SlideShare a Scribd company logo
1 of 15
Download to read offline
Blurring	
  the	
  boundary	
  between	
  linear	
  scaling	
  QM,	
  
                   QM/MM	
  and	
  polarizable	
  force	
  fields	
  

             The	
  Effec(ve	
  Fragment	
  Molecular	
  Orbital	
  Method	
  

Jan	
  H.	
  Jensen,	
  Casper	
  Steinmann,	
  Mikael	
  Wisto1	
  Ibsen,	
  Kasper	
  Tho1e	
  
                                  University	
  of	
  Copenhagen	
  

                                     Dmitri	
  Fedorov	
  
                                      AIST,	
  Japan	
  




                                                                                                    1	
  
The	
  Fragment	
  Molecular	
  Orbital	
  (FMO2)	
  method	
  
   (and	
  most	
  other	
  fragmentaEon	
  methods)	
  




                                                                  2	
  
The	
  Fragment	
  Molecular	
  Orbital	
  (FMO2)	
  method	
  
                            (and	
  most	
  other	
  fragmentaEon	
  methods)	
  




Many-­‐body	
  PolarizaEon:

  Monomer	
  SCF	
  in	
  the	
  	
  
  Coulomb	
  field	
  of	
  all	
  	
  
   other	
  monomers	
  

        Iterated	
  to	
  
     self-­‐consistency	
  	
  




                                                                                           3	
  
The	
  Fragment	
  Molecular	
  Orbital	
  (FMO2)	
  method	
  
                            (and	
  most	
  other	
  fragmentaEon	
  methods)	
  




Non-­‐Coulomb	
  effects:

  Dimer	
  SCF	
  in	
  the	
  	
  
 Coulomb	
  field	
  of	
  all	
  	
  
  other	
  monomers	
  

      Iterated	
  to	
  
    self-­‐consistency	
  	
  




                                                                                           4	
  
The	
  Fragment	
  Molecular	
  Orbital	
  (FMO2)	
  method	
  
                           (and	
  most	
  other	
  fragmentaEon	
  methods)	
  




  Coulomb	
  effects: 	
  	
  

Coulomb	
  energy	
  in	
  the	
  	
  
 Coulomb	
  field	
  of	
  all	
  	
  
  other	
  monomers	
  




                                                                                          5	
  
The	
  EffecEve	
  Fragment	
  Molecular	
  Orbital	
  (EFMO)	
  method	
  
            (Using	
  ideas	
  from	
  the	
  EffecPve	
  Fragment	
  PotenPal	
  (EFP)	
  method)	
  




  Monomer	
  SCF	
  in	
  the	
  
      gas	
  phase	
  

  Extract	
  mulPpoles	
  
and	
  dipole	
  polarizability	
  




                                                                                                        6	
  
The	
  EffecEve	
  Fragment	
  Molecular	
  Orbital	
  (EFMO)	
  method	
  
              (Using	
  ideas	
  from	
  the	
  EffecPve	
  Fragment	
  PotenPal	
  (EFP)	
  method)	
  




Many-­‐body	
  polarizaEon	
  

 Computed	
  classically	
  
 using	
  induced	
  dipoles	
  
   for	
  enPre	
  system	
  




                                                                                                          7	
  
The	
  EffecEve	
  Fragment	
  Molecular	
  Orbital	
  (EFMO)	
  method	
  
          (Using	
  ideas	
  from	
  the	
  EffecPve	
  Fragment	
  PotenPal	
  (EFP)	
  method)	
  




   Coulomb	
  and	
  
Non-­‐Coulomb	
  effects	
  

   dimer	
  SCF	
  in	
  the	
  
      gas	
  phase	
  




                                                                                                      8	
  
The	
  EffecEve	
  Fragment	
  Molecular	
  Orbital	
  (EFMO)	
  method	
  
      (Using	
  ideas	
  from	
  the	
  EffecPve	
  Fragment	
  PotenPal	
  (EFP)	
  method)	
  




Coulomb	
  effects	
  

Computed	
  using	
  
staPc	
  mulPpoles	
  




                                                                                                  9	
  
MP2	
  
(DFT	
  doesn’t	
  scale	
  well)	
  

                                        +	
  0	
  




                                                     10	
  
Covalent	
  FragmentaEon	
  
(ElectrostaPc	
  screening	
  crucial)	
  




                                             11	
  
Implemented	
  in	
  GAMESS	
  
                                                                                                                               With	
  gradients	
  

                                                                                                                                     Trp	
  cage	
  (20	
  residues)	
  
