Contribution of electrostatic interactions, compactness and quaternary structure to protein thermostability: lessons from structural genomics of Thermotoga maritima.

Studies of the structural basis of protein thermostability have produced a confusing picture. Small sets of proteins have been analyzed from a variety of thermophilic species, suggesting different structural features as responsible for protein thermostability. Taking advantage of the recent advances in structural genomics, we have compiled a relatively large protein structure dataset, which was constructed very carefully and selectively; that is, the dataset contains only experimentally determined structures of proteins from one specific organism, the hyperthermophilic bacterium Thermotoga maritima, and those of close homologs from mesophilic bacteria. In contrast to the conclusions of previous studies, our analyses show that oligomerization order, hydrogen bonds, and secondary structure play minor roles in adaptation to hyperthermophily in bacteria. On the other hand, the data exhibit very significant increases in the density of salt-bridges and in compactness for proteins from T.maritima. The latter effect can be measured by contact order or solvent accessibility, and network analysis shows a specific increase in highly connected residues in this thermophile. These features account for changes in 96% of the protein pairs studied. Our results provide a clear picture of protein thermostability in one species, and a framework for future studies of thermal adaptation.

Mining protein structure data

The principal topic of this work is the application of data mining techniques, in particular of machine learning, to the discovery of knowledge in a protein database. In the first chapter a general background is presented. Namely, in section 1.1 we overview the methodology of a Data Mining project and its main algorithms. In section 1.2 an introduction to the proteins and its supporting file formats is outlined. This chapter is concluded with section 1.3 which defines that main problem we pretend to address with this work: determine if an amino acid is exposed or buried in a protein, in a discrete way (i.e.: not continuous), for five exposition levels: 2%, 10%, 20%, 25% and 30%. In the second chapter, following closely the CRISP-DM methodology, whole the process of construction the database that supported this work is presented. Namely, it is described the process of loading data from the Protein Data Bank, DSSP and SCOP. Then an initial data exploration is performed and a simple prediction model (baseline) of the relative solvent accessibility of an amino acid is introduced. It is also introduced the Data Mining Table Creator, a program developed to produce the data mining tables required for this problem. In the third chapter the results obtained are analyzed with statistical significance tests. Initially the several used classifiers (Neural Networks, C5.0, CART and Chaid) are compared and it is concluded that C5.0 is the most suitable for the problem at stake. It is also compared the influence of parameters like the amino acid information level, the amino acid window size and the SCOP class type in the accuracy of the predictive models. The fourth chapter starts with a brief revision of the literature about amino acid relative solvent accessibility. Then, we overview the main results achieved and finally discuss about possible future work. The fifth and last chapter consists of appendices. Appendix A has the schema of the database that supported this thesis. Appendix B has a set of tables with additional information. Appendix C describes the software provided in the DVD accompanying this thesis that allows the reconstruction of the present work.

Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.

Crystal structure of yeast peroxisomal multifunctional enzyme: structural basis for substrate specificity of (3R)-hydroxyacyl-CoA dehydrogenase units.

(3R)-hydroxyacyl-CoA dehydrogenase is part of multifunctional enzyme type 2 (MFE-2) of peroxisomal fatty acid beta-oxidation. The MFE-2 protein from yeasts contains in the same polypeptide chain two dehydrogenases (A and B), which possess difference in substrate specificity. The crystal structure of Candida tropicalis (3R)-hydroxyacyl-CoA dehydrogenase AB heterodimer, consisting of dehydrogenase A and B, determined at the resolution of 2.2A, shows overall similarity with the prototypic counterpart from rat, but also important differences that explain the substrate specificity differences observed. Docking studies suggest that dehydrogenase A binds the hydrophobic fatty acyl chain of a medium-chain-length ((3R)-OH-C10) substrate as bent into the binding pocket, whereas the short-chain substrates are dislocated by two mechanisms: (i) a short-chain-length 3-hydroxyacyl group ((3R)-OH-C4) does not reach the hydrophobic contacts needed for anchoring the substrate into the active site; and (ii) Leu44 in the loop above the NAD(+) cofactor attracts short-chain-length substrates away from the active site. Dehydrogenase B, which can use a (3R)-OH-C4 substrate, has a more shallow binding pocket and the substrate is correctly placed for catalysis. Based on the current structure, and together with the structure of the 2-enoyl-CoA hydratase 2 unit of yeast MFE-2 it becomes obvious that in yeast and mammalian MFE-2s, despite basically identical functional domains, the assembly of these domains into a mature, dimeric multifunctional enzyme is very different.

