Author Archives: Garrett

Experimental Binding Modes of Small Molecules in Protein-Ligand Docking

Protein-ligand docking tends to be very good at generating binding modes that resemble experimental binding modes from X-ray crystallography and other methods (assuming we have a high quality structure…); but it is also very good at generating plausible models for ligands that don’t bind. These so-called “false positives” lead to reduced accuracy in structure-based virtual screening campaigns.

Structure-based methods are not the only way of approaching virtual screening: when all we know is the chemical structure of an active molecule, but nothing about its target (or targets), we can use ligand-based virtual screening methods, which operate on the principle of molecular similarity (Maggiora et al., 2014).

But what if we combine both methods?

Continue reading →

Interesting Jupyter and IPython Notebooks

Here’s a treasure trove of interesting Jupyter and iPython notebooks, with lots of diverse examples relevant to OPIG, including an RDKit notebook, but also:

Entire books or other large collections of notebooks on a topic (covering Introductory Tutorials; Programming and Computer Science; Statistics, Machine Learning and Data Science; Mathematics, Physics, Chemistry, Biology; Linguistics and Text Mining; Signal Processing; Scientific computing and data analysis with the SciPy Stack; General topics in scientific computing; Machine Learning, Statistics and Probability; Physics, Chemistry and Biology; Data visualization and plotting; Mathematics; Signal, Sound and Image Processing; Natural Language Processing; Pandas for data analysis); General Python Programming; Notebooks in languages other than Python (Julia; Haskell; Ruby; Perl; F#; C#); Miscellaneous topics about doing various things with the Notebook itself; Reproducible academic publications; and lots more!  

 

The Emerging Disorder-Function Paradigm

It’s rare to find a paper that connects all of the diverse areas of research of OPIG, but “The rules of disorder or why disorder rules” by Gsponer and Babu (2009) is one such paper. Protein folding, protein-protein interaction networks, protein loops (Schlessinger et al., 2007), and drug discovery all play a part in this story. What’s great about this paper is that it gives numerous examples of proteins and the evidence supporting that they are partially or completely unstructured. These are the so-called intrinsically unstructured proteins or IUPs, although more recently they are also being referred to as intrinsically disordered proteins, or IDPs. Intrinsically disordered regions (IDRs) “are polypeptide segments that do not contain sufficient hydrophobic amino acids to mediate co-operative folding” (Babu, 2016).

Such proteins contradict the classic “lock and key” hypothesis of Fischer, and challenge Continue reading →

Viewing 3D molecules interactively in Jupyter iPython notebooks

Greg Landrum, curator of the invaluable open source cheminformatics API, RDKit, recently blogged about viewing molecules in a 3D window within a Jupyter-hosted iPython notebook (as long as your browser supports WebGL, that is).

The trick is to use py3Dmol. It’s easy to install:

pip install py3Dmol

This is built on the object-oriented, webGL based JavaScript library for online molecular visualization 3Dmol.js (Rego & Koes, 2015); here's a nice summary of the capabilities of 3Dmol.js. It's features include:

  • support for pdb, sdf, mol2, xyz, and cube formats
  • parallelized molecular surface computation
  • sphere, stick, line, cross, cartoon, and surface styles
  • atom property based selection and styling
  • labels
  • clickable interactivity with molecular data
  • geometric shapes including spheres and arrows

I tried a simple example and it worked beautifully:

import py3Dmol
view = py3Dmol.view(query='pdb:1hvr')
view.setStyle({'cartoon':{'color':'spectrum'}})
view

py3dmol_in_jupyter_ipython

The 3Dmol.js website summarizes how to view molecules, along with how to choose representations, how to embed it, and even how to develop with it.

