Use of Artificial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad

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Use of Artificial Intelligence in the Design of
Small Peptide Antibiotics Effective against a
Broad Spectrum of Highly Antibiotic-Resistant
Superbugs
Artem Cherkasov, Kai Hilpert, Håvard Jenssen,
Christopher D. Fjell, Matt Waldbrook, Sarah C.
Mullaly, Rudolf Volkmer and Robert E.W. Hancock

• ACS Chem. Biol., 2009, 4 (1), pp 6574

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Superbugs!
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Why study the phenomenon of antibiotic resistance?
MRSA
MRSA infected human tissue

• 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.

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

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• 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
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• 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)
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SPOT 1
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SPOT 2
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SPOT 3
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• 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).

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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 organisms
  beautiful bioluminescent display will persist.
• Combinations of single or multiple AA
  substitutions led to peptides with better
  antimicrobial activity than Bac2A.

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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.
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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?

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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. 

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• 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 1060!
• 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).

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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
  metaphorthat 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
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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.
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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
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Methodological Overview
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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.

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If all else fails.
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Acknowledgements

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