An efficient Docking Method to Study Protein Interactions

1
An efficient Docking Method to Study Protein
Interactions   Yuhua Duan1,2,3, Boojala Reddy2,
David Breslauer4 and Yiannis Kaznessis1,2  1Depart
ment of Chemical Engineering and Materials
Science, 2Digital Technology Center, 3Army
High-Performance Computing and Research Center,
University of Minnesota, Minneapolis, MN 55455
4Department of Bioengineering, University of
California San Diego
Results and Discussion
Residue Conservation Filter
Introduction
Homologous sequences Using the FASTA3 sequence
similarity search tool we obtained homologous
sequences from an annotated non redundant protein
sequence data base (SWALL). Homologous sequences
with less than 30 gaps in the sequence and
greater than 35 sequence identity to the parent
sequence were used for analysis. Evolutionary
Distance Evolutionary distance among the
sequences is calculated using the structure based
amino acid substitution matrix7. A similarity
score Sii for sequence i is calculated by summing
the identical substitution values of the residues
a and b from the substitution matrix M(a,b). An
evolutionary distance (EDij) between the two
sequences is calculated

• As an example, Fig.1 gives scatter plots of the
  best 1000 ranked model structures versus the RMSD
  of the model to the experimental structure for
  complex 1TAB. Ranking based on the
  shape-complementarity (Fig.1(a)), pair-potential
  score functions (Fig.1(b)) and the minimized
  energy of all the model structures using CHARMM
  (Fig.1(c)).
• It has been noted from our previous studies that
  a significantly high number of conserved
  positions are present in the naturally occurring
  and functionally important interacting regions of
  protein complexes5. However, in the case of
  antigen-antibody complexes we have observed that
  the region with non-conserved positions is
  involved in interaction. Antibodies are made with
  appropriate variability to interact with the
  antigen and this is not a naturally occurring
  protein-protein interaction, which explains our
  finding.
• We have identified the top 8 (group 1) and top
  17 (group 2) of highly conserved and
  well-exposed surface residues as two groups, in
  each polypeptide chain of the interacting
  complex. These residues are given in Table 2 for
  1tabEI complex as an example. We have then
  counted the total number of group 1 and group 2
  positions in each modeled complex interface
  region.
• In Table 3 we summarize results from the docking
  analysis for all the 6 systems.

We have employed docking calculations and
atomistic simulations to determine the structure
and the binding affinity of protein-protein
complexes. By exploring the interaction
interface, we find that the conservation
information can improve the docking rank. Here we
present our docking studies for five complex
structures. With this procedure, we are
participating in CPARI competition rounds 4 and
5.

Docking Procedure and Energy Minimization

• We have chosen five protein complex structures
  (1TAB, 1EFU, 1FIN, 1JHL, 1KXQ) from the benchmark
  structures suggested by Chen et al1.
• For each protein complex, we employ docking
  calculations using FTDOCK package2,3 to get
  10,000 possible complexes and we obtained the
  shape complementarity rank and pair potential
  rank.
• For each possible complex, using CHARMM
  molecular mechanics simulations4 we minimized
  the side-chain structure, and obtained an
  estimate of the free energy for the generated
  complexes.
• With the weights, we computed an overall rank for
  each docked complex.
• Applied the residue conservation filter to
  improve the rank5,6.

Conclusion
Free Energy Filter

• We described the considerable improvement in
  ranking of the FTDOCK generated model complexes
  using the residue conservation filter. Using
  conservation information we significantly reduce
  the number of docking solutions.
• We also achieve ranking improvement for low RMSD
  structures, simply incorporating linear
  combinations of ranks of shape complementarity,
  pair potential, CHARMM energy, and conserved
  positions.
• As we determine residue conservation in the
  functionally interacting natural proteins, such
  as enzyme-inhibitor complexes, we need to give
  higher ranks for the models with higher number of
  conserved positions in the interface region. In
  the case of unnatural interactions such as
  antigen-antibody complexes the interacting
  regions are highly variable, and we need to give
  higher ranks for the models with low numbers of
  conserved positions.

