Comparing side chain packing in soluble proteins,protein‐protein interfaces,and transmembrane proteins

We compare side chain prediction and packing of core and non‐core regions of soluble proteins, protein‐protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high‐resolution crystal structures of these 3 protein classes. We show that the solvent‐inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein‐protein interfaces and in the membrane‐exposed regions of transmembrane proteins can be predicted by the hard‐sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent‐inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within ) up to a relative solvent accessibility, , for all 3 protein classes. Our results show that % of the interface regions in protein complexes are “core”, that is, densely packed with side chain conformations that can be accurately predicted using the hard‐sphere model. We propose packing fraction as a metric that can be used to distinguish real protein‐protein interactions from designed, non‐binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins.     

Protein side‐chain modeling with a protein‐dependent optimized rotamer library

Despite years of effort, the problem of predicting the conformations of protein side chains remains a subject of inquiry. This problem has three major issues, namely defining the conformations that a side chain may adopt within a protein, developing a sampling procedure for generating possible side‐chain packings, and defining a scoring function that can rank these possible packings. To solve the former of these issues, most procedures rely on a rotamer library derived from databases of known protein structures. We introduce an alternative method that is free of statistics. We begin with a rotamer library that is based only on stereochemical considerations; this rotamer library is then optimized independently for each protein under study. We show that this optimization step restores the diversity of conformations observed in native proteins. We combine this protein‐dependent rotamer library (PDRL) method with the self‐consistent mean field (SCMF) sampling approach and a physics‐based scoring function into a new side‐chain prediction method, SCMF–PDRL. Using two large test sets of 831 and 378 proteins, respectively, we show that this new method compares favorably with competing methods such as SCAP, OPUS‐Rota, and SCWRL4 for energy‐minimized structures. Proteins 2014; 82:2000–2017. © 2014 Wiley Periodicals, Inc.     

Backbone dependency further improves side chain prediction efficiency in the Energy‐based Conformer Library (bEBL)

Side chain optimization is an integral component of many protein modeling applications. In these applications, the conformational freedom of the side chains is often explored using libraries of discrete, frequently occurring conformations. Because side chain optimization can pose a computationally intensive combinatorial problem, the nature of these conformer libraries is important for ensuring efficiency and accuracy in side chain prediction. We have previously developed an innovative method to create a conformer library with enhanced performance. The Energy‐based Library (EBL) was obtained by analyzing the energetic interactions between conformers and a large number of natural protein environments from crystal structures. This process guided the selection of conformers with the highest propensity to fit into spaces that should accommodate a side chain. Because the method requires a large crystallographic data‐set, the EBL was created in a backbone‐independent fashion. However, it is well established that side chain conformation is strongly dependent on the local backbone geometry, and that backbone‐dependent libraries are more efficient in side chain optimization. Here we present the backbone‐dependent EBL (bEBL), whose conformers are independently sorted for each populated region of Ramachandran space. The resulting library closely mirrors the local backbone‐dependent distribution of side chain conformation. Compared to the EBL, we demonstrate that the bEBL uses fewer conformers to produce similar side chain prediction outcomes, thus further improving performance with respect to the already efficient backbone‐independent version of the library. Proteins 2014; 82:3177–3187. © 2014 Wiley Periodicals, Inc.     

All‐atom chain‐building by optimizing MODELLER energy function using conformational space annealing

We have investigated the effect of rigorous optimization of the MODELLER energy function for possible improvement in protein all‐atom chain‐building. For this we applied the global optimization method called conformational space annealing (CSA) to the standard MODELLER procedure to achieve better energy optimization than what MODELLER provides. The method, which we call MODELLERCSA , is tested on two benchmark sets. The first is the 298 proteins taken from the HOMSTRAD multiple alignment set. By simply optimizing the MODELLER energy function, we observe significant improvement in side‐chain modeling, where MODELLERCSA provides about 10.7% (14.5%) improvement for χ₁ (χ₁ + χ₂) accuracy compared to the standard MODELLER modeling. The improvement of backbone accuracy by MODELLERCSA is shown to be less prominent, and a similar improvement can be achieved by simply generating many standard MODELLER models and selecting lowest energy models. However, the level of side‐chain modeling accuracy by MODELLERCSA could not be matched either by extensive MODELLER strategies, side‐chain remodeling by SCWRL3, or copying unmutated rotamers. The identical procedure was successfully applied to 100 CASP7 template base modeling domains during the prediction season in a blind fashion, and the results are included here for comparison. From this study, we observe a good correlation between the MODELLER energy and the side‐chain accuracy. Our findings indicate that, when a good alignment between a target protein and its templates is provided, thorough optimization of the MODELLER energy function leads to accurate all‐atom models. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Analysis of a data set of paired uncomplexed protein structures: new metrics for side-chain flexibility and model evaluation

