Protein–protein interaction and quaternary structure

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Encoding the space of protein-protein binding interfaces by artificial intelligence

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

Software and Databases for Protein-Protein Docking

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Self-Assembled Protein Nanostructures via Irreversible Peptide Assembly

The quaternary structure of proteins extends the functionality of monomeric proteins. Similarly, self-assembled protein nanostructures (SPrNs) have great potential to improve the functionality and complexity of proteins; however, the difficulty associated with the fabrication of SPrNs is far greater than that associated with the fabrication of self-assembled peptides or polymers and often requires sophisticated computational design. To make the process of SPrN formation simpler and more intuitive, herein, we devise a strategy to adopt an irreversible self-assembled peptide nanostructure (SPeN) process en route to the formation of SPrNs. The strategy employs three sequential steps: first, the formation of SPeNs (an equilibrium process); second, covalent capture of SPeNs (an irreversible process); third, the final assembly of SPrNs via protein-peptide interactions (an equilibrium process). This strategy allowed us to fabricate SPrNs in which the size of the protein was approximately 9 times higher than that of the self-assembling peptide. Furthermore, we demonstrated that the irreversible SPeN could be used as a primary building block for assembly into superstructures. Overall, this strategy is conceptually as simple as SPeN fabrication and is potentially applicable to any soluble protein.

Motif-dependent binding on the intervening domain regulates O-GlcNAc transferase

The modification of intracellular proteins with O-linked β-N-acetylglucosamine (O-GlcNAc) moieties is a highly dynamic process that spatiotemporally regulates nearly every important cellular program. Despite its significance, little is known about the substrate recognition and regulation modes of O-GlcNAc transferase (OGT), the primary enzyme responsible for O-GlcNAc addition. In this study, we identified the intervening domain (Int-D), a poorly understood protein fold found only in metazoan OGTs, as a specific regulator of OGT protein-protein interactions and substrate modification. Using proteomic peptide phage display (ProP-PD) coupled with structural, biochemical and cellular characterizations, we discovered a strongly enriched peptide motif, employed by the Int-D to facilitate specific O-GlcNAcylation. We further show that disruption of Int-D binding dysregulates important cellular programs, including response to nutrient deprivation and glucose metabolism. These findings illustrate a mode of OGT substrate recognition and offer key insights into the biological roles of this unique domain.

Comparative analysis of permanent and transient domain-domain interactions in multi-domain proteins

Protein domains are structural, functional, and evolutionary units. These domains bring out the diversity of functionality by means of interactions with other co-existing domains and provide stability. Hence, it is important to study intra-protein inter-domain interactions from the perspective of types of interactions. Domains within a chain could interact over short timeframes or permanently, rather like protein-protein interactions (PPIs). However, no systematic study has been carried out between two classes, namely permanent and transient domain-domain interactions. In this work, we studied 263 two-domain proteins, belonging to either of these classes and their interfaces on the basis of several factors, such as interface area and details of interactions (number, strength, and types of interactions). We also characterized them based on residue conservation at the interface, correlation of residue motions across domains, its involvement in repeat formation, and their involvement in particular molecular processes. Finally, we could analyze the interactions arising from domains in two-domain monomeric proteins, and we observed significant differences between these two classes of domain interactions and a few similarities. This study will help to obtain a better understanding of structure-function and folding principles of multi-domain proteins.

Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Using AlphaFold Predictions in Viral Research

Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.

Computational analyses of the interactome between TNF and TNFR superfamilies

Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling.

Targeting Ras-binding domain of ELMO1 by computational nanobody design

The control of cell movement through manipulation of cytoskeletal structure has therapeutic prospects notably in the development of novel anti-metastatic drugs. In this study, we determine the structure of Ras-binding domain (RBD) of ELMO1, a protein involved in cytoskeletal regulation, both alone and in complex with the activator RhoG and verify its targetability through computational nanobody design. Using our dock-and-design approach optimized with native-like initial pose selection, we obtain Nb01, a detectable binder from scratch in the first-round design. An affinity maturation step guided by structure-activity relationship at the interface generates 23 Nb01 sequence variants and 17 of them show enhanced binding to ELMO1-RBD and are modeled to form major spatial overlaps with RhoG. The best binder, Nb29, inhibited ELMO1-RBD/RhoG interaction. Molecular dynamics simulation of the flexibility of CDR2 and CDR3 of Nb29 reveal the design of stabilizing mutations at the CDR-framework junctions potentially confers the affinity enhancement.

