Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity

The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators ("potentiators" and "correctors"), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called "stabilizers") that rescue ΔF508-CFTR activity. To design the "stabilizers", we extended our structural ensemble-based computational protein redesign algorithm K* to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods.     

Computational design of orthogonal ribosomes

Orthogonal ribosomes (o-ribosomes), also known as specialized ribosomes, are able to selectively translate mRNA not recognized by host ribosomes. As a result, they are powerful tools for investigating translational regulation and probing ribosome structure. To date, efforts directed towards engineering o-ribosomes have involved random mutagenesis-based approaches. As an alternative, we present here a computational method for rationally designing o-ribosomes in bacteria. Working under the assumption that base-pair interactions between the 16S rRNA and mRNA serve as the primary mode for ribosome binding and translational initiation, the algorithm enumerates all possible extended recognition sequences for 16S rRNA and then chooses those candidates that: (i) have a similar binding strength to their target mRNA as the canonical, wild-type ribosome/mRNA pair; (ii) do not bind mRNA with the wild-type, canonical Shine-Dalgarno (SD) sequence and (iii) minimally interact with host mRNA irrespective of whether a recognizable SD sequence is present. In order to test the algorithm, we experimentally characterized a number of computationally designed o-ribosomes in Escherichia coli.     

Computational design of protein self-assembly

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Computational design of protein-protein interactions

Computational protein design strategies have been developed to reengineer protein-protein interfaces in an automated, generalizable fashion. In the past two years, these methods have been successfully applied to generate chimeric proteins and protein pairs with specificities different from naturally occurring protein-protein interactions. Although there are shortcomings in current approaches, both in the way conformational space is sampled and in the energy functions used to evaluate designed conformations, the successes suggest we are now entering an era in which computational methods can be used to modulate, reengineer and design protein-protein interaction networks in living cells.     

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 examines how redesigned membrane proteins are being used to facilitate structural and functional studies.     

Rational design and synthesis of peptide ligands for an anti-carbohydrate antibody and their immunochemical characterization

Molecular mimics of carbohydrates present an alternative source of compounds to target pathways involving protein-carbohydrate interactions. Certain peptides act as molecular mimics of carbohydrates in binding to anti-carbohydrate antibodies. A series of potential peptide ligands for the anti-carbohydrate antibody SYA/J6, directed against Shigella flexneri Y, was designed by molecular modeling based on a crystal structure of the antibody complex with a carbohydrate-mimetic peptide. These octapeptides were synthesized using solid-phase peptide synthesis, and their recognition by the antibody was investigated. The results shed light on the nature of peptide-carbohydrate mimicry.     

Differential activation of formyl peptide receptor-like 1 by peptide ligands

Formyl peptide receptor-like 1 (FPRL1) plays a key role in the regulation of immune responses. The activation of FPRL1 induces a complicated pattern of cellular signaling, which results in the regulation of several immune responses, such as chemotactic migration and the production of reactive oxygen species (ROS). Because some of these cellular responses are not beneficial to the host, ligands that selectively modulate these cellular responses are useful. His-Phe-Tyr-Leu-Pro-Met (HFYLPM) is a synthetic peptide that binds to FPRL1. In this study, we generated various HFYLPM analogues and examined their effects on cellular responses via FPRL1 in FPRL1-expressing rat basophilic leukemia-2H3 cells or in primary human neutrophils. Among the HXYLPM analogues, His-Arg-Tyr-Leu-Pro-Met (HRYLPM) activated a broad spectrum of cellular signaling events, including an intracellular Ca(2+) concentration increase, phosphoinositide 3-kinase, extracellular signal-regulated kinase, and Akt activation, however, His-Glu-Tyr-Leu-Pro-Met (HEYLPM) activated only intracellular Ca(2+) concentration and Akt but did not increase Ca(2+). In addition, HRYLPM was found to stimulate chemotaxis and ROS generation via phosphoinositide 3-kinase and an intracellular Ca(2+) concentration increase, respectively, whereas HEYLPM stimulated chemotaxis but not ROS generation. With respect to the molecular mechanisms involved in the differential action of HRYLPM and HEYLPM, we found that HRYLPM but not HEYLPM competitively inhibited the binding of (125)I-labeled Trp-Lys-Tyr-Met-Val-D-Met-NH(2) (WKYMVm, a FPRL1 ligand) to FPRL1. This study demonstrates that the important chemoattractant receptor, FPRL1, may be differentially modulated by distinct peptide ligands. We also suggest that HRYLPM and HEYLPM may be used to selectively modulate FPRL1.     

Computational analyses of interactions between ALK-5 and bioactive ligands: insights for the design of potential anticancer agents

Activin Receptor-Like Kinase 5 (ALK-5) is related to some types of cancer, such as breast, lung, and pancreas. In this study, we have used molecular docking, molecular dynamics simulations, and free energy calculations in order to explore key interactions between ALK-5 and six bioactive ligands with different ranges of biological activity. The motivation of this work is the lack of crystal structure for inhibitor–protein complexes for this set of ligands. The understanding of the molecular structure and the protein–ligand interaction could give support for the development of new drugs against cancer. The results show that the calculated binding free energy using MM-GBSA, MM-PBSA, and SIE is correlated with experimental data with r² = 0.88, 0.80, and 0.94, respectively, which indicates that the calculated binding free energy is in excellent agreement with experimental data. In addition, the results demonstrate that H bonds with Lys232, Glu245, Tyr249, His283, Asp351, and one structural water molecule play an important role for the inhibition of ALK-5. Overall, we discussed the main interactions between ALK-5 and six inhibitors that may be used as starting points for designing new molecules to the treatment of cancer.     

