Predicting the helix‐helix interactions from correlated residue mutations

Helix‐helix interactions are crucial in the structure assembly, stability and function of helix‐rich proteins including many membrane proteins. In spite of remarkable progresses over the past decades, the accuracy of predicting protein structures from their amino acid sequences is still far from satisfaction. In this work, we focused on a simpler problem, the prediction of helix‐helix interactions, the results of which could facilitate practical protein structure prediction by constraining the sampling space. Specifically, we started from the noisy 2D residue contact maps derived from correlated residue mutations, and utilized ridge detection to identify the characteristic residue contact patterns for helix‐helix interactions. The ridge information as well as a few additional features were then fed into a machine learning model HHConPred to predict interactions between helix pairs. In an independent test, our method achieved an F‐measure of ～60% for predicting helix‐helix interactions. Moreover, although the model was trained mainly using soluble proteins, it could be extended to membrane proteins with at least comparable performance relatively to previous approaches that were generated purely using membrane proteins. All data and source codes are available at http://166.111.152.91/Downloads.html or https://github.com/dpxiong/HHConPred .     

Novel approach for alpha-helical topology prediction in globular proteins: generation of interhelical restraints

The protein folding problem represents one of the most challenging problems in computational biology. Distance constraints and topology predictions can be highly useful for the folding problem in reducing the conformational space that must be searched by deterministic algorithms to find a protein structure of minimum conformational energy. We present a novel optimization framework for predicting topological contacts and generating interhelical distance restraints between hydrophobic residues in alpha-helical globular proteins. It should be emphasized that since the model does not make assumptions about the form of the helices, it is applicable to all alpha-helical proteins, including helices with kinks and irregular helices. This model aims at enhancing the ASTRO-FOLD protein folding approach of Klepeis and Floudas (Journal of Computational Chemistry 2003;24:191-208), which finds the structure of global minimum conformational energy via a constrained nonlinear optimization problem. The proposed topology prediction model was evaluated on 26 alpha-helical proteins ranging from 2 to 8 helices and 35 to 159 residues, and the best identified average interhelical distances corresponding to the predicted contacts fell below 11 A in all 26 of these systems. Given the positive results of applying the model to several protein systems, the importance of interhelical hydrophobic-to-hydrophobic contacts in determining the folding of alpha-helical globular proteins is highlighted.     

Improving accuracy of protein contact prediction using balanced network deconvolution

Residue contact map is essential for protein three‐dimensional structure determination. But most of the current contact prediction methods based on residue co‐evolution suffer from high false‐positives as introduced by indirect and transitive contacts (i.e., residues A–B and B–C are in contact, but A–C are not). Built on the work by Feizi et al. (Nat Biotechnol 2013; 31:726–733), which demonstrated a general network model to distinguish direct dependencies by network deconvolution, this study presents a new balanced network deconvolution (BND) algorithm to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems. The algorithm was used to filter contact predictions of five widely used co‐evolution methods. On the test of proteins from three benchmark datasets of the 9th critical assessment of protein structure prediction (CASP9), CASP10, and PSICOV (precise structural contact prediction using sparse inverse covariance estimation) database experiments, the BND can improve the medium‐ and long‐range contact predictions at the L/5 cutoff by 55.59% and 47.68%, respectively, without additional central processing unit cost. The improvement is statistically significant, with a P‐value < 5.93 × 10^?3 in the Student's t‐test. A further comparison with the ab initio structure predictions in CASPs showed that the usefulness of the current co‐evolution‐based contact prediction to the three‐dimensional structure modeling relies on the number of homologous sequences existing in the sequence databases. BND can be used as a general contact refinement method, which is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/BND/ . Proteins 2015; 83:485–496. © 2014 Wiley Periodicals, Inc.     

