Equilibrium transitions between side‐chain conformations in leucine and isoleucine

Despite recent improvements in computational methods for protein design, we still lack a quantitative, predictive understanding of the intrinsic probabilities for amino acids to adopt particular side‐chain conformations. Surprisingly, this question has remained unsettled for many years, in part because of inconsistent results from different experimental approaches. To explicitly determine the relative populations of different side‐chain dihedral angles, we performed all‐atom hard‐sphere Langevin Dynamics simulations of leucine (Leu) and isoleucine (Ile) dipeptide mimetics with stereo‐chemical constraints and repulsive‐only steric interactions between non‐bonded atoms. We determine the relative populations of the different χ₁ and χ₂ dihedral angle combinations as a function of the backbone dihedral angles ? and ψ. We also propose, and test, a mechanism for inter‐conversion between the different side‐chain conformations. Specifically, we discover that some of the transitions between side‐chain dihedral angle combinations are very frequent, whereas others are orders of magnitude less frequent, because they require rare coordinated motions to avoid steric clashes. For example, to transition between different values of χ₂, the Leu side‐chain bond angles κ₁ and κ₂ must increase, whereas to transition in χ₁, the Ile bond angles λ₁ and λ₂ must increase. These results emphasize the importance of computational approaches in stimulating further experimental studies of the conformations of side‐chains in proteins. Moreover, our studies emphasize the power of simple steric models to inform our understanding of protein structure, dynamics, and design. Proteins 2015; 83:1488–1499. © 2015 Wiley Periodicals, Inc.     

Assessment of protein side‐chain conformation prediction methods in different residue environments

Computational prediction of side‐chain conformation is an important component of protein structure prediction. Accurate side‐chain prediction is crucial for practical applications of protein structure models that need atomic‐detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side‐chain prediction methods in reproducing the side‐chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane‐spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side‐chains at protein interfaces and membrane‐spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane‐spanning regions as for modeling monomers. Proteins 2014; 82:1971–1984. © 2014 Wiley Periodicals, Inc.     

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.     

Comparison of multiple Amber force fields and development of improved protein backbone parameters

The ff94 force field that is commonly associated with the Amber simulation package is one of the most widely used parameter sets for biomolecular simulation. After a decade of extensive use and testing, limitations in this force field, such as over-stabilization of alpha-helices, were reported by us and other researchers. This led to a number of attempts to improve these parameters, resulting in a variety of "Amber" force fields and significant difficulty in determining which should be used for a particular application. We show that several of these continue to suffer from inadequate balance between different secondary structure elements. In addition, the approach used in most of these studies neglected to account for the existence in Amber of two sets of backbone phi/psi dihedral terms. This led to parameter sets that provide unreasonable conformational preferences for glycine. We report here an effort to improve the phi/psi dihedral terms in the ff99 energy function. Dihedral term parameters are based on fitting the energies of multiple conformations of glycine and alanine tetrapeptides from high level ab initio quantum mechanical calculations. The new parameters for backbone dihedrals replace those in the existing ff99 force field. This parameter set, which we denote ff99SB, achieves a better balance of secondary structure elements as judged by improved distribution of backbone dihedrals for glycine and alanine with respect to PDB survey data. It also accomplishes improved agreement with published experimental data for conformational preferences of short alanine peptides and better accord with experimental NMR relaxation data of test protein systems.     

