Proteins and the formation of large protein complexes are essential parts of living organisms. Proteins are present in all aspects of life processes, performing a multitude of various functions ranging from being structural components of cells, to facilitating the passage of certain molecules between various regions of cells. The 'protein docking problem' refers to the computational method of predicting the appropriate matching pair of a protein (receptor) with respect to another protein (ligand), when attempting to bind to one another to form a stable complex. Research shows that matching the three-dimensional (3D) geometric structures of candidate proteins plays a key role in determining a so-called docking pair, which is one of the key aspects of the Computer Aided Drug Design process. However, the active sites which are responsible for binding do not always present a rigid-body shape matching problem. Rather, they may undergo sufficient deformation when docking occurs, which complicates the problem of finding a match. To address this issue, we present an isometry-invariant and topologically robust partial shape matching method for finding complementary protein binding sites, which we call the ProtoDock algorithm. The ProtoDock algorithm comes in two variations. The first version performs a partial shape complementarity matching by initially segmenting the underlying protein object mesh into smaller portions using a spectral mesh segmentation approach. The Heat Kernel Signature (HKS), the underlying basis of our shape descriptor, is subsequently computed for the obtained segments. A final descriptor vector is constructed from the Heat Kernel Signatures and used as the basis for the segment matching. The three different descriptor methods employed are, the accepted Bag of Features (BoF) technique, and our two novel approaches, Closest Medoid Set (CMS) and Medoid Set Average (MSA). The second variation of our ProtoDock algorithm aims to perform the partial matching by utilizing the pointwise HKS descriptors. The use of the pointwise HKS is mainly motivated by the suggestion that, at adequate times, the Heat Kernel Signature of a point on a surface sufficiently describes its neighbourhood. Hence, the HKS of a point may serve as the representative descriptor of its given region of which it forms a part. We propose three (3) sampling methods---Uniform, Random, and Segment-based Random sampling---for selecting these points for the partial matching. Random and Segment-based Random sampling both prove superior to the Uniform sampling method. Our experimental results, run against the Protein-Protein Benchmark 4.0, demonstrate the viability of our approach, in that, it successfully returns known binding segments for known pairing proteins. Furthermore, our ProtoDock-1 algorithm still still yields good results for low resolution protein meshes. This results in even faster processing and matching times with sufficiently reduced computational requirements when obtaining the HKS.

The major goal of this study was to develop an experimental proteomics approach for deciphering protein complexes and protein interaction networks in the budding and fission yeasts. Key steps of the employed analytical routine, including the purification of complexes and mass spectrometric identification of their subunits, were investigated in detail. Archiving, storage and handling of polyacylamide gels, visualization of protein bands and their effect on the efficiency of in-gel digestion and mass spectrometric identification of proteins were quantitatively evaluated. It was further demonstrated that a combination of several mass spectrometric techniques based on MALDI and ES ionization provided complementary data and enabled comprehensive characterization of protein digests. The optimized analytical procedures were employed in deciphering protein complexes and protein interaction networks in the budding and fission yeasts. A combination of Tandem Affinity Purification (TAP) and mass spectrometric identification of gel separated protein subunits is generic and robust strategy that provided accurate and reproducible data. The evaluation of TAP success rate, reproducibility and typical protein background presented in this work is based on TAP tagging and immunoprecepitation of 75 genes in S. cerevisiae and 22 in S. pombe. The molecular composition of characterized protein complexes was compared with protein-protein interactions uncovered by other established methods, such as yeast two hybrid screens or proteome-wide purification of protein complexes. We found that repetitive purification of protein complexes using different subunits as baits is crucially important for confident charting of proteomic environments. Accurate dissection of individual protein complexes and identification of their proteomic hyperlinks enabled to consider proteomic environments in the phylogenetic perspective and paved the way to reliable projection of proteomics data obtained in lower eukaryotic model organisms to higher eukaryotes, including humans.