Evaluation of transmembrane helix predictions in 2014

Experimental structure determination continues to be challenging for membrane proteins. Computational prediction methods are therefore needed and widely used to supplement experimental data. Here, we re‐examined the state of the art in transmembrane helix prediction based on a nonredundant dataset with 190 high‐resolution structures. Analyzing 12 widely‐used and well‐known methods using a stringent performance measure, we largely confirmed the expected high level of performance. On the other hand, all methods performed worse for proteins that could not have been used for development. A few results stood out: First, all methods predicted proteins in eukaryotes better than those in bacteria. Second, methods worked less well for proteins with many transmembrane helices. Third, most methods correctly discriminated between soluble and transmembrane proteins. However, several older methods often mistook signal peptides for transmembrane helices. Some newer methods have overcome this shortcoming. In our hands, PolyPhobius and MEMSAT‐SVM outperformed other methods. Proteins 2015; 83:473–484. © 2014 Wiley Periodicals, Inc.     

Dissecting the roles of local packing density and longer‐range effects in protein sequence evolution

What are the structural determinants of protein sequence evolution? A number of site‐specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site‐specific sequence variability in proteins. We show that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as contact number and the weighted contact number, represent the combined effects of local packing density and longer‐range effects. As an alternative, we propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino‐acid side chains, behaves nearly identically to the relative solvent accessibility, and each individually can explain, on average, approximately 34% of the site‐specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10% of variation can be explained by nonlocal effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined effects of the immediate amino‐acid neighbors of that site and effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize on the relative importance of immediate contacts and longer‐range effects on evolutionary variation. Proteins 2016; 84:841–854. © 2016 Wiley Periodicals, Inc.     

Energetics and stability of transmembrane helix packing: a replica-exchange simulation with a knowledge-based membrane potential

The energetics and stability of the packing of transmembrane helices were investigated by Monte Carlo simulations with the replica-exchange method. The helices were modeled with a united atom representation, and the CHARMM19 force field was employed. Based on known experimental structures of membrane proteins, an implicit knowledge-based potential was developed to describe the helix-membrane interactions at the residue level, whose validity was tested through prediction of the orientations when single helices were inserted into a membrane. Two systems were studied in this article, namely the glycophorin A dimer, and helices A and B of Bacteriorhodopsin. For the glycophorin A dimer, the most stable structure (0.5 A away from the experimental structure) is mainly stabilized by the favorable helix-helix interactions, and has the most population regardless of the helix-membrane interaction. However, for helices A and B of Bacteriorhodopsin, it was found that the packing determined by helix-helix interactions is nonspecific, and a native-like structure (0.2 A from the experimental one) can be identified from several structural analogs as the most stable one only after applying the membrane potential. Our results suggest that the contribution from the helix-membrane interaction could be critical in the correct packing of transmembrane helices in the membrane.     

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug‐specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans‐membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α‐helical MPs as well as β‐barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge‐based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. Proteins 2015; 83:1–24. © 2014 Wiley Periodicals, Inc.     

A Continuum theory for the prediction of lateral and rotational positioning of α-helices in membrane proteins: Bacteriorhodopsin

We have developed a new method for the prediction of the lateral and the rotational positioning of transmembrane helices, based upon the present status of knowledge about the dominant interaction of the tertiary structure formation. The basic assumption about the interaction is that the interhelix binding is due to the polar interactions and that very short extramembrane loop segments restrict the relative position of the helices. Another assumption is made for the simplification of the prediction that a helix may be regarded as a continuum rod having polar interaction fields around it. The polar interaction field is calculated by a probe helix method, using a copolymer of serine and alanine as probe helices. The lateral position of helices is determined by the strength of the interhelix binding estimated from the polar interaction field together with the length of linking loop segments. The rotational positioning is determined by the polar interaction field, assuming the optimum lateral configuration. The structural change due to the binding of a prosthetic group is calculated, fixing the rotational freedom of a helix that is connected to the prosthetic group. Applying this method to bacteriorhodopsin, the optimum lateral and rotational positioning of transmembrane helices that are very similar to the experimental configuration was obtained. This method was implemented by a software system, which was developed for this work, and automatic calculation became possible for membrane proteins comprised of several transmembrane helices. © 1995 Wiley-Liss, Inc.     

