An efficient algorithm for multistate protein design based on FASTER

Most of the methods that have been developed for computational protein design involve the selection of side‐chain conformations in the context of a single, fixed main‐chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful when the design target is an ensemble of related states rather than a single structure, or when a protein sequence must assume several distinct conformations to function. MSD can also be used with explicit negative design to suggest sequences with altered structural, binding, or catalytic specificity. We report implementation details of an efficient multistate design optimization algorithm based on FASTER (MSD‐FASTER). We subjected the algorithm to a battery of computational tests and found it to be generally applicable to various multistate design problems; designs with a large number of states and many designed positions are completely feasible. A direct comparison of MSD‐FASTER and multistate design Monte Carlo indicated that MSD‐FASTER discovers low‐energy sequences much more consistently. MSD‐FASTER likely performs better because amino acid substitutions are chosen on an energetic basis rather than randomly, and because multiple substitutions are applied together. Through its greater efficiency, MSD‐FASTER should allow protein designers to test experimentally better‐scoring sequences, and thus accelerate progress in the development of improved scoring functions and models for computational protein design. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Modeling loop backbone flexibility in receptor‐ligand docking simulations

The relevance of receptor conformational change during ligand binding is well documented for many pharmaceutically relevant receptors, but is still not fully accounted for in in silico docking methods. While there has been significant progress in treatment of receptor side chain flexibility sampling of backbone flexibility remains challenging because the conformational space expands dramatically and the scoring function must balance protein–protein and protein–ligand contributions. Here, we investigate an efficient multistage backbone reconstruction algorithm for large loop regions in the receptor and demonstrate that treatment of backbone receptor flexibility significantly improves binding mode prediction starting from apo structures and in cross docking simulations. For three different kinase receptors in which large flexible loops reconstruct upon ligand binding, we demonstrate that treatment of backbone flexibility results in accurate models of the complexes in simulations starting from the apo structure. At the example of the DFG‐motif in the p38 kinase, we also show how loop reconstruction can be used to model allosteric binding. Our approach thus paves the way to treat the complex process of receptor reconstruction upon ligand binding in docking simulations and may help to design new ligands with high specificity by exploitation of allosteric mechanisms. © 2012 Wiley Periodicals, Inc.     

Absolute free energies of biomolecules from unperturbed ensembles

Computing the absolute free energy of a macromolecule's structural state, F, is a challenging problem of high relevance. This study presents a method that computes F using only information from an unperturbed simulation of the macromolecule in the relevant conformational state, ensemble, and environment. Absolute free energies produced by this method, dubbed V aluation of L ocal C onfiguration I ntegral with D ynamics (VALOCIDY), enable comparison of alternative states. For example, comparing explicitly solvated and vaporous states of amino acid side‐chain analogs produces solvation free energies in good agreement with experiments. Also, comparisons between alternative conformational states of model heptapeptides (including the unfolded state) produce free energy differences in agreement with data from μs molecular‐dynamics simulations and experimental propensities. The potential of using VALOCIDY in computational protein design is explored via a small design problem of stabilizing a β‐turn structure. When VALOCIDY‐based estimation of folding free energy is used as the design metric, the resulting sequence folds into the desired structure within the atomistic force field used in design. The VALOCIDY‐based approach also recognizes the distinct status of the native sequence regardless of minor details of the starting template structure, in stark contrast with a traditional fixed‐backbone approach. © 2013 Wiley Periodicals, Inc.     

Simulated evolution of emergent chiral structures in polyalanine

The relationship between monomer chirality and polymer structure has been studied using both theoretical and experimental methods. Atomistic models, such as the ones employed in computational protein folding and design, can be used to study the relationship between monomer chirality and the properties of polypeptides. Using a simulated evolution approach that combines side-chain epimerization with backbone flexibility, we recapitulate the relationship between basic forces that drive secondary structure formation and sequence homochirality. Additionally, we find heterochiral motifs including a C-terminal helix capping interaction and stable helix-reversals that result in bent helix structures. Our studies show that simulated evolution of chirality with backbone flexibility can be a powerful tool in the design of novel heteropolymers with tuned stereochemical properties.     

