Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems

Multiple Functions of Compatible Solute Ectoine and Strategies for Constructing Overproducers for Biobased Production

Ectoine and its derivative 5-hydroxyectoine are compatible solutes initially found in the hyperhalophilic bacterium Ectothiorhodospira halochloris, which inhabits the desert in Egypt. The habitat of ectoine producers implies the primary function of ectoine as a cytoprotectant against harsh conditions such as high salinity, drought, and high radiation. More extensive and in-depth studies have revealed the multiple functions of ectoine in its native producer bacterial cells and other types of cells and its biomolecular components (such as proteins and DNA) as a general protective agent. Its chemical properties as a bio-based amino acid derivative make it attractive for basic scientific research and related industries, such as the food/agricultural industry, cosmetic manufacturing, biologics, and therapeutic agent preparation. This article first discusses the functions and applications of ectoine and 5-hydroxyectoine. Subsequently, more emphasis was placed on advances in bio-based ectoine and/or 5-hydroxyectoine production. Strategies for developing more robust cell factories for highly efficient ectoine and/or 5-hydroxyectoine production are further discussed. We hope this review will provide a valuable reference for studies on the bio-based production of ectoine and 5-hydroxyectoine.

Crystal structure of a human MUC16 SEA domain reveals insight into the nature of the CA125 tumor marker

MUC16 is a membrane bound glycoprotein involved in the progression and metastasis of pancreatic and ovarian cancer. The protein is shed into the serum and the resulting cancer antigen 125 (CA125) can be detected by immunoassays. The CA125 epitope is used for monitoring ovarian cancer treatment progression, and has emerged as a potential target for antibody mediated immunotherapy. The extracellular tandem repeat domain of the protein is composed of repeating segments of heavily glycosylated sequence intermixed with homologous SEA (Sperm protein, Enterokinase and Agrin) domains. Here we report the purification and the first X-ray structure of a human MUC16 SEA domain. The structure was solved by molecular replacement using a Rosetta generated structure as a search model. The SEA domain reacted with three different MUC16 therapeutic antibodies, confirming that the CA125 epitope is localized to the SEA domain. The structure revealed a canonical ferredoxin-like fold, and contained a conserved disulfide bond. Analysis of the relative solvent accessibility of side chains within the SEA domain clarified the assignment of N-linked and O-linked glycosylation sites within the domain. A model of the glycosylated SEA domain revealed two major accessible faces, which likely represent the binding sites of CA125 specific antibodies. The results presented here will serve to accelerate future work to understand the functional role of MUC16 SEA domains and antibody recognition of the CA125 epitope.

X-ray Crystal Structure Analysis of VHH-Protein Antigen Complexes

VHHs are antigen-binding domains cloned from heavy-chain antibodies found in camelids. These proteins have generated considerable interest in a variety of applications as research reagents, crystallization chaperones, and therapeutics. The evolutionary adaptations of VHHs have resulted in biophysical properties and antigen-binding modalities which are distinct from those of conventional antibodies. A detailed molecular analysis of VHH interactions with their cognate protein antigens is valuable for understanding structure-function relationships and for protein engineering. The majority of VHHs bind to folded proteins and thus recognize discontinuous three-dimensional epitopes. While multiple approaches exist for dissecting the interaction between a protein antigen and a VHH, X-ray crystallography remains the highest resolution method available. Here, we provide an updated procedure for determining and analyzing the X-ray structure of a VHH in complex with a protein antigen. We describe the recombinant expression and purification of VHHs and protein antigens, purification and analysis of protein complexes, crystallization, and optimization, X-ray structure determination by molecular replacement, and analysis of the complex.

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.

Current directions in combining simulation-based macromolecular modeling approaches with deep learning

Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.

Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8

The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre-existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for MR. The ORF8 protein of SARS-CoV-2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all β-sheet fold and few orthologs. We previously determined experimentally the structure of ORF8 using the single anomalous dispersion (SAD) phasing method, having been unable to find an MR solution to the crystallographic phase problem. Following a report of an accurate prediction of the ORF8 structure, we assessed whether the predicted model would have succeeded as an MR search model. A phase problem solution was found, and the resulting structure was refined, yielding structural parameters equivalent to the original experimental solution.

Synthesis and evaluation of disulfide-rich cyclic α-conotoxin [S9A]TxID analogues as novel α3β4 nAChR antagonists

Head-to-tail cyclization is an effective strategy to improve the biological stability of peptides. The α-conotoxin [S9A]TxID is a peptide that inhibits α3β4 nAChR with high activity and selectivity. Herein, we established a method for cyclizing and oxidative folding of [S9A]TxID, and six cyclic analogues of [S9A]TxID were chemically synthesized with various linker lengths. We used the electrophysiology assay to measure activity values of these cyclic analogues, and obtained the most potent analogue c[S9A]TxID-6, which was more stable than native [S9A]TxID against proteinase K.

Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8

The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre-existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for molecular replacement. The ORF8 protein of SARS-CoV-2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all β-sheet fold and few orthologs. We previously determined experimentally the structure of ORF8 using the single anomalous dispersion (SAD) phasing method, having been unable to find an MR solution to the crystallographic phase problem. Following a report of an accurate prediction of the ORF8 structure, we assessed whether the predicted model would have succeeded as an MR search model. A phase problem solution was found, and the resulting structure was refined, yielding structural parameters equivalent to the original experimental solution.

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pK_a determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.