Protein microenvironments for topology analysis

Previously held under moratorium from 1st December 2016 until 1st December 2021Amino Acid Residues are often the focus of research on protein structures. However, in a folded protein, each residue finds itself in an environment that is defined by the properties of its surrounding residues. The term microenvironment is used herein to refer to these local ensembles. Not only do they have chemical properties but also topological properties which quantify concepts such as density, boundaries between domains and junction complexity. These quantifications are used to project a protein’s backbone structure into a series of scores. The hypothesis was that these sequences of scores can be used to discover protein domains and motifs and that they can be used to align and compare groups of 3D protein structures. This research sought to implement a system that could efficiently compute microenvironments such that they can be applied routinely to large datasets. The computation of the microenvironments was the most challenging aspect in terms of performance, and the optimisations required are described. Methods of scoring microenvironments were developed to enable the extraction of domain and motif data without 3D alignment. The problem of allosteric site detection was addressed with a classifier that gave high rates of allosteric site detection. Overall, this work describes the development of a system that scales well with increasing dataset sizes. It builds on existing techniques, in order to automatically detect the boundaries of domains and demonstrates the ability to process large datasets by application to allosteric site detection, a problem that has not previously been adequately solved.Amino Acid Residues are often the focus of research on protein structures. However, in a folded protein, each residue finds itself in an environment that is defined by the properties of its surrounding residues. The term microenvironment is used herein to refer to these local ensembles. Not only do they have chemical properties but also topological properties which quantify concepts such as density, boundaries between domains and junction complexity. These quantifications are used to project a protein’s backbone structure into a series of scores. The hypothesis was that these sequences of scores can be used to discover protein domains and motifs and that they can be used to align and compare groups of 3D protein structures. This research sought to implement a system that could efficiently compute microenvironments such that they can be applied routinely to large datasets. The computation of the microenvironments was the most challenging aspect in terms of performance, and the optimisations required are described. Methods of scoring microenvironments were developed to enable the extraction of domain and motif data without 3D alignment. The problem of allosteric site detection was addressed with a classifier that gave high rates of allosteric site detection. Overall, this work describes the development of a system that scales well with increasing dataset sizes. It builds on existing techniques, in order to automatically detect the boundaries of domains and demonstrates the ability to process large datasets by application to allosteric site detection, a problem that has not previously been adequately solved

Local pre-processing for node classification in networks : application in protein-protein interaction

Network modelling provides an increasingly popular conceptualisation in a wide range of domains, including the analysis of protein structure. Typical approaches to analysis model parameter values at nodes within the network. The spherical locality around a node provides a microenvironment that can be used to characterise an area of a network rather than a particular point within it. Microenvironments that centre on the nodes in a protein chain can be used to quantify parameters that are related to protein functionality. They also permit particular patterns of such parameters in node-centred microenvironments to be used to locate sites of particular interest. This paper evaluates an approach to index generation that seeks to rapidly construct microenvironment data. The results show that index generation performs best when the radius of microenvironments matches the granularity of the index. Results are presented to show that such microenvironments improve the utility of protein chain parameters in classifying the structural characteristics of nodes using both support vector machines and neural networks

BIOCHEMICAL INVESTIGATION INTO INITIATION OF FATTY ACID SYNTHESIS IN THE AFRICAN TRYPANOSOMES

My doctoral studies focused on studying FA metabolism in the deadly protozoan parasite T. brucei. In my dissertation, I will be addressing various aspects of the regulation of TbACC, which catalyzes the first committed step in FA synthesis. In the second chapter, I hypothesized that TbACC is regulated in response to environmental lipids. I examined changes in TbACC RNA, protein, and activity in response to different levels of environmental lipids in both BF and PF cells. I also delineated the mechanisms by which TbACC expression and activity is regulated by phosphorylation in response to altered lipid environments. In the third chapter, which has been published, we tested the effects of a compound in green tea extract known as epigallocatechin gallate (EGCG), a known inducer of AMPK, which phosphorylates ACC in other organisms. We tested the effect of EGCG on BF and PF growth. We also examined the effect of EGCG on TbACC activity and phosphorylation. In the fourth chapter, I demonstrated that TbACC in PF is also regulated by various allosteric regulators. I also showed that TbACC might form oligomers. Together these studies have given an insight on the ability of T. brucei to regulate its FA synthesis and the role this pathway may play in the survival of this deadly parasite in its hosts. This knowledge may be exploited in the future to find a better cure for Trypanosomiasis

A combinatorial extracellular matrix platform identifies cell-extracellular matrix interactions that correlate with metastasis

Extracellular matrix interactions have essential roles in normal physiology and many pathological processes. Although the importance of extracellular matrix interactions in metastasis is well documented, systematic approaches to identify their roles in distinct stages of tumorigenesis have not been described. Here we report a novel-screening platform capable of measuring phenotypic responses to combinations of extracellular matrix molecules. Using a genetic mouse model of lung adenocarcinoma, we measure the extracellular matrix-dependent adhesion of tumour-derived cells. Hierarchical clustering of the adhesion profiles differentiates metastatic cell lines from primary tumour lines. Furthermore, we uncovered that metastatic cells selectively associate with fibronectin when in combination with galectin-3, galectin-8 or laminin. We show that these molecules correlate with human disease and that their interactions are mediated in part by α3β1 integrin. Thus, our platform allowed us to interrogate interactions between metastatic cells and their microenvironments, and identified extracellular matrix and integrin interactions that could serve as therapeutic targets.National Institutes of Health (U.S.) (Grant K99-CA151968)National Institutes of Health (U.S.). Ruth L. Kirschstein National Research Service AwardStand Up To Cancer (SU2C/AACR)David H. Koch Institute for Integrative Cancer Research at MIT (CTC Project)Harvard Stem Cell Institute (SG-0046-08-00)National Cancer Center (Postdoctoral Fellowship)National Cancer Institute (U.S.) (U54CA126515)National Cancer Institute (U.S.) (U54CA112967)Howard Hughes Medical InstituteMassachusetts Institute of Technology. Ludwig Center for Molecular Oncolog

Binding site matching in rational drug design: Algorithms and applications

© 2018 The Author(s) 2018. Published by Oxford University Press. All rights reserved. Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning

AMP Is a True Physiological Regulator of AMP-Activated Protein Kinase by Both Allosteric Activation and Enhancing Net Phosphorylation

SummaryWhile allosteric activation of AMPK is triggered only by AMP, binding of both ADP and AMP has been reported to promote phosphorylation and inhibit dephosphorylation at Thr172. Because cellular concentrations of ADP and ATP are higher than AMP, it has been proposed that ADP is the physiological signal that promotes phosphorylation and that allosteric activation is not significant in vivo. However, we report that: AMP is 10-fold more potent than ADP in inhibiting Thr172 dephosphorylation; only AMP enhances LKB1-induced Thr172 phosphorylation; and AMP can cause >10-fold allosteric activation even at concentrations 1–2 orders of magnitude lower than ATP. We also provide evidence that allosteric activation by AMP can cause increased phosphorylation of acetyl-CoA carboxylase in intact cells under conditions in which there is no change in Thr172 phosphorylation. Thus, AMP is a true physiological regulator of AMPK, and allosteric regulation is an important component of the overall activation mechanism