On the ability of molecular dynamics force fields to recapitulate NMR derived protein side chain order parameters

Molecular dynamics (MD) simulations have become a central tool for investigating various biophysical questions with atomistic detail. While many different proxies are used to qualify MD force fields, most are based on largely structural parameters such as the root mean square deviation from experimental coordinates or nuclear magnetic resonance (NMR) chemical shifts and residual dipolar couplings. NMR derived Lipari–Szabo squared generalized order parameter (O²) values of amide N? H bond vectors of the polypeptide chain were also often employed for refinement and validation. However, with a few exceptions, side chain methyl symmetry axis order parameters have not been incorporated into experimental reference sets. Using a test set of five diverse proteins, the performance of several force fields implemented in the NAMDD simulation package was examined. It was found that simulations employing explicit water implemented using the TIP3 model generally performed significantly better than those using implicit water in reproducing experimental methyl symmetry axis O² values. Overall the CHARMM27 force field performs nominally better than two implementations of the Amber force field. It appeared that recent quantum mechanics modifications to side chain torsional angles of leucine and isoleucine in the Amber force field have significantly hindered proper motional modeling for these residues. There remained significant room for improvement as even the best correlations of experimental and simulated methyl group Lipari–Szabo generalized order parameters fall below an R² of 0.8.     

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.     

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.     

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.     

Structure refinement of protein model decoys requires accurate side‐chain placement

In this study, the application of temperature‐based replica‐exchange (T‐ReX) simulations for structure refinement of decoys taken from the I‐TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self‐guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T‐ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T‐ReX simulation model is provided. Additionally, the effect of side‐chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near‐native backbone conformations among the starting decoys, the determinant of their refinement is side‐chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T‐ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I‐TASSER decoy sets and a 25% reduction in values of C_α root‐mean‐square deviation. The hybrid model succeeded in obtaining a sharper funnel to low‐energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near‐native packing of side chains, the T‐ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem. Proteins 2013. 2012 Published by 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 .     

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.     

Deuteration of nonexchangeable protons on proteins affects their thermal stability,side‐chain dynamics,and hydrophobicity

We have investigated the effect of deuteration of non‐exchangeable protons on protein global thermal stability, hydrophobicity, and local flexibility using well‐known thermostable model systems such as the villin headpiece subdomain (HP36) and the third immunoglobulin G‐binding domain of protein G (GB3). Reversed‐phase high‐performance liquid chromatography (RP‐HPLC) measurements as a function of temperature probe global thermal stability in the presence of acetonitrile, while differential scanning calorimetry determines thermal stability in solution. Both indicate small but measurable changes in the order of several degrees. RP‐HPLC also permitted quantification of the effect of deuteration of just three core phenylalanine side chains of HP36. NMR dynamics investigation has focused on methyl axes motions using cross‐correlated relaxation measurements. The analysis of order parameters provided a complex picture indicating that deuteration generally increases motional amplitudes of sub‐nanosecond motion in GB3 but decreases those in HP36. Combined with earlier dynamics measurements at C_α–C_β sites and backbone sites of GB3, which probed slower time scales, the results point to the need to probe multiple atoms in the protein and variety of time scales to the discern the full complexity of the effects of deuteration on dynamics.     

Molprobity's ultimate rotamer‐library distributions for model validation

Here we describe the updated MolProbity rotamer‐library distributions derived from an order‐of‐magnitude larger and more stringently quality‐filtered dataset of about 8000 (vs. 500) protein chains, and we explain the resulting changes and improvements to model validation as seen by users. To include only side‐chains with satisfactory justification for their given conformation, we added residue‐specific filters for electron‐density value and model‐to‐density fit. The combined new protocol retains a million residues of data, while cleaning up false‐positive noise in the multi‐ datapoint distributions. It enables unambiguous characterization of conformational clusters nearly 1000‐fold less frequent than the most common ones. We describe examples of local interactions that favor these rare conformations, including the role of authentic covalent bond‐angle deviations in enabling presumably strained side‐chain conformations. Further, along with favored and outlier, an allowed category (0.3–2.0% occurrence in reference data) has been added, analogous to Ramachandran validation categories. The new rotamer distributions are used for current rotamer validation in MolProbity and PHENIX, and for rotamer choice in PHENIX model‐building and refinement. The multi‐dimensional distributions and Top8000 reference dataset are freely available on GitHub. These rotamers are termed “ultimate” because data sampling and quality are now fully adequate for this task, and also because we believe the future of conformational validation should integrate side‐chain with backbone criteria. Proteins 2016; 84:1177–1189. © 2016 Wiley Periodicals, Inc.     