With some approximation, the free energy change
can be divided into several terms ?G ?Ges
?Gcav ?Gbonding ... ?Gcoulomb
?Gpol SskAk ?Gbonding The individual terms
can be calculated separately. ?Gcoulomb and
?Gpol are calculated by the Generalized Born
model with the Debye-Huckel approximation
Conservation Index of Residue Position As
described above evolutionary distances between
the reference sequence and its homologues were
used to calculate residue conservation index
(CIl) for each position l using amino acid
substitution matrix, similar to the amino acid
conservation used by Valdar and
Thornton8.Conservation Index (CIl) is a
weighted sum of all pair wise similarities
between all residues present at the position. The
CIl value is calculated in a given alignment and
takes a value in the range 0.0 to 1.0.
Where N is the number of homologous sequences in
the alignment si(l) and sj(l) are the amino
acids at the alignment position l of sequences si
and sj respectively ED(si) and ED(sj) are the
average evolutionary distance of s(i) and s(j)
from the remaining homologues. Mut(a,b) measures
the similarity among the amino acids a and b as
derived from amino acid substitution matrix
M(a,b) and defined as
Current and Future Work

• We have used the group 1 and group 2 conservation
  positions as a filter to reduce the total number
  of docked models. We selected only the models,
  which have at least 4 of group 1 positions and 6
  of group 2 positions in the interface region of
  the enzyme-inhibitor model complexes. In the case
  of antigen-antibody complexes (1JHL, 1KXQ) we
  have reversed the selection, limiting to 2 or
  less group1 positions and 4 or less group2
  positions. With the conservation positions
  filter we reduced the number of complexes by
  about 55 to 88 (see Reduced column in Table 3).
  
• In Fig. 2 we have plotted the RMSD versus model
  rank for the remaining models after using the
  conservation positions filter for 1TAB. In Table
  3 we summarized these results for all the six
  systems.
• When we compare Fig 1 with Fig 2 and the
  corresponding rows in the Table 3, when filter is
  on we still select all of the low RMSD models
  plus we also obtain many additional low RMSD
  models. This can be clearly seen by comparing
  Fig. 1(a)-(d) with Fig. 2(a)-(d) of 1TAB. This
  shows that conservation filter not only decreases
  the number of possible docked structures but also
  improves the ranking of the low RMSD models.

• Optimizing the weights for each rank property to
  come up a global rank by working on a larger data
  sample.
• Dissecting the structures of known
  repressor-operator complexes we use
  computationally efficient simulations to
  calculate the binding affinity of
  repressor-operator complexes and identify the
  protein residues that play a central role in
  binding and are amenable to mutations.

where fGB(rij2aij2e-D)1/2, aij(aiaj)1/2,
Drij2/(2aij)2, ai is the effective Born radius
of atom i

• The desolvation term SskAk can be obtained by
  calculating the solvent-accessible-surface-area
  Ak for each residue k and the optimizing weight
  sk
• The bonding term ?Gbonding can be expressed with
  by using self-consistent Lennard-Jones 12-6
  parameters (e, s ) which have been used in AMBER
  and CHARMM software with the form

a,b are the pairs of amino acids at a given
alignment position l. M(a,b)low is the lowest
value in the substitution matrix and M(a,b)max is
the maximum value among all the possible
substitution pairs in that position. Thus the
Mut(a,b) takes a value in the range 0 to 1.
Solvent accessible contact area (SACA) values
were used to identify surface residues and buried
residues. We have identified the top 8 and 17
of highly conserved residues, which have solvent
accessibility greater than 25 of their total
surface area. As an example, in Table 2, we
listed the highly conserved surface residues of
complex 1TABs E and I chains.
.
References

• Chen, R, et al., Proteins. 52, 88-91(2003).
• Gabb, H.A. et al., J. Mol. Biol. 272,
  109-120(1997).
• Moont, G. Et al., Proteins. 35, 364-373(1999).
• Brooks, B.R., et al., J. Comp. Chem. 4,
  187-217(1983).
• Reddy,B.V.B., et al., Submitted to ISMB 2004.
• Duan, Y., et al., To be published.
• Gonnet, G.H., et al, Science 256,
  1443-1445(1992).
• Valdar, W.S., et al., Proteins. 42, 108-124(2001).

• By studying the residue conservation in each
  sequence of heterocomplex structures of
  interacting proteins as a filter we improved our
  docking results.
• From the binding affinity calculation, ?G -RT
  lnkb, we can get the binding constant kb for the
  protein-protein system. By substituting residues
  in certain proteins, combining with molecular
  simulation (CHARMM), we plan to obtain the free
  energy change (??G) which could be strongly
  related to mutation experiments (the work is in
  progress).

1FIN
Acknowledgements
1VIN
1HCL
This work is partially supported by the Army High
Performance Computing Center (AHPCRC) under the
auspices of the Department of the Army, Army
Research Laboratory (ARL) under contract number
DAAD19-01-2-0014. We also thanks the University
of Minnesota Digital Technology Center for
support.
docking
Docked Cyclin-Dependent Kinase 2 Complex(1FIN)
from 1HCL 1VIN, the smallest RMSD we get is
0.41A with rank 2.