                                                                                                                                      2	
  residues/fragment	
  




	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  EFMO	
  	
  	
  FMO2	
  
Error	
  in	
  energy	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  -­‐4.3	
  	
  	
  	
  	
  	
  	
  	
  6.4	
  	
  kcal/mol	
  

MP2/6-­‐31G(d)	
  gradient	
  	
  	
  	
  	
  	
  	
  	
  	
  314	
  	
  	
  	
  	
  	
  	
  409	
  	
  minutes	
  
20	
  cores	
  
(most	
  Pme	
  spent	
  in	
  MP2	
  dimers)	
  




                                                                                                                                                                                                                                   12	
  
To	
  Do	
  

                                               QM/”MM”	
  
                                                 PCM	
  




Large	
  parts	
  of	
  MM	
  region	
  	
  
          o1en	
  	
  frozen	
  	
  
                   =	
  
 Requires	
  only	
  monomer	
  	
  
  gas	
  phase	
  calculaPons	
  
         for	
  that	
  region	
  
                   =	
  
            Very	
  fast	
  




                                                                13	
  
To	
  Do	
  

Flexible	
  EFP/Polarizable	
  “Force	
  Field”	
  
                    covalent
                    dimers

                     ∑ (E                                 )
           N
E EFMO = ∑ EI0 +                0
                                IJ    − EI0 − EJ − EIJ
                                               0    POL

           I           IJ


                (                           )
           N
        + ∑ EIJ + EIJ /CT + EIJ + Etot
             ES    XR        Disp  POL

           IJ




         Important	
  miscellanea	
  

  EFMO	
  GUI:	
  FRAGIT	
  (Mikael	
  Ibsen)	
  

TS	
  search	
  algorithms	
  (Kasper	
  Tho1e)	
  

                                                              14	
  
Funding:	
  	
  EU	
  (IRENE	
  collab	
  program)	
  




                                         Thank	
  You!	
  

                                     QuesEons	
  Now?	
  


                                     QuesEons	
  Later?	
  

                                  Leave	
  a	
  comment	
  on	
  

hgp://proteinsandwavefuncEons.blogspot.com/2011/07/my-­‐presentaPon-­‐for-­‐watoc-­‐2011.html	
  


                                                                                          15	
  

More Related Content

More from molmodbasics

xyz2mol for organometallic compounds
xyz2mol for organometallic compoundsxyz2mol for organometallic compounds
xyz2mol for organometallic compoundsmolmodbasics
 
Chemical Space Exploration
Chemical Space ExplorationChemical Space Exploration
Chemical Space Explorationmolmodbasics
 
ChemRxiv, Plan S, and OA publishing
ChemRxiv, Plan S, and OA publishingChemRxiv, Plan S, and OA publishing
ChemRxiv, Plan S, and OA publishingmolmodbasics
 
A Quantum Chemist Meets Cheminformatics
A Quantum Chemist Meets CheminformaticsA Quantum Chemist Meets Cheminformatics
A Quantum Chemist Meets Cheminformaticsmolmodbasics
 
Can We Automate Computational Studies of Enzymes? Lessons from Small-Molecul...
Can We Automate Computational Studies of Enzymes?  Lessons from Small-Molecul...Can We Automate Computational Studies of Enzymes?  Lessons from Small-Molecul...
Can We Automate Computational Studies of Enzymes? Lessons from Small-Molecul...molmodbasics
 
Proteiner du kan regne med
Proteiner du kan regne med Proteiner du kan regne med
Proteiner du kan regne med molmodbasics
 
Using semiempirical methods for fast and automated predictions
Using semiempirical methods for fast and automated predictionsUsing semiempirical methods for fast and automated predictions
Using semiempirical methods for fast and automated predictionsmolmodbasics
 
Jan H. Jensen: profile
Jan H. Jensen: profileJan H. Jensen: profile
Jan H. Jensen: profilemolmodbasics
 
Can semiempirical methods be used for high throughput screening (for enzyme m...
Can semiempirical methods be used for high throughput screening (for enzyme m...Can semiempirical methods be used for high throughput screening (for enzyme m...
Can semiempirical methods be used for high throughput screening (for enzyme m...molmodbasics
 
Thermodynamics for Biochemists: a YouTube textbook
Thermodynamics for Biochemists: a YouTube textbookThermodynamics for Biochemists: a YouTube textbook
Thermodynamics for Biochemists: a YouTube textbookmolmodbasics
 
