BRIKARD: Builder of Recursive Inverse Kinematic Assembly and Ring Design

Background

Macrocycles (MCs) are an extremely promising class of new drugs with a broad range of therapeutic applications. The challenge is they are hard to design using conventional “wet” combinatorial methods which makes in silico approaches especially important for design of MCs. Design of new drugs tailored to specific targets requires combining conformational sampling with combinatorial design and docking algorithms. Existing algorithms developed for small molecules perform poorly for large and complex macrocycles because they are unable to efficiently sample conformations consistent with ring closure. This intrinsic limitation impedes computational design of novel MC drugs by pharmaceutical companies.

Technology

Researchers at Stony Brook University have developed and successfully tested a novel tool specifically designed for computational prediction of new MC drugs. The input is a standard depiction of a molecule, and the possible outputs are conformational ensembles of geometrically valid shapes, low energy ensembles, or ensembles in complex with a given target. BRIKARD TM is a fast and low resource demanding algorithm that solves the sampling problem in a rigorous analytical way using techniques from robotics. The researchers have demonstrated superior performance, speed, and efficacy and can generate macrocycle conformation satisfying a wide range of structural constraints such as amine bonds, cysteine bridges, five and six atom rings fused to the macrocycle, cages, lassos, etc.

Advantages

Explores the whole conformational space theoretically available for the molecule of interest using either a fully de novo approach, or in combination with a limited set of data (ie NMR).

Application

Instrumental for high throughput in silico screening. Tested against MMBS, and LowMode MD, achieving lower average Root‑Mean‑Square deviation, 2 orders of magnitude increase in speed using standard protocol, successful in cases for which currently available sampling methods fail

Patent Status

Stage Of Development

Licensing Potential

Licensing

Licensing Status

End user software licensing.

Additional Info

https://stonybrook.technologypublisher.com/files/sites/ltoikksuq4w8s9ivnwvd_in-silico-mechanistic-analysis-of-irf3-inactivation-and-high-risk-hpv-e6-species-dependent-drug-srep13446-s4.ogv.jpg Please note, header image is purely illustrative. Source: Shah M, Anwar M, Park S, Jafri S, Choi S, Wikimedia Commons, CC BY 4.0.

Category(s):

Campus > Stony Brook University

Technology Classifications > Drug Design and/or Synthesis

Case ID: R8743

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For Information, Contact:

Donna Tumminello

Assistant Director

State University of New York at Stony Brook

6316324163

donna.tumminello@stonybrook.edu

Inventors:

Evangelos Coutsias

Michael Wester

Keywords:

Technologies