Glu contacts are seen more frequently than Lys-Glu contacts. Based on observation of the 10 proteins with both native and methylated structures, we propose that the increased rate of contact for dmLys-Glu is due to both a slight increase in the number of amine-carboxyl H-bonds and to the formation of methyl C–H···O interactions. By comparing the relative contact frequencies of dmLys with other residues, the mechanism by which methylation of lysines improves the formation of crystal contacts appears to be similar to that of Lys to Arg mutation. Moreover, analysis of methylated structures with the surface entropy reduction (SER) prediction server suggests that in many cases SLM of predicted SER sites may contribute to improved crystallization. Thus, tools that analyze protein sequences and mark residues for SER mutation may identify proteins with good candidate sites for SLM.     

Improvement of the crystallizability and expression of an RNA crystallization chaperone

Crystallizing RNA has been an imperative and challenging task in the world of RNA research. Assistive methods such as chaperone-assisted RNA crystallography (CARC), employing monoclonal antibody fragments (Fabs) as crystallization chaperones have enabled us to obtain RNA crystal structures by forming crystal contacts and providing initial phasing information. Despite the early successes, the crystallization of large RNA-Fab complex remains a challenge in practice. The possible reason for this difficulty is that the Fab scaffold has not been optimized for crystallization in complex with RNA. Here, we have used the surface entropy reduction (SER) technique for the optimization of ΔC209 P4-P6/Fab2 model system. Protruding lysine and glutamate residues were mutated to a set of alanines or serines to construct Fab2SMA or Fab2SMS. Expression with the shake flask approach was optimized to allow large scale production for crystallization. Crystal screening shows that significantly higher crystal-forming ratio was observed for the mutant complexes. As the chosen SER residues are far away from the CDR regions of the Fab, the same set of mutations can now be directly applied to other Fabs binding to a variety of ribozymes and riboswitches to improve the crystallizability of Fab-RNA complex.     

Expression, purification, and crystallization of the RGS-like domain from the Rho nucleotide exchange factor, PDZ-RhoGEF, using the surface entropy reduction approach

Lsc-homology domains are found in several eukaryotic nucleotide exchange factors which act on Rho-family GTPases. They show limited amino acid sequence similarity to RGS proteins, which down-regulate the cellular signaling by the alpha-subunits of trimeric G-proteins and have been shown to interact with Galpha12 and Galpha13. It is believed that the RGS-like (RGSL) domain constitutes the functional link between G-protein-coupled receptors and cytosolic Rho-GTPases. We report here the expression, purification, and crystallization of the RGSL domain from the PDZ-RhoGEF. To obtain X-ray-grade crystals we have used the recently proposed approach of crystallization by mutational surface entropy reduction, in which selected Lys --> Ala, Glu --> Ala, and/or combined point mutations are introduced into the target protein to reduce the cumulative conformational entropy of surface residues. Of the five mutants that were designed and prepared, the second one tried (K463A, E465A, E466A) yielded crystals suitable for further analysis and diffracted X-rays to 2.8 A resolution on a home source. The crystals exhibit hexagonal symmetry, space group P6(1) 22 or P6(5) 22, with unit cell parameters a = b = 63.1 A, c = 202.1 A, and contain one molecule in the asymmetric unit.     

Atomic-resolution crystal structure of Borrelia burgdorferi outer surface protein A via surface engineering

Outer surface protein A (OspA) from Borrelia burgdorferi has an unusual dumbbell-shaped structure in which two globular domains are connected with a "single-layer" beta-sheet (SLB). The protein is highly soluble, and it has been recalcitrant to crystallization. Only OspA complexes with Fab fragments have been successfully crystallized. OspA contains a large number of Lys and Glu residues, and these "high entropy" residues may disfavor crystal packing because some of them would need to be immobilized in forming a crystal lattice. We rationally designed a total of 13 surface mutations in which Lys and Glu residues were replaced with Ala or Ser. We successfully crystallized the mutant OspA without a bound Fab fragment and extended structure analysis to a 1.15 Angstroms resolution. The new high-resolution structure revealed a unique backbone hydration pattern of the SLB segment in which water molecules fill the "weak spots" on both faces of the antiparallel beta-sheet. These well-defined water molecules provide additional structural links between adjacent beta-strands, and thus they may be important for maintaining the rigidity of the SLB that inherently lacks tight packing afforded by a hydrophobic core. The structure also revealed new information on the side-chain dynamics and on a solvent-accessible cavity in the core of the C-terminal globular domain. This work demonstrates the utility of extensive surface mutation in crystallizing recalcitrant proteins and dramatically improving the resolution of crystal structures, and provides new insights into the stabilization mechanism of OspA.     