References

Nicholas Rego & David Koes (2015). “3Dmol.js: molecular visualization with WebGL”.
Bioinformatics, 31 (8): 1322-1324. doi:10.1093/bioinformatics/btu829

The Protein World

This week’s issue of Nature has a wonderful “Insight” supplement titled, “The Protein World” (Vol. 537 No. 7620, pp 319-355). It begins with an editorial from Joshua Finkelstein, Alex Eccleston & Sadaf Shadan (Nature, 537: 319, doi:10.1038/537319a), and introduces four reviews, covering:

  • the computational de novo design of proteins that spontaneously fold and assemble into desired shapes (“The coming of age of de novo protein design“, by Po-Ssu Huang, Scott E. Boyken & David Baker, Nature, 537: 320–327, doi:10.1038/nature19946). Baker et al. point out that much of protein engineering until now has involved modifying naturally-occurring proteins, but assert, “it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology”;
  • the cellular proteome is a dynamic structural and regulatory network that constantly adapts to the needs of the cell—and through genetic alterations, ranging from chromosome imbalance to oncogene activation, can become imbalanced due to changes in speed, fidelity and capacity of protein biogenesis and degradation systems. Understanding these complex systems can help us to develop better ways to treat diseases such as cancer (“Proteome complexity and the forces that drive proteome imbalance“, by J. Wade Harper & Eric J. Bennett, Nature, 537: 328–338, doi:10.1038/nature19947);
  • the new challenger to X-ray crystallography, the workhorse of structural biology: cryo-EM. Cryo-electron microscopy has undergone a renaissance in the last 5 years thanks to new detector technologies, and is starting to give us high-resolution structures and new insights about processes in the cell that are just not possible using other techniques (“Unravelling biological macromolecules with cryo-electron microscopy“, by Rafael Fernandez-Leiro & Sjors H. W. Scheres, Nature, 537: 339–346, doi:10.1038/nature19948); and
  • the growing role of mass spectrometry in unveiling the higher-order structures and composition, function, and control of the networks of proteins collectively known as the proteome. High resolution mass spectrometry is helping to illuminate and elucidate complex biological processes and phenotypes, to “catalogue the components of proteomes and their sites of post-translational modification, to identify networks of interacting proteins and to uncover alterations in the proteome that are associated with diseases” (“Mass-spectrometric exploration of proteome structure and function“, by Ruedi Aebersold & Matthias Mann, Nature, 537: 347–355, doi:10.1038/nature19949).

Baker points out that the majority of de novo designed proteins consist of a single, deep minimum energy state, and that we have a long way to go to mimic the subtleties of naturally-occurring proteins: things like allostery, signalling, and even recessed binding pockets for small moleculecules, functional sites, and hydrophobic binding interfaces present their own challenges. Only by increasing our understanding, developing better models and computational tools, will we be able to accomplish this.

Comp Chem Kitchen

I recently started  “Comp Chem Kitchen” with Richard Cooper and Rob Paton in the Department of Chemistry here in Oxford. It’s a regular forum and seminar series  for molecular geeks and hackers, in the original, untarnished sense of the word: people using and developing computational methods to tackle problems in chemistry, biochemistry and drug discovery. Our hope is that we will share best practices, even code snippets and software tools, and avoid re-inventing wheels.

In addition to local researchers, we invite speakers from industry and non-profits from time to time, and occasionally organize software demos and tutorials.

We also provide refreshments including free beer. (We are grateful to Prof. Phil Biggin and the MRC Proximity to Discovery Fund for offering to support CCK.)

CCK-1

Our first meeting, CCK-1,  was held in the Abbot’s Kitchen on May 24, 2016, at 5 pm, and was a great success—standing room only, in fact! The Abbot’s Kitchen—originally a laboratory—is a beautiful stone building built in 1860 in the Victorian Gothic style, alongside the Natural History Museum, at a time when Chemistry was first recognized as a discipline.

