Knowledge-based approaches for understanding structure-dynamics-function relationship in proteins

Proteins accomplish their functions through conformational changes, often brought about by changes in environmental conditions or ligand binding. Predicting the functional mechanisms of proteins is impossible without a deeper understanding of conformational transitions. Dynamics is the key link between the structure and function of proteins. The protein data bank (PDB) contains multiple structures of the same protein, which have been solved under different conditions, using different experimental methods or in complexes with different ligands. These alternate conformations of the same protein (or similar proteins) can provide important information about what conformational changes take place and how they are brought about. Though there have been multiple computational approaches developed to predict dynamics from structure information, little work has been done to exploit this apparent, but potentially informative, redundancy in the PDB. In this work I bridge this gap by exploring various knowledge-based approaches to understand the structure-dynamics relationship and how it translates into protein function. First, a novel method for constructing free energy landscapes for conformational changes in proteins is proposed by combining principal motions with knowledge-based potential energies and entropies from coarse-grained models of protein dynamics. Second, an innovative method for computing knowledge-based entropies for proteins using an inverse Boltzmann approach is introduced, similar to the manner in which statistical potentials were previously extracted. We hypothesize that amino acid contact changes observed in the course of conformational changes within a large set of proteins can provide information about local pairwise flexibilities or entropies. By combining this new entropy measure with knowledge-based potential functions, we formulate a knowledge-based free energy (KBF) function that we demonstrate outperforms other statistical potentials in its ability to identify native protein structures embedded with sets of decoys. Third, I apply the methods developed above in collaboration with experimentalists to understand the molecular mechanisms of conformational changes in several protein systems including cadherins and membrane transporters. This work introduces several ways that the huge data in the PDB can be utilized to understand the underlying principles behind the structure-dynamics-function relationships of proteins. Results from this work have several important applications in structural bioinformatics such as structure prediction, molecular docking, protein engineering and design. In particular, the new KBFs developed in this dissertation have immediate applications in emerging topics such as prediction of 3D structure from coevolving residues in sequence alignments as well as in identifying the phenotypic effects of mutants

DYNAMICS OF AN UMBILICAL CABLE FOR UNDERGROUND BORE-WELL APPLICATIONS

A general model for an umbilical cable for underground bore-well applications is considered. The response of one-degree-of-freedom, nonlinear system under external excitation forces and the effect of the parameters 2, β and f on the excited system are investigated. Variation of the parameter 2 leads to multi-valued amplitudes and hence to jump phenomena. The simulation results are achieved using MATLAB 7.12.0 (R2011a) Simulink

Modelling of crowdsourced Wi-Fi fingerprint data

The lack of and/or the unreliability of GPS signals indoors poses unique challenges with accurate indoor navigation. This thesis proposes an idea that aims to addresses these challenges by leveraging Wi-Fi fingerprinting to augment Pedestrian Dead Reckoning (PDR) based Inertial Navigation Systems (INS). Wi-Fi fingerprinting involves the collection of Wi-Fi signal strengths from multiple access points, which are then used to model the relationship between fingerprint dissimilarity and real-world distances. Wi-Fi fingerprint data can be modelled through crowdsourced Wi-Fi fingerprint data. This model is crucial for enhancing indoor navigation accuracy where GPS data is unavailable. The research introduces a sophisticated approach using Weighted Least Squares regression with linear scaling weights to refine the estimation process. The Wi-Fi fingerprint model is used to filter out unreliable PDR data, considerably improving the location estimation accuracy. It employs a dual-model approach that allows utilisation of known reference points such as GPS fixes when available or Bluetooth beacons as indoor landmarks to further enhance the reliability of the navigation system. A weighted algorithm prioritizing data points based on their estimated reliability, effectively reducing the influence of poor-quality data on the overall system performance is used. This method shows a marked improvement in positioning accuracy, thus demonstrating the feasibility and effectiveness of integrating Wi-Fi fingerprinting with traditional inertial navigation methods. The findings showcase the potential of using Wi-Fi fingerprint modelling as a powerful augmenting technology for PDR-based INS (Inertial Navigation System), offering improvements over existing methods, particularly in complex indoor environments. The research also lays the groundwork for future advancements in indoor navigation technologies, opening avenues for more reliable and accurate indoor positioning solutions that can operate without GPS

Cu K-absorption edge study of cuprate superconductors

Cu K-absorption edges of YBa2Cu3O6.9, Tl2CaBa2Cu2O8 and Bi2CaSr2Cu2O8 show similar features. Copper is mainly in the 2+ state in these cuprates suggesting the likely presence of oxygen holes

