Validation and Applications of Protein-Ligand Docking Approaches Improved for Metalloligands with Multiple Vacant Sites

Altres ajuts: COST Action CM1306Decoding the interaction between coordination compounds and proteins is of fundamental importance in biology, pharmacy, and medicine. In this context, protein-ligand docking represents a particularly interesting asset to predict how small compounds could interact with biomolecules, but to date, very little information is available to adapt these methodologies to metal-containing ligands. Here, we assessed the predictive capability of a metal-compatible parameter set for the docking program GOLD for metalloligands with multiple vacant sites and different geometries. The study first presents a benchmark of 25 well-characterized X-ray metalloligand-protein adducts. In 100% of the cases, the docking solutions are superimposable to the X-ray determination, and in 92% the value of the root-mean-square deviation between the experimental and calculated structures is lower than 1.5 Å. After the validation step, we applied these methods to five case studies for the prediction of the binding of pharmacological active metal species to proteins: (i) the anticancer copper(II) complex [Cu II (Br)(2-hydroxy-1-naphthaldehyde benzoyl hydrazine)(indazole)] to human serum albumin (HSA); (ii) one of the active species of antidiabetic and antitumor vanadium compounds, V IV O 2+ ion, to carboxypeptidase; (iii) the antiarthritic species [Au I (PEt 3 )] + to HSA; (iv) the antitumor oxaliplatin to ubiquitin; (v) the antitumor ruthenium(II) compound RAPTA-PentaOH to cathepsin B. The calculations suggested that the binding modes are in good agreement with the partial information retrieved from spectroscopic and spectrometric analysis and allowed us, in certain cases, to propose additional hypotheses. This method is an important update in protein-metalloligand docking, which could have a wide field of application, from biology and inorganic biochemistry to medicinal chemistry and pharmacology

AtomLbs: An Atom Based Convolutional Neural Network for Druggable Ligand Binding Site Prediction

Despite advances in drug research and development, there are few and ineffective treatments for a variety of diseases. Virtual screening can drastically reduce costs and accelerate the drug discovery process. Binding site identification is one of the initial and most important steps in structure-based virtual screening. Identifying and defining protein cavities that are likely to bind to a small compound is the objective of this task. In this research, we propose four different convolutional neural networks for predicting ligand-binding sites in proteins. A parallel optimized data pipeline is created to enable faster training of these neural network models on minimal hardware. The effectiveness of each method is assessed on well-established ligand binding site datasets. It is then compared with the state-of-the-art and widely used methods for ligand binding site identification. The result shows that our methods outperform most of the other methods and are comparable to the state-of-the-art methods

Computational approaches to shed light on molecular mechanisms in biological processes

Computational approaches based on Molecular Dynamics simulations, Quantum Mechanical methods and 3D Quantitative Structure-Activity Relationships were employed by computational chemistry groups at the University of Milano-Bicocca to study biological processes at the molecular level. The paper reports the methodologies adopted and the results obtained on Aryl hydrocarbon Receptor and homologous PAS proteins mechanisms, the properties of prion protein peptides, the reaction pathway of hydrogenase and peroxidase enzymes and the defibrillogenic activity of tetracyclines. © Springer-Verlag 2007

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Data mining in computational proteomics and genomics

This dissertation addresses data mining in bioinformatics by investigating two important problems, namely peak detection and structure matching. Peak detection is useful for biological pattern discovery while structure matching finds many applications in clustering and classification. The first part of this dissertation focuses on elastic peak detection in 2D liquid chromatographic mass spectrometry (LC-MS) data used in proteomics research. These data can be modeled as a time series, in which the X-axis represents time points and the Y-axis represents intensity values. A peak occurs in a set of 2D LC-MS data when the sum of the intensity values in a sliding time window exceeds a user-determined threshold. The elastic peak detection problem is to locate all peaks across multiple window sizes of interest in the dataset. A new method, called PeakID, is proposed in this dissertation, which solves the elastic peak detection problem in 2D LC-MS data without yielding any false negative. PeakID employs a novel data structure, called a Shifted Aggregation Tree or AggTree for short, to find the different peaks in the dataset. This method works by first constructing an AggTree in a bottom-up manner from the dataset, and then searching the AggTree for the peaks in a top-down manner. PeakID uses a state-space algorithm to find the topology and structure of an efficient AggTree. Experimental results demonstrate the superiority of the proposed method over other methods on both synthetic and real-world data. The second part of this dissertation focuses on RNA pseudoknot structure matching and alignment. RNA pseudoknot structures play important roles in many genomic processes. Previous methods for comparative pseudoknot analysis mainly focus on simultaneous folding and alignment of RNA sequences. Little work has been done to align two known RNA secondary structures with pseudoknots taking into account both sequence and structure information of the two RNAs. A new method, called RKalign, is proposed in this dissertation for aligning two known RNA secondary structures with pseudoknots. RKalign adopts the partition function methodology to calculate the posterior log-odds scores of the alignments between bases or base pairs of the two RNAs with a dynamic programming algorithm. The posterior log-odds scores are then used to calculate the expected accuracy of an alignment between the RNAs. The goal is to find an optimal alignment with the maximum expected accuracy. RKalign employs a greedy algorithm to achieve this goal. The performance of RKalign is investigated and compared with existing tools for RNA structure alignment. An extension of the proposed method to multiple alignment of pseudoknot structures is also discussed. RKalign is implemented in Java and freely accessible on the Internet. As more and more pseudoknots are revealed, collected and stored in public databases, it is anticipated that a tool like RKalign will play a significant role in data comparison, annotation, analysis, and retrieval in these databases

A marine viral halogenase that iodinates diverse substrates

We thank the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013/ERC grant agreement no. 614779 GenoChemetics to R.J.M.G.), Syngenta and Wellcome ISSF (grant no. 204821/Z/16/Z to D.S.G.) for generous financial support.Oceanic cyanobacteria are the most abundant oxygen-generating phototrophs on our planet and are therefore important to life. These organisms are infected by viruses called cyanophages, which have recently shown to encode metabolic genes that modulate host photosynthesis, phosphorus cycling and nucleotide metabolism. Herein we report the characterization of a wild-type flavin-dependent viral halogenase (VirX1) from a cyanophage. Notably, halogenases have been previously associated with secondary metabolism, tailoring natural products. Exploration of this viral halogenase reveals it capable of regioselective halogenation of a diverse range of substrates with a preference for forming aryl iodide species; this has potential implications for the metabolism of the infected host. Until recently, a flavin-dependent halogenase that is capable of iodination in vitro had not been reported. VirX1 is interesting from a biocatalytic perspective as it shows strikingly broad substrate flexibility and a clear preference for iodination, as illustrated by kinetic analysis. These factors together render it an attractive tool for synthesis.PostprintPeer reviewe

Advancing Desalination

This book is a companion volume to two published in 2011 by INTECH titled “Desalination, Trends and Technologies” and “Expanding Issues in Desalination”. The term “desalination” used in this series is in the broadest sense of the removal of dissolved, suspended, visible and invisible impurities in seawater, brackish water and wastewater. The purpose of desalination is to make water drinkable, or pure enough for industrial applications like in the processes for the production of steam, power, pharmaceuticals and microelectronics, or simply for attaining acceptable qualities for discharge back into the environment. This volume touches on Membranes and Systems, Solar Desalination, Reverse Osmosis Process Chemistry and Control, Drinking Water Quality, and Selective Waste Product Removal. The value of these volumes on the vast topic of desalination is to present the landscape to students, teachers and practitioners, with key concepts and keywords useful in gathering publications through internet search engines. The technologies of desalination of water are advancing as rapidly as the cry of human kind for more availability of quality water supply while minimizing environmental pollution. Contributions to the knowledge-base of desalination are expected to continue to grow exponentially in the coming years

The novel homozygous KCNJ10 c.986T>C (p.(Leu329Pro)) variant is pathogenic for the SeSAME/EAST homologue in Malinois dogs.

SeSAME/EAST syndrome is a multisystemic disorder in humans, characterised by seizures, sensorineural deafness, ataxia, developmental delay and electrolyte imbalance. It is exclusively caused by homozygous or compound heterozygous variations in the KCNJ10 gene. Here we describe a similar syndrome in two families belonging to the Malinois dog breed, based on clinical, neurological, electrodiagnostic and histopathological examination. Genetic analysis detected a novel pathogenic KCNJ10 c.986T>C (p.(Leu329Pro)) variant that is inherited in an autosomal recessive way. This variant has an allele frequency of 2.9% in the Belgian Malinois population, but is not found in closely related dog breeds or in dog breeds where similar symptoms have been already described. The canine phenotype is remarkably similar to humans, including ataxia and seizures. In addition, in half of the dogs clinical and electrophysiological signs of neuromyotonia were observed. Because there is currently no cure and treatment is nonspecific and unsatisfactory, this canine translational model could be used for further elucidating the genotype/phenotype correlation of this monogenic multisystem disorder and as an excellent intermediate step for drug safety testing and efficacy evaluations before initiating human studies