Computational Protein Design: Advances in the Design and Redesign of Biomolecular Nanostructures

Computational protein design facilitates the continued development of methods for the design of biomolecular structure, sequence and function. Recent applications include the design of novel protein sequences and structures, proteins incorporating nonbiological components, protein assemblies, soluble variants of membrane proteins, and proteins that modulate membrane function

Computational Protein Design: Engineering Molecular Diversity, Nonnatural Enzymes, Nonbiological Cofactor Complexes, and Membrane Proteins

Computational and theoretical methods are advancing protein design as a means to create and investigate proteins. Such efforts further our capacity to control, design and understand biomolecular structure, sequence and function. Herein, the focus is on some recent applications that involve using theoretical and computational methods to guide the design of protein sequence ensembles, new enzymes, proteins with novel cofactors, and membrane proteins

Statistical Theory of Protein Combinatorial Libraries

Combinatorial experiments provide new ways to probe the determinants of protein folding and to identify novel folding amino acid sequences. These types of experiments, however, are complicated both by enormous conformational complexity and by large numbers of possible sequences. Therefore, a quantitative computational theory would be helpful in designing and interpreting these types of experiment. Here, we present and apply a statistically based, computational approach for identifying the properties of sequences compatible with a given main-chain structure. Protein side-chain conformations are included in an atom-based fashion. Calculations are performed for a variety of similar backbone structures to identify sequence properties that are robust with respect to minor changes in main-chain structure. Rather than specific sequences, the method yields the likelihood of each of the amino acids at preselected positions in a given protein structure. The theory may be used to quantify the characteristics of sequence space for a chosen structure without explicitly tabulating sequences. To account for hydrophobic effects, we introduce an environmental energy that it is consistent with other simple hydrophobicity scales and show that it is effective for side-chain modeling. We apply the method to calculate the identity probabilities of selected positions of the immunoglobulin light chain-binding domain of protein L, for which many variant folding sequences are available. The calculations compare favorably with the experimentally observed identity probabilities

Computational Design of Membrane Proteins

Membrane proteins are involved in a wide variety of cellular processes, and are typically part of the first interaction a cell has with extracellular molecules. As a result, these proteins comprise a majority of known drug targets. Membrane proteins are among the most difficult proteins to obtain and characterize, and a structure-based understanding of their properties can be difficult to elucidate. Notwithstanding, the design of membrane proteins can provide stringent tests of our understanding of these crucial biological systems, as well as introduce novel or targeted functionalities. Computational design methods have been particularly helpful in addressing these issues and this review discusses recent studies that tailor membrane proteins to display specific structures or functions, and how redesigned membrane proteins are being used to facilitate structural and functional studies

Human μ Opioid Receptor Models with Evaluation of the Accuracy Using the Crystal Structure of the Murine μ Opioid Receptor

Models of the human μ opioid receptor were constructed using available G-protein-coupled receptor (GPCR) structures using homology (comparative) modeling techniques. The recent publication of a high-resolution crystal structure of a construct based on the murine μ opioid receptor offers a unique opportunity to evaluate the reliability of the homology models and test the relevance of introducing more templates (known structures) to increase the accuracy of the comparative models. In the first model two templates were used: the β2 adrenergic and bovine rhodopsin receptors. For the second model, four templates were utilized: the β2adrenergic, bovine rhodopsin, β1 adrenergic, and A2A adenosine receptors. Including additional templates improved the accuracy of structural motifs and other features of the model when the same sequence alignment was used. The predicted structures were especially relevant in the case of important receptor regions such as the DRY motif, which has been associated with receptor activation. Additionally, this study showed that receptor sequence similarity is crucial in homology modeling, as indicated in the case of the highly diverse EC2 loop. This study demonstrates the reliability of the homology modeling technique in the case of the μ opioid receptor, a member of the rhodopsin-like family class of GPCRs. The addition of more templates improved the accuracy of the model. The findings regarding the modeling has significant implication to other GPCRs where the crystal structure is still unknown and suggest that homology modeling techniques can provide high quality structural models for interpreting experimental findings and formulating structurally based hypotheses regarding the activity of these important receptors