X‐ray Crystallography and Vibrational Spectroscopy Reveal the Key Determinants of Biocatalytic Dihydrogen Cycling by [NiFe] Hydrogenases

[NiFe] hydrogenases are complex model enzymes for the reversible cleavage of dihydrogen (H₂). However, structural determinants of efficient H₂ binding to their [NiFe] active site are not properly understood. Here, we present crystallographic and vibrational‐spectroscopic insights into the unexplored structure of the H₂‐binding [NiFe] intermediate. Using an F₄₂₀‐reducing [NiFe]‐hydrogenase from Methanosarcina barkeri as a model enzyme, we show that the protein backbone provides a strained chelating scaffold that tunes the [NiFe] active site for efficient H₂ binding and conversion. The protein matrix also directs H₂ diffusion to the [NiFe] site via two gas channels and allows the distribution of electrons between functional protomers through a subunit‐bridging FeS cluster. Our findings emphasize the relevance of an atypical Ni coordination, thereby providing a blueprint for the design of bio‐inspired H₂‐conversion catalysts.     

Identifying and reducing error in cluster‐expansion approximations of protein energies

Protein design involves searching a vast space for sequences that are compatible with a defined structure. This can pose significant computational challenges. Cluster expansion is a technique that can accelerate the evaluation of protein energies by generating a simple functional relationship between sequence and energy. The method consists of several steps. First, for a given protein structure, a training set of sequences with known energies is generated. Next, this training set is used to expand energy as a function of clusters consisting of single residues, residue pairs, and higher order terms, if required. The accuracy of the sequence‐based expansion is monitored and improved using cross‐validation testing and iterative inclusion of additional clusters. As a trade‐off for evaluation speed, the cluster‐expansion approximation causes prediction errors, which can be reduced by including more training sequences, including higher order terms in the expansion, and/or reducing the sequence space described by thecluster expansion. This article analyzes the sources of error and introduces a method whereby accuracy can be improved by judiciously reducing the described sequence space. The method is applied to describe the sequence–stability relationship for several protein structures: coiled‐coil dimers and trimers, a PDZ domain, and T4 lysozyme as examples with computationally derived energies, and SH3 domains in amphiphysin‐1 and endophilin‐1 as examples where the expanded pseudo‐energies are obtained from experiments. Our open‐source software package Cluster Expansion Version 1.0 allows users to expand their own energy function of interest and thereby apply cluster expansion to custom problems in protein design. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

The Influence of the Amino Acid Side Chains on the Raman Optical Activity Spectra of Proteins

The Raman optical activity (ROA) spectra of proteins show distinct patterns arising from the secondary structure. It is generally believed that the spectral contributions of the side-chains largely cancel out because of their flexibility and the occurrence of many side-chains with different conformations. Yet, the influence of the side-chains on the ROA patterns assigned to different secondary structures is unknown. Here, the first systematic study of the influence of all amino acid side-chains on the ROA patterns is presented based on density functional theory (DFT) calculations of an extensive collection of peptide models that include many different side-chain and secondary structure conformations. It was shown that the contributions of the side-chains to a large extent average out with conformational flexibility. However, specific side-chain conformations can have significant contributions to the ROA patterns. It was also shown that α-helical structure is very sensitive to both the exact backbone conformation and the side-chain conformation. Side-chains with χ₁≈−60° generate ROA patterns alike those in experiment. Aromatic side-chains strongly influence the amide III ROA patterns. Because of the huge structural sensitivity of ROA, the spectral patterns of proteins arise from extensive conformational averaging of both the backbone and the side-chains. The averaging results in the fine spectral details and relative intensity differences observed in experimental spectra.     

High–quality protein backbone reconstruction from alpha carbons using Gaussian mixture models

Coarse‐grained protein structure models offer increased efficiency in structural modeling, but these must be coupled with fast and accurate methods to revert to a full‐atom structure. Here, we present a novel algorithm to reconstruct mainchain models from C traces. This has been parameterized by fitting Gaussian mixture models (GMMs) to short backbone fragments centered on idealized peptide bonds. The method we have developed is statistically significantly more accurate than several competing methods, both in terms of RMSD values and dihedral angle differences. The method produced Ramachandran dihedral angle distributions that are closer to that observed in real proteins and better Phaser molecular replacement log‐likelihood gains. Amino acid residue sidechain reconstruction accuracy using SCWRL4 was found to be statistically significantly correlated to backbone reconstruction accuracy. Finally, the PD2 method was found to produce significantly lower energy full‐atom models using Rosetta which has implications for multiscale protein modeling using coarse‐grained models. A webserver and C++ source code is freely available for noncommercial use from: http://www.sbg.bio.ic.ac.uk/phyre2/PD2_ca2main/ . © 2013 Wiley Periodicals, Inc.     