The use of protein structure/activity relationships in the rational design of stable particulate delivery systems

The recombinant heat shock protein (18 kDa-hsp) from Mycobacterium leprae was studied as a T-epitope model for vaccine development. We present a structural analysis of the stability of recombinant 18 kDa-hsp during different processing steps. Circular dichroism and ELISA were used to monitor protein structure after thermal stress, lyophilization and chemical modification. We observed that the 18 kDa-hsp is extremely resistant to a wide range of temperatures (60% of activity is retained at 80ºC for 20 min). N-Acylation increased its ordered structure by 4% and decreased its ß-T1 structure by 2%. ELISA demonstrated that the native conformation of the 18 kDa-hsp was preserved after hydrophobic modification by acylation. The recombinant 18 kDa-hsp resists to a wide range of temperatures and chemical modifications without loss of its main characteristic, which is to be a source of T epitopes. This resistance is probably directly related to its lack of organization at the level of tertiary and secondary structures.

Insights into the role of hydration in protein structure and stability obtained through hydrostatic pressure studies

A thorough understanding of protein structure and stability requires that we elucidate the molecular basis for the effects of both temperature and pressure on protein conformational transitions. While temperature effects are relatively well understood and the change in heat capacity upon unfolding has been reasonably well parameterized, the state of understanding of pressure effects is much less advanced. Ultimately, a quantitative parameterization of the volume changes (at the basis of pressure effects) accompanying protein conformational transitions will be required. The present report introduces a qualitative hypothesis based on available model compound data for the molecular basis of volume change upon protein unfolding and its dependence on temperature.

From protein structure to function with bioinformatics

It has long been known that amino acids are the building blocks for proteins and govern their folding into specific three-dimensional structures. However, the details of this process are still unknown and represent one of the main problems in structural bioinformatics, which is a highly active research area with the focus on the prediction of three-dimensional structure and its relationship to protein function. The protein structure prediction procedure encompasses several different steps from searches and analyses of sequences and structures, through sequence alignment to the creation of the structural model. Careful evaluation and analysis ultimately results in a hypothetical structure, which can be used to study biological phenomena in, for example, research at the molecular level, biotechnology and especially in drug discovery and development. In this thesis, the structures of five proteins were modeled with templatebased methods, which use proteins with known structures (templates) to model related or structurally similar proteins. The resulting models were an important asset for the interpretation and explanation of biological phenomena, such as amino acids and interaction networks that are essential for the function and/or ligand specificity of the studied proteins. The five proteins represent different case studies with their own challenges like varying template availability, which resulted in a different structure prediction process. This thesis presents the techniques and considerations, which should be taken into account in the modeling procedure to overcome limitations and produce a hypothetical and reliable three-dimensional structure. As each project shows, the reliability is highly dependent on the extensive incorporation of experimental data or known literature and, although experimental verification of in silico results is always desirable to increase the reliability, the presented projects show that also the experimental studies can greatly benefit from structural models. With the help of in silico studies, the experiments can be targeted and precisely designed, thereby saving both money and time. As the programs used in structural bioinformatics are constantly improved and the range of templates increases through structural genomics efforts, the mutual benefits between in silico and experimental studies become even more prominent. Hence, reliable models for protein three-dimensional structures achieved through careful planning and thoughtful executions are, and will continue to be, valuable and indispensable sources for structural information to be combined with functional data.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering or consensus based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ - a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilising the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from: http://www.reading.ac.uk/bioinf/downloads/ CONTACT: l.j.mcguffin@reading.ac.uk.

