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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., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

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.

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., (© 2015 Wiley Periodicals, Inc.)

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.

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.)

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.

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.