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The aim of the diploma thesis is to analyze the mechanisms involved in the coagulation of peptides and proteins contained in cellular organic matter produced by Microcystis aeruginosa, and to describe their influence on the coagulation of hydrophobic kaolin suspension. According to the results of jar tests, the coagulation effectiveness and removability of COM peptides/proteins and kaolin particles are heavily dependent on pH value which determines charge characteristics of peptides/proteins, kaolin and hydrolysis products of coagulant and therefore the prevailing mechanisms of interactions between them. Efficient coagulation and the highest removal of COM peptides and proteins were achieved in the pH range of 4-6 due to charge neutralization of peptide/protein negative surface by positively charged hydrolysis products of ferric coagulant. Peptides and proteins contributed to the coagulation of kaolin particles under the reaction conditions mentioned above, too. Charge neutralization and adsorption were found to be the dominant coagulation mechanisms under these conditions. At a low COM/Fe concentration ratio (COM/Fe < 0.33), adsorption of peptides/proteins onto ferric oxide-hydroxide particles, described as the electrostatic patch model, enabled the coagulation in the pH range of 6-8. On the...

Estudos teóricos dos mecanismos moleculares responsáveis pela formação e estabilidade de complexos moleculares vêm ganhando relevância pelas possibilidades práticas que oferecem, por exemplo, na compreensão de diversas doenças e no desenho racional de fármacos. Neste projeto, nossa ênfase está no estudo de complexos de proteínas, extraídos do banco de dados de proteínas (PDB), onde desenvolvemos ferramentas computacionais as quais permitem efetuar análises em duas direções: 1) efetuar previsões básicas, através do emprego de propriedades eletrostáticas de proteínas, em diferentes condições e níveis preditivos e 2) realização de um conjunto de análises estatísticas, como freqüência de contato, em busca de preditores de complexos de proteínas e identificar padrões de interação entre seus aminoácidos em função da distância de separação. Com base nos resultados obtidos por ambos os estudos, objetivamos quantificar as forças físicas envolvidas na formação dos complexos protéicos. O foco do projeto, a longo prazo, é prever o fenômeno da complexação através da fusão dessas duas linhas de estudos: preditor básico de complexos protéicos e análise do potencial estatístico entre os aminoácidos que formam o complexo. O presente projeto é concluído com a construção de portais web que disponibilizarão os resultados obtidos por nossos trabalhos bem como a possibilidade de qualquer usuário, efetuar consultas por propriedades de proteínas e/ou grupo de proteínas. / Theoretical studies of the molecular mechanisms responsible for the formation and stability of molecular complexes are gaining relevance for the practical possibilities that they offer, for example, in the understanding of diverse diseases and rational drug design. In this project, our emphasis is on the study of protein complexes, extracted from protein data bank (PDB). We have developed computational tools which allow to perform analyses in two directions: 1) to make basic complexation forecasts, through the use of electrostatics properties of proteins, in different conditions and predictive levels, and 2) to carry out a set of statistical analyses, as contacts frequency, in order to build up predictor of protein complexes and to identify patters of interactions between the amino acids as a function of their separation distance. Based on the results obtained on both studies, we aim quantify the physical forces involved in the formation of protein complexes. The focus of the project, in the long run, is to foresee the phenomenon of the protein complexes through the fusing of these two study lines: a coarse-grained predictor of protein complexes and analysis of the statistical potentials between the amino acids that form the complex. The present project is concluded with the construction of web services where we make available the results obtained on our works. This server also has the possibility to be used by any computer user, that wishes to perform search on protein and/or protein group properties

Molecular interaction among proteins drives the cellular processes through the formation of complexes that perform the requisite biochemical function. While some of the complexes are obligate (i.e., they fold together while complexation) others are non-obligate, and are formed through macromolecular recognition. Macromolecular recognition in proteins is highly specific, yet it can be both permanent and non permanent in nature. Hallmarks of permanent recognition complexes include large surface of interaction, stabilization by hydrophobic interaction and other noncovalent forces. Several amino acids which contribute critically to the free energy of binding at these interfaces are called as “hot spot” residues. The non permanent recognition complexes, on the other hand, usually show small interface of interaction, with limited stabilization from non covalent forces. For both the permanent and non permanent complexes, the specificity of molecular interaction is governed by the geometric compatibility of the interaction surface, and the noncovalent forces that anchor them. A great deal of studies has already been performed in understanding the basis of protein macromolecular recognition.1; 2 Based on these studies efforts have been made to develop protein-protein