Andrzej KolinskiAndrzej Kolinski

LABORATORY OF THEORY OF LABORATORY OF THEORY OF BIOPOLYMERS WARSAW UNIVERSITYBIOPOLYMERS WARSAW UNIVERSITY

http://www.biocomp.chem.uw.edu.plhttp://www.biocomp.chem.uw.edu.pl

Structure and Function of Biomolecules, Bedlewo, May 12-15, Structure and Function of Biomolecules, Bedlewo, May 12-15, 20042004

Andrzej KolinskiAndrzej Kolinski

LABORATORY OF THEORY OF LABORATORY OF THEORY OF BIOPOLYMERS WARSAW UNIVERSITYBIOPOLYMERS WARSAW UNIVERSITY

http://www.biocomp.chem.uw.edu.plhttp://www.biocomp.chem.uw.edu.pl

Structure and Function of Biomolecules, Bedlewo, May 12-15, Structure and Function of Biomolecules, Bedlewo, May 12-15, 20042004

HIGH RESOLUTION LATTICE MODELS OF HIGH RESOLUTION LATTICE MODELS OF PROTEINS: DESIGN & APPLICATIONSPROTEINS: DESIGN & APPLICATIONS

HIGH RESOLUTION LATTICE MODELS OF HIGH RESOLUTION LATTICE MODELS OF PROTEINS: DESIGN & APPLICATIONSPROTEINS: DESIGN & APPLICATIONS

WHY REDUCED MODELS?WHY REDUCED MODELS?WHY REDUCED MODELS?WHY REDUCED MODELS?

• Classical Molecular Mechanics study of the large scale Classical Molecular Mechanics study of the large scale conformational rearrangements of biomolecules are still conformational rearrangements of biomolecules are still impractical (proteins fold in a time frame of 0.001s to impractical (proteins fold in a time frame of 0.001s to 100s - “long” MD simulations cover 100 nanoseconds).100s - “long” MD simulations cover 100 nanoseconds).

• The number of degrees of freedom treated in an explicit The number of degrees of freedom treated in an explicit way needs to be reduced and the energy landscape way needs to be reduced and the energy landscape smoothened. smoothened.

• Knowledge-based force fields of reduced models seem Knowledge-based force fields of reduced models seem to have frequently a higher predictive power than the to have frequently a higher predictive power than the all-atom potentials of the Molecular Mechanics.all-atom potentials of the Molecular Mechanics.

• We know about 1000 times more protein sequences We know about 1000 times more protein sequences than protein structures (ca. 30M against ca. 30k). This than protein structures (ca. 30M against ca. 30k). This gap increases.gap increases.

• Classical Molecular Mechanics study of the large scale Classical Molecular Mechanics study of the large scale conformational rearrangements of biomolecules are still conformational rearrangements of biomolecules are still impractical (proteins fold in a time frame of 0.001s to impractical (proteins fold in a time frame of 0.001s to 100s - “long” MD simulations cover 100 nanoseconds).100s - “long” MD simulations cover 100 nanoseconds).

• The number of degrees of freedom treated in an explicit The number of degrees of freedom treated in an explicit way needs to be reduced and the energy landscape way needs to be reduced and the energy landscape smoothened. smoothened.

• Knowledge-based force fields of reduced models seem Knowledge-based force fields of reduced models seem to have frequently a higher predictive power than the to have frequently a higher predictive power than the all-atom potentials of the Molecular Mechanics.all-atom potentials of the Molecular Mechanics.

• We know about 1000 times more protein sequences We know about 1000 times more protein sequences than protein structures (ca. 30M against ca. 30k). This than protein structures (ca. 30M against ca. 30k). This gap increases.gap increases.

OUTLINEOUTLINEOUTLINEOUTLINE

• Reduced protein models of an intermediate and high Reduced protein models of an intermediate and high resolution (representation, sampling and force field)resolution (representation, sampling and force field)

• Ab initio folding (an illustration)Ab initio folding (an illustration)

• Loops (or fragments) modeling using various Loops (or fragments) modeling using various reduced representations: SICHO, CABS and REFINER reduced representations: SICHO, CABS and REFINER models. Comparison with standard modeling tools: models. Comparison with standard modeling tools: MODELLER and SWISS-MODELMODELLER and SWISS-MODEL

• Comparative modeling starting from multiple Comparative modeling starting from multiple threading alignments threading alignments