The Double‐Histidine Cu2+‐Binding Motif: A Highly Rigid,Site‐Specific Spin Probe for Electron Spin Resonance Distance Measurements

The development of ESR methods that measure long‐range distance distributions has advanced biophysical research. However, the spin labels commonly employed are highly flexible, which leads to ambiguity in relating ESR measurements to protein‐backbone structure. Herein we present the double‐histidine (dHis) Cu²⁺‐binding motif as a rigid spin probe for double electron–electron resonance (DEER) distance measurements. The spin label is assembled in situ from natural amino acid residues and a metal salt, requires no postexpression synthetic modification, and provides distance distributions that are dramatically narrower than those found with the commonly used protein spin label. Simple molecular modeling based on an X‐ray crystal structure of an unlabeled protein led to a predicted most probable distance within 0.5 Å of the experimental value. Cu²⁺ DEER with the dHis motif shows great promise for the resolution of precise, unambiguous distance constraints that relate directly to protein‐backbone structure and flexibility.     

Water‐in‐Oil Micro‐Emulsion Enhances the Secondary Structure of a Protein by Confinement

A scheme is presented in which an organic solvent environment in combination with surfactants is used to confine a natively unfolded protein inside an inverse microemulsion droplet. This type of confinement allows a study that provides unique insight into the dynamic structure of an unfolded, flexible protein which is still solvated and thus under near‐physiological conditions. In a model system, the protein osteopontin (OPN) is used. It is a highly phosphorylated glycoprotein that is expressed in a wide range of cells and tissues for which limited structural analysis exists due to the high degree of flexibility and large number of post‐translational modifications. OPN is implicated in tissue functions, such as inflammation and mineralisation. It also has a key function in tumour metastasis and progression. Circular dichroism measurements show that confinement enhances the secondary structural features of the protein. Small‐angle X‐ray scattering and dynamic light scattering show that OPN changes from being a flexible protein in aqueous solution to adopting a less flexible and more compact structure inside the microemulsion droplets. This novel approach for confining proteins while they are still hydrated may aid in studying the structure of a wide range of natively unfolded proteins.     

Using support vector machines for prediction of protein structural classes based on discrete wavelet transform

The prediction of secondary structure is a fundamental and important component in the analytical study of protein structure and functions. How to improve the predictive accuracy of protein structural classification by effectively incorporating the sequence‐order effects is an important and challenging problem. In this study, a new method, in which the support vector machine combines with discrete wavelet transform, is developed to predict the protein structural classes. Its performance is assessed by cross‐validation tests. The predicted results show that the proposed approach can remarkably improve the success rates, and might become a useful tool for predicting the other attributes of proteins as well. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009     

Catalysis and Electron Transfer in De Novo Designed Helical Scaffolds

The relationship between protein structure and function is one of the greatest puzzles within biochemistry. De novo metalloprotein design is a way to wipe the board clean and determine what is required to build in function from the ground up in an unrelated structure. This Review focuses on protein design efforts to create de novo metalloproteins within alpha‐helical scaffolds. Examples of successful designs include those with carbonic anhydrase or nitrite reductase activity by incorporating a ZnHis₃ or CuHis₃ site, or that recapitulate the spectroscopic properties of unique electron‐transfer sites in cupredoxins (CuHis₂Cys) or rubredoxins (FeCys₄). This work showcases the versatility of alpha helices as scaffolds for metalloprotein design and the progress that is possible through careful rational design. Our studies cover the invariance of carbonic anhydrase activity with different site positions and scaffolds, refinement of our cupredoxin models, and enhancement of nitrite reductase activity up to 1000‐fold.     