Residue conservation and dimer-interface analysis of olfactory receptor molecular models

Olfactory Receptors (ORs) are members of the Class A rhodopsin like G-protein coupled receptors (GPCRs) which are the initial players in the signal transduction cascade, leading to the generation of nerve impulses transmitted to the brain and resulting in the detection of odorant molecules. Despite the accumulation of thousands of olfactory receptor sequences, no crystal structures of ORs are known tο date. However, the recent availability of crystallographic models of a few GPCRs allows us to generate homology models of ORs and analyze their amino acid patterns, as there is a huge diversity in OR sequences. In this study, we have generated three-dimensional models of 100 representative ORs from Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Sacharomyces cerevisiae which were selected on the basis of a composite classification scheme and phylogenetic analysis. The crystal structure of bovine rhodopsin was used as a template and it was found that the full-length models have more than 90% of their residues in allowed regions of the Ramachandran plot. The structures were further used for analysis of conserved residues in the transmembrane and extracellular loop regions in order to identify functionally important residues. Several ORs are known to be functional as dimers and hence dimer interfaces were predicted for OR models to analyse their oligomeric functional state

The role of Signal Processing in Meeting Privacy Challenges [an overview]

International audienceWith the increasing growth and sophistication of information technology, personal information is easily accessible electronically. This flood of released personal data raises important privacy concerns. However, electronic data sources exist to be used and have tremendous value (utility) to their users and collectors, leading to a tension between privacy and utility. This article aims to quantify that tension by means of an information-theoretic framework and motivate signal processing approaches to privacy problems. The framework is applied to a number of case studies to illustrate concretely how signal processing can be harnessed to provide data privacy

Insights from the analysis of conserved motifs and permitted amino acid exchanges in the human, the fly and the worm GPCR clusters

G-protein coupled receptors (GPCRs) belong to biologically important and functionally diverse and largest super family of membrane proteins. GPCRs retain a characteristic membrane topology of seven alpha helices with three intracellular, three extracellular loops and flanking N' and C' terminal residues. Subtle differences do exist in the helix boundaries (TM-domain), loop lengths, sequence features such as conserved motifs, and substituting amino acid patterns and their physiochemical properties amongst these sequences (clusters) at intra-genomic and inter-genomic level (please re-phrase into 2 statements for clarity). In the current study, we employ prediction of helix boundaries and scores derived from amino acid substitution exchange matrices to identify the conserved amino acid residues (motifs) as consensus in aligned set of homologous GPCR sequences. Co-clustered GPCRs from human and other genomes, organized as 32 clusters, were employed to study the amino acid conservation patterns and species-specific or cluster-specific motifs. Critical analysis on sequence composition and properties provide clues to connect functional relevance within and across genome for vast practical applications such as design of mutations and understanding of disease-causing genetic abnormalities

Structural differences and differential expression among rhabdomeric opsins reveal functional change after gene duplication in the bay scallop, Argopecten irradians (Pectinidae)

Background Opsins are the only class of proteins used for light perception in image-forming eyes. Gene duplication and subsequent functional divergence of opsins have played an important role in expanding photoreceptive capabilities of organisms by altering what wavelengths of light are absorbed by photoreceptors (spectral tuning). However, new opsin copies may also acquire novel function or subdivide ancestral functions through changes to temporal, spatial or the level of gene expression. Here, we test how opsin gene copies diversify in function and evolutionary fate by characterizing four rhabdomeric (Gq-protein coupled) opsins in the scallop, Argopecten irradians, identified from tissue-specific transcriptomes. Results Under a phylogenetic analysis, we recovered a pattern consistent with two rounds of duplication that generated the genetic diversity of scallop Gq-opsins. We found strong support for differential expression of paralogous Gq-opsins across ocular and extra-ocular photosensitive tissues, suggesting that scallop Gq-opsins are used in different biological contexts due to molecular alternations outside and within the protein-coding regions. Finally, we used available protein models to predict which amino acid residues interact with the light-absorbing chromophore. Variation in these residues suggests that the four Gq-opsin paralogs absorb different wavelengths of light. Conclusions Our results uncover novel genetic and functional diversity in the light-sensing structures of the scallop, demonstrating the complicated nature of Gq-opsin diversification after gene duplication. Our results highlight a change in the nearly ubiquitous shadow response in molluscs to a narrowed functional specificity for visual processes in the eyed scallop. Our findings provide a starting point to study how gene duplication may coincide with eye evolution, and more specifically, different ways neofunctionalization of Gq-opsins may occur