Characterization of Cofactor-Induced Folding Mechanism of a Zinc Binding Peptide Using Computationally Designed Mutants

Metals are the most commonly encountered protein cofactors, and they play important structural and functional roles in biology. In many cases, metal binding provides a major driving force for a polypeptide chain to fold. While there are many studies on the structure, stability, and function of metal-binding proteins, there are few studies focusing on understanding the kinetic mechanism of metal-induced folding. Herein, the Zn(2+)-induced folding kinetics of a small zinc-binding protein are studied; the CH1(1) peptide is derived from the first cysteine/histidine-rich region (CH1 domain) of the protein interaction domains of the transcriptional coregulator CREB-binding protein. Computational design is used to introduce tryptophan and histidine mutations that are structurally consistent with CH1(1); these mutants are studied using stopped-flow tryptophan fluorescence experiments. The Zn(2+)-induced CH1(1) folding kinetics are consistent with two parallel pathways, where the initial binding of Zn(2+) occurs at two sites. However, the initially formed Zn(2+)-bound complexes can proceed either directly to the folded state where zinc adopts a tetrahedral coordination or to an off-pathway misligated intermediate. While elimination of those ligands responsible for misligation simplifies the folding kinetics, it also leads to a decrease in the zinc binding constant. Therefore, these results suggest why these nonnative zinc ligands in the CH1(1) motif are conserved in several distantly related organisms and why the requirement for function can lead to kinetic frustration in folding. In addition, the loop closure rate of the CH1(1) peptide is determined based on the proposed model and temperature-dependent kinetic measurements

Binding Site and Affinity Prediction of General Anesthetics to Protein Targets Using Docking

BACKGROUND: The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explored whether a computational method, AutoDock, could serve as such a tool. METHODS: High-resolution crystal data of water-soluble proteins (cytochrome C, apoferritin, and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus [GLIC]) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (http://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants were compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent cocrystallization data. Docking calculations for 6 general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known 50% effective concentration (EC50) values were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC50 values and octanol/water partition coefficients for the 6 general anesthetics. RESULTS: All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (P = 0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC50 values for the 6 frequently used anesthetics in GLIC for the site identified in the experimental crystal data (P = 0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these 6 anesthetics\u27 known experimental EC50values. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. CONCLUSION: We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (AutoDock) for both water-soluble and membrane proteins. Correlation of predicted affinity and EC50 for 6 frequently used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism

Progress in the development and application of computational methods for probabilistic protein design

Proteins exhibit a wide range of physical and chemical properties, including highly selective molecular recognition and catalysis, and are also key components in biological metabolic, catabolic, and signaling pathways. Given that proteins are well-structured and can now be rapidly synthesized, they are excellent targets for engineering of both molecular structure and biological function. Computational analysis of the protein design problem allows scientists to explore sequence space and systematically discover novel protein molecules. Nonetheless, the complexity of proteins, the subtlety of the determinants of folding, and the exponentially large number of possible sequences impede the search for peptide sequences compatible with a desired structure and function. Directed search algorithms, which identify directly a small number of sequences, have achieved some success in identifying sequences with desired structures and functions. Alternatively, one can adopt a probabilistic approach. Instead of a finite number of sequences, such calculations result in a probabilistic description of the sequence ensemble. In particular, by casting the formalism in the language of statistical mechanics, the site-specific amino acid probabilities of sequences compatible with a target structure may be readily identified. The computational probabilities are well suited for both de novo protein design of particular sequences as well as combinatorial, library-based protein engineering. The computed site-specific amino acid profile may be converted to a nucleotide base distribution to allow assembly of a partially randomized gene library. The ability to synthesize readily such degenerate oligonucleotide sequences according to the prescribed distribution is key to constructing a biased peptide library genuinely reflective of the computational design. Herein we illustrate how a standard DNA synthesizer can be used with only a slight modification to the synthesis protocol to generate a pool of degenerate DNA sequences, which encodes a predetermined amino acid distribution with high fidelity