Red fluorescent protein DsRed: Parametrization of its chromophore as an amino acid residue for computer modeling in the OPLS-AA force field

Topology of the neutral form of the DsRed fluorescent protein chromophore as a residue of [(4-cis)-2-[(1-cis)-4-amino-4-oxobutanimidoyl]-4-(4-hydroxybenzylidene)-5-oxo-4,5-dihydro-1H-imidazol-1-yl]acetic acid was calculated with OPLS-AA force field. Use of this topology and molecular dynamics simulation allows calculating the parameters of proteins that contain such residue in their polypeptide chains. The chromophore parameters were obtained by ab initio (RHF/6-31G**) quantum chemical calculations applying density functional theory (B3LYP). Using this chromophore, we have calculated the molecular dynamics trajectory of tetrameric fluorescent protein DsRed in solution at 300 K (4 nsec). Correctness of the chromophore parametrization was revealed by comparison of quantitative characteristics of the chromophore structure obtained from the molecular dynamic simulations of DsRed protein with the quantitative characteristics of the chromophore based on the crystallographic X-ray data of fluorescent protein DsRed (PDB ID: 1ZGO, 1G7K, and 1GGX), and also with the quantitative characteristics of the chromophore obtained by quantum chemical calculations. Inclusion of the neutral form of DsRed protein chromophore topology into the OPLS-AA force field yielded the extended force field OPLS-AA/DsRed. This force field can be used for molecular dynamics calculations of proteins containing the DsRed chromophore. The parameter set presented in this study can be applied for similar extension in any other force fields.     

Banding 2of NMR‐derived methyl order parameters: Implications for protein dynamics

Our understanding of protein folding, stability, and function has begun to more explicitly incorporate dynamical aspects. Nuclear magnetic resonance has emerged as a powerful experimental method for obtaining comprehensive site‐resolved insight into protein motion. It has been observed that methyl‐group motion tends to cluster into three “classes” when expressed in terms of the popular Lipari‐Szabo model‐free squared generalized order parameter. Here the origins of the three classes or bands in the distribution of order parameters are examined. As a first step, a Bayesian based approach, which makes no a priori assumption about the existence or number of bands, is developed to detect the banding of values derived either from NMR experiments or molecular dynamics simulations. The analysis is applied to seven proteins with extensive molecular dynamics simulations of these proteins in explicit water to examine the relationship between O² and fine details of the motion of methyl bearing side chains. All of the proteins studied display banding, with some subtle differences. We propose a very simple yet plausible physical mechanism for banding. Finally, our Bayesian method is used to analyze the measured distributions of methyl group motions in the catabolite activating protein and several of its mutants in various liganded states and discuss the functional implications of the observed banding to protein dynamics and function. Proteins 2014; 82:2106–2117. © 2014 Wiley Periodicals, Inc.     

Characterization of threonine side chain dynamics in an antifreeze protein using natural abundance 13C NMR spectroscopy

The dynamics of threonine side chains of the Tenebrio molitor antifreeze protein (TmAFP) were investigated using natural abundance (13)C NMR. In TmAFP, the array of threonine residues on one face of the protein is responsible for conferring its ability to bind crystalline ice and inhibit its growth. Heteronuclear longitudinal and transverse relaxation rates and the [(1)H]-(13)C NOE were determined in this study. The C alpha H relaxation measurements were compared to the previously measured (15)N backbone parameters and these are found to be in agreement. For the analysis of the threonine side chain motions, the model of restricted rotational diffusion about the chi(1) dihedral angle was employed [London and Avitabile (1978) J. Am. Chem. Soc., 100, 7159-7165]. We demonstrate that the motion experienced by the ice binding threonine side chains is highly restricted, with an approximate upper limit of less than +/-25 degrees.