Crystal contact-free conformation of an intrinsically flexible loop in protein crystal: Tim21 as the case study

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.

Engineering of an in-cell protein crystal for fastening a metastable conformation of a target miniprotein

Protein crystals can be utilized as porous scaffolds to capture exogenous molecules. Immobilization of target proteins using protein crystals is expected to facilitate X-ray structure analysis of proteins that are difficult to be crystallized. One of the advantages of scaffold-assisted structure determination is the analysis of metastable structures that are not observed in solution. However, efforts to fix target proteins within the pores of scaffold protein crystals have been limited due to the lack of strategies to control protein-protein interactions formed in the crystals. In this study, we analyze the metastable structure of the miniprotein, CLN025, which forms a β-hairpin structure in solution, using a polyhedra crystal (PhC), an in-cell protein crystal. CLN025 is successfully fixed within the PhC scaffold by replacing the original loop region. X-ray crystal structure analysis and molecular dynamics (MD) simulation reveal that CLN025 is fixed as a helical structure in a metastable state by non-covalent interactions in the scaffold crystal. These results indicate that modulation of intermolecular interactions can trap various protein conformations in the engineered PhC and provides a new strategy for scaffold-assisted structure determination.

Crystal structure of potato 14-3-3 protein St14f revealed the importance of helix I in StFDL1 recognition

In potato (Solanum tuberosum L.), 14-3-3 protein forms a protein complex with the FLOWERING LOCUS T (FT)-like protein StSP6A and the FD-like protein StFDL1 to activate potato tuber formation. Eleven 14-3-3 isoforms were reported in potato, designated as St14a-k. In this study, the crystal structure of the free form of St14f was determined at 2.5 Å resolution. Three chains were included in the asymmetric unit of the St14f free form crystal, and the structural deviation among the three chain structures was found on the C-terminal helix H and I. The St14f free form structure in solution was also investigated by nuclear magnetic resonance (NMR) residual dipolar coupling analysis, and the chain B in the crystal structure was consistent with NMR data. Compared to other crystal structures, St14f helix I exhibited a different conformation with larger B-factor values. Larger B-factor values on helix I were also found in the 14-3-3 free form structure with higher solvent contents. The mutation in St14f Helix I stabilized the complex with StFDL1. These data clearly showed that the flexibility of helix I of 14-3-3 protein plays an important role in the recognition of target protein.

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Loops are the most variable and unorganized elements of the secondary structure of proteins. Their ability to shift their shape can play a role in the binding of small ligands, enzymatic catalysis, or protein–protein interactions. Due to the loop flexibility, the positions of their residues in solved structures show the largest B-factors, or in a worst-case scenario can be unknown. Based on the loops’ movements’ timeline, they can be divided into slow (static) and fast (flexible). Although most of the loops that are missing in experimental structures belong to the flexible loops group, the computational tools for loop reconstruction use a set of static loop conformations to predict the missing part of the structure and evaluate the model. We believe that these two loop types can adopt different conformations and that using scoring functions appropriate for static loops is not sufficient for flexible loops. We showed that common model evaluation methods, are insufficient in the case of flexible solvent-exposed loops. Instead, we recommend using the potential energy to evaluate such loop models. We provide a novel model selection method based on a set of geometrical parameters to distinguish between flexible and static loops without the use of molecular dynamics simulations. We have also pointed out the importance of water network and interactions with the solvent for the flexible loop modeling.