We compiled and analyzed a data set of paired protein structures containing proteins for which multiple high-quality uncomplexed atomic structures were available in the Protein Data Bank. Side-chain flexibility was quantified, yielding a set of residue- and environment-specific confidence levels describing the range of motion around chi1 and chi2 angles. As expected, buried residues were inflexible, adopting similar conformations in different crystal structure analyses. Ile, Thr, Asn, Asp, and the large aromatics also showed limited flexibility when exposed on the protein surface, whereas exposed Ser, Lys, Arg, Met, Gln, and Glu residues were very flexible. This information is different from and complementary to the information available from rotamer surveys. The confidence levels are useful for assessing the significance of observed side-chain motion and estimating the extent of side-chain motion in protein structure prediction. We compare the performance of a simple 40 degrees threshold with these quantitative confidence levels in a critical evaluation of side-chain prediction with the program SCWRL.     

Minimal ensembles of side chain conformers for modeling protein–protein interactions

The goal of this article is to reduce the complexity of the side chain search within docking problems. We apply six methods of generating side chain conformers to unbound protein structures and determine their ability of obtaining the bound conformation in small ensembles of conformers. Methods are evaluated in terms of the positions of side chain end groups. Results for 68 protein complexes yield two important observations. First, the end‐group positions change less than 1 Å on association for over 60% of interface side chains. Thus, the unbound protein structure carries substantial information about the side chains in the bound state, and the inclusion of the unbound conformation into the ensemble of conformers is very beneficial. Second, considering each surface side chain separately in its protein environment, small ensembles of low‐energy states include the bound conformation for a large fraction of side chains. In particular, the ensemble consisting of the unbound conformation and the two highest probability predicted conformers includes the bound conformer with an accuracy of 1 Å for 78% of interface side chains. As more than 60% of the interface side chains have only one conformer and many others only a few, these ensembles of low‐energy states substantially reduce the complexity of side chain search in docking problems. This approach was already used for finding pockets in protein–protein interfaces that can bind small molecules to potentially disrupt protein–protein interactions. Side‐chain search with the reduced search space will also be incorporated into protein docking algorithms. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

How do side chains orient globally in protein structures?

An angle Omega is defined to serve as a metric for global side-chain orientations, which reflects the orientation of the side chain relative to the radial vector from the center of the protein to an amino acid. The side-chain orientations of buried residues exhibit characteristically different orientations than do exposed residues, in both monomeric and dimeric structures. Overall, buried side chains point mostly inward, whereas surface side chains tend to point outward from the surface. This difference in behavior also correlates well with the residue hydrophobicity; so a global side-chain orientation can be viewed as a direct structural manifestation of hydrophobicity. When various solvent-accessible layers are considered, the behavior is relatively continuous between centrally located and exposed residues. In the case of interfacial residues between subunits, there are statistically significant differences between exposed residues and interface residues for ALA, ARG, ASN, ASP, GLU, HIS, LYS, THR, VAL, MET, PRO, and overall the interface residues have an increased tendency to point inward. Presumably, these substantial differences in orientations of side chains may be a manifestation of hydrophobic forces.     

Binding interface prediction by combining protein–protein docking results

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein–protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision‐recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta‐PPISP (AUC = 0.43), which is one of the best monomer‐based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta‐PPISP and another monomer‐based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta‐PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues. Proteins 2014; 82:57–66. © 2013 Wiley Periodicals, Inc.     

Antibody side chain conformations are position‐dependent

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone‐dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody‐specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position‐dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ₁ and accuracy (78.7% and 64.8%) compared to three leading backbone‐dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain–side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears .     

Assignment of polar states for protein amino acid residues using an interaction cluster decomposition algorithm and its application to high resolution protein structure modeling

We have developed a new method (Independent Cluster Decomposition Algorithm, ICDA) for creating all-atom models of proteins given the heavy-atom coordinates, provided by X-ray crystallography, and the pH. In our method the ionization states of titratable residues, the crystallographic mis-assignment of amide orientations in Asn/Gln, and the orientations of OH/SH groups are addressed under the unified framework of polar states assignment. To address the large number of combinatorial possibilities for the polar hydrogen states of the protein, we have devised a novel algorithm to decompose the system into independent interacting clusters, based on the observation of the crucial interdependence between the short range hydrogen bonding network and polar residue states, thus significantly reducing the computational complexity of the problem and making our algorithm tractable using relatively modest computational resources. We utilize an all atom protein force field (OPLS) and a Generalized Born continuum solvation model, in contrast to the various empirical force fields adopted in most previous studies. We have compared our prediction results with a few well-documented methods in the literature (WHATIF, REDUCE). In addition, as a preliminary attempt to couple our polar state assignment method with real structure predictions, we further validate our method using single side chain prediction, which has been demonstrated to be an effective way of validating structure prediction methods without incurring sampling problems. Comparisons of single side chain prediction results after the application of our polar state prediction method with previous results with default polar state assignments indicate a significant improvement in the single side chain predictions for polar residues.     

