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.     

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.     

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.     

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.     

Methoxinine – an alternative stable amino acid substitute for oxidation‐sensitive methionine in radiolabelled peptide conjugates

Radiolabelled peptides with high specificity and affinity towards receptors that are overexpressed by tumour cells are used in nuclear medicine for the diagnosis (imaging) and therapy of cancer. In some cases, the sequences of peptides under investigations contain methionine (Met), an amino acid prone to oxidation during radiolabelling procedures. The formation of oxidative side products can affect the purity of the final radiopharmaceutical product and/or impair its specificity and affinity towards the corresponding receptor. The replacement of Met with oxidation resistant amino acid analogues, for example, norleucine (Nle), can provide a solution. While this approach has been applied successfully to different radiolabelled peptides, a Met → Nle switch only preserves the length of the amino acid side chain important for hydrophobic interactions but not its hydrogen‐bonding properties. We report here the use of methoxinine (Mox), a non‐canonical amino acid that resembles more closely the electronic properties of Met in comparison to Nle. Specifically, we replaced Met¹⁵ by Mox¹⁵ and Nle¹⁵ in the binding sequence of a radiometal‐labelled human gastrin derivative [d ‐Glu¹⁰]HG(10‐17), named MG11 (d ‐Glu‐Ala‐Tyr‐Gly‐Trp‐Met‐Asp‐Phe‐NH₂). A comparison of the physicochemical properties of ¹⁷⁷Lu‐DOTA[ X ¹⁵]MG11 ( X = Met, Nle, Mox) in vitro (cell internalization/externalization properties, receptor affinity (IC₅₀), blood plasma stability and logD) showed that Mox indeed represents a suitable, oxidation‐stable amino acid substitute of Met in radiolabelled peptide conjugates. Copyright © 2017 European Peptide Society and John Wiley & Sons, Ltd.     

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.     

New Insights into the Interdependence between Amino Acid Stereochemistry and Protein Structure

To successfully design new proteins and understand the effects of mutations in natural proteins, we must understand the geometric and physicochemical principles underlying protein structure. The side chains of amino acids in peptides and proteins adopt specific dihedral angle combinations; however, we still do not have a fundamental quantitative understanding of why some side-chain dihedral angle combinations are highly populated and others are not. Here we employ a hard-sphere plus stereochemical constraint model of dipeptide mimetics to enumerate the side-chain dihedral angles of leucine (Leu) and isoleucine (Ile), and identify those conformations that are sterically allowed versus those that are not as a function of the backbone dihedral angles ? and ψ. We compare our results with the observed distributions of side-chain dihedral angles in proteins of known structure. With the hard-sphere plus stereochemical constraint model, we obtain agreement between the model predictions and the observed side-chain dihedral angle distributions for Leu and Ile. These results quantify the extent to which local, geometrical constraints determine protein side-chain conformations.     

New Insights into the Interdependence between Amino Acid Stereochemistry and Protein Structure

To successfully design new proteins and understand the effects of mutations in natural proteins, we must understand the geometric and physicochemical principles underlying protein structure. The side chains of amino acids in peptides and proteins adopt specific dihedral angle combinations; however, we still do not have a fundamental quantitative understanding of why some side-chain dihedral angle combinations are highly populated and others are not. Here we employ a hard-sphere plus stereochemical constraint model of dipeptide mimetics to enumerate the side-chain dihedral angles of leucine (Leu) and isoleucine (Ile), and identify those conformations that are sterically allowed versus those that are not as a function of the backbone dihedral angles ϕ and ψ. We compare our results with the observed distributions of side-chain dihedral angles in proteins of known structure. With the hard-sphere plus stereochemical constraint model, we obtain agreement between the model predictions and the observed side-chain dihedral angle distributions for Leu and Ile. These results quantify the extent to which local, geometrical constraints determine protein side-chain conformations.     

Site-specific ¹⁹F NMR chemical shift and side chain relaxation analysis of a membrane protein labeled with an unnatural amino acid

Site‐specific ¹⁹F chemical shift and side chain relaxation analysis can be applied on large size proteins. Here, one‐dimensional ¹⁹F spectra and T₁, T₂ relaxation data were acquired on a SH3 domain in aqueous buffer containing 60% glycerol, and a nine‐transmembrane helices membrane protein diacyl‐glycerol kinase (DAGK) in dodecyl phosphochoine (DPC) micelles. The high quality of the data indicates that this method can be applied to site‐specifically analyze side chain internal mobility of membrane proteins or large size proteins.     

