Computational analysis of novel drug opportunities (CANDO)
Discover new cures and treatments with us!
We have a developed a unique computational multitarget
fragment-based docking with dynamics protocol to implement a
comprehensive and efficient drug discovery pipeline with higher
efficiency, lowered cost, and increased success rates, compared to
We have applied this pipeline to evaluate how all FDA approved and
other human ingestible drugs (such as certain phenethylamines,
tryptamines, psychoactives, and dietary supplements) interact with all
protein structures in Homo sapiens and several pathogenic
species. Currently we have determined 3733 compound to 42,223 protein
structure interactions. The compound-proteome interaction signatures
are combined with pharmacalogical, physiological, and cheminformatics
data to predict new therapeutics through repurposing drugs already
approved for other indications. The top predictions are verified in
the laboratory and clinic by our collaborators.
The project represents an integration of our group's applied
research on therapeutic
discovery, building upon basic protein structure,
function, and interaction prediction research. Funding sources
include the National Institutes Health (specifically the 2010 NIH Director's Pioneer
Award), the National Science Foundation, the Kinship Foundation,
the University of Washington Technology Gap Innovation Fund, and the
Washington Research Foundation.
We are currently working with almost 30 collaborators throughout the world to find cures for over 20 indications/diseases. See a full list of our
indications and collaborators and some results in progress.
We have developed BINDNET, a novel method for predicting likely binding partners for a given ligand within a proteome of interest.
Drug discovery is protein folding with a compound.
This section is in progress. There's a lot of novelty to
this project, technically in terms of the methods used, and also in
terms of philosophy and paradigms employed (ergo, the reason for the
Director's Pioneer Award). Here are a few of them:
- Docking with protein structure + ligand dynamics.
- Automated binding site identification.
- Can be used to computationally assess new compounds from combinations of fragments (+).
- Using all the known information about current drug and drug like compounds.
- Learning from affinity measures separating entropy and enthalpy.
- Predict toxicity through nonspecific binding.
- Predict ligand-target networks.
- Fragmentation of drugs to identify pharmacophore.
- Drug comparisons to substrates and metabolites to find NCEs in the structural context of the binding site
- Drug profiles across multiple targets (not single drug per target paradigm).
- Molecular and systems level integration because of drug profile (i.e., how each compound interacts with the interactome).
- Exploiting the fact that all drug discovery thusfar has been a feature of Evolution.
- Consolidates almost all one off inhibitor discovery in one shotgun approach.
- Systems based drug discovery.
- New compounds (+) predicted to be nontoxic can be used to explore beyond the CANDO space for very intractable diseases.
- Can be used to create a system of existing and novel small molecules to manipulate living (and nonliving) systems
- If successful, it will move compbio frameworks forward unlike never before.
Ultimately the goal is personalisation to improve quality of life,
including personalised medicine. When I first came across genetics, my
dream was that each person would have their genome sequence and a
powerful computing cluster (these days, one
can get a personal supercomputer for ~$6000) where they could
evaluate the response of their proteins and proteomes (corresponding
to their specific genes and genomes) against entities in the
environment, such as bioactive chemical compounds, to improve their
quality of life, i.e., to treat and/or cure diseases as well develop
vaccines. This project is part of that dream and we're going to
rigourously evaluate whether it can come to fruition.
Even though everyone has a major responsibility, keep in mind that
there's a lot of overlap.
- Ram Samudrala - PIon.
- Andrew Ho - personalisation, individualised webbot.
- Brian Buttrick - function prediction for docking site identification.
- Gaurav Chopra - fragment based docking with dynamics, shotgun systems and synthetic biology, guide.
- Geetika Sethi - pipeline management, benchmarking.
- George White - collaborations, verifications, all rounder.
- Janez Konc - fragment based docking with dynamics.
- Jeremy Li - personalisation, individualised webbot.
- Kaushik Hatti - web application design.
- Mark Minie - writing, all rounder.
- Amrbish Roy - in virtuale multitargeting shotgun drug discovery pipeline, and beyond.
- Brady Bernard - all around consultant, 3dtherapeutics, commercialisation.
- Brian Buttrick - in virtuale multitargeting shotgun drug discovery pipeline, and beyond.
- David Beck - all around consultant.
- Ekachai Jenwitheesuk - original developer v1.
- Jeremy Horst - original developer, all around consultant.
- Ling-Hong Hung - shotgun structural and functional biology.
- Haychoi Taing - systems and database administrator/programmer.
- Michael Shannon - former systems administrator.
- Thomas Wood - shotgun systems and synthetic biology.
- US NIH Director's Pioneer Award (2010-2015).
- US NSF CAREER Award IIS-0448502 (2005-2010).
- US NIH F30DE017522 (2006-2010).
- The University of Washington's Technology Gap Innovation Fund (2006-2007).
- Washington Research Foundation (2006-2007).
- Puget Sound Partners in Global Health (2004-2005).
- Searle Scholar Award to Ram Samudrala (2002-2005).
- The University of Washington's Advanced Technology Initiative in Infectious Diseases (2001-).
our publications related to therapeutic discovery and all
Samudrala Computational Biology Research Group ||