BRIKARD: Builder of Recursive Inverse Kinematic Assembly and Ring Design
Predicting conformations of artificial macrocycles and their interaction with target proteins. 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 Overview: 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. BRIKARDTM 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 sixâ€‘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). Applications: 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 Intellectual Property Summary: Copyright Stage of Development: Licensing Potential: Licensing Licensing Status: End user software licensing. 8743 Additional Information: drug design,target protein,sampling,computational,analytical,amine,cysteine,high-throughput,in silico,screening,software,root,macromolecule,macromolecules,new drugs,combinatorial chemistry,in silico model,computational chemistry,drug prediction,algorithms,algorithm development,algorithm efficiency,pharmaceutical chemistry,high-throughput screening,high throughput screening,target,new,combinatorial,methods,small molecules,pharmaceutical,tools,cage,cas,nmr 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.