Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides.

There is interest in peptide drug design, especially for targeting intracellular protein-protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

Inhibiting IL-2 signaling and the regulatory T-cell pathway using computationally designed peptides.

Background Increased serum levels of soluble interleukin-2 (IL-2) receptor alpha (sIL-2Rα) are an indicator of poor prognosis in patients with B-cell non-Hodgkin lymphoma (NHL). By binding to IL-2, sIL-2Rα upregulates Foxp3 expression and induces the development of regulatory T (Treg) cells. Methods To inhibit the binding of IL-2 to sIL-2Rα with the goal of suppressing the induction of Foxp3 and decreasing Treg cell numbers, we developed peptides by structure-based computational design to disrupt the interaction between IL-2 and sIL-2Rα. Each peptide was screened using an enzyme-linked immunosorbent assay (ELISA), and 10 of 22 peptides showed variable capacity to inhibit IL-2/sIL-2Rα binding. Results We identified a lead candidate peptide, CMD178, which consistently reduced the expression of Foxp3 and STAT5 induced by IL-2/sIL-2Rα signaling. Furthermore, production of cytokines (IL-2/interferon gamma [IFN-γ]) and granules (perforin/granzyme B) was preserved in CD8+ T cells co-cultured with IL-2-stimulated CD4+ T cells that had been pretreated with CMD178 compared to CD8+ cells co-cultured with untreated IL-2-stimulated CD4+ T cells where it was inhibited. Conclusions We conclude that structure-based peptide design can be used to identify novel peptide inhibitors that block IL-2/sIL-2Rα signaling and inhibit Treg cell development. We anticipate that these peptides will have therapeutic potential in B-cell NHL and other malignancies.

An unexpected way forward: towards a more accurate and rigorous protein-protein binding affinity scoring function by eliminating terms from an already simple scoring function.

A fundamental and unsolved problem in biophysical chemistry is the development of a computationally simple, physically intuitive, and generally applicable method for accurately predicting and physically explaining protein-protein binding affinities from protein-protein interaction (PPI) complex coordinates. Here, we propose that the simplification of a previously described six-term PPI scoring function to a four term function results in a simple expression of all physically and statistically meaningful terms that can be used to accurately predict and explain binding affinities for a well-defined subset of PPIs that are characterized by (1) crystallographic coordinates, (2) rigid-body association, (3) normal interface size, and hydrophobicity and hydrophilicity, and (4) high quality experimental binding affinity measurements. We further propose that the four-term scoring function could be regarded as a core expression for future development into a more general PPI scoring function. Our work has clear implications for PPI modeling and structure-based drug design.

PeptideNavigator: An interactive tool for exploring large and complex data sets generated during peptide-based drug design projects.

There is growing interest in peptide-based drug design and discovery. Due to their relatively large size, polymeric nature, and chemical complexity, the design of peptide-based drugs presents an interesting "big data" challenge. Here, we describe an interactive computational environment, PeptideNavigator, for naturally exploring the tremendous amount of information generated during a peptide drug design project. The purpose of PeptideNavigator is the presentation of large and complex experimental and computational data sets, particularly 3D data, so as to enable multidisciplinary scientists to make optimal decisions during a peptide drug discovery project. PeptideNavigator provides users with numerous viewing options, such as scatter plots, sequence views, and sequence frequency diagrams. These views allow for the collective visualization and exploration of many peptides and their properties, ultimately enabling the user to focus on a small number of peptides of interest. To drill down into the details of individual peptides, PeptideNavigator provides users with a Ramachandran plot viewer and a fully featured 3D visualization tool. Each view is linked, allowing the user to seamlessly navigate from collective views of large peptide data sets to the details of individual peptides with promising property profiles. Two case studies, based on MHC-1A activating peptides and MDM2 scaffold design, are presented to demonstrate the utility of PeptideNavigator in the context of disparate peptide-design projects.

Efficient synthesis of α-fluoromethylhistidine di-hydrochloride and demonstration of its efficacy as a glutathione S-transferase inhibitor.

