Ligand Binding and Circular Permutation Modify Residue Interaction Network in DHFR

Residue interaction networks and loop motions are important for catalysis in dihydrofolate reductase (DHFR). Here, we investigate the effects of ligand binding and chain connectivity on network communication in DHFR. We carry out systematic network analysis and molecular dynamics simulations of the native DHFR and 19 of its circularly permuted variants by breaking the chain connections in ten folding element regions and in nine nonfolding element regions as observed by experiment. Our studies suggest that chain cleavage in folding element areas may deactivate DHFR due to large perturbations in the network properties near the active site. The protein active site is near or coincides with residues through which the shortest paths in the residue interaction network tend to go. Further, our network analysis reveals that ligand binding has “network-bridging effects” on the DHFR structure. Our results suggest that ligand binding leads to a modification, with most of the interaction networks now passing through the cofactor, shortening the average shortest path. Ligand binding at the active site has profound effects on the network centrality, especially the closeness

Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology

Prediction of protein-binding areas by small-world residue networks and application to docking

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions are involved in most cellular processes, and their detailed physico-chemical and structural characterization is needed in order to understand their function at the molecular level. In-silico docking tools can complement experimental techniques, providing three-dimensional structural models of such interactions at atomic resolution. In several recent studies, protein structures have been modeled as networks (or graphs), where the nodes represent residues and the connecting edges their interactions. From such networks, it is possible to calculate different topology-based values for each of the nodes, and to identify protein regions with high centrality scores, which are known to positively correlate with key functional residues, hot spots, and protein-protein interfaces.</p> <p>Results</p> <p>Here we show that this correlation can be efficiently used for the scoring of rigid-body docking poses. When integrated into the pyDock energy-based docking method, the new combined scoring function significantly improved the results of the individual components as shown on a standard docking benchmark. This improvement was particularly remarkable for specific protein complexes, depending on the shape, size, type, or flexibility of the proteins involved.</p> <p>Conclusions</p> <p>The network-based representation of protein structures can be used to identify protein-protein binding regions and to efficiently score docking poses, complementing energy-based approaches.</p

Characterizing early drug resistance-related events using geometric ensembles from HIV protease dynamics:

The use of antiretrovirals (ARVs) has drastically improved the life quality and expectancy of HIV patients since their introduction in health care. Several millions are still afflicted worldwide by HIV and ARV resistance is a constant concern for both healthcare practitioners and patients, as while treatment options are finite, the virus constantly adapts via complex mutation patterns to select for resistant strains under the pressure of drug treatment. The HIV protease is a crucial enzyme for viral maturation and has been a game changing drug target since the first application. Due to similarities in protease inhibitor designs, drug cross-resistance is not uncommon across ARVs of the same class

Computer-Based Screening of Functional Conformers of Proteins

A long-standing goal in biology is to establish the link between function, structure, and dynamics of proteins. Considering that protein function at the molecular level is understood by the ability of proteins to bind to other molecules, the limited structural data of proteins in association with other bio-molecules represents a major hurdle to understanding protein function at the structural level. Recent reports show that protein function can be linked to protein structure and dynamics through network centrality analysis, suggesting that the structures of proteins bound to natural ligands may be inferred computationally. In the present work, a new method is described to discriminate protein conformations relevant to the specific recognition of a ligand. The method relies on a scoring system that matches critical residues with central residues in different structures of a given protein. Central residues are the most traversed residues with the same frequency in networks derived from protein structures. We tested our method in a set of 24 different proteins and more than 260,000 structures of these in the absence of a ligand or bound to it. To illustrate the usefulness of our method in the study of the structure/dynamics/function relationship of proteins, we analyzed mutants of the yeast TATA-binding protein with impaired DNA binding. Our results indicate that critical residues for an interaction are preferentially found as central residues of protein structures in complex with a ligand. Thus, our scoring system effectively distinguishes protein conformations relevant to the function of interest

Functional correlation of bacterial LuxS with their quaternary associations: interface analysis of the structure networks

<p>Abstract</p> <p>Background</p> <p>The genome of a wide variety of prokaryotes contains the <it>luxS </it>gene homologue, which encodes for the protein S-ribosylhomocysteinelyase (LuxS). This protein is responsible for the production of the quorum sensing molecule, AI-2 and has been implicated in a variety of functions such as flagellar motility, metabolic regulation, toxin production and even in pathogenicity. A high structural similarity is present in the LuxS structures determined from a few species. In this study, we have modelled the structures from several other species and have investigated their dimer interfaces. We have attempted to correlate the interface features of LuxS with the phenotypic nature of the organisms.</p> <p>Results</p> <p>The protein structure networks (PSN) are constructed and graph theoretical analysis is performed on the structures obtained from X-ray crystallography and on the modelled ones. The interfaces, which are known to contain the active site, are characterized from the PSNs of these homodimeric proteins. The key features presented by the protein interfaces are investigated for the classification of the proteins in relation to their function. From our analysis, structural interface motifs are identified for each class in our dataset, which showed distinctly different pattern at the interface of LuxS for the probiotics and some extremophiles. Our analysis also reveals potential sites of mutation and geometric patterns at the interface that was not evident from conventional sequence alignment studies.</p> <p>Conclusion</p> <p>The structure network approach employed in this study for the analysis of dimeric interfaces in LuxS has brought out certain structural details at the side-chain interaction level, which were elusive from the conventional structure comparison methods. The results from this study provide a better understanding of the relation between the <it>luxS </it>gene and its functional role in the prokaryotes. This study also makes it possible to explore the potential direction towards the design of inhibitors of LuxS and thus towards a wide range of antimicrobials.</p