Observations concerning topology and locations of helix ends of membrane proteins of known structure

Summary Hydropathy plots of amino acid sequences reveal the approximate locations of the transbilayer helices of membrane proteins of known structure and are thus used to predict the helices of proteins of unknown structure. Because the threedimensional structures of membrane proteins are difficult to obtain, it is important to be able to extract as much information as possible from hydropathy plots. We describe an augmented hydropathy plot analysis of the three membrane proteins of known structure, which should be useful for the systematic examination and comparison of membrane proteins of unknown structure. The sliding-window analysis utilizes the floating interfacial hydrophobicity scale [IFH(h)] of Jacobs and White (Jacobs, R.E., White, S.H., 1989.Biochemistry 28:3421–3437) and the reverse-turn (RT) frequencies of Levitt (Levitt, M., 1977,Biochemistry 17:4277–4285). The IFH(h) scale allows one to examine the consequences of different assumptions about the average hydrogen bond status (h=0 to 1) of polar side chains. Hydrophobicity plots of the three proteins show that (i) the intracellular helix-connecting links and chain ends can be distinguished from the extracellular ones and (ii) the main peaks of hydrophobicity are bounded by minor ones which bracket the helix ends. RT frequency plots show that (iii) the centers of helices are usually very close to wide-window minima of average RT frequency and (iv) helices are always bounded by narrowwindow maxima of average RT frequency. The analysis suggests that side-chain hydrogen bonding with membrane components during folding may play a key role in insertion.     

Bihelix: Towards de novo structure prediction of an ensemble of G‐protein coupled receptor conformations

G‐Protein Coupled Receptors (GPCRs) play a critical role in cellular signal transduction pathways and are prominent therapeutic targets. Recently there has been major progress in obtaining experimental structures for a few GPCRs. Each GPCR, however, exhibits multiple conformations that play a role in their function and we have been developing methods aimed at predicting structures for all these conformations. Analysis of available structures shows that these conformations differ in relative helix tilts and rotations. The essential issue is, determining how to orient each of the seven helices about its axis since this determines how it interacts with the other six helices. Considering all possible helix rotations to ensure that no important packings are overlooked, and using rotation angle increments of 30° about the helical axis would still lead to 12⁷ or 35 million possible conformations each with optimal residue positions. We show in this paper how to accomplish this. The fundamental idea is to optimize the interactions between each pair of contacting helices while ignoring the other 5 and then to estimate the energies of all 35 million combinations using these pair‐wise interactions. This BiHelix approach dramatically reduces the effort to examine the complete set of conformations and correctly identifies the crystal packing for the experimental structures plus other near‐native packings we believe may play an important role in activation. This approach also enables a detailed structural analysis of functionally distinct conformations using helix‐helix interaction energy landscapes and should be useful for other helical transmembrane proteins as well. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

TMSEG: Novel prediction of transmembrane helices

Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non‐redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state‐of‐the‐art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98 ± 2% and a false positive rate as low as 3 ± 1%. Individual TMHs were predicted with a precision of 87 ± 3% and recall of 84 ± 3%. Furthermore, in 63 ± 6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the second and third best method available, and 4400 fewer mistakes than by a simple hydrophobicity‐based method. Second, TMSEG provides an add‐on improvement for any existing method to benefit from. Proteins 2016; 84:1706–1716. © 2016 Wiley Periodicals, Inc.     

Sequence and conformational preferences at termini of α‐helices in membrane proteins: Role of the helix environment

α‐helices are amongst the most common secondary structural elements seen in membrane proteins and are packed in the form of helix bundles. These α‐helices encounter varying external environments (hydrophobic, hydrophilic) that may influence the sequence preferences at their N and C‐termini. The role of the external environment in stabilization of the helix termini in membrane proteins is still unknown. Here we analyze α‐helices in a high‐resolution dataset of integral α‐helical membrane proteins and establish that their sequence and conformational preferences differ from those in globular proteins. We specifically examine these preferences at the N and C‐termini in helices initiating/terminating inside the membrane core as well as in linkers connecting these transmembrane helices. We find that the sequence preferences and structural motifs at capping (Ncap and Ccap) and near‐helical (N' and C') positions are influenced by a combination of features including the membrane environment and the innate helix initiation and termination property of residues forming structural motifs. We also find that a large number of helix termini which do not form any particular capping motif are stabilized by formation of hydrogen bonds and hydrophobic interactions contributed from the neighboring helices in the membrane protein. We further validate the sequence preferences obtained from our analysis with data from an ultradeep sequencing study that identifies evolutionarily conserved amino acids in the rat neurotensin receptor. The results from our analysis provide insights for the secondary structure prediction, modeling and design of membrane proteins. Proteins 2014; 82:3420–3436. © 2014 Wiley Periodicals, Inc.     