• Reduced protein models of an intermediate and high Reduced protein models of an intermediate and high resolution (representation, sampling and force field)resolution (representation, sampling and force field)

• Ab initio folding (an illustration)Ab initio folding (an illustration)

• Loops (or fragments) modeling using various Loops (or fragments) modeling using various reduced representations: SICHO, CABS and REFINER reduced representations: SICHO, CABS and REFINER models. Comparison with standard modeling tools: models. Comparison with standard modeling tools: MODELLER and SWISS-MODELMODELLER and SWISS-MODEL

• Comparative modeling starting from multiple Comparative modeling starting from multiple threading alignments threading alignments

SICHO, CABS and REFINERSICHO, CABS and REFINERSICHO, CABS and REFINERSICHO, CABS and REFINER

1.45 Å1.45 Å 0.61Å

Gly

Ala

LeuPhe

0.61Å

Gly

Ala

LeuPhe

Gly

MetLeu

AlaGly

MetLeu

Ala

All models use knowledge-based statistical potentials derived via an analysis of structural regularities seen in the solved structures of globular proteinsAll models use knowledge-based statistical potentials derived via an analysis of structural regularities seen in the solved structures of globular proteins

Sampling of the conformational space Sampling of the conformational space of the SICHO and CABS modelsof the SICHO and CABS modelsSampling of the conformational space Sampling of the conformational space of the SICHO and CABS modelsof the SICHO and CABS models

--Single residue movesSingle residue moves

-Two-residue moves-Two-residue moves

-Three-residue moves-Three-residue moves

-Small distance (rigid body) moves of -Small distance (rigid body) moves of a randomly selected fragment of a randomly selected fragment of thethe

model chainmodel chain

-Reptation type moves-Reptation type moves

--Single residue movesSingle residue moves

-Two-residue moves-Two-residue moves

-Three-residue moves-Three-residue moves

-Small distance (rigid body) moves of -Small distance (rigid body) moves of a randomly selected fragment of a randomly selected fragment of thethe

model chainmodel chain

-Reptation type moves-Reptation type moves

ConformationalConformational Search Search SchemeSchemeConformationalConformational Search Search SchemeScheme

High Temperature

Low Temperature

Folding Transition Temp

exp (-

Isothermal MC

Replica Exchange Monte Carlo

N copies

High Temperature

Low Temperature

Folding Transition Temp

exp (-

Isothermal MC

Replica Exchange Monte Carlo

N copies

INTERACTION SCHEMEINTERACTION SCHEME INTERACTION SCHEMEINTERACTION SCHEME

• Generic “protein-like” biases Generic “protein-like” biases

• Statistical potentials for short-range Statistical potentials for short-range conformational propensitiesconformational propensities

• Model of main chain hydrogen bondsModel of main chain hydrogen bonds

• Pairwise interactions between united atoms Pairwise interactions between united atoms (including orientation- and secondary (including orientation- and secondary structure dependent potentials)structure dependent potentials)

• Generic “protein-like” biases Generic “protein-like” biases

• Statistical potentials for short-range Statistical potentials for short-range conformational propensitiesconformational propensities

• Model of main chain hydrogen bondsModel of main chain hydrogen bonds

• Pairwise interactions between united atoms Pairwise interactions between united atoms (including orientation- and secondary (including orientation- and secondary structure dependent potentials)structure dependent potentials)

Generic (sequence independent) chain Generic (sequence independent) chain stiffnessstiffness - regular secondary structure - regular secondary structure propensitiespropensities

Generic (sequence independent) chain Generic (sequence independent) chain stiffnessstiffness - regular secondary structure - regular secondary structure propensitiespropensities

i

i+1

i+2

i+3

i+4

vi

vi+1vi+2

vi+3

| ri+4-ri |

PROTEIN

PE

Generic (sequence independent) chain Generic (sequence independent) chain stiffnessstiffnessGeneric (sequence independent) chain Generic (sequence independent) chain stiffnessstiffness

1

i

i-1i+1

i+2

i+3

i+4vi-1

vi

vi+1vi+2

vi+3vi-1

vi+3

B1 = f×g

for: (vi-1 • vi+3)<0

B1 = f×g

for: (vi-1 • vi+3)<0

B2 = -f×g -g×g

for: | ri+4 –ri |< 7.0 Å and “right handed” twist

or: | ri+4 –ri |>11.0 Å and -type geometry

B2 = -f×g -g×g

for: | ri+4 –ri |< 7.0 Å and “right handed” twist

or: | ri+4 –ri |>11.0 Å and -type geometry

Generic (sequence independent) chain Generic (sequence independent) chain stiffnessstiffnessGeneric (sequence independent) chain Generic (sequence independent) chain stiffnessstiffness