Histidine decarboxylase (HDC) is an enzyme that converts histidine to histamine. Inhibition of HDC has several medical applications, and HDC inhibitors are of considerable interest for the study of histidine metabolism. (S)-α-Fluoromethylhistidine di-hydrochloride (α-FMH) is a potent HDC inhibitor that is commercially available at high cost in small amounts only. Here we report a novel, inexpensive, and efficient method for synthesis of α-FMH using methyl 2-aziridinyl-3-(N-triphenylmethyl-4-imidazolyl) propionate and HF/pyridine, with experimental yield of 57%. To identify novel targets for α-FMH, we developed a three step in silico work-flow for identifying physically plausible protein targets. The work-flow resulted in 21 protein target hits, including several enzymes involved in glutathione metabolism, and notably, two isozymes of the glutathione S-transferase (GST) superfamily, which plays a central role in drug metabolism. In view of this predictive data, the efficacy of α-FMH as a GST inhibitor was investigated in vitro. α-FMH was demonstrated to be an effective inhibitor of GST at micromolar concentration, suggesting that off-target effects of α-FMH may limit physiological drug metabolism and elimination by GST-dependent mechanisms. The present study therefore provides new avenues for obtaining α-FMH and for studying its biochemical effects, with potential implications for drug development.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

A combined cheminformatic and bioinformatic approach to address the proteolytic stability challenge in peptide-based drug discovery.

We have created models to predict cleavage sites for several human proteases including caspase-1, caspase-3, caspase-6, caspase-7, cathepsin B, cathepsin D, cathepsin G, cathepsin K, cathepsin L, elastase-2, granzyme A, granzyme B, matrix metallopeptidase-2 (MMP2), MMP7, MMP9, thrombin, and trypsin-1. Rather than representing the sequence pattern around the potential cleavage site through a series of flags with each flag representing one of the 20 standard amino acids, we first represent each amino acid by its calculated properties. For these calculated properties, we use validated cheminformatic descriptors, such as molecular weight, logP, and polar surface area, of the individual amino acids. Finally, the cleavage site-specific descriptors are calculated through various combinations of the individual amino acid descriptors for the residues surrounding the cleavage site. Some of these combinations do not take into account the location of the residue, as long as it is in a prescribed neighborhood of the potential cleavage site, whereas others are sensitive to the precise order of the residues in the sequence. The key advantage of this approach is that it allows one to perform meaningful calculations with nonstandard amino acids for which little or no data exists. Finally, using both docking and molecular dynamics simulations, we examine the potential for and limitations of protease crystal structures to impact the design of proteolytically stable peptides.

Advances in the prediction of protein-peptide binding affinities: implications for peptide-based drug discovery.

Peptides hold great promise as novel medicinal and biologic agents, and computational methods can help unlock that promise. In particular, structure-based peptide design can be used to identify and optimize peptide ligands. Successful structure-based design, in turn, requires accurate and fast methods for predicting protein-peptide binding affinities. Here, we review the development of such methods, emphasizing structure-based methods that assume rigid-body association and the single-structure approximation. We also briefly review recent applications of computational free energy prediction methods to enable and guide novel peptide drug and biomarker discovery. We close the review with a brief perspective on the future of computational, structure-based protein-peptide binding affinity prediction.

Recent work in the development and application of protein-peptide docking.

Interest in the development of novel peptide-based drugs is growing. There is, thus, a pressing need for the development of effective methods to enable novel peptide-based drug discovery. A cogent case can be made for the development and application of computational or in silico methods to assist with peptide discovery. In particular, there is a need for the development of effective protein-peptide docking methods. Here, recent work in the area of protein-peptide docking method development is reviewed and several drug-discovery projects that benefited from protein-peptide docking are discussed. In the present review, special attention is given to the search and scoring problems, the use of peptide docking to enable hit identification, and the use of peptide docking to help rationalize experimental results, and generate and test structure-based hypotheses. Finally, some recommendations are made for improving the future development and application of protein-peptide docking.

