20.320,   notes   for   11/13
Tuesday,   November   13,   2012
9:42   AM
1.
Refining   structures     pyrosetta
2.
ΔΔ G   for   small   molecules
3.
Drugs!
We   have   done   lots     approximations     order     get   reasonable   calculations
• Only     small   change     the   protein's   backbone
• We   only   consider   rotamers
○ We   work   with     discrete   set       angles
Now   we   work   on   refinements     those   first   approximations
1.
Energy   minimization
2.
Molecular   dynamics
We   spent     lot     time   figuring   out   how     calculate   overall   potential   energy       function     either   rotamer   angles     x ‐ y ‐ z   coordinates.
the   end     our   algorithm,   given   our   approximations,   we   will   most   likely   not   get     the   lowest   possible   free   energy   state.
We   aren't   considering     rotamer   angles   (only   discrete   steps),     we're   likely     not   be   able     get     the   perfect   bottom     the   energy   well.
In   mathematics   we   could   use   the   first   derivative       function     see   whether   we   were       minimum.
For     grand   multi ‐ dimensional   space   we   could   similarly   calculate   the   gradient.
This   works   great   for   smooth   functions,   but   not   for   our   messy,   empirical   relationships.
Instead,   for   our   real   case   we   use   something   called   gradient   descent .
At   any   point   on   the   energy   landscape,   we   can   compute   the   gradient       directions   and   determine   which   way     goes   down.
When   we   have   that,   we   simply   move     that   direction.
Note   that   we've   used   the   metropolis   algorithm   with   our   blunt   approximations     do   the   first   sampling     the   space.
can   search   an   enormous   space,   with   simplifying   constraints   determined     us,   and   will   hopefully   reach   the   global   minimum.
will   get     close     the   ideal,   we   hope,     which   point   gradient   descent   can   fine ‐ tune   our   final   values.
20.320 Notes Page 1

For   this   method,   we   start   assuming   that   forces   (from   molecular   interactions)   act   on   every   atom   and   affect   their   motion.
For   each   atom   we   can   calculate     current   location   and   figure   out   where     will   be   given     velocity   and   the   acceleration   caused     the   forces   around     over     small   time   step.
Atoms   will   move   until   they   reach   the   state     lowest   energy   within     well.
Note   that   both   gradient   descent   and   molecular   dynamics   are   unable     jump   over   an   energy   barrier     reach     lower   global   minimum   elsewhere.
They   are   always   stuck     whichever   local   minimum   they   start   out   at.
That's   why   we   run   the   Metropolis   Algorithm   first.
20.320 Notes Page 2

ΔΔ
Let's   shift   our   focus     the   sort     substances   that   most   drugs   are:   small   molecules.
How    do   we   apply   these   concepts   we've   learned?
For   that   we   need     look     how   drugs   are   designed.
There   are     ways:
It's   been   around   for   thousands     years.
was   the   only   way   before   computer   simulations.
The   idea       take     starting   material   known     have     therapeutic   effect   (like   salicylic   acid).
You   try     whole   lot     chemical   modifications   (like   acetylation,   among   many),   and   you   assay   for   effectiveness   until   you   find   the   best   (acetylsalicylic   acid,   Aspirin).
The   most   rigorous   and   modern   way     doing   this     through     method   called   QSAR   (Quantitative
Structure   Activity   Relationships).
The   idea     also     change   stuff   randomly   onto     starting   molecule,   but   then   you   try     see   whether   there's     pattern     the   data.
there   any   characteristic     our   molecules   that   seems     correlate   with   greater   effectiveness?
so,   we   try     maximize   more     that.
There     also     popular   equivalent     the   toxicology   world,   where   they   use   TEST   (Toxicity   Estimation
Software   Tool)     determine   the   toxicity     stuff     essentially   the   same   method.
20.320 Notes Page 3

All   this   looks   very   similar     protein   design.
They   also   calculate   free   energy   diagrams   and   what   not.
However,   there   are   some   important   differences.
The   search   space   for   drugs     much,   much   greater   than   the   already   astronomical   search   space   for   proteins.
Proteins
Limited   number     variables
(20aa)
Drugs
An   estimated   10 60 compounds   that   would   make   realistic   drug   candidates
Mostly   fixed   backbone Nope
Known   active   site     mess   with Active   site(s)   unknown
So   imagine   we   want     see   where     small   molecule   binds       given   protein.
The   way     do         search   around   the   protein,   dock   the   drug   somewhere,   and   create     score   for   that   binding   spot.
The   scoring,     course,   comes   from   the   same   tools   that   we'd   used   before.
Calculate   which   spot   shows   the   lowest   potential   energy     interaction.
To   do   our   search,   we   need   six   independent   degrees     freedom.
Three   are   for   location         and   three   for   orientation   (the   equivalents     roll,   pitch,   and   yaw,     use   the   aerospace   analogy).
This     enough     describe     potential   interaction   sites.
for   how     sample   them,   we   again   bring     our   method   for   sampling   enormous   sample   spaces:   the   Metropolis   Algorithm.
Does   this   work?
Well,     certainly   reduces   the   search   space.
good     determining   the   really   bad   binders,   even       has   more   difficulty   telling   apart   the   good   ones.
Just   knowing   what     ignore,   though,     already     great   starting   point   for   the   pharmaceutical   industry.
You   find   leads   and   then   try     improve   on   them.
The   leads   can   be   analyzed     X ‐ ray   crystallography     NMR     see   exactly   how   the   drug     fact   binds     the   protein.
The   pharma   industry   lore   has   created   the   labels     good   targets   and   good   drugs.
Good   targets   are   those   that   are   likely     be     monetary   hit.
They   have   made   lists     what   they   call
20.320 Notes Page 4

druggable   and   undruggable   targets.
The   druggable   ones   are   those   for   which   we   already   have   several   examples     working   drugs   (kinases,   GPCRs,   Ion   channels)   that   we   can   use     starting   material.
The   undruggables   are   those   that   nobody   has   tried,     that   many   have   tried   and   failed   at.
More   important   for   our   discussion,   though,     what   makes     good   drug .
How   do   you   sift   through   10 60 candidates?
Well,   there   are   considerations   other   than   the   molecular   interactions.
This     where   we   look   at   ADME/Toxicity,   the   short   list     considerations   that   will   make     break     potential   drug:
• Adsorption
• Distribution
• Metabolism
• Excretion
Most   drugs   are   small   molecules   taken   orally.
Their   life   cycle   inside   the   body   looks   something   like   this:
So   when   the   industry   looks       molecule,     needs     consider       the   above.
How   do   they   do   it?
Well,   they   rely   on   lots     calculations   and     few   famous   rules     thumb.
One     the   most   famous   was   put   together       guy   called   Lipinsky,   based   on   his   experience     what   has   and   hasn't   worked     the   history   of   the   pharma   industry.
short   list     the   things   that     successful   drug   should   not have.
A   good   drug   candidate   should   NOT have:
• More   than     H ‐ bond   donors
• Molecular   weight     500   Da
• Sum         atoms     10
• High   lipophilicity,   defined     below:
݈݋݃ ൤
ܦ
௢௖௧௔௡௢௟
ܦ
௪௔௧௘௥
൨ ൐ 5
• Also   avoid   an   area     140   square   angstroms
20.320 Notes Page 5

• Avoid       formal   charge
20.320 Notes Page 6

MIT OpenCourseWare http://ocw.mit.edu
Fall 20 1 2
For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms .