The role of flexibility and molecular shape in the crystallization of proteins by surface mutagenesis

Studying the Structures of Relaxed and Fuzzy Interactions: The Diverse World of S100 Complexes

S100 proteins are small, dimeric, Ca²⁺-binding proteins of considerable interest due to their associations with cancer and rheumatic and neurodegenerative diseases. They control the functions of numerous proteins by forming protein–protein complexes with them. Several of these complexes were found to display “fuzzy” properties. Examining these highly flexible interactions, however, is a difficult task, especially from a structural biology point of view. Here, we summarize the available in vitro techniques that can be deployed to obtain structural information about these dynamic complexes. We also review the current state of knowledge about the structures of S100 complexes, focusing on their often-asymmetric nature.

Protein crystal lattices are dynamic assemblies: the role of conformational entropy in the protein condensed phase

Until recently, the occurrence of conformational entropy in protein crystal contacts was considered to be a very unlikely event. A study based on the most accurately refined protein structures demonstrated that side-chain conformational entropy and static disorder might be common in protein crystal lattices. The present investigation uses structures refined using ensemble refinement to show that although paradoxical, conformational entropy is likely to be the major factor in the emergence and integrity of the protein condensed phase. This study reveals that the role of shape entropy and local entropic forces expands beyond the onset of crystallization. For the first time, the complete pattern of intermolecular interactions by protein atoms in crystal lattices is presented, which shows that van der Waals interactions dominate in crystal formation.

Crystallophore: a versatile lanthanide complex for protein crystallography combining nucleating effects, phasing properties, and luminescence

Macromolecular crystallography suffers from two major issues: getting well-diffracting crystals and solving the phase problem inherent to large macromolecules. Here, we describe the first example of a lanthanide complex family named "crystallophore" (Xo4), which contributes to tackling both bottlenecks. This terbium complex, Tb-Xo4, is an appealing agent for biocrystallography, combining the exceptional phasing power of the Tb(iii) heavy atom with powerful nucleating properties, providing ready-to-use crystals for structure determination. Furthermore, protein/Tb-Xo4 co-crystals can be easily detected and discriminated from other crystalline by-products using luminescence. We demonstrate the potential of this additive for the crystallisation and structure determination of eight proteins, two of whose structures were unknown.

Computational crystallization

Crystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one of trial and error. In this article, efforts in the field are discussed together with a theoretical underpinning using a solubility phase diagram. Prior knowledge has been used to develop tools that computationally predict the crystallization outcome and define mutational approaches that enhance the likelihood of crystallization. For the most part these tools are based on binary outcomes (crystal or no crystal), and the full information contained in an assembly of crystallization screening experiments is lost. The potential of this additional information is illustrated by examples where new biological knowledge can be obtained and where a target can be sub-categorized to predict which class of reagents provides the crystallization driving force. Computational analysis of crystallization requires complete and correctly formatted data. While massive crystallization screening efforts are under way, the data available from many of these studies are sparse. The potential for this data and the steps needed to realize this potential are discussed.