Unconventional interactions between water and heterocyclic nitrogens in protein structures

We report an unusual interaction in which a water molecule approaches the heterocyclic nitrogen of tryptophan and histidine along an axis that is roughly perpendicular to the aromatic plane of the side chain. The interaction is distinct from the well-known conventional aromatic hydrogen-bond, and it occurs at roughly the same frequency in protein structures. Calculations indicate that the water-indole interaction is favorable energetically, and we find several cases in which such contacts are conserved among structural orthologs. The indole-water interaction links side chains and peptide backbone in turn regions, connects the side chains in beta-sheets, and bridges secondary elements from different domains. We suggest that the water-indole interaction can be indirectly responsible for the quenching of tryptophan fluorescence that is observed in the folding of homeodomains and, possibly, many other proteins. We also observe a similar interaction between water and the imidazole nitrogens of the histidine side chain. Taken together, these observations suggest that the unconventional water-indole and water-imidazole interactions provide a small but favorable contribution to protein structures.     

Probing the structure of complex macromolecular interactions by homolog specificity scanning: the P1 and P7 plasmid partition systems.

The P1 plasmid partition locus, P1 par, actively distributes plasmid copies to Escherichia coli daughter cells. It encodes two DNA sites and two proteins, ParA and ParB. Plasmid P7 uses a similar system, but the key macromolecular interactions are species specific. Homolog specificity scanning (HSS) exploits such specificities to map critical contact points between component macromolecules. The ParA protein contacts the par operon operator for operon autoregulation, and the ParB contacts the parS partition site during partition. Here, we refine the mapping of these contacts and extend the use of HSS to map protein-protein contacts. We found that ParB participates in autoregulation at the operator site by making a specific contact with ParA. Similarly, ParA acts in partition by making a specific contact with ParB bound at parS. Both these interactions involve contacts between a C-terminal region of ParA and the extreme N-terminus of ParB. As a single type of ParA-ParB complex appears to be involved in recognizing both DNA sites, the operator and the parS sites may both be occupied by a single protein complex during partition. The general HSS strategy may aid in solving the three-dimensional structures of large complexes of macromolecules.     

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.     

Exploration and comparison of molecular mechanisms across diseases using MINERVA Net

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease‐related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open‐access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.     

Combining different 'omics' technologies to map and validate protein-protein interactions in humans.

The mapping of protein-protein interactions is key to understanding biological processes. Many technologies have been reported to map interactions and these have been systematically applied in yeast. To date, the number of reported yeast protein interactions that have been truly validated by at least one other approach is low. The mapping of human protein interaction networks is even more complicated. Thus, it is unreasonable to try to map the human interactome; instead, interaction mapping in human cell lines should be focused along the lines of diseases or changes that can be associated with specific cells. In this paper, an approach for combining different 'omics' technologies to achieve efficient mapping and validation of protein interactions in human cell lines is presented.     

An accurate,sensitive, and scalable method to identify functional sites in protein structures

Functional sites determine the activity and interactions of proteins and as such constitute the targets of most drugs. However, the exponential growth of sequence and structure data far exceeds the ability of experimental techniques to identify their locations and key amino acids. To fill this gap we developed a computational Evolutionary Trace method that ranks the evolutionary importance of amino acids in protein sequences. Studies show that the best-ranked residues form fewer and larger structural clusters than expected by chance and overlap with functional sites, but until now the significance of this overlap has remained qualitative. Here, we use 86 diverse protein structures, including 20 determined by the structural genomics initiative, to show that this overlap is a recurrent and statistically significant feature. An automated ET correctly identifies seven of ten functional sites by the least favorable statistical measure, and nine of ten by the most favorable one. These results quantitatively demonstrate that a large fraction of functional sites in the proteome may be accurately identified from sequence and structure. This should help focus structure-function studies, rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.     