1

B4 = h×g

for: (ri+5 –ri ) • (ri+10 –ri+5 ) < 0 and (ri+15 –r10 ) • (ri+5 –ri ) >0

i.e., penalty for a too crumpled main chain conformations

B4 = h×g

for: (ri+5 –ri ) • (ri+10 –ri+5 ) < 0 and (ri+15 –r10 ) • (ri+5 –ri ) >0

i.e., penalty for a too crumpled main chain conformations

For known or strongly predicted secondary structure fragments an additional bias towards proper values of the medium-range distances along the chain could be superimposed

For known or strongly predicted secondary structure fragments an additional bias towards proper values of the medium-range distances along the chain could be superimposed

Short-range conformational Short-range conformational propensitiespropensities

E13(ri+2,i , Ai, Ai+2)

E14(r*i+3,i , Ai+1, Ai+2

E15(ri+4,i , Ai+1, Ai+3)

Note: the reduced backbone geometry correlates better with secondary structure than the phi-psi angles

E13(ri+2,i , Ai, Ai+2)

E14(r*i+3,i , Ai+1, Ai+2

E15(ri+4,i , Ai+1, Ai+3)

Note: the reduced backbone geometry correlates better with secondary structure than the phi-psi angles

i+1 i+3

ii+2 i+4

-10 -1 0 1 10_______________________________________________________________________________________________________ALA ALA -0.25 -0.45 -0.39 0.73 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 -1.12 -2.55 0.44 0.56 0.25 0.76 0.51 VAL THR -1.71 -1.83 0.06 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 0.11 -1.51 0.56 0.56 0.44 -0.57 -0.75 _______________________________________________________________________________________________________

Left-handed beta unlike or prohibited Alpha Right-handed beta

-10 -1 0 1 10_______________________________________________________________________________________________________ALA ALA -0.25 -0.45 -0.39 0.73 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 -1.12 -2.55 0.44 0.56 0.25 0.76 0.51 VAL THR -1.71 -1.83 0.06 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 0.11 -1.51 0.56 0.56 0.44 -0.57 -0.75 _______________________________________________________________________________________________________

Left-handed beta unlike or prohibited Alpha Right-handed beta

E/kT ~ -ln

(nk,A1,A2/nk,Ai,Aj>)

< > average over the database

E/kT ~ -ln

(nk,A1,A2/nk,Ai,Aj>)

< > average over the database

CABS reduced representationCABS reduced representationCABS reduced representationCABS reduced representation

Model of the main chain hydrogen Model of the main chain hydrogen bondsbondsModel of the main chain hydrogen Model of the main chain hydrogen bondsbonds

i

i-1i+1

vi-1 vi

j

j-1

vj-1 vj-1

j+1hj

-hi

bi

bj

Hydrogen bonds cause specific spatial arrangement of the -trace vectors and the -carbon united atoms

Hydrogen bonds cause specific spatial arrangement of the -trace vectors and the -carbon united atoms

The united atoms i and j are “hydrogen bonded” when:

- at least one of the vectors h points into the vicinity of the -carbon i or j

- vectors h are “almost” parallel (or antiparallel)

- (bi * bj) >0 (“roughly” parallel)

The strength of the hydrogen bond is moderated by a cooperative component dependent on the distance between the corresponding centers of the C-C virtual bonds (minimum of the potential at 4.25 Å )

The united atoms i and j are “hydrogen bonded” when:

- at least one of the vectors h points into the vicinity of the -carbon i or j

- vectors h are “almost” parallel (or antiparallel)

- (bi * bj) >0 (“roughly” parallel)

The strength of the hydrogen bond is moderated by a cooperative component dependent on the distance between the corresponding centers of the C-C virtual bonds (minimum of the potential at 4.25 Å )Additional rules: No hydrogen bonds between pairs assigned as (HE) and (HH for |i-

j|>3)

The C-based model of hydrogen bonds correlates very well with the real hydrogen bonds. When “translating” the indices need to be properly shifted (by +/- 1) depending on type of secondary structure

Additional rules: No hydrogen bonds between pairs assigned as (HE) and (HH for |i-j|>3)

The C-based model of hydrogen bonds correlates very well with the real hydrogen bonds. When “translating” the indices need to be properly shifted (by +/- 1) depending on type of secondary structure

Pairwise interactions (CPairwise interactions (C, C, C, Side Groups), Side Groups)Pairwise interactions (CPairwise interactions (C, C, C, Side Groups), Side Groups)

• Hard-core excluded volume for CHard-core excluded volume for C-C-C, C, C-C-C and and CC-C-C pairs (the cut-off distances are amino acid pairs (the cut-off distances are amino acid independent).independent).

