Symmetric cell division in gram-negative bacteria is essential for generating two equal-sized daughter cells, each containing cellular material crucial for growth and future replication. The Min system, comprised of proteins MinC, MinD and MinE, is particularly important for this process since its deletion leads to minicells incapable of further replication. This thesis focuses on the interactions involving MinE that are important for allowing cell division at the mid-cell and for directing the dynamic localization of MinD that is observed in vivo. Previous experiments have shown that the MinE protein contains an N-terminal region that is required to stimulate MinD-catalyzed ATP hydrolysis in the Min protein interaction cycle. However, MinD-binding residues in MinE identified by in vitro MinD ATPase assays were subsequently found to be buried in the hydrophobic dimeric interface in the MinE structure, raising the possibility that these residues are not directly involved in the interaction. To address this issue, the ability of N-terminal MinE peptides to stimulate MinD activity was studied to determine the role of these residues in MinD activation. Our results implied that MinE likely undergoes a change in conformation or oligomerization state before binding MinD. In addition we performed circular dichroism spectroscopy of MinE. The data suggest that direct interactions between MinE and the lipid membrane can lead to conformational changes in MinE. Using NMR spectroscopy in an attempt to observe this structure change, different membrane-mimetic environments were tested. However the results strongly suggest that structural studies on the membrane-bound state of MinE will pose significant challenges. Taken together, the results in this thesis open the door for further exploration of the interactions involving MinE in order to gain a better understanding of the dynamic localization patterns formed by these proteins in vivo.

Indiana University-Purdue University Indianapolis (IUPUI) / The discovery and isolation of naturally occurring fluorescent proteins, FPs, have provided much needed tools for molecular and cellular level studies. Specifically the cloning of green fluorescent protein, GFP, revolutionized the field of biotechnology and biochemical research. Recently, a red fluorescent protein, DsRed, isolated from the Discosoma coral has further expanded the pallet of available fluorescent tools. DsRed shares only 23 % amino acid sequence homology with GFP, however the X-ray crystal structures of the two proteins are nearly identical. DsRed has been subjected to a number of mutagenesis studies, which have been found to offer improved physical and spectral characteristics. One such mutant, DsRed-Monomer, with a total of 45 amino acid substitutions in native DsRed, has shown improved fluorescence characteristics without the toxic oligomerization seen for the native protein. In our laboratory, we have demonstrated that DsRed proteins have a unique and selective copper-binding affinity, which results in fluorescence quenching. This copper-binding property was utilized in the purification of DsRed proteins using copper-bound affinity columns. The work presented here has explored the mechanism of copper-binding by DsRed-Monomer using binding studies, molecular biology, and other biochemical techniques. Another focus of this thesis work was to demonstrate the applications of DsRed-Monomer in biochemical studies based on the copper-binding affinity and fluorescence properties of the protein. To achieve this, we have focused on genetic fusions of DsRed-Monomer with peptides and proteins. The work with these fusions have demonstrated the feasibility of using DsRed-Monomer as a dual functional tag, as both an affinity tag and as a label in the development of a fluorescence assay to detect a ligand of interest. Further, a complex between DsRed-Monomer-bait peptide/protein fusion and an interacting protein has been isolated taking advantage of the copper-binding affinity of DsRed-Monomer. We have also demonstrated the use of non-natural amino acid analogues, incorporated into the fluorophore of DsRed-Monomer, as a tool for varying the spectral properties of the protein. These mutations demonstrated not only shifted fluorescence emission compared to the native protein, but also improved extinction coefficients and quantum yields.

Gene expression is regulated in response to metabolic necessities and environmental changes throughout the life of a cell. A major part of this regulation is governed at the level of transcription, deciding whether messengers to specific genes are produced or not. This decision is triggered by the action of transcription factors, proteins which interact with specific sites on DNA and thus influence the rate of transcription of proximal genes. Mapping the organisation of these transcription factor binding sites sheds light on potential causal relations between genes and is the key to establishing networks of genetic interactions, which determine how the cell adapts to external changes. In this work I review briefly the basics of genetics and summarise popular approaches to describe transcription factor binding sites, from the most straight forward to finally discuss a biophysically motivated representation based on the estimation of free energies of molecular interactions. Two articles on transcription factors are contained in this thesis, one published (Aurell, Fouquier d'Hérouël, Malmnäs and Vergassola, 2007) and one submitted (Fouquier d'Hérouël, 2008). Both rely strongly on the representation of binding sites by matrices accounting for the affinity of the proteins to specific nucleotides at the different positions of the binding sites. The importance of non-specific binding of transcription factors to DNA is briefly addressed in the text and extensively discussed in the first appended article: In a study on the affinity of yeast transcription factors for their binding sites, we conclude that measured in vivo protein concentrations are marginally sufficient to guarantee the occupation of functional sites, as opposed to unspecific emplacements on the genomic sequence. A common task being the inference of binding site motifs, the most common statistical method is reviewed in detail, upon which I constructed an alternative biophysically motivated approach, exemplified in the second appended article. / QC 20101110

京都大学 / 新制・課程博士 / 博士(工学) / 甲第25264号 / 工博第5223号 / 新制||工||1996(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 高岡 昌輝, 教授 米田 稔, 教授 松田 知成 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Insulin-like growth factor-I is a small growth factor known to signal in a variety of mammalian cells through the IGF-I cell surface receptor (IGF-IR). A unique feature of the IGF-I system is the regulation of this binding by soluble IGF binding proteins. Recent studies from our laboratory show that there is a pH dependence in the association of IGF-I with the cell surface in the presence of IGFBP-3 which suggested increased association of IGF-I with IGFBP-3 at low pH. We studied cell free interaction of IGF-I and IGFBP-3 as a function of pH using surface plasmon resonance (SPR) in order to understand the mechanism that causes the increased association. In our studies three different SPR instruments with different surfaces for immobilization of one of the binding partners were used: a Leica Bio-SPR 9000 with a low molecular weight carboxymethylated dextran (CMD) surface, a BIAcore 2000 with a high molecular weight CMD surface and a Leica SPR 2001 Alpha with a planar mixed self-assembled monolayer (mSAM) surface. Since the experimental system we used was transport sensitive, only the mSAM surface, under optimized conditions, produced results that fit to a single site model. Results suggest that use of CMD layers for immobilization of one partner of a high-affinity binding complex can result in transport limited binding for which simple analysis is inappropriate. Future studies are planned to expand the work with the mSAM surface to elucidate whether a significant difference between the binding parameters as a function of pH exists. / Master of Science

The development of medicine is generally a long and costly process, and one big factor is estimating the affinity of protein-drug binding. Leveraging machine learning in this field is a promising approach as it can streamline the prediction process and reduce the need for expensive experimental methods. Machine learning methods have already enabled significant advances in predicting protein-drug binding affinity, yet there remains room for improvement. The primary challenge is the quality of data used for these machine learning models. In this work, two ensemble machine learning models, Random Forest and Extreme Gradient Boosting Machine, have been tested and compared with a recent database of protein-ligand complex features calculated from molecular dynamics simulation. Additional features were also extracted from the PDB database through PLIP (Protein-Ligand interaction Profiler), aiming to improve the predictions further. The results indicate that while the features from the PDB database provided strong predictive power, including features from molecular dynamic simulations did not improve the models’ performance.

Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional mutants and demonstrate a ~30 fold improvement in initial positive predictive value compared with choosing randomly from a sequence space of 160 000 variants in the top 20 mutants. We also demonstrate qualitative differences in the predictions of the two scores, primarily a tendency for the coevolutionary score to miss out on one class of functional mutants with enriched frequency of the amino acid threonine in one position.

Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.

Abstract Spontaneous changes in protein systems, such as the binding of a ligand to an enzyme or receptor, are characterized by a decrease of free energy. Despite the recent developments in computing power and methodology, it remains challenging to accurately estimate free energy changes. Major issues are still concerned with the accuracy of the underlying model to describe the protein system and how well the calculation in fact emulates the behaviour of the system. This thesis is largely concerned with the quality of current free energy calculation methods as applied to protein-ligand systems. Several methodologies were employed to calculate Gibbs standard free energies of binding for a collection of protein-ligand complexes, for which experimental affinities were available. Calculations were performed using system description with different levels of accuracy and included a continuum approach, which considers the protein and the ligand at the atomic level but includes solvent as a polarizable continuum, and an all-atom approach that relies on molecular dynamics simulations. In most such applications, the effects of ionic strength are neglected. However, the severity of this approximation, in particular when calculating free energies of charged ligands, is not very clear. The issue of incorporating ionic strength in free energy calculations by means of explicit ions was investigated in greater detail and considerable attention was given to the affinities of charged peptides in the presence of explicit counter-ions. A second common approximation is concerned with the description of ligands that exhibit multiple protonation states. Because most of current methods do not model changes in the acid dissociation constants of titrating groups upon binding, protonation equilibria of such ligands are not taken into account in free energy calculations. The implications of this approximation when predicting affinities were analysed. Finally, when calculating free energies of binding, a correct description of the interactions between the protein and the ligand is of fundamental importance. However, active sites of enzymes, where strained conformations may hold a functional role, are not always accurately modelled by molecular mechanics force fields. The case of a strained planar proline in the active site of triosephosphate isomerase was investigated using an hybrid quantum mechanics/molecular mechanics method, which implies a higher level of accuracy.

Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e., the property of certain protein-ligand complexes to exhibit different ligand binding modes, has been shown to occur in nature for a variety of molecules. We conducted a high-throughput docking study and implemented multipose binding in the scoring procedure by considering multiple docking solutions in binding affinity prediction. In general, improvement of the agreement between docking scores and experimental data was observed, and this was most pronounced in complexes with large and flexible ligands and high binding affinities. Further developments of the selection criteria for docking solutions for each individual complex are still necessary for a general utilization of the multipose binding concept for accurate binding affinity prediction by molecular docking.