Structural Diversity of Biological Ligands and their Binding Sites in Proteins

The phenomenon of molecular recognition, which underpins almost all biological processes, is dynamic, complex and subtle. Establishing an interaction between a pair of molecules involves mutual structural rearrangements guided by a highly convoluted energy landscape, the accurate mapping of which continues to elude us. The analysis of interactions between proteins and small molecules has been a focus of intense interest for many years, offering as it does the promise of increased insight into many areas of biology, and the potential for greatly improved drug design methodologies. Computational methods for predicting which types of ligand a given protein may bind, and what conformation two molecules will adopt once paired, are particularly sought after. The work presented in this thesis aims to quantify the amount of structural variability observed in the ways in which proteins interact with ligands. This diversity is considered from two perspectives: to what extent ligands bind to different proteins in distinct conformations, and the degree to which binding sites specific for the same ligand have different atomic structures. The first study could be of value to approaches which aim to predict the bound pose of a ligand, since by cataloguing the range of conformations previously observed, it may be possible to better judge the biological likelihood of a newly predicted molecular arrangement. The findings show that several common biological ligands exhibit considerable conformational diversity when bound to proteins. Although binding in predominantly extended conformations, the analysis presented here highlights several cases in which the biological requirements of a given protein force its ligand to adopt a highly compact form. Comparing the conformational diversity observed within several protein families, the hypothesis that homologous proteins tend to bind ligands in a similar arrangement is generally upheld, but several families are identified in which this is demonstrably not the case. Consideration of diversity in the binding site itself, on the other hand, may be useful in guiding methods which search for binding sites in uncharacterised protein structures: identifying those regions of known sites which are less variable could help to focus the search only on the most important features. Analysis of the diversity of a non-redundant dataset of adenine binding sites shows that a small number of key interactions are conserved, with the majority of the fragment environment being highly variable. Just as ligand conformation varies between protein families, so the degree of binding site diversity is observed to be significantly higher in some families than others. Taken together, the results of this work suggest that the repertoire of strategies produced by nature for the purposes of molecular recognition are extremely extensive. Moreover, the importance of a given ligand conformation or pattern of interaction appears to vary greatly depending on the function of the particular group of proteins studied. As such, it is proposed that diversity analysis may form a significant part of future large-scale studies of ligand-protein interactions

Estudis computacionals sobre beta-amilasa, una molècula enzimàtica tipus barril (beta/alfa) 8: relacions evolutives i transicions estructurals al centre catalític..

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Structural approaches to protein sequence analysis

Various protein sequence analysis techniques are described, aimed at improving the prediction of protein structure by means of pattern matching. To investigate the possibility that improvements in amino acid comparison matrices could result in improvements in the sensitivity and accuracy of protein sequence alignments, a method for rapidly calculating amino acid mutation data matrices from large sequence data sets is presented. The method is then applied to the membrane-spanning segments of integral membrane proteins in order to investigate the nature of amino acid mutability in a lipid environment. Whilst purely sequence analytic techniques work well for cases where some residual sequence similarity remains between a newly characterized protein and a protein of known 3-D structure, in the harder cases, there is little or no sequence similarity with which to recognize proteins with similar folding patterns. In the light of these limitations, a new approach to protein fold recognition is described, which uses a statistically derived pairwise potential to evaluate the compatibility between a test sequence and a library of structural templates, derived from solved crystal structures. The method, which is called optimal sequence threading, proves to be highly successful, and is able to detect the common TIM barrel fold between a number of enzyme sequences, which has not been achieved by any previous sequence analysis technique. Finally, a new method for the prediction of the secondary structure and topology of membrane proteins is described. The method employs a set of statistical tables compiled from well-characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization. The statistical tables show definite biases towards certain amino acid species on the inside, middle and outside of a cellular membrane

Spacelab Science Results Study

Beginning with OSTA-1 in November 1981 and ending with Neurolab in March 1998, a total of 36 Shuttle missions carried various Spacelab components such as the Spacelab module, pallet, instrument pointing system, or mission peculiar experiment support structure. The experiments carried out during these flights included astrophysics, solar physics, plasma physics, atmospheric science, Earth observations, and a wide range of microgravity experiments in life sciences, biotechnology, materials science, and fluid physics which includes combustion and critical point phenomena. In all, some 764 experiments were conducted by investigators from the U.S., Europe, and Japan. The purpose of this Spacelab Science Results Study is to document the contributions made in each of the major research areas by giving a brief synopsis of the more significant experiments and an extensive list of the publications that were produced. We have also endeavored to show how these results impacted the existing body of knowledge, where they have spawned new fields, and if appropriate, where the knowledge they produced has been applied