Computational prediction and analysis of the DR6-NAPP interaction.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that involves a devastating clinical course and that lacks an effective treatment. A biochemical model for neuronal development, recently proposed by Nikolaev et al., that may also have implications for AD, hinges on a novel protein­protein interaction between the death cell receptor 6 (DR6) ectodomain and an Nterminal fragment of amyloid precursor protein (NAPP), specifically, the growth factor-like domain of NAPP (GFD NAPP). Given all of this, we used a pure computational work-flow to dock a binding competent homology model of the DR6 ectodomain to a binding competent crystal structure of GFD NAPP. The DR6 homology model was built according to a template supplied by the neurotrophin p75 receptor. The best docked model was selected according to an empirical estimate of the binding affinity and represents a high quality model of probable structural accuracy, especially with respect to the residue-level contribution of GFD NAPP. The final model was tested and verified against a variety of biophysical and theoretical data sets. Particularly, worth noting is the excellent observed agreement between the theoretically calculated DR6­GFD NAPP binding free energy and the experimental quantity. The model is used to provide a satisfying structural and energetic interpretation of DR6­GFD NAPP binding and to suggest the possibility of and a mechanism for spontaneous apoptosis. The evidence suggests that the DR6­NAPP model proposed here is of probable accuracy and that it will prove useful in future studies, modeling work, and structure-based AD drug design.

Continued development of an empirical function for predicting and rationalizing protein-protein binding affinities.

Here we summarize recent work on the continued development of our fast and simple empirical equation for predicting and structurally rationalizing protein-protein and protein-peptide binding affinities. Our empirical expression consists of six regression-weighted physical descriptors and derives from two key simplifying assumptions: (1) the assumption of rigid-body association and (2) the assumption that all contributions not explicitly considered in the equation make a net contribution to binding of approximately 0 kcal. Within the strict framework of rigid-body association, we tested relative binding affinity predictions using our empirical equation against the corresponding experimental binding free energy data for 197 interface alanine mutants. Our methodology produced excellent agreement between prediction and experiment for 79% of the mutations considered. These encouraging results further suggest the basic validity of our approach. Further analysis suggests that the majority of the failed predictions can be accounted for in terms of mutation induced violations of assumption (2). In particular, we hypothesize that assumed away charge and aromatic side chain-mediated electrostatic interface interactions play a key role in protein-protein recognition and that such interactions must be explicitly considered for a more generally valid approach to physics-based binding affinity prediction.

Development and validation of an empirical free energy function for calculating protein-protein binding free energy surfaces.

In a previous paper, we described a novel empirical free energy function that was used to accurately predict experimental binding free energies for a diverse test set of 31 protein-protein complexes to within approximately 1.0 kcal. Here, we extend that work and show that an updated version of the function can be used to (1) accurately predict native binding free energies and (2) rank crystallographic, native-like and non-native binding modes in a physically realistic manner. The modified function includes terms designed to capture some of the unfavorable interactions that characterize non-native interfaces. The function was used to calculate one-dimensional binding free energy surfaces for 21 protein complexes. In roughly 90% of the cases tested, the function was used to place native-like and crystallographic binding modes in global free energy minima. Our analysis further suggests that buried hydrogen bonds might provide the key to distinguishing native from non-native interactions. To the best of our knowledge our function is the only one of its kind, a single expression that can be used to accurately calculate native and non-native binding free energies for a large number of proteins. Given the encouraging results presented in this paper, future work will focus on improving the function and applying it to the protein-protein docking problem.

A novel empirical free energy function that explains and predicts protein-protein binding affinities.

A free energy function can be defined as a mathematical expression that relates macroscopic free energy changes to microscopic or molecular properties. Free energy functions can be used to explain and predict the affinity of a ligand for a protein and to score and discriminate between native and non-native binding modes. However, there is a natural tension between developing a function fast enough to solve the scoring problem but rigorous enough to explain and predict binding affinities. Here, we present a novel, physics-based free energy function that is computationally inexpensive, yet explanatory and predictive. The function results from a derivation that assumes the cost of polar desolvation can be ignored and that includes a unique and implicit treatment of interfacial water-bridged interactions. The function was parameterized on an internally consistent, high quality training set giving R2=0.97 and Q2=0.91. We used the function to blindly and successfully predict binding affinities for a diverse test set of 31 wild-type protein-protein and protein-peptide complexes (R2=0.79, rmsd=1.2 kcal mol(-1)). The function performed very well in direct comparison with a recently described knowledge-based potential and the function appears to be transferable. Our results indicate that our function is well suited for solving a wide range of protein/peptide design and discovery problems.