Biomedical subjects

Ryan G Coleman

Publications and source records attributed to Ryan G Coleman.

5 recordsLinked to original sources

PubMed2007

Structure-based maximal affinity model predicts small-molecule druggability.

Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.

Algorithms↗

PubMed2006

Protonation of excited state pyrene-1-carboxylate by phosphate and organic acids in aqueous solution studied by fluorescence spectroscopy.

Pyrene-1-carboxylic acid has a pK of 4.0 in the ground state and 8.1 in the singlet electronic excited state. In the pH range of physiological interest (pH approximately 5-8), the ground state compound is largely ionized as pyrene-1-carboxylate, but protonation of the excited state molecule occurs when a proton donor reacts with the carboxylate during the excited state lifetime of the fluorophore. Both forms of the pyrene derivatives are fluorescent, and in this work the protonation reaction was measured by monitoring steady-state and time-resolved fluorescence. The rate of protonation of pyrene-COO(-) by acetic, chloroacetic, lactic, and cacodylic acids is a function of DeltapK, as predicted by Marcus theory. The rate of proton transfer from these acids saturates at high concentration, as expected for the existence of an encounter complex. Trihydrogen-phosphate is a much better proton donor than dihydrogen- and monohydrogen-phosphate, as can be seen by the pH dependence. The proton-donating ability of phosphate does not saturate at high concentrations, but increases with increasing phosphate concentration. We suggest that enhanced rate of proton transfer at high phosphate concentrations may be due to the dual proton donating and accepting nature of phosphate, in analogy to the Grotthuss mechanism for proton transfer in water. It is suggested that in molecular structures containing multiple phosphates, such as membrane surfaces and DNA, proton transfer rates will be enhanced by this mechanism.

Acids↗

PubMed2006

Travel depth, a new shape descriptor for macromolecules: application to ligand binding.

Depth is a term frequently applied to the shape and surface of macromolecules, describing for example the grooves in DNA, the shape of an enzyme active site, or the binding site for a small molecule in a protein. Yet depth is a difficult property to define rigorously in a macromolecule, and few computational tools exist to quantify this notion, to visualize it, or analyze the results. We present our notion of travel depth, simply put the physical distance a solvent molecule would have to travel from a surface point to a suitably defined reference surface. To define the reference surface, we use the limiting form of the molecular surface with increasing probe size: the convex hull. We then present a fast, robust approximation algorithm to compute travel depth to every surface point. The travel depth is useful because it works for pockets of any size and complexity. It also works for two interesting special cases. First, it works on the grooves in DNA, which are unbounded in one direction. Second, it works on the case of tunnels, that is pockets that have no "bottom", but go through the entire macromolecule. Our algorithm makes it straightforward to quantify discussions of depth when analyzing structures. High-throughput analysis of macromolecule depth is also enabled by our algorithm. This is demonstrated by analyzing a database of protein-small molecule binding pockets, and the distribution of bound magnesium ions in RNA structures. These analyses show significant, but subtle effects of depth on ligand binding localization and strength.

Algorithms↗

PubMed2005

An intuitive approach to measuring protein surface curvature.

A natural way to measure protein surface curvature is to generate the least squares fitted (LSF) sphere to a surface patch and use the radius as the curvature measure. While the concept is simple, the sphere-fitting problem is not trivial and known means of protein surface curvature measurement use alternative schemes that are arguably less straightforward to interpret. We have developed an approach to solve the LSF sphere problem by turning the sphere-fitting problem into a solvable plane-fitting problem using a transformation known as geometric inversion. The approach works on any arbitrary surface patch, and returns a radius of curvature that has direct physical interpretation. Additionally, it is flexible in its ability to find the curvature of an arbitrary surface patch, and the "resolution" can be adjusted to highlight atomic features or larger features such as peptide binding sites. We include examples of applying the method to visualization of peptide recognition pockets and protein conformational change, as well as a comparison with a commonly used solid-angle curvature method showing that the LSF method produces more pronounced curvature results.

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PubMedDate not supplied

Structure-based identification of small molecule binding sites using a free energy model.

We separately have shown that the maximal druglike affinity of a given binding site on a protein can be calculated on the basis of the binding-site structure alone by using a desolvation-based free energy model along with the notion that druglike ligands fall into certain physiochemical property ranges. Here, we present an approach where we reformulate the calculated druggability affinity as an additive free energy to facilitate the searching of whole protein surfaces for druglike binding sites. The highest-scoring patches in many cases represent known ligand-binding sites for druggable targets, but not for difficult targets. This approach differs from other approaches in that it does not simply identify pockets with the greatest volume but instead identifies pockets that are likely to be amenable to druglike small-molecule binding. Combining the method with a functional residue prediction method called SCA (statistical coupling analysis) results in the prediction of potentially druggable allosteric binding sites on p38alpha kinase.

Allosteric Site↗