Abbot's Kitchen, Oxford, Watercolor, Detailed, 512x683We heard a fascinating talk from Jerome Wicker from the Department of Chemistry who spoke about “Machine learning for classification of solid form data extracted from CSD and ZINC”, and described a method that could successfully discriminate (~80%) whether a small molecule would crystallize or not. The software tools he discussed included RDKit, CSD, and scikit-learn. There were also two lightning talks, each 5 minutes long, one from OPIG member Hannah Patel,  from the Department of Statistics, on “Novelty Score: Prioritising compounds that potentially form novel protein-ligand interactions and novel scaffolds using an interaction centric approach”, who briefly described her Django-based web interface to her RDKit-based tool to analyse structures of ligands bound to proteins and help guide future medicinal chemistry to find novel compounds. We also had a talk from Dr Michael Charlton from InhibOx spoke about “Antibacterial Drug Discovery and Machine Learning”.

CCK-2

Our next Comp Chem Kitchen, CCK-2, will be held next Tuesday (June 14th, 2016), and you can register free for CCK-2 here.

We will have talks from:

 

Hope to see you there!  (Did I say free beer?)

RDKit User Group Meeting 2016

If you’re interested in small molecules and cheminformatics, you might like to know that registration for the 2016 RDKit User Group Meeting is now open. The meeting will be held from the 26th-28th of October in Basel, Switzerland. From their announcement:

This year’s RDKit User Group Meeting will take place from 26-28 October at the Novartis Campus in Basel, Switzerland and is co-organized by people from Roche and Novartis.

Registration for the RDKit UGM is free:

https://www.eventbrite.com/e/5th-rdkit-user-group-meeting-tickets-22539677783

The previous years’ format seemed to work pretty well and the feedback was positive, so we will stick to the same format this year:

Days 1 and 2: Talks, lightning talks, roundtable(s), discussion, and talktorials. For those who haven’t attended before, talktorials are somewhere between a talk and a tutorial, they cover something interesting done with the RDKit and include the code used to do the work. During the presentation you’ll give an overview of what you did and also show the pieces of the code that are central to the work. The idea is to mix the science up with the tutorial aspects.

Day 3 will be a sprint: those who choose to stay will spend an intense day working in small groups to produce useful artifacts: new bits of code, KNIME nodes, KNIME workflows, tutorials, documentation, IPython notebooks, etc. We will once again try to structure this a bit by collecting a bunch of ideas for things to work on in advance.

There will also be, of course, social activities.

Looks like fun!

Advances in Conformer Generation: ETKDG and ETDG

Predicting the possible shapes a small molecule can adopt is essential to understanding its chemistry and the possible biological roles of candidate drugs. Thus, conformer generation, the process of converting a topological description of a molecule into a set of 3D positions of its constituent atoms, is an essential component of computational drug discovery.

A former member of OPIG, Dr Jean-Paul Ebejer, myself, and Prof. Charlotte Deane, compared the ability of freely-available conformer generation methods:

“(i) to identify which tools most accurately reproduce experimentally determined structures;
(ii) to examine the diversity of the generated conformational set; and
(iii) to benchmark the computational time expended.”

in Ebejer et al., 2012.  JP assembled a set of 708 crystal structures of drug-like molecules from the OMEGA validation set and the Astex Diverse Set with which to test the various methods. We found that RDKit, combining its Distance Geometry (DG) algorithm with energy minimization  using the MMFF94 force field proved to be a “valid free alternative to commercial, proprietary software”, and was able to generate “a diverse and representative set of conformers which also contains a close conformer to the known structure”.

Following on from our work at InhibOx, and building on the same benchmark set JP assembled, Greg Landrum and Sereina Riniker recently described (Riniker & Landrum, 2015) two new conformer generation methods, ETDG and ETKDG, that improve upon the classical distance geometry (DG) algorithm. They do this by combining DG with knowledge of preferred torsional angles derived from experimentally determined crystal structures (ETDG), and also by further adding constraints from chemical knowledge, such as ‘aromatic rings are be flat’, or ‘bonds connected to triple bonds are colinear’ (ETKDG). They compared DG, ETDG, ETKDG, and a knowledge-based method, CONFECT, and found:

“ETKDG was found to outperform standard DG and the knowledge-based conformer generator CONFECT in reproducing crystal conformations from both small-molecule crystals (CSD data set) and protein−ligand complexes (PDB data set). With ETKDG, 84% of a set of 1290 small-molecule crystal structures from the CSD could be reproduced within an RMSD of 1.0 Å and 38% within an RMSD of 0.5 Å. The experimental torsional-angle preferences or the K terms alone each performed better than standard DG but were not sufficient to obtain the full performance of ETKDG.