A synergistic approach to protein crystallization: Combination of a fixed‐arm carrier with surface entropy reduction

Protein crystallographers are often confronted with recalcitrant proteins not readily crystallizable, or which crystallize in problematic forms. A variety of techniques have been used to surmount such obstacles: crystallization using carrier proteins or antibody complexes, chemical modification, surface entropy reduction, proteolytic digestion, and additive screening. Here we present a synergistic approach for successful crystallization of proteins that do not form diffraction quality crystals using conventional methods. This approach combines favorable aspects of carrier‐driven crystallization with surface entropy reduction. We have generated a series of maltose binding protein (MBP) fusion constructs containing different surface mutations designed to reduce surface entropy and encourage crystal lattice formation. The MBP advantageously increases protein expression and solubility, and provides a streamlined purification protocol. Using this technique, we have successfully solved the structures of three unrelated proteins that were previously unattainable. This crystallization technique represents a valuable rescue strategy for protein structure solution when conventional methods fail.     

Importance of mutant position in Ramachandran plot for predicting protein stability of surface mutations

Understanding the mechanisms by which mutations affect protein stability is one of the most important problems in molecular biology. In this work, we analyzed the relationship between changes in protein stability caused by surface mutations and changes in 49 physicochemical, energetic, and conformational properties of amino acid residues. We found that the hydration entropy was the major contributor to the stability of surface mutations in helical segments; other properties responsible for size and volume of molecule also correlated significantly with stability. Classification of coil mutations based on their locations in the (phi-psi) map improved the correlation significantly, demonstrating the existence of a relationship between stability and strain energy, which indicates that the role of strain energy is very important for the stability of surface mutations. We observed that the inclusion of sequence and structural information raised the correlation, indicating the influence of surrounding residues on the stability of surface mutations. Further, we examined the previously reported "inverse relationship" between stability and hydrophobicity, and observed that the inverse hydrophobic effect was generally applicable only to coil mutations. The present study leads to a simple method for predicting protein stability changes caused by amino acid substitutions, which will be useful for protein engineering in designing novel proteins with increased stability and altered function.     

Metal ion-mediated reduction in surface entropy improves diffraction quality of crystals of the IRAK-4 death domain.

Interleukin-1 receptor-associated kinase-4 (IRAK-4) is an essential component of innate immunity in mice and humans. IRAK-4 is a bipartite protein composed of a death domain (DD) that mediates molecular recognition, and a catalytic kinase domain. Structure determination of the proteolytically stable, soluble IRAK-4 DD was hampered by poor diffraction quality. Addition of manganese (II) chloride to the crystallization solution produced significant improvements in diffraction, and the structure has been determined to 1.7-Angstrom resolution. Examination of the IRAK-4 DD crystal structure reveals a single manganese ion coordinated to surface residues lysine-21 and aspartate-24. Coordination of the manganese ion resulted in a reduction in the surface entropy at this region of the molecule, by generating a contact-forming and conformationally homogenous surface patch. Prior studies have shown that surface entropy reduction by mutation of surface residues with large flexible side chains (i.e., Lys and Glu) to smaller side chains results in the production of diffraction-quality crystals. The intrinsic high surface entropy of Lys residues can also be decreased by reductive methylation. Our results suggest that screening of manganese ions as a crystallization additive may also facilitate ordered crystallization by reduction of surface entropy. Given the quick and inexpensive nature of screening, this technique is likely to be amenable to high-throughput methods such as those employed by Protein Structure Initiatives.     

Fully automated ab initio protein structure prediction using I-SITES,HMMSTR and ROSETTA

MOTIVATION: The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm. RESULTS: The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6A. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.     

Improvement in protein functional site prediction by distinguishing structural and functional constraints on protein family evolution using computational design

The prediction of functional sites in newly solved protein structures is a challenge for computational structural biology. Most methods for approaching this problem use evolutionary conservation as the primary indicator of the location of functional sites. However, sequence conservation reflects not only evolutionary selection at functional sites to maintain protein function, but also selection throughout the protein to maintain the stability of the folded state. To disentangle sequence conservation due to protein functional constraints from sequence conservation due to protein structural constraints, we use all atom computational protein design methodology to predict sequence profiles expected under solely structural constraints, and to compute the free energy difference between the naturally occurring amino acid and the lowest free energy amino acid at each position. We show that functional sites are more likely than non-functional sites to have computed sequence profiles which differ significantly from the naturally occurring sequence profiles and to have residues with sub-optimal free energies, and that incorporation of these two measures improves sequence based prediction of protein functional sites. The combined sequence and structure based functional site prediction method has been implemented in a publicly available web server.     