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

Unnaturally Distorted Hexagonal Protein Ring Alternatingly Reorganized from Two Distinct Chemically Modified Proteins

In this study, we constructed a semiartificial protein assembly of alternating ring type, which was modified from the natural assembly state via incorporation of a synthetic component at the protein interface. For the redesign of a natural protein assembly, a scrap-and-build approach employing chemical modification was used. Two different protein dimer units were designed based on peroxiredoxin from Thermococcus kodakaraensis, which originally forms a dodecameric hexagonal ring with six homodimers. The two dimeric mutants were reorganized into a ring by reconstructing the protein-protein interactions via synthetic naphthalene moieties introduced by chemical modification. Cryo-electron microscopy revealed the formation of a uniquely shaped dodecameric hexagonal protein ring with broken symmetry, distorted from the regular hexagon of the wild-type protein. The artificially installed naphthalene moieties were arranged at the interfaces of dimer units, forming two distinct protein-protein interactions, one of which is highly unnatural. This study deciphered the potential of the chemical modification technique that constructs semiartificial protein structures and assembly hardly accessible by conventional amino acid mutations.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

A novel binding site on the cryptic intervening domain is a motif-dependent regulator of O-GlcNAc transferase

The modification of intracellular proteins with O-linked β- N -acetylglucosamine (O-GlcNAc) moieties is a highly dynamic process that spatiotemporally regulates nearly every important cellular program. Despite its significance, little is known about the substrate recognition and regulation modes of O-GlcNAc transferase (OGT), the primary enzyme responsible for O-GlcNAc addition. In this study, we have identified the intervening domain (Int-D), a poorly understood protein fold found only in metazoan OGTs, as a specific regulator of OGT protein-protein interactions and substrate modification. Utilizing an innovative proteomic peptide phage display (ProP-PD) coupled with structural, biochemical, and cellular characterizations, we discovered a novel peptide motif, employed by the Int-D to facilitate specific O-GlcNAcylation. We further show that disruption of Int-D binding dysregulates important cellular programs including nutrient stress response and glucose metabolism. These findings illustrate a novel mode of OGT substrate recognition and offer the first insights into the biological roles of this unique domain.

Fluorous-Directed Assembly of DNA Origami Nanostructures

An orthogonal, noncovalent approach to direct the assembly of higher-order DNA origami nanostructures is described. By incorporating perfluorinated tags into the edges of DNA origami tiles we control their hierarchical assembly via fluorous-directed recognition. When we combine this approach with Watson-Crick base-pairing we form discrete dimeric constructs in significantly higher yield (8x) than when either molecular recognition method is used in isolation. This integrated "catch-and-latch" approach, which combines the strength and mobility of the fluorous effect with the specificity of base-pairing, provides an additional toolset for DNA nanotechnology, one that enables increased assembly efficiency while requiring significantly fewer DNA sequences. As a result, our integration of fluorous-directed assembly into origami systems represents a cheap, atom-efficient means to produce discrete superstructures.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

Molecular Determinants of Human Rhinovirus Infection, Assembly, and Conformational Stability at Capsid Protein Interfaces

Human rhinovirus (HRV), one of the most frequent human pathogens, is the major causative agent of common colds. HRVs also cause or exacerbate severe respiratory diseases, such as asthma or chronic obstructive pulmonary disease. Despite the biomedical and socioeconomic importance of this virus, no anti-HRV vaccines or drugs are available yet. Protein-protein interfaces in virus capsids have increasingly been recognized as promising virus-specific targets for the development of antiviral drugs. However, the specific structural elements and residues responsible for the biological functions of these extended capsid regions are largely unknown. In this study, we performed a thorough mutational analysis to determine which particular residues along the capsid interpentamer interfaces are relevant to HRV infection as well as the stage(s) in the viral cycle in which they are involved. The effect on the virion infectivity of the individual mutation to alanine of 32 interfacial residues that, together, removed most of the interpentamer interactions was analyzed. Then, a representative sample that included many of those 32 single mutants were tested for capsid and virion assembly as well as virion conformational stability. The results indicate that most of the interfacial residues, and the interactions they establish, are biologically relevant, largely because of their important roles in virion assembly and/or stability. The HRV interpentamer interface is revealed as an atypical protein-protein interface, in which infectivity-determining residues are distributed at a high density along the entire interface. Implications for a better understanding of the relationship between the molecular structure and function of HRV and the development of novel capsid interface-binding anti-HRV agents are discussed. IMPORTANCE The rising concern about the serious medical and socioeconomic consequences of respiratory infections by HRV has elicited a renewed interest in the development of anti-HRV drugs. The conversion into effective drugs of compounds identified via screening, as well as antiviral drug design, rely on the acquisition of fundamental knowledge about the targeted viral elements and their roles during specific steps of the infectious cycle. The results of this study provide a detailed view on structure-function relationships in a viral capsid protein-protein interface, a promising specific target for antiviral intervention. The high density and scattering of the interfacial residues found to be involved in HRV assembly and/or stability support the possibility that any compound designed to bind any particular site at the interface will inhibit infection by interfering with virion morphogenesis or stabilization of the functional virion conformation.