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

pi-pi, Cation-pi, and hydrophobic packing interactions contribute specificity to protein folding and stability to the native state. As a step towards developing improved models of these interactions in proteins, we compare the side-chain packing arrangements in native proteins to those found in compact decoys produced by the Rosetta de novo structure prediction method. We find enrichments in the native distributions for T-shaped and parallel offset arrangements of aromatic residue pairs, in parallel stacked arrangements of cation-aromatic pairs, in parallel stacked pairs involving proline residues, and in parallel offset arrangements for aliphatic residue pairs. We then investigate the extent to which the distinctive features of native packing can be explained using Lennard-Jones and electrostatics models. Finally, we derive orientation-dependent pi-pi, cation-pi and hydrophobic interaction potentials based on the differences between the native and compact decoy distributions and investigate their efficacy for high-resolution protein structure prediction. Surprisingly, the orientation-dependent potential derived from the packing arrangements of aliphatic side-chain pairs distinguishes the native structure from compact decoys better than the orientation-dependent potentials describing pi-pi and cation-pi interactions.     

Comparison analysis of primary ligand‐binding sites in seven‐helix membrane proteins

Seven‐helix transmembrane proteins, including the G‐protein‐coupled receptors (GPCRs), mediate a broad range of fundamental cellular activities through binding to a wide range of ligands. Understanding the structural basis for the ligand‐binding selectivity of these proteins is of significance to their structure‐based drug design. Comparison analysis of proteins' ligand‐binding sites provides a useful way to study their structure‐activity relationships. Various computational methods have been developed for the binding‐site comparison of soluble proteins. In this work, we applied this approach to the analysis of the primary ligand‐binding sites of 92 seven‐helix transmembrane proteins. Results of the studies confirmed that the binding site of bacterial rhodopsins is indeed different from all GPCRs. In the latter group, further comparison of the binding sites indicated a group of residues that could be responsible for ligand‐binding selectivity and important for structure‐based drug design. Furthermore, unexpected binding‐site dissimilarities were observed among adrenergic and adenosine receptors, suggesting that the percentage of the overall sequence identity between a target protein and a template protein alone is not sufficient for selecting the best template for homology modeling of seven‐helix membrane proteins. These results provided novel insight into the structural basis of ligand‐binding selectivity of seven‐helix membrane proteins and are of practical use to the computational modeling of these proteins. © 2010 Wiley Periodicals, Inc. Biopolymers 95: 31–38, 2011.     

Predicted residue–residue contacts can help the scoring of 3D models

During the 7th Critical Assessment of Protein Structure Prediction (CASP7) experiment, it was suggested that the real value of predicted residue–residue contacts might lie in the scoring of 3D model structures. Here, we have carried out a detailed reassessment of the contact predictions made during the recent CASP8 experiment to determine whether predicted contacts might aid in the selection of close‐to‐native structures or be a useful tool for scoring 3D structural models. We used the contacts predicted by the CASP8 residue–residue contact prediction groups to select models for each target domain submitted to the experiment. We found that the information contained in the predicted residue–residue contacts would probably have helped in the selection of 3D models in the free modeling regime and over the harder comparative modeling targets. Indeed, in many cases, the models selected using just the predicted contacts had better GDT‐TS scores than all but the best 3D prediction groups. Despite the well‐known low accuracy of residue–residue contact predictions, it is clear that the predictive power of contacts can be useful in 3D model prediction strategies. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

CASP10–BCL::Fold efficiently samples topologies of large proteins

During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template‐based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native‐like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE‐only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native‐like assembly of SSEs for further refinement and submission. It was also observed that for some β‐strand proteins model refinement failed as β‐strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non‐natural topologies that require loop regions to pass through the center of the protein. Proteins 2015; 83:547–563. © 2015 Wiley Periodicals, Inc.     

