New Haven, CT, United States
New Haven, CT, United States
SEARCH FILTERS
Time filter
Source Type

Audie J.,CMD Bioscience | Audie J.,American International College
Current Pharmaceutical Design | Year: 2010

The case for peptide-based drugs is compelling. Due to their chemical, physical and conformational diversity, and relatively unproblematic toxicity and immunogenicity, peptides represent excellent starting material for drug discovery. Nature has solved many physiological and pharmacological problems through the use of peptides, polypeptides and proteins. If nature could solve such a diversity of challenging biological problems through the use of peptides, it seems reasonable to infer that human ingenuity will prove even more successful. And this, indeed, appears to be the case, as a number of scientific and methodological advances are making peptides and peptide-based compounds ever more promising pharmacological agents. Chief among these advances are powerful chemical and biological screening technologies for lead identification and optimization, methods for enhancing peptide in vivo stability, bioavailability and cell-permeability, and new delivery technologies. Other advances include the development and experimental validation of robust computational methods for peptide lead identification and optimization. Finally, scientific analysis, biology and chemistry indicate the prospect of designing relatively small peptides to therapeutically modulate so-called 'undruggable' protein-protein interactions. Taken together a clear picture is emerging: through the synergistic use of the scientific imagination and the computational, chemical and biological methods that are currently available, effective peptide therapeutics for novel targets can be designed that surpass even the proven peptidic designs of nature. © 2010 Bentham Science Publishers Ltd.


Audie J.,CMD Bioscience | Audie J.,Sacred Heart University at Connecticut | Swanson J.,ChemModeling LLC
Chemical Biology and Drug Design | Year: 2013

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. © 2012 John Wiley & Sons A/S.


Diller D.J.,CMD Bioscience | Swanson J.,ChemModeling LLC | Bayden A.S.,CMD Bioscience | Jarosinski M.,CMD Bioscience | And 2 more authors.
Future Medicinal Chemistry | Year: 2015

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. © 2015 Future Science Ltd.


PubMed | ChemModeling LLC and CMD Bioscience
Type: Journal Article | Journal: Future medicinal chemistry | Year: 2015

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.


Ponomarev S.Y.,CMD Bioscience | Audie J.,CMD Bioscience | Audie J.,Sacred Heart University at Connecticut
Proteins: Structure, Function and Bioinformatics | Year: 2011

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 protein-protein interaction between the death cell receptor 6 (DR6) ectodomain and an N-terminal 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 DR6-GFD NAPP binding free energy and the experimental quantity. The model is used to provide a satisfying structural and energetic interpretation of DR6-GFD NAPP binding and to suggest the possibility of and a mechanism for spontaneous apoptosis. The evidence suggests that the DR6-NAPP model proposed here is of probable accuracy and that it will prove useful in future studies, modeling work, and structure-based AD drug design. © 2010 Wiley-Liss, Inc.


Bayden A.S.,Astrazeneca | Bayden A.S.,CMD Bioscience | Moustakas D.T.,Astrazeneca | Moustakas D.T.,Alkermes | And 3 more authors.
Journal of Chemical Information and Modeling | Year: 2015

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. (Figure Presented). © 2015 American Chemical Society.


Diller D.J.,Snowdon Inc. | Diller D.J.,CMD Bioscience | Connell N.D.,Rutgers University | Welsh W.J.,Rutgers University
Journal of Computer-Aided Molecular Design | Year: 2015

This report introduces a new ligand-based virtual screening tool called Avalanche that incorporates both shape- and feature-based comparison with three-dimensional (3D) alignment between the query molecule and test compounds residing in a chemical database. Avalanche proceeds in two steps. The first step is an extremely rapid shape/feature based comparison which is used to narrow the focus from potentially millions or billions of candidate molecules and conformations to a more manageable number that are then passed to the second step. The second step is a detailed yet still rapid 3D alignment of the remaining candidate conformations to the query conformation. Using the 3D alignment, these remaining candidate conformations are scored, re-ranked and presented to the user as the top hits for further visualization and evaluation. To provide further insight into the method, the results from two prospective virtual screens are presented which show the ability of Avalanche to identify hits from chemical databases that would likely be missed by common substructure-based or fingerprint-based search methods. The Avalanche method is extended to enable patent landscaping, i.e., structural refinements to improve the patentability of hits for deployment in drug discovery campaigns. © 2015 Springer International Publishing Switzerland.


PubMed | LLC Suite 101 and CMD Bioscience
Type: | Journal: Journal of biomolecular structure & dynamics | Year: 2016

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.


PubMed | CMD Bioscience
Type: Journal Article | Journal: Chemical biology & drug design | Year: 2012

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.


Loading CMD Bioscience collaborators
Loading CMD Bioscience collaborators