Harnessing Growth Selections in Saccharomyces cerevisiae for Biological Engineering

Directed evolution is a powerful tool that mimics the natural selection process to engineer biomolecules with improved and altered functionalities for a wide variety of applications. The advance of biological engineering based on directed evolution techniques depends upon selection assays that can practically search large, diverse libraries for the most improved variants. In Chapter 1, we begin by discussing the potential of growth selections to serve as accessible and robust assays for directed evolution. We then delve into the existing approaches to expand the scope of targets for growth selections beyond those that are intrinsically linked to growth and the strategies implemented to improve their throughput and sensitivity. The yeast three-hybrid (Y3H) assay is a versatile system that can expand the field of directed evolution if implemented as a growth selection for the search of large variant libraries. Although the Y3H assay has been successfully applied as a positive selection to evolve proteins with improved functions, its expansion into applications requiring a high-throughput, versatile selection against transcriptional activation has been hindered by its limited dynamic range as a counter selection. To address the limited dynamic range of the Y3H assay, we undertook a multi-pronged approach to reengineer our Y3H counter selection to have a high dynamic range. In Chapter 2, we discuss strategies to improve the dynamic range of the Y3H counter selection by maximizing the growth between cells with activated and basal reporter gene expression levels. Specifically, we elaborate on the development and characterization of two Y3H counter selections that were based on either reporter gene degradation or an alternative phototoxic reporter gene. In Chapter 3, we present our most successful strategy to improve the dynamic range of the Y3H counter selection that uses the dual tetracycline (Tet) system to increase transcriptional regulation of the reporter gene. We employed a guided strategy based on both rational design and library approaches to find the best Tet Y3H reporter gene construct with the highest dynamic range. We believe our method to engineer the best Tet Y3H reporter construct will be widely useful to synthetic biologists developing sophisticated in vivo assays that require fine-tuned reporter gene expression levels. Finally, in Chapter 4, we demonstrate the versatility of the Y3H system by developing a screen for the detection of natural product biosynthesis. This assay should have an impact for metabolic engineers that are employing directed evolution techniques to generate large metabolic pathway libraries for the overproduction of high-value small molecules in heterologous producer strains

Studies of site-selective glycosylation of dihydrofolate reductase

Glycosylation is important for many molecular processes, although how glycosylation contributes to glycoprotein structure and function is not entirely clear. Other than that, the study of many of these events is complicated by the fact that natural glycoproteins normally occur as mixtures of glycoforms, therefore the isolation or synthesis of homogenous glycoproteins is an important task. Previous studies on deglycosylated proteins have shown that glycosylation reduces their catalytic activity and increase thermal stability. Therefore, we hypothesize that similar effects may be observed in the naturally unglycosylated dihydrofolate reductase from Escherichia coli (EcDHFR). Four different surface-exposed sites for the incorporation of a single cysteine residue were selected based on the protein crystal structure, which may or may not affect its dynamics. Homogeneous glycoproteins have been synthesized via chemoselective ligation of a glycosyl haloacetammide with the thiol of a cysteine, to produce site- selectively glycosylated forms of EcDHFR. Techniques such as mass spectrometry, circular dichroism (CD) spectroscopy, fluorescence spectroscopy, ultraviolet (UV) visible spectroscopy and stopped-flow spectrophotometry have been used to identify and study the physical properties of different glycoforms of DHFR. Although there were some changes of the kinetic activity of the mutants of EcDHFR. the values were comparable to those of the wild-type protein. Interestingly, in four of the five cases studied. EcDHFRDM D87C, there was an increase in thermal stability upon site-selective glycosylation. The other mutants showed no effect. With the exception of the effect seen for the thermal stability of the D87C mutant, this is not in accordance with the original hypothesis. This suggest that the effect seen in the D87C mutant may be due to specific interactions of the carbohydrate moiety at certain points on the protein. An increase in resistance to thermal denaturation observed for proteins in sugar solutions may therefore also be due to binding of the sugars to specific sites on the protein. In conclusion, an effective method for the synthesis of homogeneous glycosylated and non-glycosylated proteins has been developed and applied to the site selective glycosylation of EcDHFR. The results also suggested that the kinetic properties of EcDHFR are not significantly affected by glycosylation. It may be the large effects in terms of protein stability which due to glycosylation only occur in naturally glycosylated proteins, and not in the naturally unglycosylated EcDHFR

Genetic Code Expansion: A Brief History and Perspective

Since the establishment of site-specific mutagenesis of single amino acids to interrogate protein function in the 1970s, biochemists have sought to tailor protein structure in the native cell environment. Fine-tuning the chemical properties of proteins is an indispensable way to address fundamental mechanistic questions. Unnatural amino acids (UAAs) offer the possibility to expand beyond the 20 naturally occurring amino acids in most species and install new and useful chemical functions. Here, we review the literature about advances in UAA incorporation technology from chemoenzymatic aminoacylation of modified tRNAs to in vitro translation systems to genetic encoding of UAAs in the native cell environment and whole organisms. We discuss innovative applications of the UAA technology to challenges in bioengineering and medicine