Protein Design Using Continuous Rotamers

Optimizing amino acid conformation and identity is a central problem in computational protein design. Protein design algorithms must allow realistic protein flexibility to occur during this optimization, or they may fail to find the best sequence with the lowest energy. Most design algorithms implement side-chain flexibility by allowing the side chains to move between a small set of discrete, low-energy states, which we call rigid rotamers. In this work we show that allowing continuous side-chain flexibility (which we call continuous rotamers) greatly improves protein flexibility modeling. We present a large-scale study that compares the sequences and best energy conformations in 69 protein-core redesigns using a rigid-rotamer model versus a continuous-rotamer model. We show that in nearly all of our redesigns the sequence found by the continuous-rotamer model is different and has a lower energy than the one found by the rigid-rotamer model. Moreover, the sequences found by the continuous-rotamer model are more similar to the native sequences. We then show that the seemingly easy solution of sampling more rigid rotamers within the continuous region is not a practical alternative to a continuous-rotamer model: at computationally feasible resolutions, using more rigid rotamers was never better than a continuous-rotamer model and almost always resulted in higher energies. Finally, we present a new protein design algorithm based on the dead-end elimination (DEE) algorithm, which we call iMinDEE, that makes the use of continuous rotamers feasible in larger systems. iMinDEE guarantees finding the optimal answer while pruning the search space with close to the same efficiency of DEE. Availability: Software is available under the Lesser GNU Public License v3. Contact the authors for source code

TransCent: Computational enzyme design by transferring active sites and considering constraints relevant for catalysis

BACKGROUND: Computational enzyme design is far from being applicable for the general case. Due to computational complexity and limited knowledge of the structure-function interplay, heuristic methods have to be used. RESULTS: We have developed TransCent, a computational enzyme design method supporting the transfer of active sites from one enzyme to an alternative scaffold. In an optimization process, it balances requirements originating from four constraints. These are 1) protein stability, 2) ligand binding, 3) pKa values of active site residues, and 4) structural features of the active site. Each constraint is handled by an individual software module. Modules processing the first three constraints are based on state-of-the-art concepts, i.e. RosettaDesign, DrugScore, and PROPKA. To account for the fourth constraint, knowledge-based potentials are utilized. The contribution of modules to the performance of TransCent was evaluated by means of a recapitulation test. The redesign of oxidoreductase cytochrome P450 was analyzed in detail. As a first application, we present and discuss models for the transfer of active sites in enzymes sharing the frequently encountered triosephosphate isomerase fold. CONCLUSION: A recapitulation test on native enzymes showed that TransCent proposes active sites that resemble the native enzyme more than those generated by RosettaDesign alone. Additional tests demonstrated that each module contributes to the overall performance in a statistically significant manner

Directed evolution of bioactive compounds: oxa(thia)zole-containing post-translationally modified peptides

Thiazole/Oxazole Modified Microcins (TOMMs) are a diverse class of post-translationally modified peptides including many bioactive compounds; a potential new source for drug discovery. Despite a limited understanding of the TOMM synthase heterotrimeric complex biosynthetic mechanism, a variable degree of substrate plasticity is present in the family. This makes them attractive targets for developing novel oxazole- and thiazole-containing compounds from synthetic peptides. Available annotation on complex members suggests the presence of different biochemical activities among homologous proteins, precluding the use of established prediction methods for identification of functional residues. A novel algorithm was developed (Normalised Shannon Entropy, NoSE) for functional prediction from sequence alignments containing mixed functions. NoSE was applied, along with established conservation- and coevolution-based metrics, to detect functional residues in the well-characterised bacterial Solute Binding Protein family, which could be validated against the extensively reported characterisation. The strategy was applied for functional residue prediction in the TOMM synthase complex and candidate functional residues were mutated in McbC dehydrogenase of Escherichia coli. Mutants were assessed using a bacterial growth inhibition bioassay and six out of sixteen mutations reduced TOMM production, demonstrating the value of employing a prediction strategy to improve characterization of proteins. Attempts at establishing an in vitro assay for TOMM biosynthesis were unsuccessful due to difficulties in protein expression and purification, as well as inconsistent assay results. Finally, a framework for directed evolution of length-variable proteins was developed, with the aim of engineering synthetic TOMM products. A method was developed for assembly of high-quality libraries at a low cost, along with a workflow for enriched motif detection in selection experiments. The approach was validated by isolating seven novel variants of the β-lactamase TEM-1 active on a non-cognate substrate. Together, the developed methods represent a foundation for establishing TOMM biosynthesis as a platform for discovery of novel bioactive compounds