docking algorithms that can predict the geometric orientation of the interacting molecules from their individual unbound states. Despite advances in docking methodologies, several significant difficulties remain.1 Therefore, in this thesis, we start with literature review to understand the individual merits and demerits of the existing approaches (Chapter 1),3 and then, we attempt to address some of the problems by developing methods to infer protein quaternary structure from the crystalline state, and improve structural and chemical understanding of protein-protein interactions through biological complex prediction. The understanding of the interaction geometry is the first step in a protein-protein interaction study. Yet, no consistent method exists to assess the geometric compatibility of the interacting interface because of its highly rugged nature. This suggested that new sensitive measures and methods are needed to tackle the problem. We, therefore, developed two new and conceptually different measures using the Delaunay tessellation and interface slice selection to compute the surface complementarity and atom packing at the protein-protein interface (Chapter 2).4 We called these Normalized Surface Complementarity (NSc) and Normalized Interface Packing (NIP). We rigorously benchmarked the measures on the non redundant protein complexes available in the Protein Data Bank (PDB) and found that they efficiently segregate the biological protein-protein contacts from the non biological ones, especially those derived from X-ray crystallography. Sensitive surface packing/complementarity recognition algorithms are usually computationally expensive and thus limited in application to high-throughput screening. Therefore, special emphasis was given to make our measure compute-efficient as well. Our final evaluation showed that NSc, and NIP have very strong correlation among themselves, and with the interface area normalized values available from the Surface Complementarity program (CCP4 Suite: <http://smb.slac.stanford.edu/facilities/software/ccp4/html/sc.html>); but at a fraction of the computing cost. After building the geometry based surface complementarity and packing assessment methods to assess the rugged protein surface, we advanced our goal to determine the stabilities of the geometrically compatible interfaces formed. For doing so, we needed to survey the quaternary structure of proteins with various affinities. The emphasis on affinity arose due to its strong relationship with the permanent and non permanent life-time of the complex. We, therefore, set up data mining studies on two databases named PQS (Protein Quaternary structure database: http://pqs.ebi.ac.uk) and PISA (Protein Interfaces, Surfaces and Assemblies: www.ebi.ac.uk/pdbe/prot_int/pistart.html) that offered downloads on quaternary structure data on protein complexes derived from X-ray crystallographic methods. To our surprise, we found that above mentioned databases provided the valid quaternary structure mostly for moderate to strong affinity complexes. The limitation could be ascertained by browsing annotations from another curated database of protein quaternary structure (PiQSi:5 supfam.mrc-lmb.cam.ac.uk/elevy/piqsi/piqsi_home.cgi) and literature surveys. This necessitated that we at first develop a more robust method to infer quaternary structures of all affinity available from the PDB. We, therefore, developed a new scheme focused on covering all affinity category complexes, especially the weak/very weak ones, and heteromeric quaternary structures (Chapter 3).6 Our scheme combined the naïve Bayes classifier and point-group symmetry under a Boolean framework to detect all categories of protein quaternary structures in crystal lattice. We tested it on a standard benchmark consisting of 112 recognition heteromeric complexes, and obtained a correct recall in 95% cases, which are significantly better than 53% achieved by the PISA,7 a state-of-art quaternary structure detection method hosted at the European Bioinformatics Institute, Hinxton, UK. A few cases that failed correct detection through our scheme, offered interesting insights into the intriguing nature of protein contacts in the lattice. The findings have implications for accurate inference of quaternary states of proteins, especially weak affinity complexes, where biological protein contacts tend to be sacrificed for the energetically optimal ones that favor the formation/stabilization of the crystal lattice. We expect our method to be used widely by all researchers interested in protein quaternary structure and interaction. Having developed a method that allows us to sample all categories of quaternary structures in PDB, we set our goal in addressing the next problem that of accurately determining stabilities of the geometrically compatible protein surfaces involved in interaction. Reformulating the question in terms of protein-protein docking, we sought to ask how we could reliably infer the stabilities of any arbitrary interface that is formed when two protein molecules are brought sterically closer. In a real protein docking exercise this question is asked innumerable times during energy-based screening of thousands of decoys geometrically sampled (through rotation+translation) from the unbound subunits. The current docking methods face problems in two counts: (i), the number of interfaces from decoys to evaluate energies is rather large (64320 for a 9º rotation and translation for a dimeric complex), and (ii) the energy based screening is not quite efficient such that the decoys with native-like quaternary structure are