Prediction of Protein Structure Using Surface Accessibility Data

An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemical shift‐based CS‐Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach.     

Method to assess packing quality of transmembrane alpha-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.     

Method to assess packing quality of transmembrane alpha-helices in proteins. 1. Parametrization using structural data

Integral membrane proteins (MPs) are pharmaceutical targets of exceptional importance. Modern methods of three-dimensional protein structure determination often fail to supply the fast growing field of structure-based drug design with the requested MPs' structures. That is why computational modeling techniques gain a special importance for these objects. Among the principal difficulties limiting application of these methods is the low quality of the MPs' models built in silico. In this series of two papers we present a computational approach to the assessment of the packing "quality" of transmembrane (TM) alpha-helical domains in proteins. The method is based on the concept of protein environment classes, whereby each amino acid residue is described in terms of its environment polarity and accessibility to the membrane. In the first paper we analyze a nonredundant set of 26 TM alpha-helical domains and compute the residues' propensities to five predefined classes of membrane-protein environments. Here we evaluate the proposed approach only by various test sets, cross-validation protocols and ability of the method to delimit the crystal structure of visual rhodopsin, and a number of its erroneous theoretical models. More advanced validation of the method is given in the second article of this series. We assume that the developed "membrane score" method will be helpful in optimizing computer models of TM domains of MPs, especially G-protein coupled receptors.     

Membrane protein native state discrimination by implicit membrane models

Four implicit membrane models [IMM1, generalized Born (GB)‐surface area‐implicit membrane (GBSAIM), GB with a simple switching (GBSW), and heterogeneous dielectric GB (HDGB)] were tested for their ability to discriminate the native conformation of five membrane proteins from 450 decoys generated by the Rosetta‐Membrane program. The energy ranking of the native state and Z‐scores were used to assess the performance of the models. The effect of membrane thickness was examined and was found to be substantial. Quite satisfactory discrimination was achieved with the all‐atom IMM1 and GBSW models at 25.4 Å thickness and with the HDGB model at 28.5 Å thickness. The energy components by themselves were not discriminative. Both van der Waals and electrostatic interactions contributed to native state discrimination, to a different extent in each model. Computational efficiency of the models decreased in the order: extended‐atom IMM1 > all‐atom IMM1 > GBSAIM > GBSW > HDGB. These results encourage the further development and use of implicit membrane models for membrane protein structure prediction. © 2012 Wiley Periodicals, Inc.     

A sequence-based computational model for the prediction of the solvent accessible surface area for α-helix and β-barrel transmembrane residues

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and β-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and β-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.     

LEAP: Highly accurate prediction of protein loop conformations by integrating coarse‐grained sampling and optimized energy scores with all‐atom refinement of backbone and side chains

Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template‐based modeling as well as ab initio protein‐structure prediction. Here, we developed a coarse‐grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side‐chain conformations and ranked by all‐atom energy scoring functions. The final integrated technique called loop prediction by energy‐assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12‐residue test loops and 2.0 Å RMSD for 45 12‐residue loops from critical assessment of structure‐prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side‐chain conformations in protein cores were predicted in the absence of the target loop, loop‐prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12‐residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks‐lab.org . © 2013 Wiley Periodicals, Inc.     

Predicting three-dimensional structures of transmembrane domains of β-barrel membrane proteins

β-Barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank. We have developed a computational method for predicting structures of the transmembrane (TM) domains of β-barrel membrane proteins. Based on physical principles, our method can predict structures of the TM domain of β-barrel membrane proteins of novel topology, including those from eukaryotic mitochondria. Our method is based on a model of physical interactions, a discrete conformational state space, an empirical potential function, as well as a model to account for interstrand loop entropy. We are able to construct three-dimensional atomic structure of the TM domains from sequences for a set of 23 nonhomologous proteins (resolution 1.8-3.0 ?). The median rmsd of TM domains containing 75-222 residues between predicted and measured structures is 3.9 ? for main chain atoms. In addition, stability determinants and protein-protein interaction sites can be predicted. Such predictions on eukaryotic mitochondria outer membrane protein Tom40 and VDAC are confirmed by independent mutagenesis and chemical cross-linking studies. These results suggest that our model captures key components of the organization principles of β-barrel membrane protein assembly.     

TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. We have developed a method based on radial basis function networks and position specific scoring matrix (PSSM) profiles generated by PSI-BLAST and non-redundant protein database. Our approach with PSSM profiles has correctly predicted the OMPs with a cross-validated accuracy of 96.4% in a set of 1251 proteins, which contain 206 OMPs, 667 globular proteins and 378 alpha-helical inner membrane proteins. Furthermore, we applied our method on a dataset containing 114 OMPs, 187 TMH proteins and 195 globular proteins obtained with less than 20% sequence identity and obtained the cross-validated accuracy of 95%. This accuracy of discriminating OMPs is higher than other methods in the literature and our method could be used as an effective tool for dissecting OMPs from genomic sequences. We have developed a prediction server, TMBETADISC-RBF, which is available at http://rbf.bioinfo.tw/~sachen/OMP.html.     