• Soft core excluded volume for interactions with the Soft core excluded volume for interactions with the side groups.side groups.

• Pairwise potentials for side groups derived from a Pairwise potentials for side groups derived from a statistical analysis of known protein structures.statistical analysis of known protein structures.

• Two side groups are assumed to be “in contact” Two side groups are assumed to be “in contact” when any pair of their heavy atoms is “in contact” when any pair of their heavy atoms is “in contact” (4.5 (4.5 Å cut-off) – the average distance between the cut-off) – the average distance between the centers of mass are then taken as a contact centers of mass are then taken as a contact distance for a pair of side groups.distance for a pair of side groups.

• Side group pairwise potentials are “context” Side group pairwise potentials are “context” dependent (mutual orientation, conformation of the dependent (mutual orientation, conformation of the main chain)main chain)

• Hard-core excluded volume for CHard-core excluded volume for C-C-C, C, C-C-C and and CC-C-C pairs (the cut-off distances are amino acid pairs (the cut-off distances are amino acid independent).independent).

• Soft core excluded volume for interactions with the Soft core excluded volume for interactions with the side groups.side groups.

• Pairwise potentials for side groups derived from a Pairwise potentials for side groups derived from a statistical analysis of known protein structures.statistical analysis of known protein structures.

• Two side groups are assumed to be “in contact” Two side groups are assumed to be “in contact” when any pair of their heavy atoms is “in contact” when any pair of their heavy atoms is “in contact” (4.5 (4.5 Å cut-off) – the average distance between the cut-off) – the average distance between the centers of mass are then taken as a contact centers of mass are then taken as a contact distance for a pair of side groups.distance for a pair of side groups.

• Side group pairwise potentials are “context” Side group pairwise potentials are “context” dependent (mutual orientation, conformation of the dependent (mutual orientation, conformation of the main chain)main chain)

Pairwise interactions of the side Pairwise interactions of the side groupsgroupsPairwise interactions of the side Pairwise interactions of the side groupsgroups

Distance between centers of mass of the side groups

Between centers of mass (all heavy atoms of a side group + C).

Cut-off distances pairwise dependent (not additive, account for some packing details).

Square-well shape of the potential (for charged residues a tail added).

Soft (however relatively large) excluded volume potential – the height is amino acid independent.

For a given pair of amino acids the strength of interactions and the cut-off distances depend on mutual orientation of the interacting side groups and on the local geometry of the main chain.

Between centers of mass (all heavy atoms of a side group + C).

Cut-off distances pairwise dependent (not additive, account for some packing details).

Square-well shape of the potential (for charged residues a tail added).

Soft (however relatively large) excluded volume potential – the height is amino acid independent.

For a given pair of amino acids the strength of interactions and the cut-off distances depend on mutual orientation of the interacting side groups and on the local geometry of the main chain.

CONTEXT-DEPENDENT STATISTICAL CONTEXT-DEPENDENT STATISTICAL POTENTIALSPOTENTIALSCONTEXT-DEPENDENT STATISTICAL CONTEXT-DEPENDENT STATISTICAL POTENTIALSPOTENTIALS

Three types of the mutual orientations of the side groups: A-antiparallel, M-intermediate, P-parallel

Three types of the mutual orientations of the side groups: A-antiparallel, M-intermediate, P-parallel

Two types of the main chain conformations: C- compact and E-extendedTwo types of the main chain conformations: C- compact and E-extended

Derived pairwise contact potentials from the statistics of the numbers of parallel, antiparllel and semi-orthogonal contacts for a given residue type and two types of the main chain conformations.

Derived pairwise contact potentials from the statistics of the numbers of parallel, antiparllel and semi-orthogonal contacts for a given residue type and two types of the main chain conformations.