Mechanism of chorismate dehydratase MqnA, the first enzyme of the futalosine pathway, proceeds via substrate-assisted catalysis

MqnA, the only chorismate dehydratase known so far, catalyzes the initial step in the biosynthesis of menaquinone via the futalosine pathway. Details of the MqnA reaction mechanism remain unclear. Here, we present crystal structures of Streptomyces coelicolor MqnA and its active site mutants in complex with chorismate and the product 3-enolpyruvyl-benzoate, produced during heterologous expression in Escherichia coli. Together with activity studies, our data are in line with dehydration proceeding via substrate assisted catalysis, with the enol pyruvyl group of chorismate acting as catalytic base. Surprisingly, structures of the mutant Asn17Asp with copurified ligand suggest that the enzyme converts to a hydrolase by serendipitous positioning of the carboxyl group. All complex structures presented here exhibit a closed Venus flytrap fold, with the enzyme exploiting the characteristic ligand binding properties of the fold for specific substrate binding and catalysis. The conformational rearrangements that facilitate complete burial of substrate/product, with accompanying topological changes to the enzyme surface, could foster substrate channeling within the biosynthetic pathway.

ProDFace: A web-tool for the dissection of protein-DNA interfaces

Protein-DNA interactions play a crucial role in gene expression and regulation. Identifying the DNA binding surface of proteins has long been a challenge–in comparison to protein-protein interactions, limited progress has been made in the development of efficient DNA binding site prediction and protein-DNA docking methods. Here we present ProDFace, a web tool that characterizes the binding region of a protein-DNA complex based on amino acid propensity, hydrogen bond (HB) donor capacity (number of solvent accessible HB donor groups), sequence conservation at the interface core and rim region, and geometry. The program takes as input the structure of a protein-DNA complex in PDB (Protein Data Bank) format, and outputs various physicochemical and geometric parameters of the interface, as well as conservation of the interface residues in the protein component. Values are provided for the whole interface, and after dissecting it into core and rim regions. Details of water mediated HBs between protein and DNA, potential HB donor groups present at the binding surface of protein, and conserved interface residues are also provided as downloadable text files. These parameters can be useful in evaluating and validating protein-DNA docking solutions, structures derived from simulation as well as solutions from the available prediction tools, and facilitate the development of more efficient prediction methods. The web-tool is freely available at structbioinfo.iitj.ac.in/resources/bioinfo/pd_interface.

Molecular and in vivo studies of a glutamate-class prolyl-endopeptidase for coeliac disease therapy

The digestion of gluten generates toxic peptides, among which a highly immunogenic proline-rich 33-mer from wheat α-gliadin, that trigger coeliac disease. Neprosin from the pitcher plant is a reported prolyl endopeptidase. Here, we produce recombinant neprosin and its mutants, and find that full-length neprosin is a zymogen, which is self-activated at gastric pH by the release of an all-β pro-domain via a pH-switch mechanism featuring a lysine plug. The catalytic domain is an atypical 7+8-stranded β-sandwich with an extended active-site cleft containing an unprecedented pair of catalytic glutamates. Neprosin efficiently degrades both gliadin and the 33-mer in vitro under gastric conditions and is reversibly inactivated at pH > 5. Moreover, co-administration of gliadin and the neprosin zymogen at the ratio 500:1 reduces the abundance of the 33-mer in the small intestine of mice by up to 90%. Neprosin therefore founds a family of eukaryotic glutamate endopeptidases that fulfils requisites for a therapeutic glutenase.

Celiac disease is characterized by intolerance to gluten, a cereal protein. Here, the authors show that neprosin, a glutamate peptidase from the pitcher plant, efficiently cleaves gluten components under physiological conditions in vitro and in the gut of mice.

In silico analysis of the predicted protein-protein interaction of syntaxin-18, a putative receptor of Peregrinus maidis Ashmead (Hemiptera: Delphacidae) with Maize mosaic virus glycoprotein

The corn planthopper, Peregrinus maidis Ashmead (Hemiptera:Delphacidae), is a widely distributed insect pest which serves as a vector of two phytopathogenic viruses, Maize mosaic virus (MMV) and Maize stripe virus (MStV). It transmits the viruses in a persistent and propagative manner. MMV is an alphanucleorhabdovirus with a negative-sense, single-stranded RNA unsegmented genome. One identified insect vector protein that may serve as receptor to MMV is Syntaxin-18 (PmStx18) which belongs to the SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) proteins. SNAREs play major roles in the final stage of docking and subsequent fusion of diverse vesicle-mediated transport events. In this work, in silico analysis of the interaction of MMV glycoprotein (MMV G) and PmStx18 was performed. Various freely available protein-protein docking web servers were used to predict the 3 D complex of MMV G and PmStx18. Analysis and protein-protein interaction (PPI) count showed that the complex predicted by the ZDOCK server has the highest number of interaction and highest affinity, as suggested by the calculated solvation free energy gain upon formation of the interface (ΔⁱG = -31 kcal/mol). Molecular dynamics simulation of the complex revealed important interactions at the interface over the course of 25 ns. This is the first in silico analysis performed for the interaction on a putative receptor of P. maidis and MMV G. The results of the PPI prediction provide novel information for studying the role of Stx18 in the transport, docking and fusion events involved in virus particle transport in the insect vector cells and its release.Communicated by Ramaswamy H. Sarma.

