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ACS Chem. Biol. , 2009 , 4 (1), pp 65–74

Use of Artificial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad Spectrum of Highly Antibiotic-Resistant Superbugs. ACS Chem. Biol. , 2009 , 4 (1), pp 65–74.

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ACS Chem. Biol. , 2009 , 4 (1), pp 65–74

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  1. Use of Artificial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad Spectrum of Highly Antibiotic-Resistant Superbugs ACS Chem. Biol., 2009, 4 (1), pp 65–74 Artem Cherkasov, Kai Hilpert, Håvard Jenssen, Christopher D. Fjell, Matt Waldbrook, Sarah C. Mullaly, Rudolf Volkmer § and Robert E.W. Hancock

  2. Superbugs!

  3. Why study the phenomenon of antibiotic resistance? Consider the following harrowing facts: • In 2002, 57.1 percent (an estimated 102,000 cases) of the staph bacteria found in U.S. hospitals were methicillin-resistant (MRSA), according to CDC. • The total cost of antimicrobial resistance to U.S. society is nearly $5 billion annually, according to the Institute of Medicine (IOM). • About 2 million people acquire bacterial infections in U.S. hospitals each year, and 90,000 die as a result. About 70 percent of those infections are resistant to at least one drug, according to the Centers for Disease Control and Prevention. • Recent CDC data show that in 2002, nearly 33 percent of tested samples from ICUs were resistant to fluoroquinolones. P. aeruginosa causes infections of the urinary tract, lungs, and wounds and other infections commonly found in intensive care units. MRSA MRSA infected human tissue

  4. Antibiotic: a substance that kills or inhibits the growth of bacteria (e.g. penicillin, erythromycin, anisomycin,etc) Penicillin Anisomycin Erythromycin

  5. Bacterial Antibiotic Resistance Mechanisms Different types of bacteria exhibit different ways of resistance. Some contain enzymes to change the chemical structure of the antibiotic. Some contain enzymes capable of splitting the antibiotic molecule apart. Some are able to “flush” the antibiotic out of the cell before it can fatally wreck the little creature. Each of these abilities are encoded by resistance genes often found in bacterial plasmid.

  6. Peptide: a polymer made up of amino acid monomers (e.g. the 9-mer KRWWKWIRW in Hancock et al) • Peptides antibiotics are simply antibiotics that are composed either partially or wholly of amino acids. • Almost all species have evolved antimicrobial peptides capable of attacking microbes directly, or, indirectly, by bringing about an innate or inflammatory immune response. Actinomycin D Various peptide antibiotic ribbon structures

  7. Scientific goal: Based on a antimicrobial peptide found in nature, in this case the bovine (as in cow) neutrophil cationic peptide bactenecin (RLCRIVVIRVCR-NH-2), that is known to serve a desirable function per this study, perhaps we can scramble its AA sequence to determine if there are even better antimicrobial peptides of the same length. Cattle neutrophil bactenecin RLCRIVVIRVCR-NH2 (from left to right)

  8. SPOT 1

  9. SPOT 2

  10. SPOT 3

  11. Each circular region contains a synthesized peptide. • The tiny penciled-in dots are the actual specific peptides. • Each of these can be punched out and tested for various biological functions (e.g. antimicrobial activity).

  12. Proof of Bac2A variant antimicrobial activity • Lux assay (no graphical representation depicted in reference 20) is accomplished by taking the peptides from the SPOT synthesis, punching them out, transferring them to microtiter plates, and seeing if they reduce the ability of P. aeruginosa to bioluminescence. • An active antimicrobial peptide will destroy the P. aeruginosa and stop its from luminescence. • An inactive antimicrobial peptide will not destroy P. aeruginosa and thus the organism’s beautiful bioluminescent display will persist. • Combinations of single or multiple AA substitutions led to peptides with better antimicrobial activity than Bac2A.

