From 2007.igem.org

Introduction

Protein Design

Designing efficient proteins for a broad of different processes is of tremendous practical fun both in science and industry. [1]. Computational design is well concerned for its efficiency and convenience. Redesigning the protein-DNA complexes is so important that it is the first step concerned at the beginning of redesigning the active sites. Milestones have been reported[2-5]. Here we are trying to construct several artificial repressor-operator pairs to serve as the connecting wires of our system.

There are several steps in protein design[6]. Firstly, generate a random structure with random sequence. Secondly, optimize the structure of the side-chains for each random sequence. Thirdly, give each random sequence a score and select the sequence with the best score. In almost all the conditions, sequence candidates is countless. There will be 20²⁰ candidates if we redesign 20 positions. Therefore, great efforts have been made to reduce the computation complexity[1,7-14], for example, using rotamer representing several discrete conformation, to work out the status of sidechains, employing pair-wise energy function to score the sequence, searching with Monte Carlo, Genetic Algorithm and etc. Another key here is the score function. Up till now, no efficient method in silico has been given to examine the computational design results. One way to examine them is to express the sequences designed in practical experiments.

Lac Repressor

Figure 1 Solution structure of a dimer of Lac repressor DNA-binding domain complexed to its natural operator O1 ([http://www.rcsb.org/pdb/explore.do?structureId=1L1M From RCSB])

The lac repressor is a DNA-binding protein which inhibits the expression of genes coding for proteins involved in the metabolism of lactose in bacteria[15]. There are three distinct regions in the protein. The headpiece, a fragment contains approximately 51 amino acids from its N-terminal together with a helix-turn-helix motif, is the only region that serves to bind DNA. Figure 1 shows the NMR structure of the complex with two headpieces of the lac repressor and its nature operator[16]. A series of experiments have shown that the mutation on the 7th, 9th site of the operator can sharply reduce the stability of the complex[15-17]. Figure 2 shows the structure of DNA sequence on site 7,8,9 and the residues related to them. From this structure, we come to know that Residue 17 and 18, which are YQ in native repressor sequence, take charge in recognizing the specific DNA sequence. The binding specificity may be adapted by changing the two positions.

As to the wires exempt from interference, the binding specificity is highly required for the repressor-operator pairs. It is reported that the mutated repressor with VA at Position 17,18 can bind to the transitionally mutated operator on Site 7 [18]. Transversion might lead to changes too big for us to rebuild the DNA structure according to the native structure, thus, no mutated lac repressor can bind to it. Therefore, only transition mutation made on the DNA, and the structure was built manually according to native structure and then optimized with gromos96, 43a1 forcefield. In this way, we have obtained four DNA structures. Furthermore, we aims at accomplish in the near future redesigning the recognition region of the repressor for specific bindings to the operator DNA.

Figure 2 The structure of DNA sequence on site 7,8,9 of the recognition helix of Lac repressor and the residues interaction with them

Computational Model

Score function

Our score function here is a combination of physic-based potentials and knowledge-based potentials. The energy items should be modified to suit the rotamer library. The modified van der waal interaction, hydrogen bond energy, solvent accessible surface area (SASA) and electrostatic interaction are applied as items for score function.

Linearize van der waal interaction

The vdw interaction energy[19] between atom i and atom j at the distance of r is:

Hydrogen bond energy

Clashes in redesigned structures will occurs more often than those in native structure, caused by discrete represent proteins sidechains. A tolerate acception of clash is given by linearizing the resplusive item in the vdw energy, and the parameters are decided by test. given the distance between the donor and acceptor, the hydrogen bond energy is:

Describing sidechain structure with rotamers is changing the distance between the donor and acceptor too, and the position of hydrogen atom can not be obtained in a easy way as it rotates. Here we build a knowledge-base potential base d on the distance of donor and acceptor in the coarse-grained structures.

SASA and Electrostatic Interaction

The energy items for SASA and electostatic interaction are defined by TM Handel and her co-workers[8].

Parameterization

We optimize sidechain of the proteins and select sequence with different score function. Because they are totally different processes on concept. Sidechain optimization tells us the 3-dimensional structure of the complex after changing the amino acids in the protein and the amino acid sequence selection tells us which sequence is more suitable for binding. The role of each energy item is not the same in two process . The parameters for sidechain optimized are obtained by training with 49 structures picked from PDBbind library [20], and they for sequence selection are by training with the results of directed evolution.

Results

In this particular problem, only 2 positions on the protein to be changed, so there are only 400 candidates. The computational complexity is no more a problem here. We optimized the sidechain of each candidate, and get the each energy items. With the directed evolution, we find the suitable parameters for sequence selection and predict that some other chains may work in this problem. And the experiments of test are processing.

References

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