Modeling Organorhodium Catalysis

The first project considered in this dissertation was the improvement of an existing global optimization algorithm that uses extended dimensionality to find global minima of Lennard-Jones clusters. The speed of this algorithm was increased by three orders of magnitude, primarily by improving the algorithm for compressing the system from 4D to 3D at constant energy. The second project was modeling the adsorption of H2 molecules on the Si(100) surface using density functional theory (DFT) with the PW91 functional. Consistent with the experiments, the calculations predicted an energetic preference for clustering of occupied sites in a dimer row. However, our calculations did not verify the unbuckling induced by H2 adsorption reported by Buehler and Boland. The third project was modeling molybdenum and rhodium-catalyzed [2 + 2 + 1] cycloisomerization reaction using DFT with the B3LYP functional. We found that in the rhodium-catalyzed [2 + 2 + 1] cycloisomerization reactions of allenes the oxidative addition step determined both the rate and the product of the reaction. For the molybdenum-catalyzed reaction the rate was controlled not by oxidative addition, but by the next step, the attachment of a carbon monoxide molecule from the media to the molybdenum atom. The fourth project was modeling the transfer of hydrogen from one side of the heterocyclic ring to another in rhodium(I) catalyzed allenic Pauson-Khand type reactions. Our calculations showed that this process occurs after the cyclization step. We have also discovered a novel mechanism for this process - hydride transfer; however, we believe that in most cases the reaction proceeds by beta-hydride elimination

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

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

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 \u27undruggable\u27 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 \u27drug-like\u27 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

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

Evaluating Free Energies of Binding and Conservation of Crystallographic Waters Using SZMAP

The SZMAP method computes binding free energies and the corresponding thermodynamic components for water molecules in the binding site of a protein structure [SZMAP, 1.0.0; OpenEye Scientific Software Inc.: Santa Fe, NM, USA, 2011]. In this work, the ability of SZMAP to predict water structure and thermodynamic stability is examined for the X-ray crystal structures of a series of protein–ligand complexes. SZMAP results correlate with higher-level replica exchange thermodynamic integration double decoupling calculations of the absolute free energy of bound waters in the test set complexes. In addition, SZMAP calculations show good agreement with experimental data in terms of water conservation (across multiple crystal structures) and B-factors over a subset of the test set. In particular, the SZMAP neutral entropy difference term calculated at crystallographic water positions within each of the complex structures correlates well with whether that crystallographic water is conserved or displaceable. Furthermore, the calculated entropy of the water probe relative to the continuum shows a significant degree of correlation with the B-factors associated with the oxygen atoms of the water molecules. Taken together, these results indicate that SZMAP is capable of quantitatively predicting water positions and their energetics and is potentially a useful tool for determining which waters to attempt to displace, maintain, or build in through water-mediated interactions when evolving a lead series during a drug discovery program

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