  13. Training Sets (A & B) • Preferred AAs are found in the best antimicrobial peptides from (refs. 20, 21). They tend to be hydrophobic and amphiphathic AAs. • Using these preferred AAs from (refs. 20, 21) the authors design sets of 943 and 500 cellulose peptides (sets A and B respectively). • Best set A amino acid preferences were used to adjust the amino acid composition of set B. • Adjustments made to set B resulted in better antimicrobial activity than set A relative to Bac2A. • Amino acid composition of set B thus formed to the lead amino acids to be tested in silico. Figure 1. Occurrence of amino acids in the training and QSAR predicted data sets. The predicted activity quartiles from the 100,000 virtual peptide library are marked as Q1−Q4.

  14. The cream of the crop: Set B peptides • Set B peptide AA preferences, representing the best amino acid sequences (increased Ile, Arg, Val, and Trp) were used for random computer generation of 100, 000 virtual peptides (out of an astounding 70 billion possible 9-mer variants. • No position-specific requirements and 16 out of 20 natural AAs used in simulation. • 4 peptides were not used (3 residues were found not make for good antimicrobials in previous libraries and cysteine results in dimerization via disulphide formation. • QSAR solutions from sets A and B were used to help evaluate the effectiveness of these 100, 000 virtual peptides. • QSAR?

  15. What is QSAR? • Quantative Structure-Activity Relationships • A QSAR is a mathematical association between a biological goings-on of a molecular system and its geometric and chemical characteristics. • QSAR attempts to find reliable relationship between biological activity and molecular properties, so that these “rules” can be used to assess the activity of new compounds. • Sets A and B were used to create QSAR models relating chemical characteristics to antimicrobial activity.  • Artificial neural network was used to relate chemical descriptors to antimicrobial activity for the 100, 000 computer generated peptides. • 100, 000 peptides were broken down into four quartiles based on activity predicted : high, medium, low, and completely inactive.

  16. “Chemical space” is the short qualitative answer to the following question: “How many different types of chemical compounds are theoretically capable of existing?” • Chemical space includes: biopolymers, synthetic polymers, metallic clusters, small carbon-based compounds, organometallic systems, etc. • Not all of chemical space may be biologically relevant. Even so, the number of small carbon based molecules with a molecular weight of less than 500 daltons (the molecular mass of many compounds found in living systems) is estimated to be 1 x 10^60! • The number of compounds required for synthesis in order to place 10 different groups in 4 positions of benzene ring is 104 • In silico modeling is thus necessary to search through small parts of chemical space in a reasonable time and cost-effective manner. • A type of chemoinformatic computer modeling called QSAR is one of the methods by which a virtual library of compounds can be generated from lead compounds with certain desirable “drug-like” characteristics. • But first, for the purposes of this study, a lead antimicrobial peptide must be developed and its biological activity determined (set B in Hancock et al 2009).

  17. ANN (Artificial Neural Network) • neural networks are attempt to make computers process information like human neurons. • The human brain is essentially a vast array of interconnected neurons that respond differently to different types of information • Massive interconnectivity allows for many parameters to be looked all at once as opposed to regression analysis which typically deals with a much smaller number of variables. • Authors refer to ANN using a “black box” metaphor—that is, they are not totally sure how the neural network is coming up with its results. The authors leave to a future paper an attempt to explain how the ANN is working its magic (Hancock et al, 2009 in prep.) Artificial Neural Network

  18. Figure 2. Antimicrobial activity and physical parameters for antimicrobial peptides from Training Sets A and B and peptides from the 100,000 peptide virtual library.

  19. Tests of Candidate Peptide Antibiotic Effectiveness Figure 3: Ability of the Peptides HHC-10 and HHC-36 to protect mice against invasive S. Aureus infection

  20. Methodological Overview

  21. Future directions and outstanding issues • Not just theory: peptide antibiotic MX-226 has been shown to significantly limit catheter colonization in phase IIIa clinical trials. • QSAR/ANN is not a one cycle process. It exhibits positive feedback: lead compound to improved virtual compound to drug candidate which may then be used in turn as a lead compound, ad infinitum. • Peptide antibiotics have some negative characteristics such as unknown toxicities, degradation by proteases (enzymes that break down proteins), and high cost (amino acids are expensive building blocks). • Not all of the structural characteristics of what makes a good peptide antibiotic are known at this time.

  22. If all else fails….

  23. Acknowledgements • Dr. Case • The students of Chem258 • Antibiotics and bacteria

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