BCL::Fold—Protein topology determination from limited NMR restraints

When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge‐based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton–proton distances to C_β–C_β distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets. Proteins 2014; 82:587–595. © 2013 Wiley Periodicals, Inc.     

Validation of protein structure models using network similarity score

Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test‐total structure (GDT‐TS), TM‐score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side‐chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS—network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side‐chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X‐ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network‐based method to analyze fluctuations in side chain interactions (edge‐weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories.     

Prediction and evaluation of side-chain conformations for protein backbone structures

A common approach to protein modeling is to propose a backbone structure based on homology or threading and then to attempt to build side chains onto this backbone. A fast algorithm using the simple criteria of atomic overlap and overall rotamer probability is proposed for this purpose. The method was first tested in the context of exhaustive searches of side chain configuration space in protein cores and was then applied to all side chains in 49 proteins of known structure, using simulated annealing to sample space. The latter procedure obtains the correct rotamer for 57% and the correct χ₁ value for 74% of the 6751 residues in the sample. When low-temperature Monte-Carlo simulations are initiated from the results of the simulated-annealing processes, consensus configurations are obtained which exhibit slightly more accurate predictions. The Monte-Carlo procedure also allows converged side chain entropies to be calculated for all residues. These prove to be accurate indicators of prediction reliability. For example, the correct rotamer is obtained for 79% and the correct χ₁ value is obtained for 84% of the half of the sample residues exhibiting the lowest entropies. Side chain entropy and predictability are nearly completely uncorrelated with solvent-accessible area. Some precedents for and implications of this observation are discussed. © 1996 Wiley-Liss, Inc.     

Fragment‐based modeling of membrane protein loops: Successes,failures, and prospects for the future

Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment‐based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5–1 Å median root‐mean‐square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8–1.6 Å) rather than SPs (1–4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment‐based loop modeling algorithms for MPs. The current version of our proof‐of‐concept loop modeling protocol produces high‐accuracy loop models for MPs and is available as a web server at http://medeller.info/fread . Proteins 2014; 82:175–186. © 2013 Wiley Periodicals, Inc.     

RBRDetector: Improved prediction of binding residues on RNA‐binding protein structures using complementary feature‐ and template‐based strategies

Computational prediction of RNA‐binding residues is helpful in uncovering the mechanisms underlying protein‐RNA interactions. Traditional algorithms individually applied feature‐ or template‐based prediction strategy to recognize these crucial residues, which could restrict their predictive power. To improve RNA‐binding residue prediction, herein we propose the first integrative algorithm termed RBRDetector (RNA‐Binding Residue Detector) by combining these two strategies. We developed a feature‐based approach that is an ensemble learning predictor comprising multiple structure‐based classifiers, in which well‐defined evolutionary and structural features in conjunction with sequential or structural microenvironment were used as the inputs of support vector machines. Meanwhile, we constructed a template‐based predictor to recognize the putative RNA‐binding regions by structurally aligning the query protein to the RNA‐binding proteins with known structures. The final RBRDetector algorithm is an ingenious fusion of our feature‐ and template‐based approaches based on a piecewise function. By validating our predictors with diverse types of structural data, including bound and unbound structures, native and simulated structures, and protein structures binding to different RNA functional groups, we consistently demonstrated that RBRDetector not only had clear advantages over its component methods, but also significantly outperformed the current state‐of‐the‐art algorithms. Nevertheless, the major limitation of our algorithm is that it performed relatively well on DNA‐binding proteins and thus incorrectly predicted the DNA‐binding regions as RNA‐binding interfaces. Finally, we implemented the RBRDetector algorithm as a user‐friendly web server, which is freely accessible at http://ibi.hzau.edu.cn/rbrdetector . Proteins 2014; 82:2455–2471. © 2014 Wiley Periodicals, Inc.     