Predicting accurate absolute binding energies in aqueous solution: thermodyn...
Predicting accurate absolute binding energies in aqueous solution: thermodyn...Predicting accurate absolute binding energies in aqueous solution: thermodyn...
Predicting accurate absolute binding energies in aqueous solution: thermodyn...molmodbasics
 
Teaching Tools and Tips
Teaching Tools and TipsTeaching Tools and Tips
Teaching Tools and Tipsmolmodbasics
 
Short answer questions on thermodynamics
Short answer questions on thermodynamicsShort answer questions on thermodynamics
Short answer questions on thermodynamicsmolmodbasics
 
Different kinds of peer instruction questions for thermodynamics
Different kinds of peer instruction questions for thermodynamicsDifferent kinds of peer instruction questions for thermodynamics
Different kinds of peer instruction questions for thermodynamicsmolmodbasics
 
Teaching Tools and Tips
Teaching Tools and TipsTeaching Tools and Tips
Teaching Tools and Tipsmolmodbasics
 
Quantum Biochemistry: the rise of semiempirical methods
Quantum Biochemistry: the rise of semiempirical methodsQuantum Biochemistry: the rise of semiempirical methods
Quantum Biochemistry: the rise of semiempirical methodsmolmodbasics
 

More from molmodbasics (20)

xyz2mol for organometallic compounds
xyz2mol for organometallic compoundsxyz2mol for organometallic compounds
xyz2mol for organometallic compounds
 
Chemical Space Exploration
Chemical Space ExplorationChemical Space Exploration
Chemical Space Exploration
 
ChemRxiv, Plan S, and OA publishing
ChemRxiv, Plan S, and OA publishingChemRxiv, Plan S, and OA publishing
ChemRxiv, Plan S, and OA publishing
 
A Quantum Chemist Meets Cheminformatics
A Quantum Chemist Meets CheminformaticsA Quantum Chemist Meets Cheminformatics
A Quantum Chemist Meets Cheminformatics
 
Can We Automate Computational Studies of Enzymes? Lessons from Small-Molecul...
Can We Automate Computational Studies of Enzymes?  Lessons from Small-Molecul...Can We Automate Computational Studies of Enzymes?  Lessons from Small-Molecul...
Can We Automate Computational Studies of Enzymes? Lessons from Small-Molecul...
 
Open is Better
Open is BetterOpen is Better
Open is Better
 
Proteiner du kan regne med
Proteiner du kan regne med Proteiner du kan regne med
Proteiner du kan regne med
 
Using semiempirical methods for fast and automated predictions
Using semiempirical methods for fast and automated predictionsUsing semiempirical methods for fast and automated predictions
Using semiempirical methods for fast and automated predictions
 
Jan H. Jensen: profile
Jan H. Jensen: profileJan H. Jensen: profile
Jan H. Jensen: profile
 
Why I blog
Why I blogWhy I blog
Why I blog
 
Why I tweet
Why I tweetWhy I tweet
Why I tweet
 
Can semiempirical methods be used for high throughput screening (for enzyme m...
Can semiempirical methods be used for high throughput screening (for enzyme m...Can semiempirical methods be used for high throughput screening (for enzyme m...
Can semiempirical methods be used for high throughput screening (for enzyme m...
 
Thermodynamics for Biochemists: a YouTube textbook
Thermodynamics for Biochemists: a YouTube textbookThermodynamics for Biochemists: a YouTube textbook
Thermodynamics for Biochemists: a YouTube textbook
 
Predicting accurate absolute binding energies in aqueous solution: thermodyn...
Predicting accurate absolute binding energies in aqueous solution: thermodyn...Predicting accurate absolute binding energies in aqueous solution: thermodyn...
Predicting accurate absolute binding energies in aqueous solution: thermodyn...
 
I lecture nomore
I lecture nomoreI lecture nomore
I lecture nomore
 
Teaching Tools and Tips
Teaching Tools and TipsTeaching Tools and Tips
Teaching Tools and Tips
 
Short answer questions on thermodynamics
Short answer questions on thermodynamicsShort answer questions on thermodynamics
Short answer questions on thermodynamics
 
Different kinds of peer instruction questions for thermodynamics
Different kinds of peer instruction questions for thermodynamicsDifferent kinds of peer instruction questions for thermodynamics
Different kinds of peer instruction questions for thermodynamics
 
Teaching Tools and Tips
Teaching Tools and TipsTeaching Tools and Tips
Teaching Tools and Tips
 
Quantum Biochemistry: the rise of semiempirical methods
Quantum Biochemistry: the rise of semiempirical methodsQuantum Biochemistry: the rise of semiempirical methods
Quantum Biochemistry: the rise of semiempirical methods
 