Chromosome rearrangements and the evolution of genome structuring and adaptability

Eukaryotes appear to evolve by micro and macro rearrangements. This is observed not only for long-term evolutionary adaptation, but also in short-term experimental evolution of yeast, Saccharomyces cerevisiae. Moreover, based on these and other experiments it has been postulated that repeat elements, retroposons for example, mediate such events. We study an evolutionary model in which genomes with retroposons and a breaking/repair mechanism are subjected to a changing environment. We show that retroposon-mediated rearrangements can be a beneficial mutational operator for short-term adaptations to a new environment. But simply having the ability of rearranging chromosomes does not imply an advantage over genomes in which only single-gene insertions and deletions occur. Instead, a structuring of the genome is needed: genes that need to be amplified (or deleted) in a new environment have to cluster. We show that genomes hosting retroposons, starting with a random order of genes, will in the long run become organized, which enables (fast) rearrangement-based adaptations to the environment. In other words, our model provides a "proof of principle" that genomes can structure themselves in order to increase the beneficial effect of chromosome rearrangements.     

The structure and function of a foot-and-mouth disease virus-oligosaccharide receptor complex.

Heparan sulfate has an important role in cell entry by foot-and-mouth disease virus (FMDV). We find that subtype O1 FMDV binds this glycosaminoglycan with a high affinity by immobilizing a specific highly abundant motif of sulfated sugars. The binding site is a shallow depression on the virion surface, located at the junction of the three major capsid proteins, VP1, VP2 and VP3. Two pre-formed sulfate-binding sites control receptor specificity. Residue 56 of VP3, an arginine in this virus, is critical to this recognition, forming a key component of both sites. This residue is a histidine in field isolates of the virus, switching to an arginine in adaptation to tissue culture, forming the high affinity heparan sulfate-binding site. We postulate that this site is a conserved feature of FMDVs, such that in the infected animal there is a biological advantage to low affinity, or more selective, interactions with glycosaminoglycan receptors.     

Simple and versatile restraints for the accurate modeling of alpha-helical coiled-coil structures of multiple strandedness, orientation and composition

We present a minimalist approach for the modeling of the three-dimensional structure of multistranded alpha-helical coiled coils. The approach is based on empirical principles introduced by F. H. C. Crick (F. H. C. Crick, Acta Crystallogr, 1953, Vol. 6, pp. 689-697). Crick hypothesized that keeping the distance between the residues at the interacting interface of alpha-helices constant would lead to supercoiling or the formation of a coiled coil through the knobs-into-holes mode of packing. We have implemented the latter hypothesis in a simulating annealing protocol in the simple form of interhelical distance restraints (two per heptad) between Calpha at the interfacial positions and. To demonstrate the authenticity of Crick's hypothesis and the precision and accuracy of our approach, we have modeled the crystal structures of six synthetic coiled coils in dimeric, trimeric, and tetrameric states. The mean root mean square deviations (RMSDs) between the backbone atoms of the ensemble of structures calculated and those of the corresponding geometric averages is always below 0.76 A, indicating that our protocol has an excellent degree of convergence and precision. The RMSDs between the backbone atoms of each of the six geometric average structures and the backbone of the corresponding crystal structures all range between 0.43 and 0.95 A, indicative of excellent accuracy and proving the authenticity of Crick's hypothesis. Moreover, without specifying any dihedral angles, we found that in 81% of the occurrences, the most populated conformer of the side chains at positions and in the ensembles calculated were identical to those observed in the crystal structures. This shows that our simple approach, which is the simplest reported so far, can generate accurate results for the backbone and side chains. Finally, as a test case for a wider application of our approach in the field of structural proteomics, we describe the successful modeling of the overall structure of SNARE and the organization of its interfacial ionic layer known to play an important functional role.     

Crystal structures of SnoaL2 and AclR: two putative hydroxylases in the biosynthesis of aromatic polyketide antibiotics

