Challenges in the computational design of proteins

Abstract

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

Figure 1

(a) The folding problem. Given an amino acid sequence, the goal is to predict the final structure it will adopt after the folding process. (b) The inverse folding problem. Among the astronomically large number of possible sequences, we find those stabilizing a given fold. The sequences are ranked, for the chosen fold, by evaluating their folding free energy and only the ones with the lowest energy are kept. The goal in computational protein design is to find at least one sequence able to adopt the chosen structure.

Figure 2

Placement of water molecules in the solvated rotamers. Studies on the distribution of water molecules around backbone and polar residues lead to the construction of solvated rotamers ((a) carbonyl oxygen in Asp, Glu, Gln and Asn; (b) amide nitrogen in Lys, Arg, Gln and Asn; (c) hydroxyl oxygen in Ser and Thr; (d) hydroxyl oxygen in Tyr; (e) aromatic hydrogen in His). Although only modifications to the side-chains have been included here, extra water molecules can also be included in the backbone description. The solvated rotamers can afterwards be introduced in computational protein design (Jiang et al. 2005).

Figure 3

(a) Iterative protocol for backbone and sequence simultaneous optimization. The use of the same energy function for backbone and sequence optimization guarantees that all interactions will be equally treated. This protocol was employed by Kuhlman et al. (2003) in the design of Top7, a 93-residue α/β protein designed to have a novel sequence and fold. The iterative combinatorial optimization protocol used in this design was based on the use of heuristic methods: Monte Carlo protocol with Metropolis criterion. (b) The structure of Top7 is shown. PDB, Protein Data Bank.

Figure 4

Example of positive and negative design states: schematic of competing states included in the design of ligand binding-induced allosteric changes ((a) open conformation, no binding; (b) open conformation with binding; (c) aggregated state; (d) closed conformation, no binding ligand; (e) closed conformation with binding; (f) unfolded state and ligand). The optimization has to consider two target states corresponding to the two desired conformations (a,e), guaranteeing stability of the open and closed conformations. Competing structures are included for stability (f), sensitivity (d), affinity (b) and solubility (c), and we will have to maximize their free energies simultaneously. The energies to minimize are and . reflects the probability of the ligand binding and not inducing the corresponding conformational change. reflects the probability of the conformational transition taking place in the absence of the ligand. The probability that a designed protein will adopt the target state is given by .

Figure 5

Redesign of ribose-binding protein for specificity towards trinitrotoluene. (a) Wild-type protein binding a ribose molecule (Protein Data Bank access code 2DRI). (b) Protein redesigned to recognize trinitrotoluene by Looger et al. (2003).

Figure 6

Redesign of the interface between the DNase E7 and immunity protein Im7. Wild-type DNase E7 has PDB access code 7CEI. The redesign of the interface produced a mutant with a 300-fold higher specificity (PDB access code 2ERH). The figure shows the following mutations: D35Y, T51Q (Im7) and T539Q, K528Q (E7) and the changes they introduce in the H-bond network in the interface between the protein and the inhibitor.