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 .     

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.     

Protein side chain conformation predictions with an MMGBSA energy function

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an “MMGBSA” energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803–819. © 2016 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.     

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.     

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.     

Prediction of protein mutation effects based on dehydration and hydrogen bonding – A large‐scale study

Reliable computational prediction of protein side chain conformations and the energetic impact of amino acid mutations are the key aspects for the optimization of biotechnologically relevant enzymatic reactions using structure‐based design. By improving the protein stability, higher yields can be achieved. In addition, tuning the substrate selectivity of an enzymatic reaction by directed mutagenesis can lead to higher turnover rates. This work presents a novel approach to predict the conformation of a side chain mutation along with the energetic effect on the protein structure. The HYDE scoring concept applied here describes the molecular interactions primarily by evaluating the effect of dehydration and hydrogen bonding on molecular structures in aqueous solution. Here, we evaluate its capability of side‐chain conformation prediction in classic remutation experiments. Furthermore, we present a new data set for evaluating “cross‐mutations,” a new experiment that resembles real‐world application scenarios more closely. This data set consists of protein pairs with up to five point mutations. Thus, structural changes are attributed to point mutations only. In the cross‐mutation experiment, the original protein structure is mutated with the aim to predict the structure of the side chain as in the paired mutated structure. The comparison of side chain conformation prediction (“remutation”) showed that the performance of HYDE_protein is qualitatively comparable to state‐of‐the art methods. The ability of HYDE_protein to predict the energetic effect of a mutation is evaluated in the third experiment. Herein, the effect on protein stability is predicted correctly in 70% of the evaluated cases. Proteins 2017; 85:1550–1566. © 2017 Wiley Periodicals, Inc.     

Physical�Cchemical determinants of coil conformations in globular proteins

We present a method with the potential to generate a library of coil segments from first principles. Proteins are built from α‐helices and/or β‐strands interconnected by these coil segments. Here, we investigate the conformational determinants of short coil segments, with particular emphasis on chain turns. Toward this goal, we extracted a comprehensive set of two‐, three‐, and four‐residue turns from X‐ray–elucidated proteins and classified them by conformation. A remarkably small number of unique conformers account for most of this experimentally determined set, whereas remaining members span a large number of rare conformers, many occurring only once in the entire protein database. Factors determining conformation were identified via Metropolis Monte Carlo simulations devised to test the effectiveness of various energy terms. Simulated structures were validated by comparison to experimental counterparts. After filtering rare conformers, we found that 98% of the remaining experimentally determined turn population could be reproduced by applying a hydrogen bond energy term to an exhaustively generated ensemble of clash‐free conformers in which no backbone polar group lacks a hydrogen‐bond partner. Further, at least 90% of longer coil segments, ranging from 5‐ to 20 residues, were found to be structural composites of these shorter primitives. These results are pertinent to protein structure prediction, where approaches can be divided into either empirical or ab initio methods. Empirical methods use database‐derived information; ab initio methods rely on physical–chemical principles exclusively. Replacing the database‐derived coil library with one generated from first principles would transform any empirically based method into its corresponding ab initio homologue.     

A new approach to the rapid determination of protein side chain conformations

Two efficient algorithms have been developed which allow amino acid side chain conformations to be optimized rapidly for a given peptide backbone conformation. Both these approaches are based on the assumption that each side chain can be represented by a small number of rotameric states. These states have been obtained by a dynamic cluster analysis of a large data base of known crystallographic structures. Successful applications of these algorithms to the prediction of known protein conformations are presented.     

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.     

Advantages of fine-grained side chain conformer libraries

We compare the modelling accuracy of two common rotamer libraries, the Dunbrack-Cohen and the 'Penultimate' rotamer libraries, with that of a novel library of discrete side chain conformations extracted from the Protein Data Bank. These side chain conformer libraries are extracted automatically from high-quality protein structures using stringent filters and maintain crystallographic bond lengths and angles. This contrasts with traditional rotamer libraries defined in terms of chi angles under the assumption of idealized covalent geometry. We demonstrate that side chain modelling onto native and near-native main chain conformations is significantly more successful with the conformer libraries than with the rotamer libraries when solely considering excluded-volume interactions. The rotamer libraries are inadequate to model side chains without atomic clashes on over 20% of targets if the backbone is held fixed in the native conformation. An algorithm is described for simultaneously modelling both main chain and side chain atoms during discrete ab initio sampling. The resulting models have equivalent root mean square deviations from the experimentally determined protein loops as models from backbone-only ensembles, indicating that all-atom modelling does not detract from the accuracy of conformational sampling.     

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.     

应用死端排除法考虑分子对接中的侧链柔性

假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用９个晶体结构对这一假设进行检验。结果表明假设基本正确。对２个蛋白酶和抑制剂的应用比较成功。９个配体中的７个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对     

应用死端排除法考虑分子对接中的侧链柔性

假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用９个晶体结构对这一假设进行检验,结果表明假设基本正确。对２个蛋白酶与抑制剂的应用比较成功。９个配体中的７个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对接后加入对接面中氨基酸残基的侧链构象预测。对一个蛋白酶与抑制剂的复合结构的应用表明对接中的正确解的信号与噪音比相对错误解增加了。     

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.     

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.     

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.