Effect of Alkylsilyl Side‐Chain Structure on Photovoltaic Properties of Conjugated Polymer Donors

Side‐chain engineering is an important strategy for optimizing photovoltaic properties of organic photovoltaic materials. In this work, the effect of alkylsilyl side‐chain structure on the photovoltaic properties of medium bandgap conjugated polymer donors is studied by synthesizing four new polymers J70 , J72 , J73 , and J74 on the basis of highly efficient polymer donor J71 by changing alkyl substituents of the alkylsilyl side chains of the polymers. And the photovoltaic properties of the five polymers are studied by fabricating polymer solar cells (PSCs) with the polymers as donor and an n‐type organic semiconductor (n‐OS) m‐ITIC as acceptor. It is found that the shorter and linear alkylsilyl side chain could afford ordered molecular packing, stronger absorption coefficient, higher charge carrier mobility, thus results in higher J_sc and fill factor values in the corresponding PSCs. While the polymers with longer or branched alkyl substituents in the trialkylsilyl group show lower‐lying highest occupied molecular orbital energy levels which leads to higher V_oc of the PSCs. The PSCs based on J70 :m‐ITIC and J71 :m‐ITIC achieve power conversion efficiency (PCE) of 11.62 and 12.05%, respectively, which are among the top values of the PSCs reported in the literatures so far.     

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 .     

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.     

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.     

A statistical analysis of side-chain conformations in proteins: Comparison with ECEPP predictions

A comparison of the statistical distributions of side-chain conformations of 17 amino acids (Gly, Ala, and Pro excluded), observed in 63 nonhomologous globular proteins (covering 10,832 residues), is made with similar distributions calculated from the low-energy conformational states for the same amino acids (blocked with acetyl and N-methylamide groups at the N- and C-termini, respectively) obtained by Vásquezet al. [(1983),Macromolecules 16, 1043–1049 using the ECEPP/2 force field. Those residues (i) with linear side chains (Arg, Lys, Met, Cys, Ser), or those that are unbranched through the-carbon atom (Glu, Gln) show good agreement, whereas (ii) those with side chains that are branched at C or C show poor agreement with ECEPP calculations. A possible explanation for this is shown to be the greater tendency for side-chain atoms in class (ii) to interact with the backbone and/or adjacent side chains. Accordingly, ECEPP/3 calculations, carried out after elongating the backbone chain of the model peptide unit (by adding three Ala residues on each side of the central residue, and then blocking the termini as before), result in distributions that are often closer to the observed side-chain distributions. The implications of these results for the relative importance of short-range versus long-range interactions in determining protein structure are discussed.     

General trends of dihedral conformational transitions in a globular protein

Dihedral conformational transitions are analyzed systematically in a model globular protein, cytochrome P450cam, to examine their structural and chemical dependences through combined conventional molecular dynamics (cMD), accelerated molecular dynamics (aMD) and adaptive biasing force (ABF) simulations. The aMD simulations are performed at two acceleration levels, using dihedral and dual boost, respectively. In comparison with cMD, aMD samples protein dihedral transitions approximately two times faster on average using dihedral boost, and ～3.5 times faster using dual boost. In the protein backbone, significantly higher dihedral transition rates are observed in the bend, coil, and turn flexible regions, followed by the β bridge and β sheet, and then the helices. Moreover, protein side chains of greater length exhibit higher transition rates on average in the aMD‐enhanced sampling. Side chains of the same length (particularly N_χ = 2) exhibit decreasing transition rates with residues when going from hydrophobic to polar, then charged and aromatic chemical types. The reduction of dihedral transition rates is found to be correlated with increasing energy barriers as identified through ABF free energy calculations. These general trends of dihedral conformational transitions provide important insights into the hierarchical dynamics and complex free energy landscapes of functional proteins. Proteins 2016; 84:501–514. © 2016 Wiley Periodicals, Inc.     

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.     

Side‐Chain Engineering for Enhancing the Properties of Small Molecule Solar Cells: A Trade‐off Beyond Efficiency

Three small molecules with different substituents on bithienyl‐benzo[1,2‐b:4,5‐b′]dithiophene (BDTT) units, BDTT‐TR (meta‐alkyl side chain), BDTT‐O‐TR (meta‐alkoxy), and BDTT‐S‐TR (meta‐alkylthio), are designed and synthesized for systematically elucidating their structure–property relationship in solution‐processed bulk heterojunction organic solar cells. Although all three molecules show similar molecular structures, thermal properties and optical band gaps, the introduction of meta‐alkylthio‐BDTT as the central unit in the molecular backbone substantially results in a higher absorption coefficient, slightly lower highest occupied molecular orbital level and significantly more efficient and balanced charge transport property. The bridging atom in the meta‐position to the side chain is found to impact the microstructure formation which is a subtle but decisive way: carrier recombination is suppressed due to a more balanced carrier mobility and BDTT based devices with the meta‐alkylthio side chain (BDTT‐S‐TR) show a higher power conversion efficiency (PCE of 9.20%) as compared to the meta‐alkoxy (PCE of 7.44% for BDTT‐TR) and meta‐alkyl spacer (PCE of 6.50% for BDTT‐O‐TR). Density functional density calculations suggest only small variations in the torsion angle of the side chains, but the nature of the side chain linkage is further found to impact the thermal as well as the photostability of corresponding devices. The aim is to provide comprehensive insight into fine‐tuning the structure–property interrelationship of the BDTT material class as a function of side chain engineering.     