NEW STATISTICAL POTENTIALS (AN EXAMPLE)NEW STATISTICAL POTENTIALS (AN EXAMPLE)NEW STATISTICAL POTENTIALS (AN EXAMPLE)NEW STATISTICAL POTENTIALS (AN EXAMPLE)

LYS-GLU POTENTIAL

P M A

CC -0.9 -0.4 0.9

EE -1.1 -0.4 0.6

CE -0.2 0.1 0.8

EC -0.2 0.0 0.8

LYS-GLU POTENTIAL

P M A

CC -0.9 -0.4 0.9

EE -1.1 -0.4 0.6

CE -0.2 0.1 0.8

EC -0.2 0.0 0.8

GAPLESS THREADING

%NATIVE Z-scoreQUASI 86 % 6.72QUASI3 94 % 7.84QUASI3S 97 % 9.96

When tested on a large set of decoys the orientation and backbone conformation dependent potentials QUASI3S exhibits better correlation between energy and RMSD from native than the more “generic” potentials

When tested on a large set of decoys the orientation and backbone conformation dependent potentials QUASI3S exhibits better correlation between energy and RMSD from native than the more “generic” potentials

Ab initio foldingAb initio foldingAb initio foldingAb initio folding

• ““Pure” Pure” ab initioab initio (with only statistical (with only statistical potentials) protein folding and potentials) protein folding and macromolecular assembly (results macromolecular assembly (results for the SICHO model)for the SICHO model)

• ““Pure” Pure” ab initioab initio (with only statistical (with only statistical potentials) protein folding and potentials) protein folding and macromolecular assembly (results macromolecular assembly (results for the SICHO model)for the SICHO model)

LOOP MODELING – STRUCTURE COMPLETIONLOOP MODELING – STRUCTURE COMPLETIONLOOP MODELING – STRUCTURE COMPLETIONLOOP MODELING – STRUCTURE COMPLETION

• Fixed template (and an “ideal” alignment) from PDB Fixed template (and an “ideal” alignment) from PDB with removed fragments of their native structurewith removed fragments of their native structure

• Random starting conformation of the loops (non-Random starting conformation of the loops (non-entangled) entangled)

• Loop optimization using SICHO, CABS and REFINER Loop optimization using SICHO, CABS and REFINER (sampling via Replica Exchange Monte Carlo)(sampling via Replica Exchange Monte Carlo)

• The lowest energy structure taken for a comparison with The lowest energy structure taken for a comparison with MODELLER and SWISS-MODEL (automatic version)MODELLER and SWISS-MODEL (automatic version)

• No human intervention during the modeling proceduresNo human intervention during the modeling procedures

• Fixed template (and an “ideal” alignment) from PDB Fixed template (and an “ideal” alignment) from PDB with removed fragments of their native structurewith removed fragments of their native structure

• Random starting conformation of the loops (non-Random starting conformation of the loops (non-entangled) entangled)

• Loop optimization using SICHO, CABS and REFINER Loop optimization using SICHO, CABS and REFINER (sampling via Replica Exchange Monte Carlo)(sampling via Replica Exchange Monte Carlo)

• The lowest energy structure taken for a comparison with The lowest energy structure taken for a comparison with MODELLER and SWISS-MODEL (automatic version)MODELLER and SWISS-MODEL (automatic version)

• No human intervention during the modeling proceduresNo human intervention during the modeling procedures

EXAMPLES EXAMPLES (a-SICHO, b-CABS, c-REFINER, d-(a-SICHO, b-CABS, c-REFINER, d-MODELLER)MODELLER)EXAMPLES EXAMPLES (a-SICHO, b-CABS, c-REFINER, d-(a-SICHO, b-CABS, c-REFINER, d-MODELLER)MODELLER)

Gray – template

Green – native fragment or loop removed from the PDB structure

Red – Modeled fragment

Gray – template

Green – native fragment or loop removed from the PDB structure

Red – Modeled fragment

EXAMPLES EXAMPLES (a-SICHO, b-CABS, c-REFINER, d-(a-SICHO, b-CABS, c-REFINER, d-MODELLER)MODELLER)EXAMPLES EXAMPLES (a-SICHO, b-CABS, c-REFINER, d-(a-SICHO, b-CABS, c-REFINER, d-MODELLER)MODELLER)

• Green – native fragment or loop removed from the PDB structure

• Red – Modeled fragment

• Gray – template

• Green – native fragment or loop removed from the PDB structure

• Red – Modeled fragment

• Gray – template

COMPARATIVE MODELING WITH MULTIPLE COMPARATIVE MODELING WITH MULTIPLE TEMPLATESTEMPLATESCOMPARATIVE MODELING WITH MULTIPLE COMPARATIVE MODELING WITH MULTIPLE TEMPLATESTEMPLATES