Protein structure prediction servers at University College London

A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction.

The PSIPRED protein structure prediction server

The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.

PILZ Protein Structure and Interactions with PILB and the FIMX EAL Domain: Implications for Control of Type IV Pilus Biogenesis

The PilZ protein was originally identified as necessary for type IV pilus (T4P) biogenesis. Since then, a large and diverse family of bacterial PilZ homology domains have been identified, some of which have been implicated in signaling pathways that control important processes, including motility, virulence and biofilm formation. Furthermore, many PilZ homology domains, though not PilZ itself, have been shown to bind the important bacterial second messenger bis(3`-> 5`)cyclic diGMP (c-diGMP). The crystal structures of the PilZ orthologs from Xanthomonas axonopodis pv Citri (PilZ(XAC1133), this work) and from Xanthomonas campestris pv campestris (XC1028) present significant structural differences to other PilZ homologs that explain its failure to bind c-diGMP. NMR analysis of PilZ(XAC1133) shows that these structural differences are maintained in solution. In spite of their emerging importance in bacterial signaling, the means by which NZ proteins regulate specific processes is not clear. In this study, we show that PilZ(XAC1133) binds to PilB, an ATPase required for TV polymerization, and to the EAL domain of FiMX(XAC2398), which regulates TV biogenesis and localization in other bacterial species. These interactions were confirmed in NMR, two-hybrid and far-Western blot assays and are the first interactions observed between any PilZ domain and a target protein. While we were unable to detect phosphodiesterase activity for FimXX(AC2398) in vitro, we show that it binds c-diGMP both in the presence and in the absence of PilZ(XAC1133). Site-directed mutagenesis studies for conserved and exposed residues suggest that PilZ(XAC1133) interactions with FimX(XAC2398) and PilB(XAC3239) are mediated through a hydrophobic surface and an unstructured C-terminal extension conserved only in PilZ orthologs. The FimX-PilZ-PilB interactions involve a full set of ""degenerate"" GGDEF, EAL and PilZ domains and provide the first evidence of the means by which PilZ orthologs and FimX interact directly with the TP4 machinery. (C) 2009 Elsevier Ltd. All rights reserved.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

EPR spectroscopic investigation of membrane protein structure and folding on light harvesting complex LHCIIb

Structure and folding of membrane proteins are important issues in molecular and cell biology. In this work new approaches are developed to characterize the structure of folded, unfolded and partially folded membrane proteins. These approaches combine site-directed spin labeling and pulse EPR techniques. The major plant light harvesting complex LHCIIb was used as a model system. Measurements of longitudinal and transversal relaxation times of electron spins and of hyperfine couplings to neighboring nuclei by electron spin echo envelope modulation(ESEEM) provide complementary information about the local environment of a single spin label. By double electron electron resonance (DEER) distances in the nanometer range between two spin labels can be determined. The results are analyzed in terms of relative water accessibilities of different sites in LHCIIb and its geometry. They reveal conformational changes as a function of micelle composition. This arsenal of methods is used to study protein folding during the LHCIIb self assembly and a spatially and temporally resolved folding model is proposed. The approaches developed here are potentially applicable for studying structure and folding of any protein or other self-assembling structure if site-directed spin labeling is feasible and the time scale of folding is accessible to freeze-quench techniques.

Preservation of high resolution protein structure by cryo-electron microscopy of vitreous sections

We have quantitated the degree of structural preservation in cryo-sections of a vitrified biological specimen. Previous studies have used sections of periodic specimens to assess the resolution present, but preservation before sectioning was not assessed and so the damage due particularly to cutting was not clear. In this study large single crystals of lysozyme were vitrified and from these X-ray diffraction patterns extending to better than 2.1A were obtained. The crystals were high pressure frozen in 30% dextran, and cryo-sectioned using a diamond knife. In the best case, preservation to a resolution of 7.9A was shown by electron diffraction, the first observation of sub-nanometre structural preservation in a vitreous section.