Bridging of partially negative atoms by hydrogen bonds from main‐chain NH groups in proteins: The crown motif

The backbone NH groups of proteins can form N1N3‐bridges to δ‐ve or anionic acceptor atoms when the tripeptide in which they occur orients them appropriately, as in the RL and LR nest motifs, which have dihedral angles 1,2‐α_Rα_L and 1,2‐α_Lα_R, respectively. We searched a protein database for structures with backbone N1N3‐bridging to anionic atoms of the polypeptide chain and found that RL and LR nests together accounted for 92% of examples found (88% RL nests, 4% LR nests). Almost all the remaining 8% of N1N3‐bridges were found within a third tripeptide motif which has not been described previously. We term this a “crown,” because of the disposition of the tripeptide CO groups relative to the three NH groups and the acceptor oxygen anion, and the crown together with its bridged anion we term a “crown bridge.” At position 2 of these structures the dihedral angles have a tight α_R distribution, but at position 1 they have a wider distribution, with ? and ψ values generally being lower than those at position 1. Over half of crown bridges involve the backbone CO group three residues N‐terminal to the tripeptide, the remainder being to other main‐chain or side‐chain carbonyl groups. As with nests, bridging of crowns to oxygen atoms within ligands was observed, as was bridging to the sulfur atom of an iron‐sulfur cluster. This latter property may be of significance for protein evolution. Proteins 2015; 83:2067–2076. © 2015 Wiley Periodicals, Inc.     

ω‐Turn: A novel β‐turn mimic in globular proteins stabilized by main‐chain to side‐chain CH···O interaction

Mimicry of structural motifs is a common feature in proteins. The 10‐membered hydrogen‐bonded ring involving the main‐chain C?O in a β‐turn can be formed using a side‐chain carbonyl group leading to Asx‐turn. We show that the N? H component of hydrogen bond can be replaced by a C^γ‐H group in the side chain, culminating in a nonconventional C? H···O interaction. Because of its shape this β‐turn mimic is designated as ω‐turn, which is found to occur ～three times per 100 residues. Three residues (i to i + 2) constitute the turn with the C? H···O interaction occurring between the terminal residues, constraining the torsion angles ?_{i + 1}, ψ_{i + 1}, ?_{i + 2} and χ′_{1(i + 2)} (using the interacting C^γ atom). Based on these angles there are two types of ω‐turns, each of which can be further divided into two groups. C^β‐branched side‐chains, and Met and Gln have high propensities to occur at i + 2; for the last two residues the carbonyl oxygen may participate in an additional interaction involving the S and amino group, respectively. With Cys occupying the i + 1 position, such turns are found in the metal‐binding sites. N‐linked glycosylation occurs at the consensus pattern Asn‐Xaa‐Ser/Thr; with Thr at i + 2, the sequence can adopt the secondary structure of a ω‐turn, which may be the recognition site for protein modification. Location between two β‐strands is the most common occurrence in protein tertiary structure, and being generally exposed ω‐turn may constitute the antigenic determinant site. It is a stable scaffold and may be used in protein engineering and peptide design. Proteins 2015; 83:203–214. © 2014 Wiley Periodicals, Inc.     

The Dynameomics rotamer library: amino acid side chain conformations and dynamics from comprehensive molecular dynamics simulations in water

We have recently completed systematic molecular dynamics simulations of 807 different proteins representing 95% of the known autonomous protein folds in an effort we refer to as Dynameomics. Here we focus on the analysis of side chain conformations and dynamics to create a dynamic rotamer library. Overall this library is derived from 31,000 occurrences of each of 86,217 different residues, or 2.7 × 10(9) rotamers. This dynamic library has 74% overlap of rotamer distributions with rotamer libraries derived from static high-resolution crystal structures. Seventy-five percent of the residues had an assignable primary conformation, and 68% of the residues had at least one significant alternate conformation. The average correlation time for switching between rotamers ranged from 22 ps for Met to over 8 ns for Cys; this time decreased 20-fold on the surface of the protein and modestly for dihedral angles further from the main chain. Side chain S(2) axis order parameters were calculated and they correlated well with those derived from NMR relaxation experiments (R = 0.9). Relationships relating the S(2) axis order parameters to rotamer occupancy were derived. Overall the Dynameomics rotamer library offers a comprehensive depiction of side chain rotamer preferences and dynamics in solution, and more realistic distributions for dynamic proteins in solution at ambient temperature than libraries derived from crystal structures, in particular charged surface residues are better represented. Details of the rotamer library are presented here and the library itself can be downloaded at http://www.dynameomics.org.