Inhibition of the hexamerization of SARS-CoV-2 endoribonuclease and modeling of RNA structures bound to the hexamer

Non-structural protein 15 (Nsp15) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) forms a homo hexamer and functions as an endoribonuclease. Here, we propose that Nsp15 activity may be inhibited by preventing its hexamerization through drug binding. We first explored the stable conformation of the Nsp15 monomer as the global free energy minimum conformation in the free energy landscape using a combination of parallel cascade selection molecular dynamics (PaCS-MD) and the Markov state model (MSM), and found that the Nsp15 monomer forms a more open conformation with larger druggable pockets on the surface. Targeting the pockets with high druggability scores, we conducted ligand docking and identified compounds that tightly bind to the Nsp15 monomer. The top poses with Nsp15 were subjected to binding free energy calculations by dissociation PaCS-MD and MSM (dPaCS-MD/MSM), indicating the stability of the complexes. One of the identified pockets, which is distinctively bound by inosine analogues, may be an alternative binding site to stabilize viral RNA binding and/or an alternative catalytic site. We constructed a stable RNA structure model bound to both UTP and alternative binding sites, providing a reasonable proposed model of the Nsp15/RNA complex.

EXCESP: A Structure-Based Online Database for Extracellular Interactome of Cell Surface Proteins in Humans

The interactions between ectodomains of cell surface proteins are vital players in many important cellular processes, such as regulating immune responses, coordinating cell differentiation, and shaping neural plasticity. However, while the construction of a large-scale protein interactome has been greatly facilitated by the development of high-throughput experimental techniques, little progress has been made to support the discovery of extracellular interactome for cell surface proteins. Harnessed by the recent advances in computational modeling of protein-protein interactions, here we present a structure-based online database for the extracellular interactome of cell surface proteins in humans, called EXCESP. The database contains both experimentally determined and computationally predicted interactions among all type-I transmembrane proteins in humans. All structural models for these interactions and their binding affinities were further computationally modeled. Moreover, information such as expression levels of each protein in different cell types and its relation to various signaling pathways from other online resources has also been integrated into the database. In summary, the database serves as a valuable addition to the existing online resources for the study of cell surface proteins. It can contribute to the understanding of the functions of cell surface proteins in the era of systems biology.

Cryo-EM snapshots of a native lysate provide structural insights into a metabolon-embedded transacetylase reaction

Found across all kingdoms of life, 2-keto acid dehydrogenase complexes possess prominent metabolic roles and form major regulatory sites. Although their component structures are known, their higher-order organization is highly heterogeneous, not only across species or tissues but also even within a single cell. Here, we report a cryo-EM structure of the fully active Chaetomium thermophilum pyruvate dehydrogenase complex (PDHc) core scaffold at 3.85 Å resolution (FSC = 0.143) from native cell extracts. By combining cryo-EM with macromolecular docking and molecular dynamics simulations, we resolve all PDHc core scaffold interfaces and dissect the residing transacetylase reaction. Electrostatics attract the lipoyl domain to the transacetylase active site and stabilize the coenzyme A, while apolar interactions position the lipoate in its binding cleft. Our results have direct implications on the structural determinants of the transacetylase reaction and the role of flexible regions in the context of the overall 10 MDa PDHc metabolon architecture.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Tracing protein and proteome history with chronologies and networks: folding recapitulates evolution

INTRODUCTION

While the origin and evolution of proteins remain mysterious, advances in evolutionary genomics and systems biology are facilitating the historical exploration of the structure, function and organization of proteins and proteomes. Molecular chronologies are series of time events describing the history of biological systems and subsystems and the rise of biological innovations. Together with time-varying networks, these chronologies provide a window into the past.

AREAS COVERED

Here, we review molecular chronologies and networks built with modern methods of phylogeny reconstruction. We discuss how chronologies of structural domain families uncover the explosive emergence of metabolism, the late rise of translation, the co-evolution of ribosomal proteins and rRNA, and the late development of the ribosomal exit tunnel; events that coincided with a tendency to shorten folding time. Evolving networks described the early emergence of domains and a late 'big bang' of domain combinations.

EXPERT OPINION

Two processes, folding and recruitment appear central to the evolutionary progression. The former increases protein persistence. The later fosters diversity. Chronologically, protein evolution mirrors folding by combining supersecondary structures into domains, developing translation machinery to facilitate folding speed and stability, and enhancing structural complexity by establishing long-distance interactions in novel structural and architectural designs.

Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein-Protein Interactions─A Method for All Seasons

Mass spectrometry (MS) has become one of the key technologies of structural biology. In this review, the contributions of chemical cross-linking combined with mass spectrometry (XL-MS) for studying three-dimensional structures of proteins and for investigating protein-protein interactions are outlined. We summarize the most important cross-linking reagents, software tools, and XL-MS workflows and highlight prominent examples for characterizing proteins, their assemblies, and interaction networks in vitro and in vivo. Computational modeling plays a crucial role in deriving 3D-structural information from XL-MS data. Integrating XL-MS with other techniques of structural biology, such as cryo-electron microscopy, has been successful in addressing biological questions that to date could not be answered. XL-MS is therefore expected to play an increasingly important role in structural biology in the future.

Boosting the analysis of protein interfaces with multiple interface string alignments: Illustration on the spikes of coronaviruses

We introduce multiple interface string alignment (MISA), a visualization tool to display coherently various sequence and structure based statistics at protein-protein interfaces (SSE elements, buried surface area, Δ ASA , B factor values, etc). The amino acids supporting these annotations are obtained from Voronoi interface models. The benefit of MISA is to collate annotated sequences of (homologous) chains found in different biological contexts, that is, bound with different partners or unbound. The aggregated views MISA/SSE, MISA/BSA, MISA/ΔASA, and so forth, make it trivial to identify commonalities and differences between chains, to infer key interface residues, and to understand where conformational changes occur upon binding. As such, they should prove of key relevance for knowledge-based annotations of protein databases such as the Protein Data Bank. Illustrations are provided on the receptor binding domain of coronaviruses, in complex with their cognate partner or (neutralizing) antibodies. MISA computed with a minimal number of structures complement and enrich findings previously reported. The corresponding package is available from the Structural Bioinformatics Library (http://sbl.inria.frand https://sbl.inria.fr/doc/Multiple_interface_string_alignment-user-manual.html).

Direct-MS analysis of antibody-antigen complexes

In recent decades, antibodies (Abs) have attracted the attention of academia and the biopharmaceutical industry due to their therapeutic properties and versatility in binding a vast spectrum of antigens. Different engineering strategies have been developed for optimizing Ab specificity, efficacy, affinity, stability and production, enabling systematic screening and analysis procedures for selecting lead candidates. This quality assessment is critical but usually demands time-consuming and labor-intensive purification procedures. Here, we harnessed the direct-mass spectrometry (direct-MS) approach, in which the analysis is carried out directly from the crude growth media, for the rapid, structural characterization of designed Abs. We demonstrate that properties such as stability, specificity and interactions with antigens can be defined, without the need for prior purification.

Controlling the self-assembly of human calcitonin: a theoretical approach using molecular dynamics simulations

Human calcitonin (hCT) is a 32-residue amino acid poly-peptide hormone which is secreted by the C-cells (also known as parafollicular cells) of thyroid glands. It acts to inhibit osteoclast cell hormones by reducing the cell function and regulating calcium and phosphate in blood. hCT has a high tendency to assemble into protofilaments with β-sheet conformations. Amyloid fibril formation of hCT reduces its bio-activity and limits its application as a therapeutic drug. Salmon calcitonin (sCT), which also carries the same disulfide bridge at the N and C-terminus, but differs at the 16 residue position from hCT, has less propensity to aggregate than hCT. Human calcitonin has much higher bio-activity than sCT if its aggregation propensity is reduced. Substituting the key residues which are responsible for the aggregation of hCT, is one of the ways to reduce its aggregation and fibril formation. hCT analogues with less aggregation tendency can be exploited as therapeutic drugs. In this work, we study the amyloidogenic behavior of hCT and its peptide based derivatives i.e., sCT, phCT, N17H hCT, Y12L hCT and DM hCT, through classical molecular dynamics (MD) simulations. Our study reveals that sCT is the least aggregation prone derivative, and the double mutation at position 12 and 17 can reduce the aggregation propensity of this peptide. Also, we have applied these mutant variants of hCT as peptide inhibitors in the self-aggregation of hCT. This study could help in understanding and preparing peptide-based inhibitors for hCT fibrillation and their applications as therapeutic drugs.