rarely selected at high ranks. We addressed both the problems with interesting results. Intricate decoy filtering approaches have been developed, which are either applied during the search stage or the sampling stage, or both. For filtering, usually statistical information, such as 3D conservation information of the interfacial residues, or similar facts is used; more expensive approaches screen for orientation, shape complementarity and electrostatics. We developed an interface area based decoy filter for the sampling stage, exploiting an assumption that native-like decoys must have the largest, or close to the largest, interface (Chapter 4).8 Implementation of this assumption and standard benchmarking showed that in 91% of the cases, we could recover native-like decoys of bound and unbound binary docking-targets of both strong and weak affinity. This allowed us to propose that “native-like decoys must have the largest, or close to the largest, interface” can be used as a rule to exclude non native decoys efficiently during docking sampling. This rule can dramatically clip the needle-in-a-haystack problem faced in a docking study by reducing >95% of the decoy set available from sampling search. We incorporated the rule as a central part of our protein docking strategy. While addressing the question of energy based screening to rank the native-like decoys at high rank during docking, we came across a large volume of work already published. The mainstay of most of the energy based screenings that avoid statistical potential, involve some form of the Coulomb’s potential, Lennard Jones potential and solvation energy. Different flavors of the energy functions are used with diverse preferences and weights for individual terms. Interestingly, in all cases the energy functions were of the unnormalized form. Individual energy terms were simply added to arrive at a final score that was to be used for ranking. Proteins being large molecules, offer limited scope of applying semi-empirical or quantum mechanical methods for large scale evaluation of energy. We, therefore, developed a de novo empirical scoring function in the normalized form. As already stated, we found NSc and NIP to be highly discriminatory for segregating biological and non biological interface. We, therefore, incorporated them as parameters for our scoring function. Our data mining study revealed that there is a reasonable correlation of -0.73 between normalized solvation energy and normalized nonbonding energy (Coulombs + van der Waals) at the interface. Using the information, we extended our scoring function by combining the geometric measures and the normalized interaction energies. Tests on 30 unbound binary protein-protein complexes showed that in 16 cases we could identify at least one decoy in top three ranks with ≤10 Å backbone root-mean-square-deviation (RMSD) from true binding geometry. The scoring results were compared with other state-of-art methods, which returned inferior results. The salient feature of our scoring function was exclusion of any experiment guided restraints, evolutionary information, statistical propensities or modified interaction energy equations, commonly used by others. Tests on 118 less difficult bound binary protein-protein complexes with ≤35% sequence redundancy at the interface gave first rank in 77% cases, where the native like decoy was chosen among 1 in 10,000 and had ≤5 Å backbone RMSD from true geometry. The details about the scoring function, results and comparison with the other methods are extensively discussed in Chapter 5.9 The method has been implemented and made available for public use as a web server - PROBE (http://pallab.serc.iisc.ernet.in/probe). The development and use of PROBE has been elaborated in Chapter 7.10 On course of this work, we generated huge amounts of data, which is useful information that could be used by others, especially “protein dockers”. We, therefore, developed dockYard (http://pallab.serc.iisc.ernet.in/dockYard) - a repository for protein-protein docking decoys (Chapter 6).11 dockYard offers four categories of docking decoys derived from: Bound (native dimer co-crystallized), Unbound (individual subunits as well as the target are crystallized), Variants (match the previous two categories in at least one subunit with 100% sequence identity), and Interlogs (match the previous categories in at least one subunit with ≥90% or ≥50% sequence identity). There is facility for full or selective download based on search parameters. The portal also serves as a repository to modelers who may want to share their decoy sets with the community. In conclusion, although we made several contributions in development of algorithms for improved protein-protein docking and quaternary structure inference, a lot of challenges remain (Chapter 8). The principal challenge arises by considering proteins as flexible bodies, whose conformational states may change on quaternary structure formation. In addition, solvent plays a major role in the free energy of binding, but its exact contribution is not straightforward to estimate. Undoubtedly, the cost of computation is one of the limiting factors apart from good energy functions to evaluate the docking decoys. Therefore, the next generation of algorithms must focus on improved docking studies that realistically incorporate flexibility and solvent environment in all their evaluations.

Diss. (sammanfattning) Linköping : Linköpings universitet, 2008. / Härtill 4 uppsatser. Includes bibliographical references.

Diss. (sammanfattning) Linköping : Linköpings universitet, 2008. / Härtill 4 uppsatser.