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Energy design for protein-protein interactions
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Image of FIG. 1.
FIG. 1.

The relation of vdw approximation function to the Lennard-Jones 6/12 potential (dotted line), was set to −9.

Image of FIG. 2.
FIG. 2.

The problem of learning to dock is approached as learning a linear separator w(represented by the line here) that scores the native transformation (+) above all possible transformations (−). In formulation same penalty is paid in both cases while case 2 has many false positives.

Image of FIG. 3.
FIG. 3.

Elements of the optimal cover Π* are in red and elements of the current cover Π e are in black. When the set of parameters w* is used, slack cost is paid only for points in a red cluster.

Image of FIG. 4.
FIG. 4.

The scoring function converges as iterative learning proceeds, for each iteration we plot the dot product between the parameters (normalized to have L2 norm 1) at this iteration and the previous iteration. The blip at iteration 2 arises due to switching from linear programming to quadratic programming for parameter estimation.

Image of FIG. 5.
FIG. 5.

Outline of algorithm used in CAPRI to predict mode of binding in a protein-protein interaction, testing and available as web service. We only retain 219 = 524 388 conformations due to computational limitations.

Image of FIG. 6.
FIG. 6.

In proof of the theorem for error analysis of DOCK/PIE, P is the position of the particle upon application of transformation τ* and Q is the position upon application of transformation τ. Consider spheres of radii R min  and R max  around P and Q. The difference between contact potential at P and Q is only dependent on particles in the regions 2 and 3.


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Algorithm for docking

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Algorithm for learning to dock

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Table I.

A complex is said to be explained if a high quality hit – is ranked within top N.

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Table II.

Vdw residue backbone scoring function (PIE_Vdw_Res_Bkbn).

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Table III.

Performance of Dock/PIE is comparable to Zdock (Ref. 11) with Zrank (Ref. 33), Cluspro (Ref. 34), and Gramm-X (Ref. 35). A model is said to be a hit if (irmsd ⩽ 4 Å) from native. The entries of Besthit indicate the lowest ranked model that is a hit. ZD3.0ZR is the result of rescoring transformations generated by Zdock3.0 using Zrank. We use the greedy i-RMSD based clustering developed as part of our algorithm on structures generated by Zdock3.0 with Zrank and report the results under the columns labeled ZDZR + cluster. In summary, Dock/PIE predicted correctly 0/5/8 complexes in the top 1/top 10/top 100 hits, Zdock3.0 1/3/6, ZD3.0ZR 1/1/4, ZDZR + cluster 1/1/6, Cluspro 0/5/7, and Grammx 0/2/5. Dock/PIE according to this test is at par with these leading technologies.

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Table IV.

Comparing Dock/PIE and ZDOCK + ZRANK on Zlab benchmark. Dock/PIE ranks a near native solution at the top 1/top10/top 100 in 12/28/52 cases compared to 10/21/41 by Zdock3.0.

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Table V.

Top 54 000 structures generated by Zdock3.0 are re-ranked under various schemes, for each case the rank for which the probability that a random scoring function will do better with probability 0.5 is computed, the row labeled reference summarizes this evaluation (Zdock3.0 generated a lot of near native structures, scoring at random would pick up a hit in top 100 models in 162 cases). Statistical potentials capture signal in protein-protein interfaces in the PDB, our iterative learning procedure does a better job of mining this information. Round6 potential does a better job when used for sampling and scoring rather than rescoring alone.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Energy design for protein-protein interactions