Discovery of high-affinity peptide ligands for vancomycin

Vancomycin, an important antibiotic against medically relevant gram-positive bacteria such as methicillin-resistant Staphylococcus aureus, exerts its antibacterial effects by binding with moderate affinity to the C-terminal Lys-D-Ala-D-Ala motif (Kaa) of the bacterial cell wall peptide precursor. Essential for Kaa binding to vancomcyin is the free-carboxyl group on the terminal D-Ala in Kaa. In efforts to identify other Kaa-based peptides which bind vancomycin with higher affinity, we utilized our one-bead-one-compound (OBOC) combinatorial library approach, a method which has been widely used to discover highly specific ligands against various receptors. In standard OBOC peptide libraries, the C-terminal end of the synthesized peptide is tethered to a solid-support/resin, however, this study reports development of a synthetic strategy for generating OBOC peptide libraries with a free D-Ala-D-Ala carboxyl end. We screened these "OBOC inverted" peptide libraries against vancomycin, and discovered a series of peptide ligands with strong consensus, which bind vancomycin. To further optimize these ligands, two highly focused Kaa-containing OBOC combinatorial peptidomimetic libraries were designed, synthesized, and screened against vancomycin under more stringent conditions. Peptidomimetic ligands which bind vancomycin with higher affinity than Kaa were identified. The dissociation constant of one of these ligands, Lys(Ac)-HOCit-Glu-Cha-Lys(3,5-dihydroxybenzoyl)-D-Ala-D-Ala (9), as determined by surface plasmon resonance, was 1.03 microM, roughly a 50-fold improvement in affinity compared to Kaa (K(D) = 50 microM).     

Discovering peptide ligands using epitope libraries.

Epitope libraries are large collections of peptides. Each peptide is displayed on the surface of a bacteriophage particle and is encoded by a randomly mutated region of the phage genome, thus associating each unique peptide with the DNA molecule encoding it. Antibodies and other binding proteins are used to select specifically for rare, phage-bearing peptide ligands; sequencing of the corresponding viral DNA will reveal their amino acid sequences. Relatively high-affinity peptides for a variety of peptide- and non-peptide-binding ligates have been affinity-isolated from epitope libraries. This technology has been used to map epitopes on proteins and to find peptide mimics for non-peptide-binding ligates. The current challenge lies in developing epitope library technology so that tight-binding peptide ligands can be detected for a wider variety of ligates, including those that recognize folded proteins. Should this be accomplished, many powerful applications can be envisioned in the areas of drug design and the development of diagnostic markers and vaccines.     

Synthetic antibody libraries focused towards peptide ligands

Synthetic antibody libraries have proven immensely useful for the de novo isolation of antibodies without the need for animal immunization. Recently, focused libraries designed to recognize particular classes of ligands, such as haptens or proteins, have been employed to facilitate the selection of high-affinity antibodies. Focused libraries are built using V regions encoding combinations of canonical structures that resemble the structural features of antibodies that bind the desired class of ligands and sequence diversity is introduced at residues typically involved in recognition. Here we describe the generation and experimental validation of two different single-chain antibody variable fragment libraries that efficiently generate binders to peptides, a class of molecules that has proven to be a difficult target for antibody generation. First, a human anti-peptide library was constructed by diversifying a scaffold: the human variable heavy chain (V_H) germ line gene 3-23, which was fused to a variant of the human variable light chain (V_L) germ line gene A27, in which L1 was modified to encode the canonical structure found in anti-peptide antibodies. The sequence diversity was introduced into 3-23 (V_H) only, targeting for diversification residues commonly found in contact with protein and peptide antigens. Second, a murine library was generated using the antibody 26-10, which was initially isolated based on its affinity to the hapten digoxin, but also binds peptides and exhibits a canonical structure pattern typical of anti-peptide antibodies. Diversity was introduced in the V_H only using the profile of amino acids found at positions that frequently contact peptide antigens. Both libraries yielded binders to two model peptides, angiotensin and neuropeptide Y, following screening by solution phage panning. The mouse library yielded antibodies with affinities below 20 nM to both targets, although only the V_H had been subjected to diversification.     

Computational modeling and design of renin inhibitors

The recently introduced field-based QSAR was employed to develop robust quantitative 3D QSAR models to comprehend the activity of several structurally diverse classes of small molecule renin inhibitors reported in literature. A reasonable predictive model with an r² (pred) of ～0.67 and rmse of 0.79 was achieved for an external validation set of ～150 compounds centered on the model developed using ～450 training set compounds. Based on the developed 3D QSAR models and additional insights gained from reported X-ray structures, opportunity for activity improvements in the [aza]indole scaffold was explored using a carefully designed virtual library of ～2300 compounds. The potential for success of such combined structure-guided and ligand-based approach was justified when the resulting prediction was compared against a representative with supporting experimental results.     

PEPOP: Computational design of immunogenic peptides

Background  

Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides.