Transthyretin mutagenesis: impact on amyloidogenesis and disease

Funding This work was supported by COMPETE and CENTRO-202010. 13039/501100011929 and by Fundação para a Ciência e a Tecnologia (FCT) through grants UIDB/00313/2020 and UIDP/00313/2020 (to Coimbra Chemistry Center, University of Coimbra) and doctoral fellowship SFRH/BD/137991/2018 (to Z.L.A.).Transthyretin (TTR), a homotetrameric protein found in plasma, cerebrospinal fluid, and the eye, plays a pivotal role in the onset of several amyloid diseases with high morbidity and mortality. Protein aggregation and fibril formation by wild-type TTR and its natural more amyloidogenic variants are hallmarks of ATTRwt and ATTRv amyloidosis, respectively. The formation of soluble amyloid aggregates and the accumulation of insoluble amyloid fibrils and deposits in multiple tissues can lead to organ dysfunction and cell death. The most frequent manifestations of ATTR are polyneuropathies and cardiomyopathies. However, clinical manifestations such as carpal tunnel syndrome, leptomeningeal, and ocular amyloidosis, among several others may also occur. This review provides an up-to-date listing of all single amino-acid mutations in TTR known to date. Of approximately 220 single-point mutations, 93% are considered pathogenic. Aspartic acid is the residue mutated with the highest frequency, whereas tryptophan is highly conserved. “Hot spot” mutation regions are mainly assigned to β-strands B, C, and D. This manuscript also reviews the protein aggregation models that have been proposed for TTR amyloid fibril formation and the transient conformational states that convert native TTR into aggregation-prone molecular species. Finally, it compiles the various in vitro TTR aggregation protocols currently in use for research and drug development purposes. In short, this article reviews and discusses TTR mutagenesis and amyloidogenesis, and their implications in disease onset.info:eu-repo/semantics/publishedVersio

Spatial clustering of molecular dynamics trajectories in protein unfolding simulations

Molecular dynamics simulations is a valuable tool to study protein unfolding in silico. Analyzing the relative spatial position of the residues during the simulation may indicate which residues are essential in determining the protein structure. We present a method, inspired by a popular data mining technique called Frequent Itemset Mining, that clusters sets of amino acid residues with a synchronized trajectory during the unfolding process. The proposed approach has several advantages over traditional hierarchical clustering.The authors acknowledge the support of the "Fundacao para a Ciencia e Tecnologia,Portugal, and the program FEDER, through grant PTDC/BIA-PRO/72838/2006 (to PJA and RMMB) and the Fellowships SFRH/BPD/42003/2007(to PGF) and SFRH/BD/16888/2004 (to CGS). We thank the Center for Computational Physics, Departamento de Fisica, Universidade de Coimbra, for the computer resources provided for the MD simulations

Mining approximate motifs in time series

The problem of discovering previously unknown frequent patterns in time series, also called motifs, has been recently introduced. A motif is a subseries pattern that appears a significant number of times. Results demonstrate that motifs may provide valuable insights about the data and have a wide range of applications in data mining tasks. The main motivation for this study was the need to mine time series data from protein folding/unfolding simulations. We propose an algorithm that extracts approximate motifs, i.e. motifs that capture portions of time series with a similar and eventually symmetric behaviour. Preliminary results on the analysis of protein unfolding data support this proposal as a valuable tool. Additional experiments demonstrate that the application of utility of our algorithm is not limited to this particular problem. Rather it can be an interesting tool to be applied in many real world problems.Fundação para a Ciência e a Tecnologia (FCT).Fundo Europeu de Desenvolvimento Regional (FEDER) - POCTI/BME/49583/2002; SFRH/BD/13462/2003; SFRH/BD/16888/2004

On mining protein unfolding simulation data with inductive logic programming

The detailed study of folding and unfolding events in proteins is becoming central to develop rational therapeutic strategies against maladies such asAlzheimer and Parkinson disease. A promising approach to study the unfolding processes of proteins is through computer simulations. However, these computer simulations generate huge amounts of data that require computational methods for their analysis.In this paper we report on the use of Inductive Logic Programming (ILP) techniques to analyse the trajectories of protein unfolding simulations. The paper describes ongoing work on one of several problems of interest in the protein unfolding setting. The problem we address here is that of explaining what makes secondary structure elements to break down during the unfolding process. We tackle such problem collecting examples of contexts where secondary structures break and (automatically) constructing rules that may be used to suggest the explanations

Acute left main coronary occlusion after transcatheter aortic valve implantation: life-saving intervention using the snare technique-a case report

Transcatheter aortic valve implantation (TAVI) has rapidly evolved and changed the field of structural cardiovascular intervention. Its advances lead to a marked reduction in the risk of complications and improved outcomes. However, TAVI is still associated with potential serious complications.publishersversionpublishe

Experimental investigation of the flow field in the vicinity of an oscillating wave surge converter

PTDC/CTAOHR/30561/2017 PD/BD/705970/2014The main objective of this paper is to characterize the flow field on the front face of an oscillating wave surge converter (OWSC) under a regular wave. For this purpose, the longitudinal and vertical velocity components were measured using an Ultrasonic Velocity Profiler (UVP). In order to explain the main trends of the OWSC’s dynamics, the experimental data were firstly compared with the analytical results of potential theory. A large discrepancy was observed between experimental and analytical results, caused by the nonlinear behavior of wave-OWSC interaction that determine the turbulent field and the boundary layer. The experimental velocity field shows a strong ascendant flow generated by the mass transfer over the flap (overtopping) and flow rotation generated by the beginning of the flap deceleration and acceleration. These features (overtopping and flow rotation) have an important role on the power capture of OWSC and, therefore, analytical results are not accurate to describe the complex hydrodynamics of OWSC.publishersversionpublishe

Prediction of drug targets in human pathogens

The identification of new and druggable targets in bacteria is a critical endeavour in pharmaceutical research of novel antibiotics to fight infectious agents. The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. A new model that combines the singular value decomposition technique with biological filters comprised of a set of protein properties associated with bacterial drug targets and similarity to protein-coding essential genes of E. coli has been developed to predict potential drug targets in the Enterobacteriaceae family [1]. This model identified 99 potential target proteins amongst the studied bacterial family, exhibiting eight different functions that suggest that the disruption of the activities of these proteins is critical for cells. Out of these candidates, one was selected for target confirmation. To find target modulators, receptor-based pharmacophore hypotheses were built and used in the screening of a virtual library of compounds. Postscreening filters were based on physicochemical and topological similarity to known Gram-negative antibiotics and applied to the retrieved compounds. Screening hits passing all filters were docked into the proteins catalytic groove and 15 of the most promising compounds were purchased from their chemical vendors to be experimentally tested in vitro. To the best of our knowledge, this is the first attempt to rationalize the search of compounds to probe the relevance of this candidate as a new pharmacological target

Monitorização in continuum da Lagoa das Sete Cidades

Nas últimas décadas, a Lagoa das Sete Cidades (Ilha de São Miguel, Açores) tem sido afectada por um lento processo de eutrofização que, recentemente, conduziu a um agravamento da sua qualidade química e ecológica. Para avaliar o estado actual da Lagoa e monitorizar a sua evolução foi implementado um sistema de monitorização in continuum, baseado numa estação remota multiparamétrica e em análises periódicas do fitoplâncton. A monitorização in continuum permitiu caracterizar a evolução sazonal dos parâmetros físico-químicos, verificando-se uma rápida transição entre os períodos de mistura e estratificação da massa de água. O desenvolvimento da estratificação térmica foi determinante na evolução da comunidade fitoplanctónica

Solution Structures of the C-Terminal Domain of Cardiac Troponin C Free and Bound to the N-Terminal Domain of Cardiac Troponin I

The N-terminal domain of cardiac troponin I (cTnI) comprising residues 33−80 and lacking the cardiac-specific amino terminus forms a stable binary complex with the C-terminal domain of cardiac troponin C (cTnC) comprising residues 81−161. We have utilized heteronuclear multidimensional NMR to assign the backbone and side-chain resonances of Ca2+-saturated cTnC(81−161) both free and bound to cTnI(33−80). No significant differences were observed between secondary structural elements determined for free and cTnI(33−80)-bound cTnC(81−161). We have determined solution structures of Ca2+-saturated cTnC(81−161) free and bound to cTnI(33−80). While the tertiary structure of cTnC(81−161) is qualitatively similar to that observed free in solution, the binding of cTnI(33−80) results mainly in an opening of the structure and movement of the loop region between helices F and G. Together, these movements provide the binding site for the N-terminal domain of cTnI. The putative binding site for cTnI(33−80) was determined by mapping amide proton and nitrogen chemical shift changes, induced by the binding of cTnI(33−80), onto the C-terminal cTnC structure. The binding interface for cTnI(33−80), as suggested from chemical shift changes, involves predominantly hydrophobic interactions located in the expanded hydrophobic pocket. The largest chemical shift changes were observed in the loop region connecting helices F and G. Inspection of available TnC sequences reveals that these residues are highly conserved, suggesting a common binding motif for the Ca2+/Mg2+-dependent interaction site in the TnC/TnI complex

A relational learning approach to Structure-Activity Relationships in drug design toxicity studies.

It has been recognized that the development of new therapeutic drugs is a complex and expensive process. A large number of factors affect the activity in vivo of putative candidate molecules and the propensity for causing adverse and toxic effects is recognized as one of the major hurdles behind the current "target-rich, lead-poor" scenario. Structure-Activity Relationship (SAR) studies, using relational Machine Learning (ML) algorithms, have already been shown to be very useful in the complex process of rational drug design. Despite the ML successes, human expertise is still of the utmost importance in the drug development process. An iterative process and tight integration between the models developed by ML algorithms and the know-how of medicinal chemistry experts would be a very useful symbiotic approach. In this paper we describe a software tool that achieves that goal--iLogCHEM. The tool allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico. It also allows the expert to guide the search for useful molecules without the need to know the details of the algorithms used. The models produced by the algorithms may be visualized using a graphical interface, that is of common use amongst researchers in structural biology and medicinal chemistry. The graphical interface enables the expert to provide feedback to the learning system. The developed tool has also facilities to handle the similarity bias typical of large chemical databases. For that purpose the user can filter out similar compounds when assembling a data set. Additionally, we propose ways of providing background knowledge for Relational Learners using the results of Graph Mining algorithms. Copyright 2011 The Author(s). Published by Journal of Integrative Bioinformatics