Identification of beta-barrel membrane proteins based on amino acid composition properties and predicted secondary structure

Unlike all-helices membrane proteins, beta-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-beta soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 beta-barrel membrane proteins versus beta-strands of 79 all-beta soluble proteins. The average and variance of the amino acid composition in these two classes are calculated. Amino acids such as Gly, Asn, Val that are most likely associated with classification are selected based on Fishers discriminant ratio. A linear classifier built with these selected amino acids composition in observed beta-strands achieves 100% classification accuracy for 12 membrane proteins and 79 soluble proteins in a four-fold cross-validation experiment. Since at present the accuracy of secondary structure prediction is quite high, a promising method to identify beta-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 beta-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855).     

ConPred_elite: a highly reliable approach to transmembrane topology predication

The function of transmembrane (TM) proteins is closely correlated to their TM topology; large quantities of highly reliable TM topology data are becoming increasingly required. We present a new consensus approach for TM topology prediction (ConPred_elite) that can predict the whole topology with accuracies of 0.98 for prokaryotic and 0.95 for eukaryotic proteins on a dataset of experimentally-characterized TM topologies. The predicted yield on the dataset is 30.4% for prokaryotic and 21.5% for eukaryotic proteins. Applying ConPred_elite to predicted TM proteins extracted from 29 prokaryotic and 10 eukaryotic proteomes, we obtained 3871 and 7271 highly reliable TM topologies (yields, 19.8 and 13.3%), respectively. The predicted TM topology data may contribute to further research into a comprehensive functional classification and identification of TM proteins based on information of the topology.     

Fast procedure for reconstruction of full-atom protein models from reduced representations

We introduce PULCHRA, a fast and robust method for the reconstruction of full-atom protein models starting from a reduced protein representation. The algorithm is particularly suitable as an intermediate step between coarse-grained model-based structure prediction and applications requiring an all-atom structure, such as molecular dynamics, protein-ligand docking, structure-based function prediction, or assessment of quality of the predicted structure. The accuracy of the method was tested on a set of high-resolution crystallographic structures as well as on a set of low-resolution protein decoys generated by a protein structure prediction algorithm TASSER. The method is implemented as a standalone program that is available for download from http://cssb.biology.gatech.edu/skolnick/files/PULCHRA.     

A method for protein accessibility prediction based on residue types and conformational states

Prediction of protein accessibility from sequence, as prediction of protein secondary structure is an intermediate step for predicting structures and consequently functions of proteins. Most of the currently used methods are based on single residue prediction, either by statistical means or evolutionary information, and accessibility state of central residue in a window predicted. By expansion of databases of proteins with known 3D structures, we extracted information of pairwise residue types and conformational states of pairs simultaneously. For solving the problem of ambiguity in state prediction by one residue window sliding, we used dynamic programming algorithm to find the path with maximum score. The three state overall per-residue accuracy, Q₃, of this method in a Jackknife test with dataset of known proteins is more than 65% which is an improvement on results of methods based on evolutionary information.     

Elastic network normal modes provide a basis for protein structure refinement

It is well recognized that thermal motions of atoms in the protein native state, the fluctuations about the minimum of the global free energy, are well reproduced by the simple elastic network models (ENMs) such as the anisotropic network model (ANM). Elastic network models represent protein dynamics as vibrations of a network of nodes (usually represented by positions of the heavy atoms or by the C(α) atoms only for coarse-grained representations) in which the spatially close nodes are connected by harmonic springs. These models provide a reliable representation of the fluctuational dynamics of proteins and RNA, and explain various conformational changes in protein structures including those important for ligand binding. In the present paper, we study the problem of protein structure refinement by analyzing thermal motions of proteins in non-native states. We represent the conformational space close to the native state by a set of decoys generated by the I-TASSER protein structure prediction server utilizing template-free modeling. The protein substates are selected by hierarchical structure clustering. The main finding is that thermal motions for some substates, overlap significantly with the deformations necessary to reach the native state. Additionally, more mobile residues yield higher overlaps with the required deformations than do the less mobile ones. These findings suggest that structural refinement of poorly resolved protein models can be significantly enhanced by reduction of the conformational space to the motions imposed by the dominant normal modes.     

Improved helix and kink characterization in membrane proteins allows evaluation of kink sequence predictors

Although the α-helical secondary structure of proteins is well-defined, the exact causes and structures of helical kinks are not. This is especially important for transmembrane (TM) helices of integral membrane proteins, many of which contain kinks providing functional diversity despite predominantly helical structure. We have developed a Monte Carlo method based algorithm, MC-HELAN, to determine helical axes alongside positions and angles of helical kinks. Analysis of all nonredundant high-resolution α-helical membrane protein structures (842 TM helices from 205 polypeptide chains) revealed kinks in 64% of TM helices, demonstrating that a significantly greater proportion of TM helices are kinked than those indicated by previous analyses. The residue proline is over-represented by a factor >5 if it is two or three residues C-terminal to a bend. Prolines also cause kinks with larger kink angles than other residues. However, only 33% of TM kinks are in proximity to a proline. Machine learning techniques were used to test for sequence-based predictors of kinks. Although kinks are somewhat predicted by sequence, kink formation appears to be driven predominantly by other factors. This study provides an improved view of the prevalence and architecture of kinks in helical membrane proteins and highlights the fundamental inaccuracy of the typical topological depiction of helical membrane proteins as series of ideal helices.     

