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.

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.

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.

A scientific and statistical analysis of accelerated aging for pharmaceuticals. Part 1: accuracy of fitting methods.

Three competing mathematical fitting models (a point-by-point estimation method, a linear fit method, and an isoconversion method) of chemical stability (related substance growth) when using high temperature data to predict room temperature shelf-life were employed in a detailed comparison. In each case, complex degradant formation behavior was analyzed by both exponential and linear forms of the Arrhenius equation. A hypothetical reaction was used where a drug (A) degrades to a primary degradant (B), which in turn degrades to a secondary degradation product (C).

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.

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.

SYBYL line notation (SLN): a single notation to represent chemical structures, queries, reactions, and virtual libraries.

SYBYL line notation (SLN) is a powerful way to represent molecular structures, reactions, libraries of structures, molecular fragments, formulations, molecular queries, and reaction queries. Nearly any chemical structure imaginable, including macromolecules, pharmaceuticals, catalysts, and even combinatorial libraries can be represented as an SLN string. The language provides a rich syntax for database queries comparable to SMARTS.

An optimal solution for enhancing ambulance safety: implementing a driver performance feedback and monitoring device in ground emergency medical service vehicles.

A prospective study was conducted to determine if emergency vehicle driver risk behavior could be improved with an onboard computer-monitoring device, with real time auditory feedback. Data were collected over 18 months from 36 vehicles in a metropolitan EMS group, with >250 drivers. In >1.