Identifying hydrophobic protein patches to inform protein interaction interfaces

Interactions between proteins lie at the heart of numerous biological processes and are essential for the proper functioning of the cell. Although the importance of hydrophobic residues in driving protein interactions is universally accepted, a characterization of protein hydrophobicity, which informs its interactions, has remained elusive. The challenge lies in capturing the collective response of the protein hydration waters to the nanoscale chemical and topographical protein patterns, which determine protein hydrophobicity. To address this challenge, here, we employ specialized molecular simulations wherein water molecules are systematically displaced from the protein hydration shell; by identifying protein regions that relinquish their waters more readily than others, we are then able to uncover the most hydrophobic protein patches. Surprisingly, such patches contain a large fraction of polar/charged atoms and have chemical compositions that are similar to the more hydrophilic protein patches. Importantly, we also find a striking correspondence between the most hydrophobic protein patches and regions that mediate protein interactions. Our work thus establishes a computational framework for characterizing the emergent hydrophobicity of amphiphilic solutes, such as proteins, which display nanoscale heterogeneity, and for uncovering their interaction interfaces.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

Modulation of the Passive Permeability of Semipeptidic Macrocycles: N- and C-Methylations Fine-Tune Conformation and Properties

Incorporating small modifications to peptidic macrocycles can have a major influence on their properties. For instance, N-methylation has been shown to impact permeability. A better understanding of the relationship between permeability and structure is of key importance as peptidic drugs are often associated with unfavorable pharmacokinetic profiles. Starting from a semipeptidic macrocycle backbone composed of a tripeptide tethered head-to-tail with an alkyl linker, we investigated two small changes: peptide-to-peptoid substitution and various methyl placements on the nonpeptidic linker. Implementing these changes in parallel, we created a collection of 36 compounds. Their permeability was then assessed in parallel artificial membrane permeability assay (PAMPA) and Caco-2 assays. Our results show a systematic improvement in permeability associated with one peptoid position in the cycle, while the influence of methyl substitution varies on a case-by-case basis. Using a combination of molecular dynamics simulations and NMR measurements, we offer hypotheses to explain such behavior.

A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning

To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.

Chimeric Cellobiose Dehydrogenases Reveal the Function of Cytochrome Domain Mobility for the Electron Transfer to Lytic Polysaccharide Monooxygenase

The natural function of cellobiose dehydrogenase (CDH) to donate electrons from its catalytic flavodehydrogenase (DH) domain via its cytochrome (CYT) domain to lytic polysaccharide monooxygenase (LPMO) is an example of a highly efficient extracellular electron transfer chain. To investigate the function of the CYT domain movement in the two occurring electron transfer steps, two CDHs from the ascomycete Neurospora crassa (NcCDHIIA and NcCDHIIB) and five chimeric CDH enzymes created by domain swapping were studied in combination with the fungus’ own LPMOs (NcLPMO9C and NcLPMO9F). Kinetic and electrochemical methods and hydrogen/deuterium exchange mass spectrometry were used to study the domain movement, interaction, and electron transfer kinetics. Molecular docking provided insights into the protein–protein interface, the orientation of domains, and binding energies. We find that the first, interdomain electron transfer step from the catalytic site in the DH domain to the CYT domain depends on steric and electrostatic interface complementarity and the length of the protein linker between both domains but not on the redox potential difference between the FAD and heme b cofactors. After CYT reduction, a conformational change of CDH from its closed state to an open state allows the second, interprotein electron transfer (IPET) step from CYT to LPMO to occur by direct interaction of the b-type heme and the type-2 copper center. Chimeric CDH enzymes favor the open state and achieve higher IPET rates by exposing the heme b cofactor to LPMO. The IPET, which is influenced by interface complementarity and the heme b redox potential, is very efficient with bimolecular rates between 2.9 × 10⁵ and 1.1 × 10⁶ M^–1 s^–1.

Rebuilding Ring-Type Assembly of Peroxiredoxin by Chemical Modification

Direct control of the protein quaternary structure (QS) is challenging owing to the complexity of the protein structure. As a protein with a characteristic QS, peroxiredoxin from Aeropyrum pernix K1 (ApPrx) forms a decamer, wherein five dimers associate to form a ring. Here, we disrupted and reconstituted ApPrx QS via amino acid mutations and chemical modifications targeting hot spots for protein assembly. The decameric QS of an ApPrx* mutant, wherein all cysteine residues in wild-type ApPrx were mutated to serine, was destructed to dimers via an F80C mutation. The dimeric ApPrx*F80C mutant was then modified with a small molecule and successfully assembled as a decamer. Structural analysis confirmed that an artificially installed chemical moiety potentially facilitates suitable protein-protein interactions to rebuild a native structure. Rebuilding of dodecamer was also achieved through an additional amino acid mutation. This study describes a facile method to regulate the protein assembly state.

Topology Results on Adjacent Amino Acid Networks of Oligomeric Proteins

In this chapter, we focus on topology measurements of the adjacent amino acid networks for a data set of oligomeric proteins and some of its subnetworks. The aim is to present many mathematical tools in order to understand the structures of proteins implicitly coded in such networks and subnetworks. We mainly investigate four important networks by computing the number of connected components, the degree distribution, and assortativity measures. We compare each result in order to prove that the four networks have quite independent topologies.