膜蛋白跨膜区段的预测分析

将连续小波变换技术的时频局部化特点和氨基酸的疏水特性相结合,提出了一种用于预测膜蛋白跨膜区段数目和位置的新方法,以代码为1YST的膜蛋白为例,对小波尺度和疏水值的种类进行了选择,同时描述了该法对跨膜螺旋区数目和位置的预测分析过程.从膜蛋白数据库中随机抽取36个蛋白质(含跨膜螺旋区232)作为测试集,采用该方法对其跨膜螺旋区进行预测,其中222个跨膜螺旋区能被准确预测,准确率为96.1%.结果表明,该法具有较高的预测准确性.     

Identification of β-barrel membrane proteins based on amino acid composition properties and predicted secondary structure

Unlike all-helices membrane proteins, β-barrel membrane proteins can not be successfully discriminated from other proteins, especially from all-β soluble proteins. This paper performs an analysis on the amino acid composition in membrane parts of 12 β-barrel membrane proteins versus β-strands of 79 all-β soluble proteins. The average and variance of the amino acid composition in these two classes are calculated. Amino acids such as Gly, Asn, Val that are most likely associated with classification are selected based on Fishers discriminant ratio. A linear classifier built with these selected amino acids composition in observed β-strands achieves 100% classification accuracy for 12 membrane proteins and 79 soluble proteins in a four-fold cross-validation experiment. Since at present the accuracy of secondary structure prediction is quite high, a promising method to identify β-barrel membrane proteins is presented based on the linear classifier coupled with predicted secondary structure. Applied to 241 β-barrel membrane proteins and 3855 soluble proteins with various structures, the method achieves 85.48% (206/241) sensitivity and 92.53% specificity (3567/3855).     

Conserved network properties of helical membrane protein structures and its implication for improving membrane protein homology modeling at the twilight zone

Homology modeling techniques remain an important tool for membrane protein studies and membrane protein-targeted drug development. Due to the paucity of available structure data, an imminent challenge in this field is to develop novel computational methods to help improve the quality of the homology models constructed using template proteins with low sequence identity. In this work, we attempted to address this challenge using the network approach developed in our group. First, a structure pair dataset of 27 high-resolution and low sequence identity (7–36%) comparative TM proteins was compiled by analyzing available X-ray structures of helical membrane proteins. Structure deviation between these pairs was subsequently confirmed by calculating their backbone RMSD and comparing their potential energy per residue. Next, this dataset was further studied using the network approach. Results of these analyses indicated that the network measure applied represents a conserved feature of TM domains of similar folds with various sequence identities. Further comparison of this salient feature between high-resolution template structures and their homology models at the twilight zone suggested a useful method to utilize this property for homology model refinement. These findings should be of help for improving the quality of homology models based on templates with low sequence identity, thus broadening the application of homology modeling techniques in TM protein studies.     

Assessment of Semiempirical Quantum Mechanical Methods for the Evaluation of Protein Structures

The ability to discriminate native structures from computer-generated misfolded ones is key to predicting the three-dimensional structure of a protein from its amino acid sequence. Here we describe an assessment of semiempirical methods for discriminating native protein structures from decoy models. The discrimination of decoys entails an analysis of a large number of protein structures, and provides a large-scale validation of quantum mechanical methods and their ability to accurately model proteins. We combine our analysis of semiempirical methods with a comparison of an AMBER force field to discriminate decoys in conjunction with a continuum solvent model. Protein decoys provide a rigorous and reliable benchmark for the evaluation of scoring functions, not only in their ability to accurately identify native structures but also to be computationally tractable to sample a large set of non-native models.     

A sampling-based method for ranking protein structural models by integrating multiple scores and features

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.     

Promoting Tag Removal of a MBP-Fused Integral Membrane Protein by TEV Protease

Tag removal is a prerequisite issue for structural and functional analysis of affinity-purified membrane proteins. The present study took a MBP-fused membrane protein, MrpF, as a model to investigate the tag removal by TEV protease. Influences of the linking sequence between TEV cleavage site and MrpF on protein expression and predicted secondary structure were investigated. The steric accessibility of TEV protease to cleavage site of MBP-fused MrpF was explored. It was found that reducing the size of hydrophilic group of detergents and/or extending the linking sequence between cleavage site and target protein can significantly improve the accessibility of the cleavage site and promote tag removal by TEV protease.