Recently uploaded

Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embeddingZilliz
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfSeasiaInfotech2
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
 

Recently uploaded (20)

Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptxE-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
Training state-of-the-art general text embedding
Training state-of-the-art general text embeddingTraining state-of-the-art general text embedding
Training state-of-the-art general text embedding
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdf
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationBeyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
 

Blurring Boundaries Between Linear Scaling QM, QM/MM and Force Fields

  • 1. Blurring  the  boundary  between  linear  scaling  QM,   QM/MM  and  polarizable  force  fields   The  Effec(ve  Fragment  Molecular  Orbital  Method   Jan  H.  Jensen,  Casper  Steinmann,  Mikael  Wisto1  Ibsen,  Kasper  Tho1e   University  of  Copenhagen   Dmitri  Fedorov   AIST,  Japan   1  
  • 2. The  Fragment  Molecular  Orbital  (FMO2)  method   (and  most  other  fragmentaEon  methods)   2  
  • 3. The  Fragment  Molecular  Orbital  (FMO2)  method   (and  most  other  fragmentaEon  methods)   Many-­‐body  PolarizaEon: Monomer  SCF  in  the     Coulomb  field  of  all     other  monomers   Iterated  to   self-­‐consistency     3  
  • 4. The  Fragment  Molecular  Orbital  (FMO2)  method   (and  most  other  fragmentaEon  methods)   Non-­‐Coulomb  effects: Dimer  SCF  in  the     Coulomb  field  of  all     other  monomers   Iterated  to   self-­‐consistency     4  
  • 5. The  Fragment  Molecular  Orbital  (FMO2)  method   (and  most  other  fragmentaEon  methods)   Coulomb  effects:     Coulomb  energy  in  the     Coulomb  field  of  all     other  monomers   5  
  • 6. The  EffecEve  Fragment  Molecular  Orbital  (EFMO)  method   (Using  ideas  from  the  EffecPve  Fragment  PotenPal  (EFP)  method)   Monomer  SCF  in  the   gas  phase   Extract  mulPpoles   and  dipole  polarizability   6  
  • 7. The  EffecEve  Fragment  Molecular  Orbital  (EFMO)  method   (Using  ideas  from  the  EffecPve  Fragment  PotenPal  (EFP)  method)   Many-­‐body  polarizaEon   Computed  classically   using  induced  dipoles   for  enPre  system   7  
  • 8. The  EffecEve  Fragment  Molecular  Orbital  (EFMO)  method   (Using  ideas  from  the  EffecPve  Fragment  PotenPal  (EFP)  method)   Coulomb  and   Non-­‐Coulomb  effects   dimer  SCF  in  the   gas  phase   8  
  • 9. The  EffecEve  Fragment  Molecular  Orbital  (EFMO)  method   (Using  ideas  from  the  EffecPve  Fragment  PotenPal  (EFP)  method)   Coulomb  effects   Computed  using   staPc  mulPpoles   9  
  • 10. MP2   (DFT  doesn’t  scale  well)   +  0   10  
  • 11. Covalent  FragmentaEon   (ElectrostaPc  screening  crucial)   11  
  • 12. Implemented  in  GAMESS   With  gradients   Trp  cage  (20  residues)   2  residues/fragment                                                                                                      EFMO      FMO2   Error  in  energy                                                -­‐4.3                6.4    kcal/mol   MP2/6-­‐31G(d)  gradient                  314              409    minutes   20  cores   (most  Pme  spent  in  MP2  dimers)   12  
  • 13. To  Do   QM/”MM”   PCM   Large  parts  of  MM  region     o1en    frozen     =   Requires  only  monomer     gas  phase  calculaPons   for  that  region   =   Very  fast   13  
  • 14. To  Do   Flexible  EFP/Polarizable  “Force  Field”   covalent dimers ∑ (E ) N E EFMO = ∑ EI0 + 0 IJ − EI0 − EJ − EIJ 0 POL I IJ ( ) N + ∑ EIJ + EIJ /CT + EIJ + Etot ES XR Disp POL IJ Important  miscellanea   EFMO  GUI:  FRAGIT  (Mikael  Ibsen)   TS  search  algorithms  (Kasper  Tho1e)   14  
  • 15. Funding:    EU  (IRENE  collab  program)   Thank  You!   QuesEons  Now?   QuesEons  Later?   Leave  a  comment  on   hgp://proteinsandwavefuncEons.blogspot.com/2011/07/my-­‐presentaPon-­‐for-­‐watoc-­‐2011.html   15