Comparison of ETKDG with the DG conformers optimized using either the Universal Force Field (UFF) or the Merck Molecular Force Field (MMFF) showed different results for the two data sets. While FF-optimized DG performed better on the CSD data set, the two approaches were comparable for the PDB data set.”

They also showed (Fig. 13) that their ETKDG method was faster than DG followed by energy minimization, but not quite as accurate in reproducing the crystal structure.

ETKDG takes 3 times as long as DG. The addition of the K terms, i.e., generating ETKDG embeddings instead of ETDG embeddings, increases runtime by only 10% over ETDG (results not shown). Despite the longer runtime per conformer, ETKDG requires on average one-quarter of the number of conformers to achieve performance similar to DG (Figure S12 and Table S4 in the Supporting Information). This results in a net performance improvement, at least when it comes to reproducing crystal conformers.

As measured by performance in reproducing experimental crystal structures, ETKDG is a viable alternative to plain DG followed by a UFF-optimization, so it is of interest how their runtimes compare. Figure 13 (right) plots the runtime for ETKDG versus the runtime for DG + UFF-optimization. The median ratio of the DG + UFF optimization and ETKDG runtimes is 1.97, i.e., DG + UFF optimization takes almost twice as long as ETKDG. Thus, although ETKDG is significantly slower than DG on a per-conformer basis, when higher-quality conformations are required it can provide structures that are the equivalent of those obtained using DG + UFF-optimization in about half the time.

ETKDG looks like a great addition to the RDKit toolbox for conformer generation (and it was great to see JP thanked in the Acknowledgments!).

 

References

Ebejer, J. P., G. M. Morris and C. M. Deane (2012). “Freely available conformer generation methods: how good are they?” J Chem Inf Model, 52(5): 1146-1158. 10.1021/ci2004658.

Riniker, S. and G. A. Landrum (2015). “Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation.” J Chem Inf Model, 55(12): 2562-2574. 10.1021/acs.jcim.5b00654.

Network Pharmacology

The dominant paradigm in drug discovery has been one of finding small molecules (or more recently, biologics) that bind selectively to one target of therapeutic interest. This reductionist approach conveniently ignores the fact that many drugs do, in fact, bind to multiple targets. Indeed, systems biology is uncovering an unsettling picture for comfortable reductionists: the so-called ‘magic bullet’ of Paul Ehrlich, a single compound that binds to a single target, may be less effective than a compound with multiple targets. This new approach—network pharmacology—offers new ways to improve drug efficacy, to rescue orphan drugs, re-purpose existing drugs, predict targets, and predict side-effects.

Building on work Stuart Armstrong and I did at InhibOx, a spinout from the University of Oxford’s Chemistry Department, and inspired by the work of Shoichet et al. (2007), Álvaro Cortes-Cabrera and I took our ElectroShape method, designed for ultra-fast ligand-based virtual screening (Armstrong et al., 2010 & 2011), and built a new way of exploring the relationships between drug targets (Cortes-Cabrera et al., 2013). Ligand-based virtual screening is predicated on the molecular similarity principle: similar chemical compounds have similar properties (see, e.g., Johnson & Maggiora, 1990). ElectroShape built on the earlier pioneering USR (Ultra-fast Shape Recognition) work of Pedro Ballester and Prof. W. Graham Richards at Oxford (Ballester & Richards, 2007).

Our new approach addressed two Inherent limitations of the network pharmacology approaches available at the time:

  • Chemical similarity is calculated on the basis of the chemical topology of the small molecule; and
  • Structural information about the macromolecular target is neglected.

Our method addressed these issues by taking into account 3D information from both the ligand and the target.

The approach involved comparing the similarity of each set ligands known to bind to a protein, to the equivalent sets of ligands of all other known drug targets in DrugBank, DrugBank is a tremendous “bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.” This analysis generated a network of related proteins, connected by the similarity of the sets of ligands known to bind to them.

2013.ElectroShapePolypharmacologyServerWe looked at two different kinds of ligand similarity metrics, the inverse Manhattan distance of our ElectroShape descriptor, and compared them to 2D Morgan fingerprints, calculated using the wonderful open source cheminformatics toolkit, RDKit from Greg Landrum. Morgan fingerprints use connectivity information similar to that used for the well known ECFP family of fingerprints, which had been used in the SEA method of Keiser et al. We also looked at the problem from the receptor side, comparing the active sites of the proteins. These complementary approaches produced networks that shared a minimal fraction (0.36% to 6.80%) of nodes: while the direct comparison of target ligand-binding sites could give valuable information in order to achieve some kind of target specificity, ligand-based networks may contribute information about unexpected interactions for side-effect prediction and polypharmacological profile optimization.

Our new target-fishing approach was able to predict drug adverse effects, build polypharmacology profiles, and relate targets from two complementary viewpoints:
ligand-based, and target-based networks. We used the DUD and WOMBAT benchmark sets for on-target validation, and the results were directly comparable to those obtained using other state-of-the-art target-fishing approaches. Off-target validation was performed using a limited set of non-annotated secondary targets for already known drugs. Comparison of the predicted adverse effects with data contained in the SIDER 2 database showed good specificity and reasonable selectivity. All of these features were implemented in a user-friendly web interface that: (i) can be queried for both polypharmacology profiles and adverse effects, (ii) links to related targets in ChEMBLdb in the three networks (2D, 4D ligand and 3D receptor), and (iii) displays the 2D structure of already annotated drugs.

2013.ElectroShapePolypharmacologyServer.Screenshot

References

Armstrong, M. S., G. M. Morris, P. W. Finn, R. Sharma, L. Moretti, R. I. Cooper and W. G. Richards (2010). “ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics.” J Comput Aided Mol Des, 24(9): 789-801. 10.1007/s10822-010-9374-0.

Armstrong, M. S., P. W. Finn, G. M. Morris and W. G. Richards (2011). “Improving the accuracy of ultrafast ligand-based screening: incorporating lipophilicity into ElectroShape as an extra dimension.” J Comput Aided Mol Des, 25(8): 785-790. 10.1007/s10822-011-9463-8.

Ballester, P. J. and W. G. Richards (2007). “Ultrafast shape recognition to search compound databases for similar molecular shapes.” J Comput Chem, 28(10): 1711-1723. 10.1002/jcc.20681.

Cortes-Cabrera, A., G. M. Morris, P. W. Finn, A. Morreale and F. Gago (2013). “Comparison of ultra-fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology.” Br J Pharmacol, 170(3): 557-567. 10.1111/bph.12294.

Johnson, A. M., & G. M. Maggiora (1990). “Concepts and Applications of Molecular Similarity.” New York: John Willey & Sons.

Landrum, G. (2011). “RDKit: Open-source cheminformatics.” from http://www.rdkit.org.

Keiser, M. J., B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin and B. K. Shoichet (2007). “Relating protein pharmacology by ligand chemistry.” Nat Biotechnol, 25(2): 197-206. 10.1038/nbt1284.

Wishart, D. S., C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang and J. Woolsey (2006). “DrugBank: a comprehensive resource for in silico drug discovery and exploration.” Nucleic Acids Res, 34(Database issue): D668-672. 10.1093/nar/gkj067.