SnoaL2 and AclR are homologous enzymes in the biosynthesis of the aromatic polyketides nogalamycin in Streptomyces nogalater and cinerubin in Streptomyces galilaeus, respectively. Evidence obtained from gene transfer experiments suggested that SnoaL2 catalyzes the hydroxylation of the C-1 carbon atom of the polyketide chain. Here we show that AclR is also involved in the production of 1-hydroxylated anthracyclines in vivo. The three-dimensional structure of SnoaL2 has been determined by multi-wavelength anomalous diffraction to 2.5A resolution, and that of AclR to 1.8A resolution using molecular replacement. Both enzymes are dimers in solution and in the crystal. The fold of the enzyme subunits consists of an alpha+beta barrel. The dimer interface is formed by packing of the beta-sheets from the two subunits against each other. In the interior of the alpha+beta barrel a hydrophobic cavity is formed that most likely binds the substrate and harbors the active site. The subunit fold and the architecture of the active site in SnoaL2 and AclR are similar to that of the polyketide cyclases SnoaL and AknH; however, they show completely different quaternary structures. A comparison of the active site pockets of the putative hydroxylases AclR and SnoaL2 with those of bona fide polyketide cyclases reveals distinct differences in amino acids lining the cavity that might be responsible for the switch in chemistry. The moderate degree of sequence similarity and the preservation of the three-dimensional fold of the polypeptide chain suggest that these enzymes are evolutionary related. Members of this enzyme family appear to have evolved from a common protein scaffold by divergent evolution to catalyze reactions chemically as diverse as aldol condensation and hydroxylation.     

Toward a systems level view of the ECM and related proteins: a framework for the systematic definition and analysis of biological systems

Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems-level view of the human extracellular matrix (ECM)--a three-dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene-disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue-specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM.     

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data

Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.     

Dynamics of Loading the Escherichia coli DNA Polymerase Processivity Clamp

ABSTRACT

Sliding clamps and clamp loaders are processivity factors required for efficient DNA replication. Sliding clamps are ring-shaped complexes that tether DNA polymerases to DNA to increase the processivity of synthesis. Clamp loaders assemble these ring-shaped clamps onto DNA in an ATP-dependent reaction. The overall process of clamp loading is dynamic in that protein–protein and protein–DNA interactions must actively change in a coordinated fashion to complete the mechanical clamp-loading reaction cycle. The clamp loader must initially have a high affinity for both the clamp and DNA to bring these macromolecules together, but then must release the clamp on DNA for synthesis to begin. Evidence is presented for a mechanism in which the clamp-loading reaction comprises a series of binding reactions to ATP, the clamp, DNA, and ADP, each of which promotes some change in the conformation of the clamp loader that alters interactions with the next component of the pathway. These changes in interactions must be rapid enough to allow the clamp loader to keep pace with replication fork movement. This review focuses on the measurement of dynamic and transient interactions required to assemble the Escherichia coli sliding clamp on DNA.     

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.     

Free energy simulations for protein ligand binding and stability

Abstract

We summarize several computational techniques to determine relative free energies for condensed-phase systems. The focus is on practical considerations which are capable of making direct contact with experiments. Particular applications include the thermodynamic stability of apo- and holo-myoglobin, insulin dimerization free energy, ligand binding in lysozyme, and ligand diffusion in globular proteins. In addition to provide differential free energies between neighboring states, converged umbrella sampling simulations provide insight into migration barriers and ligand dissociation barriers and analysis of the trajectories yield additional insight into the structural dynamics of fundamental processes. Also, such simulations are useful tools to quantify relative stability changes for situations where experiments are difficult. This is illustrated for NO-bound myoglobin. For the dissociation of benzonitrile from lysozyme it is found that long umbrella sampling simulations are required to approximately converge the free energy profile. Then, however, the resulting differential free energy between the bound and unbound state is in good agreement with estimates from molecular mechanics with generalized Born surface area simulations. Furthermore, comparing the barrier height for ligand escape suggests that ligand dissociation contains a non-equilibrium component.     

Crystal structure of chloroplastic thioredoxin z defines a type-specific target recognition

Thioredoxins (TRXs) are ubiquitous disulfide oxidoreductases structured according to a highly conserved fold. TRXs are involved in a myriad of different processes through a common chemical mechanism. Plant TRXs evolved into seven types with diverse subcellular localization and distinct protein target selectivity. Five TRX types coexist in the chloroplast, with yet scarcely described specificities. We solved the crystal structure of a chloroplastic z-type TRX, revealing a conserved TRX fold with an original electrostatic surface potential surrounding the redox site. This recognition surface is distinct from all other known TRX types from plant and non-plant sources and is exclusively conserved in plant z-type TRXs. We show that this electronegative surface endows thioredoxin z (TRXz) with a capacity to activate the photosynthetic Calvin–Benson cycle enzyme phosphoribulokinase. The distinct electronegative surface of TRXz thereby extends the repertoire of TRX–target recognitions.