Dead-end elimination with perturbations (DEEPer): a provable protein design algorithm with continuous sidechain and backbone flexibility

Methods for library-scale computational protein design

Faced with a protein engineering challenge, a contemporary researcher can choose from myriad design strategies. Library-scale computational protein design (LCPD) is a hybrid method suitable for the engineering of improved protein variants with diverse sequences. This chapter discusses the background and merits of several practical LCPD techniques. First, LCPD methods suitable for delocalized protein design are presented in the context of example design calculations for cellobiohydrolase II. Second, localized design methods are discussed in the context of an example design calculation intended to shift the substrate specificity of a ketol-acid reductoisomerase Rossmann domain from NADPH to NADH.

Improved energy bound accuracy enhances the efficiency of continuous protein design

Flexibility and dynamics are important for protein function and a protein's ability to accommodate amino acid substitutions. However, when computational protein design algorithms search over protein structures, the allowed flexibility is often reduced to a relatively small set of discrete side-chain and backbone conformations. While simplifications in scoring functions and protein flexibility are currently necessary to computationally search the vast protein sequence and conformational space, a rigid representation of a protein causes the search to become brittle and miss low-energy structures. Continuous rotamers more closely represent the allowed movement of a side chain within its torsional well and have been successfully incorporated into the protein design framework to design biomedically relevant protein systems. The use of continuous rotamers in protein design enables algorithms to search a larger conformational space than previously possible, but adds additional complexity to the design search. To design large, complex systems with continuous rotamers, new algorithms are needed to increase the efficiency of the search. We present two methods, PartCR and HOT, that greatly increase the speed and efficiency of protein design with continuous rotamers. These methods specifically target the large errors in energetic terms that are used to bound pairwise energies during the design search. By tightening the energy bounds, additional pruning of the conformation space can be achieved, and the number of conformations that must be enumerated to find the global minimum energy conformation is greatly reduced.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

De novo design of a transmembrane Zn²⁺-transporting four-helix bundle

The design of functional membrane proteins from first principles represents a grand challenge in chemistry and structural biology. Here, we report the design of a membrane-spanning, four-helical bundle that transports first-row transition metal ions Zn(2+) and Co(2+), but not Ca(2+), across membranes. The conduction path was designed to contain two di-metal binding sites that bind with negative cooperativity. X-ray crystallography and solid-state and solution nuclear magnetic resonance indicate that the overall helical bundle is formed from two tightly interacting pairs of helices, which form individual domains that interact weakly along a more dynamic interface. Vesicle flux experiments show that as Zn(2+) ions diffuse down their concentration gradients, protons are antiported. These experiments illustrate the feasibility of designing membrane proteins with predefined structural and dynamic properties.

Computational tools for designing and engineering enzymes

Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution.

Improving computational efficiency and tractability of protein design using a piecemeal approach. A strategy for parallel and distributed protein design

MOTIVATION

Accuracy in protein design requires a fine-grained rotamer search, multiple backbone conformations, and a detailed energy function, creating a burden in runtime and memory requirements. A design task may be split into manageable pieces in both three-dimensional space and in the rotamer search space to produce small, fast jobs that are easily distributed. However, these jobs must overlap, presenting a problem in resolving conflicting solutions in the overlap regions.

RESULTS

Piecemeal design, in which the design space is split into overlapping regions and rotamer search spaces, accelerates the design process whether jobs are run in series or in parallel. Large jobs that cannot fit in memory were made possible by splitting. Accepting the consensus amino acid selection in conflict regions led to non-optimal choices. Instead, conflicts were resolved using a second pass, in which the split regions were re-combined and designed as one, producing results that were closer to optimal with a minimal increase in runtime over the consensus strategy. Splitting the search space at the rotamer level instead of at the amino acid level further improved the efficiency by reducing the search space in the second pass.

AVAILABILITY AND IMPLEMENTATION

Programs for splitting protein design expressions are available at www.bioinfo.rpi.edu/tools/piecemeal.html CONTACT: bystrc@rpi.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Computational design of protein-small molecule interfaces

The computational design of proteins that bind small molecule ligands is one of the unsolved challenges in protein engineering. It is complicated by the relatively small size of the ligand which limits the number of intermolecular interactions. Furthermore, near-perfect geometries between interacting partners are required to achieve high binding affinities. For apolar, rigid small molecules the interactions are dominated by short-range van der Waals forces. As the number of polar groups in the ligand increases, hydrogen bonds, salt bridges, cation-π, and π-π interactions gain importance. These partial covalent interactions are longer ranged, and additionally, their strength depends on the environment (e.g. solvent exposure). To assess the current state of protein-small molecule interface design, we benchmark the popular computer algorithm Rosetta on a diverse set of 43 protein-ligand complexes. On average, we achieve sequence recoveries in the binding site of 59% when the ligand is allowed limited reorientation, and 48% when the ligand is allowed full reorientation. When simulating the redesign of a protein binding site, sequence recovery among residues that contribute most to binding was 52% when slight ligand reorientation was allowed, and 27% when full ligand reorientation was allowed. As expected, sequence recovery correlates with ligand displacement.

OSPREY: protein design with ensembles, flexibility, and provable algorithms

We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side-chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application.

AVAILABILITY

OSPREY is free and open source under a Lesser GPL license. The latest version is OSPREY 2.0. The program, user manual, and source code are available at www.cs.duke.edu/donaldlab/software.php.

CONTACT

osprey@cs.duke.edu.