Studying dynamics without explicit dynamics: A structure-based study of the export mechanism by AcrB

Resistance-nodulation-cell division family proteins are transmembrane proteins identified as large spectrum drug transporters involved in multidrug resistance. A prototypical case in this superfamily, responsible for antibiotic resistance in selected gram-negative bacteria, is AcrB. AcrB forms a trimer using the proton motive force to efflux drugs, implementing a functional rotation mechanism. Unfortunately, the size of the system (1049 amino acid per monomer and membrane) has prevented a systematic dynamical exploration, so that the mild understanding of this coupled transport jeopardizes our ability to counter it. The large number of crystal structures of AcrB prompts studies to further our understanding of the mechanism. To this end, we present a novel strategy based on two key ingredients, which are to study dynamics by exploiting information embodied in the numerous crystal structures obtained to date, and to systematically consider subdomains, their dynamics, and their interactions. Along the way, we identify the subdomains responsible for dynamic events, refine the states (A, B, E) of the functional rotation mechanism, and analyze the evolution of intramonomer and intermonomer interfaces along the functional cycle. Our analysis shows the relevance of AcrB's efflux mechanism as a template within the HAE1 family but not beyond. It also paves the way to targeted simulations exploiting the most relevant degrees of freedom at certain steps, and to a targeting of specific interfaces to block the drug efflux. Our work shows that complex dynamics can be unveiled from static snapshots, a strategy that may be used on a variety of molecular machines of large size.

Characterisation and Mutagenesis Study of An Alternative Sigma Factor Gene (hrpL) from Erwinia mallotivora Reveal Its Central Role in Papaya Dieback Disease

Simple Summary

Erwinia mallotivora is the causal agent of papaya dieback disease in Malaysia, and its pathogenicity is less appreciated, especially from the molecular perspective. Our previous investigations proved that the hrpL/rpoE gene was one of the significant differentially expressed genes (DEGs) during early infection of E. mallotivora in papaya, suggesting this particular gene is important for infection. In this study, an in-depth analysis was performed using bioinformatics software on hrpL from E. mallotivora (EmhrpL) and its encoded protein (EmHrpL) obtaining crucial information including the conserved function and sequence motif, protein structural similarity with related homologs, and the possibility of being inhibited by a cognate inhibitor. Moreover, knockout (insertional mutational on DNA sequence) of the hrpL gene had caused mutant E. mallotivora (ΔEmhrpL) to be avirulent in four-month-old papaya plants. Here, the conclusion was that EmHrpL is indeed a necessary factor in E. mallotivora pathogenicity, and the findings on the potential inhibitor of this protein are useful for future studies to formulate a papaya dieback disease management programme.

Abstract

The alternative sigma (σ) factor E, RpoE or HrpL, has been reported to be involved in stress- and pathogenicity-related transcription initiation in Escherichia coli and many other Gram-negative bacteria, including Erwinia spp. and Pseudomonas spp. A previous study identified the hrpL/rpoE transcript as one of the significant differentially expressed genes (DEGs) during early E. mallotivora infection in papaya and those data serve as the basis of the current project. Here, the full coding DNA sequence (CDS) of hrpL from E. mallotivora (EmhrpL) was determined to be 549 bp long, and it encoded a 21.3 kDa HrpL protein that possessed two highly conserved sigma-70 (σ⁷⁰) motifs—σR2 and σR4. Nucleotide sequence alignment revealed the hrpL from E. mallotivora shared high sequence similarity to rpoE/hrpL from E. tracheiphila (83%), E. pyrifoliae (81%), and E. tasmaniensis (80%). Phylogenetics analysis indicated hrpL from E. mallotivora to be monophyletic with rpoEs/hrpLs from Pantoea vagans, E. herbicola, and E. tracheiphila. Structural analysis postulated that the E. mallotivora’s alternative σ factor was non-transmembranic and was an extracytoplasmic function (ECF) protein—characteristics shared by other σ factors in different bacterial species. Notably, the protein–protein interaction (PPI) study through molecular docking suggested the σ factor could be possibly inhibited by an anti-σ. Finally, a knockout of hrpL in E. mallotivora (ΔEmhrpL) resulted in avirulence in four-month-old papaya plants. These findings have revealed that the hrpL is a necessary element in E. mallotivora pathogenicity and also predicted that the gene can be inhibited by an anti-σ.

Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials

Protein-protein interactions (PPIs) have pivotal roles in life processes. The studies showed that aberrant PPIs are associated with various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Therefore, targeting PPIs is a direction in treating diseases and an essential strategy for the development of new drugs. In the past few decades, the modulation of PPIs has been recognized as one of the most challenging drug discovery tasks. In recent years, some PPIs modulators have entered clinical studies, some of which been approved for marketing, indicating that the modulators targeting PPIs have broad prospects. Here, we summarize the recent advances in PPIs modulators, including small molecules, peptides, and antibodies, hoping to provide some guidance to the design of novel drugs targeting PPIs in the future.