The Double‐Histidine Cu2+‐Binding Motif: A Highly Rigid,Site‐Specific Spin Probe for Electron Spin Resonance Distance Measurements

The development of ESR methods that measure long‐range distance distributions has advanced biophysical research. However, the spin labels commonly employed are highly flexible, which leads to ambiguity in relating ESR measurements to protein‐backbone structure. Herein we present the double‐histidine (dHis) Cu²⁺‐binding motif as a rigid spin probe for double electron–electron resonance (DEER) distance measurements. The spin label is assembled in situ from natural amino acid residues and a metal salt, requires no postexpression synthetic modification, and provides distance distributions that are dramatically narrower than those found with the commonly used protein spin label. Simple molecular modeling based on an X‐ray crystal structure of an unlabeled protein led to a predicted most probable distance within 0.5 Å of the experimental value. Cu²⁺ DEER with the dHis motif shows great promise for the resolution of precise, unambiguous distance constraints that relate directly to protein‐backbone structure and flexibility.     

A gradient‐directed Monte Carlo approach for protein design

We develop a new global optimization strategy, gradient‐directed Monte Carlo (GDMC) sampling, to optimize protein sequence for a target structure using RosettaDesign. GDMC significantly improves the sampling of sequence space, compared to the classical Monte Carlo search protocol, for a fixed backbone conformation as well as for the simultaneous optimization of sequence and structure. As such, GDMC sampling enhances the efficiency of protein design. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Structural‐Deformation‐Energy‐Modulation Strategy in a Soft Porous Coordination Polymer with an Interpenetrated Framework

To achieve unique molecular‐recognition patterns, a rational control of the flexibility of porous coordination polymers (PCPs) is highly sought, but it remains elusive. From a thermodynamic perspective, the competitive relationship between the structural deformation energy (E_def) of soft PCPs and the guest interaction is key for selective a guest‐triggered structural‐transformation behavior. Therefore, it is vital to investigate and control E_def to regulate this competition for flexibility control. Driven by these theoretical insights, we demonstrate an E_def‐modulation strategy via encoding inter‐framework hydrogen bonds into a soft PCP with an interpenetrated structure. As a proof of this concept, the enhanced E_def of PCP enables a selective gate‐opening behavior toward CHCl₃ over CH₂Cl₂ by changing the adsorption‐energy landscape of the compounds. This study provides a new direction for the design of functional soft porous materials.     

Basil Seed Inspired Design for a Monodisperse Core–Shell Sn@C Hybrid Confined in a Carbon Matrix for Enhanced Lithium‐Storage Performance

Tin anode materials have attracted much attention owing to their high theoretical capacity, although rapid capacity fade is commonly observed mainly because of structural degradation resulting from volume expansion. Herein, we report a versatile strategy based on a basil seed inspired design for constructing a monodisperse core–shell Sn@C hybrid confined in a carbon matrix (Sn basil seeds). Analogous to the structure of basil seeds soaked in water, Sn basil seeds are used to tackle the volume expansion problem in lithium‐ion batteries. Monodisperse Sn cores are encapsulated by a thick carbon layer, which thus lowers the electrolyte contact area. The obtained Sn basil seeds are closely packed to construct a framework that supplies fast electron transport and provides a reinforced mechanical backbone. As a consequence, an ensemble of this hybrid network shows significantly enhanced lithium‐storage performance with a high capacity of 870 mAh g^?1 at a current density of 0.4 A g^?1 over 600 cycles. After the intense cycling, the Sn cores transform into ultrafine nanocrystals with sizes of 3–6 nm. The structural and morphological evolution of the Sn cores can reasonably explain the gradual increase in the capacity and the long‐term cycling ability of our Sn basil seeds.     

The Application of Fluorine‐Containing Reagents in Structural Proteomics

Structural proteomics refers to large‐scale mapping of protein structures in order to understand the relationship between protein sequence, structure, and function. Chemical labeling, in combination with mass‐spectrometry (MS) analysis, have emerged as powerful tools to enable a broad range of biological applications in structural proteomics. The key to success is a biocompatible reagent that modifies a protein without affecting its high‐order structure. Fluorine, well‐known to exert profound effects on the physical and chemical properties of reagents, should have an impact on structural proteomics. In this Minireview, we describe several fluorine‐containing reagents that can be applied in structural proteomics. We organize their applications around four MS‐based techniques: a) affinity labeling, b) activity‐based protein profiling (ABPP), c) protein footprinting, and d) protein cross‐linking. Our aim is to provide an overview of the research, development, and application of fluorine‐containing reagents in protein structural studies.