• Highest score templates detected by Highest score templates detected by threading procedures are used to extract threading procedures are used to extract the distance restraintsthe distance restraints

• ““Soft” implementation of the restraints in Soft” implementation of the restraints in the CABS algorithm (from the top-four the CABS algorithm (from the top-four templates –when available)templates –when available)

• Sampling via Replica Exchange Monte Sampling via Replica Exchange Monte CarloCarlo

• Almost always a single cluster of Almost always a single cluster of structures is obtained and its centroid is structures is obtained and its centroid is taken as a final model taken as a final model

• Highest score templates detected by Highest score templates detected by threading procedures are used to extract threading procedures are used to extract the distance restraintsthe distance restraints

• ““Soft” implementation of the restraints in Soft” implementation of the restraints in the CABS algorithm (from the top-four the CABS algorithm (from the top-four templates –when available)templates –when available)

• Sampling via Replica Exchange Monte Sampling via Replica Exchange Monte CarloCarlo

• Almost always a single cluster of Almost always a single cluster of structures is obtained and its centroid is structures is obtained and its centroid is taken as a final model taken as a final model

EXAMPLES OF COMPARATIVE MODELINGEXAMPLES OF COMPARATIVE MODELINGEXAMPLES OF COMPARATIVE MODELINGEXAMPLES OF COMPARATIVE MODELING

EXAMPLES OF COMPARATIVE MODELINGEXAMPLES OF COMPARATIVE MODELINGEXAMPLES OF COMPARATIVE MODELINGEXAMPLES OF COMPARATIVE MODELING

SUMMARY OF COMPARATIVE SUMMARY OF COMPARATIVE MODELINGMODELING

SUMMARY OF COMPARATIVE SUMMARY OF COMPARATIVE MODELINGMODELING

Frequently the models are closer to the native structure than to any of the templates

Frequently the models are closer to the native structure than to any of the templates

CONCLUSIONSCONCLUSIONSCONCLUSIONSCONCLUSIONS

• Algorithms employing reduced representation of Algorithms employing reduced representation of the protein conformational space are now mature the protein conformational space are now mature and efficient tools for protein modelingand efficient tools for protein modeling

• Applications: Applications: - - ab initioab initio structure prediction structure prediction - comparative modeling (also multitemplate)- comparative modeling (also multitemplate) - structure assembly from sparse experimental - structure assembly from sparse experimental

datadata - dynamics and thermodynamics of proteins, - dynamics and thermodynamics of proteins,

prionsprions - flexible docking, macromolecular assemblies- flexible docking, macromolecular assemblies

• Tools exist for the all-atom reconstruction of the Tools exist for the all-atom reconstruction of the reduced models. (See: reduced models. (See: NIH Research Resources for Multiscale Modeling Tools in Structural Biology hhtp://mmtsb.scripps.edu)

• Algorithms employing reduced representation of Algorithms employing reduced representation of the protein conformational space are now mature the protein conformational space are now mature and efficient tools for protein modelingand efficient tools for protein modeling

• Applications: Applications: - - ab initioab initio structure prediction structure prediction - comparative modeling (also multitemplate)- comparative modeling (also multitemplate) - structure assembly from sparse experimental - structure assembly from sparse experimental

datadata - dynamics and thermodynamics of proteins, - dynamics and thermodynamics of proteins,

prionsprions - flexible docking, macromolecular assemblies- flexible docking, macromolecular assemblies

• Tools exist for the all-atom reconstruction of the Tools exist for the all-atom reconstruction of the reduced models. (See: reduced models. (See: NIH Research Resources for Multiscale Modeling Tools in Structural Biology hhtp://mmtsb.scripps.edu)

AcknowledgementAcknowledgement

• Warsaw UniversityWarsaw University PolandPoland

Michal BonieckiMichal BonieckiDominik GrontDominik Gront

Sebastian KmiecikSebastian KmiecikPiotr KleinPiotr KleinPiotr PokarowskiPiotr PokarowskiPiotr RotkiewiczPiotr Rotkiewicz

Andrzej KolinskiAndrzej Kolinski

• SUNY at Buffalo (NY)SUNY at Buffalo (NY)

Piotr RotkiewiczPiotr RotkiewiczJeffrey SkolnickJeffrey Skolnick

More info: http://www.biocomp.chem.uw.edu.plMore info: http://www.biocomp.chem.uw.edu.pl