Protein–Protein Interaction Methods and Protein Phase Separation

In the last decade, newly developed experimental methods have made it possible to highlight that macromolecules in the cell milieu physically interact to support physiology. This has shifted the problem of protein–protein interaction from a microscopic, electron-density scale to a mesoscopic one. Further, nowadays there is increasing evidence that proteins in the nucleus and in the cytoplasm can aggregate in membraneless organelles for different physiological reasons. In this scenario, it is urgent to face the problem of biomolecule functional annotation with efficient computational methods, suited to extract knowledge from reliable data and transfer information across different domains of investigation. Here, we revise the present state of the art of our knowledge of protein–protein interaction and the computational methods that differently implement it. Furthermore, we explore experimental and computational features of a set of proteins involved in phase separation.

Second career of a biosynthetic enzyme: Lumazine synthase as a virus-like nanoparticle in vaccine development

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Virus-like nano-particles can be successfully applied in vaccine development.

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Scaffolds can be cage-forming highly symmetric biological macromolecules, like lumazine synthase, ferritin or self-assembling nanoparticles created computationally ab initio.

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Symmetrical nano-particle scaffolds can display structurally ordered immunogen arrays which lead to favorable reaction with B cell receptors.

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Animal-, preclinical- and clinical studies are at present pointing to the usefulness of nanoparticle antigens in creating immune responses against HIV, Borrelia, Influenza.

Naturally occurring and computationally ab initio designed protein cages can now be considered as extremely suitable materials for new developments in nanotechnology. Via self-assembly from single identical or non-identical protomers large oligomeric particles can be formed. Virus-like particles have today found a number of quite successful applications in the development of new vaccines. Complex chimeric nanoparticles can serve as suitable platforms for the presentation of natural or designed antigens to the immune system of the host. The scaffolds can be cage forming highly symmetric biological macromolecules like lumazine synthase or symmetric self-assembling virus-like particles generated by computational ab initio design. Symmetric nanoparticle carriers display a structurally ordered array of immunogens. This feature can lead to a more favorable interaction with B-cell receptors, in comparison to the administration of single recombinant immunogens. Several pre-clinical animal studies and clinical studies have recently pointed out the efficiency of nanoparticle antigens produced recombinantly in creating strong immune responses against infectious diseases like HIV, Malaria, Borrelia, Influenza.

Protein homodimers structure prediction based on deep neural network

Structural prediction of protein-protein complexes has important application in such domains as modeling of biological processes and drug design. Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. Experimental techniques to identify the structure of protein complex require enormous amount of time and resources, and each method has its own limitations. Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. Building on the idea of this approach, we developed an algorithm to model the 3D structure of homodimer based on deep learning. It consists of two major steps: at the first step a protein complex contact map is predicted with the deep convolutional neural network, and the second stage is used to predict 3D structure of homodimer based on obtained contact map and optimization procedure. The use of the neural network in combination with optimization procedure based on gradient descent method allowed to predict structures for protein homodimers. The suggested approach was tested and validated on a dataset of protein homodimers from Protein Data Bank (PDB). The developed procedure could be also used for evaluating protein homodimer models as one of the stages in drug compounds developing.

MAS NMR detection of hydrogen bonds for protein secondary structure characterization

Hydrogen bonds are essential for protein structure and function, making experimental access to long-range interactions between amide protons and heteroatoms invaluable. Here we show that measuring distance restraints involving backbone hydrogen atoms and carbonyl- or α-carbons enables the identification of secondary structure elements based on hydrogen bonds, provides long-range contacts and validates spectral assignments. To this end, we apply specifically tailored, proton-detected 3D (H)NCOH and (H)NCAH experiments under fast magic angle spinning (MAS) conditions to microcrystalline samples of SH3 and GB1. We observe through-space, semi-quantitative correlations between protein backbone carbon atoms and multiple amide protons, enabling us to determine hydrogen bonding patterns and thus to identify β-sheet topologies and α-helices in proteins. Our approach shows the value of fast MAS and suggests new routes in probing both secondary structure and the role of functionally-relevant protons in all targets of solid-state MAS NMR.

DNA-Directed Protein Packing within Single Crystals

Designed DNA-DNA interactions are investigated for their ability to modulate protein packing within single crystals of mutant green fluorescent proteins (mGFPs) functionalized with a single DNA strand (mGFP-DNA). We probe the effects of DNA sequence, length, and protein-attachment position on the formation and protein packing of mGFP-DNA crystals. Notably, when complementary mGFP-DNA conjugates are introduced to one another, crystals form with nearly identical packing parameters, regardless of sequence if the number of bases is equivalent. DNA complementarity is essential, because experiments with non-complementary sequences produce crystals with different protein arrangements. Importantly, the DNA length and its position of attachment on the protein markedly influence the formation of and protein packing within single crystals. This work shows how designed DNA interactions can be used to influence the growth and packing in X-ray diffraction quality protein single crystals and is thus an important step forward in protein crystal engineering.