There was a time when an essential part of most chemistry experiments involved glass blowing, but no longer. Stockrooms full of standardized flasks, condensers, connectors, and tubing have allowed chemists to focus a little more on chemistry and a little less on plumbing. While it’s wonderful when scientific breakthroughs allow us to take great strides, we also move forward, inch by inch, when we remove small burdens or simplify some routine task. When preparing for a computational experiment on a macromolecular complex, one common task is to segregate the components of the complex by their functional roles, keeping those we require—say, protein, cofactor, and substrate—and discarding the rest—detergent or water. It’s not particularly difficult, just a few minutes with a molecular visualizer/editor and we’re done. But while it may be quick and easy, it becomes painful when it needs to be done 50 times. Or 1,000. How hard can this be to automate? Well, it’d be trivial if a component’s functional roles were unambiguously specified. In some companies, for example, the substrate in an internally produced structure is always given the name LIG, or something along those lines. On the other hand, in structures from the Protein Databank, all small molecules are classified as ligands—nothing distinguishes the substrate from the detergent or the cofactor. There are a myriad of cases where the trivial becomes a head scratcher for anyone trying to automate this seemingly simple task. It’s a real shame that all that valuable information about functional roles was typically not captured by the authors of the structures we use, but we have to deal with the world as we find it. Because OpenEye felt it was worth the effort, I have recently spent a good bit of time chewing over the problem to come up with OESplitMolComplex() and related functions to automate this task (available in the June 2015 OEChem toolkit). The API is flexible enough to address a wide range of use cases while remaining simple. The prototype has been part of SiteHopper for more than a year. At the core of this work is the recognition that the number of substrates will continue to grow as long as there are medicinal chemists, but the number of cofactors, buffers, and solvents will grow much more slowly. Until the day each substrate is clearly marked, a good guess can be made by considering the molecules that aren’t proteins, cofactors, buffers, or solvents. Of course, there is no single “correct” solution to problems of this sort; other people might go about things differently. Our approach is primarily geared towards substrates of interest to pharmaceutical research. A side benefit is that it also allows us to count binding sites, distinguish monomeric and multimeric binding sites, identify apo-proteins, and extract covalent ligands from a protein. We’ve just begun to work with this API; our lists of cofactors, buffers, etc. will need to be expanded, and there will always be troublesome cases to work on. But this approach has already simplified a growing list of tasks, allowing us to focus more on the chemistry than on the plumbing.
Maven is a tool that manages Java projects. In addition to other features, it enforces requirements, manages dependencies, and defines artifact creation. This guide explains how to use Maven with OpenEye jars. To get familiar with Maven, please read the 5 Minute Guide and the Getting Started Guide. The rest of this article assumes that Java and Maven are installed and working. It will be helpful to follow the sample HelloMaven project, available at
https://github.com/oess/HelloMaven, while reading this guide.
How can we more quickly and efficiently extract the rich complexity of information and knowledge embodied in the three-dimensional structure of a protein-ligand complex? In the February 2015 toolkit release OpenEye extends the ability to represent complex, three-dimensional protein-ligand structures in two dimensions with the deployment of Ligand Depiction in Proteins, LDiP 1.0. This will save medicinal chemists and protein biophysicists countless hours staring at lists of complexes in 3D molecule viewers, instead enabling them to focus quickly on key compounds, key interactions or key properties.
We have moved our documentation landing page to a more memorable address http://docs.eyesopen.com. It features a fresh landing page and should deliver our content in double time. Your current bookmarks will still work but we recommend using http://docs.eyesopen.com as your number one resource for our applications and toolkits. The popular Python Cookbook has some additional examples which highlight useful functionality and new features: Visualizing Electron Density, by popular customer request. Highlight Fragments, using our new OEMedChem TK. Depicting CSV, using the new ability to read CSV in OEChem 2.0. Happy reading!
Welcome to OpenEye’s new blog site. We’ve made a number of changes and improvements to the site including separating the site into two main sections. The first section is Ant’s Rants (of course) where Anthony will continue to provide his commentary on larger issues affecting the industry as a whole. The second section is the general company blog (which is where you are now) that we are opening up to a larger body of contributors within the company. You can expect to see more regular posting here on a wide variety of topics of interest to the company and our users. Check back often (or subscribe to the RSS feeds below) to stay informed and learn more about what we’re doing! Ant’s Rants RSS OpenEye Blog RSS
Just recently, Swann et al. of Abbott Labs published “A Unified, Probabilistic Framework for Structure- and Ligand-Based Virtual Screening” in the Journal of Medicinal Chemistry. If you haven’t read it yet, I highly recommend it. The paper is a very interesting extension of previous work done by Muchmore et al., also of Abbott Labs. Muchmore’s paper, “Application of Belief Theory to Similarity Data Fusion for Use in Analog Searching and Lead Hopping,” presented a system for calculating a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity based on ligand similarity. Swann’s paper extends this work to include information obtained from structure-based virtual screening using docking. He focuses primarily on three metrics in the paper: the CGO score from the FRED docking program, the combined shape and color Tanimoto scores from ROCS, and the 2D fingerprint similarity calculated using ECFP6. This is remarkable because they have created a unified and extensible system that effectively combines both structure- and ligand-based information in a meaningful way to assign probabilities of equipotency between any two molecules.
I am frequently being asked by our users whether OpenEye’s licensing model allows them to run OpenEye software in the cloud. As this appears to be a common concern and a potential legal stumbling block for many groups, I want to make sure that our answer is unambiguous and clarified here.
The short answer to this question is YES!