Analysis of forces that determine helix formation in alpha-proteins

A model for prediction of alpha-helical regions in amino acid sequences has been tested on the mainly-alpha protein structure class. The modeling represents the construction of a continuous hypothetical alpha-helical conformation for the whole protein chain, and was performed using molecular mechanics tools. The positive prediction of alpha-helical and non-alpha-helical pentapeptide fragments of the proteins is 79%. The model considers only local interactions in the polypeptide chain without the influence of the tertiary structure. It was shown that the local interaction defines the alpha-helical conformation for 85% of the native alpha-helical regions. The relative energy contributions to the energy of the model were analyzed with the finding that the van der Waals component determines the formation of alpha-helices. Hydrogen bonds remain at constant energy independently whether alpha-helix or non-alpha-helix occurs in the native protein, and do not determine the location of helical regions. In contrast to existing methods, this approach additionally permits the prediction of conformations of side chains. The model suggests the correct values for ~60% of all chi-angles of alpha-helical residues.     

Predicting the side‐chain dihedral angle distributions of nonpolar,aromatic, and polar amino acids using hard sphere models

The side‐chain dihedral angle distributions of all amino acids have been measured from myriad high‐resolution protein crystal structures. However, we do not yet know the dominant interactions that determine these distributions. Here, we explore to what extent the defining features of the side‐chain dihedral angle distributions of different amino acids can be captured by a simple physical model. We find that a hard‐sphere model for a dipeptide mimetic that includes only steric interactions plus stereochemical constraints is able to recapitulate the key features of the back‐bone dependent observed amino acid side‐chain dihedral angle distributions of Ser, Cys, Thr, Val, Ile, Leu, Phe, Tyr, and Trp. We find that for certain amino acids, performing the calculations with the amino acid of interest in the central position of a short α‐helical segment improves the match between the predicted and observed distributions. We also identify the atomic interactions that give rise to the differences between the predicted distributions for the hard‐sphere model of the dipeptide and that of the α‐helical segment. Finally, we point out a case where the hard‐sphere plus stereochemical constraint model is insufficient to recapitulate the observed side‐chain dihedral angle distribution, namely the distribution P(χ₃) for Met. Proteins 2014; 82:2574–2584. © 2014 Wiley Periodicals, Inc.     

Structural and conformational changes concomitant with the E1-E2 transition in H(+)K(+)-ATPase: a comparative protein modeling study

Comparative modeling studies on conserved regions of the gastric H(+)K(+)-ATPase reveal that the E1-E2 conformational transition induces significant tertiary structural changes while conserving the secondary structure. The residues 516-530 of the cytoplasmic domain and TM10 within the transmembrane (TM) regions undergo maximum tertiary structural changes. The luminal regions exhibit comparatively lesser tertiary structural deviations. Residues 249-304 show maximum secondary structural deviation in the conformational transition. The Cys-815 and Cys-323 residues involved in inhibitor binding are found to have smaller buried side chain areas in the E1 conformation compared to E2. Retention of activity correlates well with the buried side chain area when selected amino acid residues in TM6 are mutated using modeling techniques with bulkier amino acid residues. Conformational specificity for ion binding is corroborated with the fraction of side chains exposed to polar atoms of the residues E345, D826, V340, A341, V343, and E822.     

Preferred side‐chain conformation of arginine residues in a triple‐helical structure

The crystal structure of the triple‐helical peptide (Pro‐Hyp‐Gly)₃‐Pro‐Arg‐Gly‐(Pro‐Hyp‐Gly)₄ (POG3‐PRG‐POG4) was determined at 1.45 Å resolution. POG3‐PRG‐POG4 was designed to permit investigation of the side‐chain conformation of the Arg residues in a triple‐helical structure. Because of the alternative structure of one of three Arg residues, four side‐chain conformations were observed in an asymmetric unit. Among them, three adopt a ttg^?t conformation and the other adopts a tg^?g^?t conformation. A statistical analysis of 80 Arg residues in various triple‐helical peptides showed that, unlike those in globular proteins, they preferentially adopt a tt conformation for χ₁ and χ₂, as observed in POG3‐PRG‐POG4. This conformation permits van der Waals contacts between the side‐chain atoms of Arg and the main‐chain atoms of the adjacent strand in the same molecule. Unlike many other host–guest peptides, in which there is a significant difference between the helical twists in the guest and the host peptides, POG3‐PRG‐POG4 shows a marked difference between the helical twists in the N‐terminal peptide and those in the C‐terminal peptide, separated near the Arg residue. This suggested that the unique side‐chain conformation of the Arg residue affects not only the conformation of the guest peptide, but also the conformation of the peptide away from the Arg residue. © 2014 Wiley Periodicals, Inc. Biopolymers 101: 1000–1009, 2014.