Specific carbohydrate-protein interactions are crucial in numerous physiological processes, disruption of which has been implicated in many different diseases like cancer. This provides researchers an opportunity to utilize carbohydrates as biomarkers and targets for therapeutics for such diseases. There has been a tremendous surge in the research being conducted towards the development of techniques to analyze carbohydrates and their specificity and affinity for different proteins. However, owing to their complex three-dimensional structure, stereochemistry, low binding affinities and broad specificity, carbohydrates have proven to be challenging to study. Therefore, new techniques and improvements in the existing methodologies are required. Here, we show that the incorporation of experimental data into molecular modeling can be used as a powerful combination to gain an understanding of the structural features of proteins and carbohydrates leading to the specificity in their interactions. Firstly, hydroxyl radical protein footprinting (HRPF) was used to establish a relationship between the oxidation of amino acids exposed on the surface of a protein and their solvent accessible surface area (SASA). Oxidation, as well as SASA, are both directly proportional to the exposure of an amino acid to the solvent. This relationship was used to estimate SASA of residues of a protein in solution, which was then successfully utilized as a score to quantify the quality of models generated through a molecular dynamics (MD) simulation and homology modeling. This relationship can also be used to study protein- carbohydrate interactions, which remains to be tested. Secondly, the functional groups of a monosaccharide essential for forming protein-carbohydrate interactions were identified by using co-crystal structures and per-atom binding energy analysis, which shows that not all chemically-equivalent functional groups are equally significant for binding. Lastly, the 3D structure of a group of monosaccharides was analyzed and it was observed that two monosaccharides can possess structural similarities depending on their alignment, which can be used to explain cross-reactivity between a protein and more than one carbohydrate.

This book is indexed in Chemical Abstracts ServiceThe interactions of proteins with other molecules are important in many cellular activities. Investigations have been carried out to understand the recognition mechanism, identify the binding sites, analyze the the binding affinity of complexes, and study the influence of mutations on diseases. Protein interactions are also crucial in structure-based drug design.This book covers computational analysis of protein-protein, protein-nucleic acid and protein-ligand interactions and their applications. It provides up-to-date information and the latest developments from experts in the field, using illustrations to explain the key concepts and applications. This volume can serve as a single source on comparative studies of proteins interacting with proteins/DNAs/RNAs/carbohydrates and small molecules.

Any molecular recognition event results in a change in the free energy of the system. The extent of this change is related to the association constant, such that the more negative the free energy change is, the tighter the interaction between receptor and ligand. Protein-carbohydrate interactions play a critical role in signal transduction, innate immunity, and metabolism. Modeling these interactions is somewhat complicated by the inherent flexibility of carbohydrates as well as their relatively large number of functional groups. An empirical scoring function for docking carbohydrates to proteins, specifically tailored to predict both the correct binding orientation and free energy of binding of the carbohydrate-ligand/protein-receptor complex, will be presented. This new scoring function can predict free energies of binding to within 1.1 kcal/mol residual standard error, a definite improvement over existing scoring functions that result in standard errors well over 2 kcal/mol. Application of automated docking methodology to determine carbohydrate recognition specificity of the C-type lectin, human surfactant protein D, will also be presented. In the second part of the thesis, the role of [pi]-stacking interactions (e.g. between Tyr side chains) in stabilizing protein folds will be discussed. A 17-residue peptide derived from the naturally occurring anti-microbial peptide tachyplesin I was investigated using NMR spectroscopy. NOE cross-peaks were observed, confirming the existence of this interaction in solution. In the final part of the thesis, a quantitative NMR investigation into the self-association behavior of the regulatory domains of several Tec family member kinases will be presented. Of particular interest, self-association within Bruton's tyrosine kinase (Btk) regulatory domains occurs through the formation of an asymmetric homodimer. Together this work demonstrates the importance of rigorous biophysical characterization of biomolecular recognition events and the interdependence of computational modeling and experimentation.

This reference offers tools for engineers, scientists, biologists, and others working with the computational techniques of nanophotonics. It introduces the key concepts of computational methods in a manner that is easily digestible for newcomers to the field. The book also examines future applications of nanophotonics in the technical industry and covers new developments and interdisciplinary research in engineering, science, and medicine. It provides an overview of the key computational nanophotonics and describes the technologies with an emphasis on how they work and their key benefits.

Currently, with the growth of experimental structural data on protein-protein interactions and larger protein complexes, the trend in computational biology and structural bioinformatics is towards applying such resources to model and analyze structures, functions, and evolutions of PPIs. Nevertheless, due to the rapid growth of the number of experimental structures, it becomes necessary to introduce bioinformatics methodologies, which rely on the advanced machine learning and information retrieval techniques, capable of handling complex and massive structural data. The research in this dissertation introduces and develops several computational methodologies to understand PPI 3D structures. First, we introduced an alignment-free similarity measure to detect structural similar PPI interfaces. This approach is capable of finding similar PPI interfaces formed by non-related protein subunits. Second, applying our similarity measure for PPIs, we showed our ability to use feature based interface similarity to classify and retrieve similar interface structures efficiently. Third, we used a set of simple protein interface structural features to test the classification and scoring performances for docked protein complexes, by using supervised and semi-supervised learning. Fourth, we analyzed the conservation patterns of charged residues located in PPI interfaces on a sampled set of PPI data. Last, we processed a genome-wide analysis of alternative splicing (AS) effects on human PPIs.