Computational protein design for given backbone: recent progresses in general method-related aspects

Highlights

• High success rate protein design for given backbone remains an important goal.

• Physic-based energies for polar interactions need to be balanced with desolvation.

• New statistical energy functions have been developed and experimentally tested.

• Several methods consider limited backbone flexibility during sequence optimization.

• Efficient experimental approaches accelerate computational method development.

To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.

Introduction

In different protein design tasks, it is often common requisites for the designed protein to possess a thermodynamically stable and well-defined three-dimensional structure. Thus a primary quest of computational protein design is to design proteins that fold into a target structure, or form a stable complex of a well-defined structure with given molecular partners. This difficult question may be split into two sub-questions to make it more tractable (Figure 1). The first sub-question is how to define or construct a backbone structure that is of high designability (which means that there exist a relatively large number of amino acid sequences that stably form the structure). The second sub-question is how to find (some of) these amino acid sequences. So far there still lacks a general approach to address the first sub-question, although there were several significant advances for the design of particular types of backbone structures lately [1, 2, 3, 4, 5] (Figure 2). For α-helical coiled-coil backbones, their designability has been analyzed mainly in the space of the so-called crick-parameters [1, 2, 6]. Besides this backbone type, analyses of secondary structure and linking loop patterns in relation to tertiary structure packing motifs have led Koga et al. [3] to design ‘idealized’ backbones, and Lin et al. [4] to design backbones of controlled secondary structure sizes and overall shapes. In another work, Park et al. demonstrated a step-wise strategy to design repeat proteins of controlled curvatures [5].

On the other hand, the sub-question of designing amino acid sequences for a given backbone structure has been systematically addressed by computational protein design (CPD), and it comprises the main topic with which the current review is concerned. Sequence design is carried out via the classical approach of minimizing a structure-dependent objective function (i.e. the energy function) with respect to the amino acid sequence (Figure 3). The same approach is employed to design protein–protein [7] or protein–small molecule binding interfaces [8]. Through CPD, the number of fully automatically designed proteins that form thermodynamically stable and well-defined structures have been increasing continuously. Despite this, current energy models used in CPD are not yet perfect and may result in low success rates and inaccurate designs [9^•]. Improvements of sequence design for given backbones can make CPD a more effective tool for different applications. In addition, being able to consistently find good sequences for arbitrary types of designable backbones will obviously accelerate solving the first sub-problem mentioned above, which is the creating of new backbones. In this review, we give an overview of works published in recent years that are related to the methods of CPD for given backbones in aspects that are associated with improving the accuracy or success rates of the method (see also Figure 3): energy functions, treating structural flexibility and/or negative design, assessing and refining individual sequences, and efficient experimental approaches that can provide extensive feedback information for method improvements.