InterProSurf: a web server for predicting interacting sites on protein surfaces

A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. AVAILABILITY: The InterProSurf web server is available at http://curie.utmb.edu/     

Predicting surface exposure of amino acids from protein sequence

The amino acid residues on a protein surface play a key role in interaction with other molecules, determined many physical properties, and constrain the structure of the folded protein. A database of monomeric protein crystal structures was used to teach computer-simulated neural networks rules for predicting surface exposure from local sequence. These trained networks are able to correctly predict surface exposure for 72% of residues in a testing set using a binary model, (buried/exposed) and for 54% of residues using a ternary model (buried/intermediate/exposed). In the ternary model, only 11% of the exposed residues are predicted as buried and only 5% of the buried residues are predicted as exposed. Also, since the networks are able to predict exposure with a quantitative confidence estimate, it is possible to assign exposure for over half of the residues in a binary model with greater than 80% accuracy. Even more accurate predictions are obtained by making a consensus prediction of exposure for a homologous family. The effect of the local environment of an amino acid on its accessibility, though smaller than expected, is significant and accounts for the higher success rate of prediction than obtained with previously used criteria. In the absence of a three-dimensional structure, the ability to predict surface accessibility of amino acids directly from the sequence is a valuable tool in choosing sites of chemical modification or specific mutations and in studies of molecular interaction.     

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure

MOTIVATION: Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. RESULTS: We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. AVAILABILITY: The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide     

Modeling disordered regions in proteins using Rosetta

Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling.     

Beta-turn propensities as paradigms for the analysis of structural motifs to engineer protein stability.

The thermodynamic stability of a protein provides an experimental metric for the relationship of protein sequence and native structure. We have investigated an approach based on an analysis of the structural database for stability engineering of an immunoglobulin variable domain. The most frequently occurring residues in specific positions of beta-turn motifs were predicted to increase the folding stability of mutants that were constructed by site-directed mutagenesis. Even in positions in which different residues are conserved in immunoglobulin sequences, the predictions were confirmed. Frequently, mutants with increased beta-turn propensities display increased folding cooperativities, suggesting pronounced effects on the unfolded state independent of the expected effect on conformational entropy. We conclude that structural motifs with predominantly local interactions can serve as templates with which patterns of sequence preferences can be extracted from the database of protein structures. Such preferences can predict the stability effects of mutations for protein engineering and design.     

Identification and application of the concepts important for accurate and reliable protein secondary structure prediction.

A protein secondary structure prediction method from multiply aligned homologous sequences is presented with an overall per residue three-state accuracy of 70.1%. There are two aims: to obtain high accuracy by identification of a set of concepts important for prediction followed by use of linear statistics; and to provide insight into the folding process. The important concepts in secondary structure prediction are identified as: residue conformational propensities, sequence edge effects, moments of hydrophobicity, position of insertions and deletions in aligned homologous sequence, moments of conservation, auto-correlation, residue ratios, secondary structure feedback effects, and filtering. Explicit use of edge effects, moments of conservation, and auto-correlation are new to this paper. The relative importance of the concepts used in prediction was analyzed by stepwise addition of information and examination of weights in the discrimination function. The simple and explicit structure of the prediction allows the method to be reimplemented easily. The accuracy of a prediction is predictable a priori. This permits evaluation of the utility of the prediction: 10% of the chains predicted were identified correctly as having a mean accuracy of > 80%. Existing high-accuracy prediction methods are "black-box" predictors based on complex nonlinear statistics (e.g., neural networks in PHD: Rost & Sander, 1993a). For medium- to short-length chains (> or = 90 residues and < 170 residues), the prediction method is significantly more accurate (P < 0.01) than the PHD algorithm (probably the most commonly used algorithm). In combination with the PHD, an algorithm is formed that is significantly more accurate than either method, with an estimated overall three-state accuracy of 72.4%, the highest accuracy reported for any prediction method.     

Prediction of protein stability changes for single-site mutations using support vector machines

Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and structural information. We evaluate our approach using cross-validation methods on a large dataset of single amino acid mutations. When only the sign of the stability changes is considered, the predictive method achieves 84% accuracy-a significant improvement over previously published results. Moreover, the experimental results show that the prediction accuracy obtained using sequence alone is close to the accuracy obtained using tertiary structure information. Because our method can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information. The web server for predictions of protein stability changes upon mutations (MUpro), software, and datasets are available at http://www.igb.uci.edu/servers/servers.html.     

Prediction of protein domain boundaries from sequence alone

We present here a simple approach to identify domain boundaries in proteins of an unknown three-dimensional structure. Our method is based on the hypothesis that a high-side chain entropy of a region in a protein chain must be compensated by a high-residue interaction energy within the region, which could correlate with a well-structured part of the globule, that is, with a domain unit. For protein domains, this means that the domain boundary is conditioned by amino acid residues with a small value of side chain entropy, which correlates with the side chain size. On the one hand, relatively high Ala and Gly content on the domain boundary results in high conformational entropy of the backbone chain between the domains. On the other hand, the presence of Pro residues leads to the formation of hinges for a relative orientation of domains. The method was applied to 646 proteins with two contiguous domains extracted from the SCOP database with a success rate of 63%. We also report the prediction of domain boundaries for CASP5 targets obtained with the same method.