Fast assessment of structural models of ion channels based on their predicted current–voltage characteristics

Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single‐model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function ‐ conducting ions ‐ can be quantitatively measured with the patch‐clamp technique providing the current–voltage characteristics. The study was performed on a set of KcsA channel models obtained from complete and incomplete contact maps. A fast continuous electrodiffusion model was used for calculating the current–voltage characteristics of structural models. We found that the computed charge selectivity and total current were sensitive to structural and electrostatic quality of models. In practical terms, we show that evaluating predicted conductance values is an appropriate method to eliminate models with an occluded pore or with multiple erroneously created pores. Moreover, filtering models on the basis of their predicted charge selectivity results in a substantial enrichment of the candidate set in highly accurate models. Tests on three other ion channels indicate that, in addition to being a proof of the concept, our function‐oriented single‐model quality assessment method can be directly applied to evaluation of structural models of some classes of protein channels. Finally, our work raises an important question whether a computational validation of functionality should be included in the evaluation process of structural models, whenever possible. Proteins 2016; 84:217–231. © 2015 Wiley Periodicals, Inc.     

DockRank: Ranking docked conformations using partner‐specific sequence homology‐based protein interface prediction

Selecting near‐native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner‐specific sequence homology‐based protein–protein interface predictor (PS‐HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state‐of‐the‐art docking scoring functions using Success Rate (the percentage of cases that have at least one near‐native conformation among the top m conformations) and Hit Rate (the percentage of near‐native conformations that are included among the top m conformations). In cases where it is possible to obtain partner‐specific (PS) interface predictions from PS‐HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state‐of‐the‐art energy‐based scoring functions (improving Success Rate by up to 4‐fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39‐fold). The latter result underscores the importance of using partner‐specific interface residues in scoring docked conformations. We show that DockRank, when used to re‐rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/ . Proteins 2014; 82:250–267. © 2013 Wiley Periodicals, Inc.     

Comparing four different approaches for the determination of inter‐residue interactions provides insight for the structure prediction of helical membrane proteins

Studying inter‐residue interactions provides insight into the folding and stability of both soluble and membrane proteins and is essential for developing computational tools for protein structure prediction. As the first step, various approaches for elucidating such interactions within protein structures have been proposed and proven useful. Since different approaches may grasp different aspects of protein structural folds, it is of interest to systematically compare them. In this work, we applied four approaches for determining inter‐residue interactions to the analysis of three distinct structure datasets of helical membrane proteins and compared their correlation to the three individual quality measures of structures in these datasets. These datasets included one of 35 structures of rhodopsin receptors and bacterial rhodopsins determined at various resolutions, one derived from the HOMEP benchmark dataset previously reported, and one comprising of 139 homology models. It was found that the correlation between the average number of inter‐residue interactions obtained by applying the four approaches and the available structure quality measures varied quite significantly among them. The best correlation was achieved by the approach focusing exclusively on favorable inter‐residue interactions. These results provide interesting insight for the development of objective quality measure for the structure prediction of helical membrane proteins. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 547–556, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

Quantitative analysis and prediction of curvature in leucine‐rich repeat proteins

Leucine‐rich repeat (LRR) proteins form a large and diverse family. They have a wide range of functions most of which involve the formation of protein–protein interactions. All known LRR structures form curved solenoids, although there is large variation in their curvature. It is this curvature that determines the shape and dimensions of the inner space available for ligand binding. Unfortunately, large‐scale parameters such as the overall curvature of a protein domain are extremely difficult to predict. Here, we present a quantitative analysis of determinants of curvature of this family. Individual repeats typically range in length between 20 and 30 residues and have a variety of secondary structures on their convex side. The observed curvature of the LRR domains correlates poorly with the lengths of their individual repeats. We have, therefore, developed a scoring function based on the secondary structure of the convex side of the protein that allows prediction of the overall curvature with a high degree of accuracy. We also demonstrate the effectiveness of this method in selecting a suitable template for comparative modeling. We have developed an automated, quantitative protocol that can be used to predict accurately the curvature of leucine‐rich repeat proteins of unknown structure from sequence alone. This protocol is available as an online resource at http://www.bioinf.manchester.ac.uk/curlrr/ . Proteins 2009. © 2009 Wiley‐Liss, Inc.