H-1, N-15 and C-13 backbone resonance assignments of pentaerythritol tetranitrate reductase from Enterobacter cloacae PB2

Pentaerythritol tetranitrate reductase (PETNR) is a flavoenzyme possessing a broad substrate specificity and is a member of the Old Yellow Enzyme family of oxidoreductases. As well as having high potential as an industrial biocatalyst, PETNR is an excellent model system for studying hydrogen transfer reactions. Mechanistic studies performed with PETNR using stopped-flow methods have shown that tunneling contributes towards hydride transfer from the NAD(P)H coenzyme to the flavin mononucleotide (FMN) cofactor and fast protein dynamics have been inferred to facilitate this catalytic step. Herein, we report the near-complete 1H, 15N and 13C backbone resonance assignments of PETNR in a stoichiometric complex with the FMN cofactor in its native oxidized form, which were obtained using heteronuclear multidimensional NMR spectroscopy. A total of 97% of all backbone resonances were assigned, with 333 out of a possible 344 residues assigned in the 1H–15N TROSY spectrum. This is the first report of an NMR structural study of a flavoenzyme from the Old Yellow Enzyme family and it lays the foundation for future investigations of functional dynamics in hydride transfer catalytic mechanism

Learning a peptide-protein binding affinity predictor with kernel ridge regression

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalize eight kernels, such as the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of accurately predicting the binding affinity of any peptide to any protein. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. On all benchmarks, our method significantly (p-value < 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. The method should be of value to a large segment of the research community with the potential to accelerate peptide-based drug and vaccine development.Comment: 22 pages, 4 figures, 5 table

Recombining Low Homology, Functionally Rich Regions of Bacterial Subtilisins by Combinatorial Fragment Exchange

Combinatorial fragment exchange was utilised to recombine key structural and functional low homology regions of bacilli subtilisins to generate new active hybrid proteases with altered substrate profiles. Up to six different regions comprising mostly of loop residues from the commercially important subtilisin Savinase were exchanged with the structurally equivalent regions of six other subtilisins. The six additional subtilisins derive from diverse origins and included thermophilic and intracellular subtilisins as well as other academically and commercially relevant subtilisins. Savinase was largely tolerant to fragment exchange; rational replacement of all six regions with 5 of 6 donating subtilisin sequences preserved activity, albeit reduced compared to Savinase. A combinatorial approach was used to generate hybrid Savinase variants in which the sequences derived from all seven subtilisins at each region were recombined to generate new region combinations. Variants with different substrate profiles and with greater apparent activity compared to Savinase and the rational fragment exchange variants were generated with the substrate profile exhibited by variants dependent on the sequence combination at each region

A review of wearable motion tracking systems used in rehabilitation following hip and knee replacement

Clinical teams are under increasing pressure to facilitate early hospital discharge for total hip replacement and total knee replacement patients following surgery. A wide variety of wearable devices are being marketed to assist with rehabilitation following surgery. A review of wearable devices was undertaken to assess the evidence supporting their efficacy in assisting rehabilitation following total hip replacement and total knee replacement. A search was conducted using the electronic databases including Medline, CINAHL, Cochrane, PsycARTICLES, and PubMed of studies from January 2000 to October 2017. Five studies met the eligibility criteria, and all used an accelerometer and a gyroscope for their technology. A review of the studies found very little evidence to support the efficacy of the technology, although they show that the use of the technology is feasible. Future work should establish which wearable technology is most valuable to patients, which ones improve patient outcomes, and the most economical model for deploying the technolog

Identification of hot-spot residues in protein-protein interactions by computational docking

<p>Abstract</p> <p>Background</p> <p>The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.</p> <p>Results</p> <p>We have applied here normalized interface propensity (<it>NIP</it>) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.</p> <p>Conclusion</p> <p>The <it>NIP </it>values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.</p

Regional differences in prostaglandin E₂ metabolism in human colorectal cancer liver metastases

Background: Prostaglandin (PG) E₂ plays a critical role in colorectal cancer (CRC) progression, including epithelial-mesenchymal transition (EMT). Activity of the rate-limiting enzyme for PGE₂ catabolism (15-hydroxyprostaglandin dehydrogenase [15-PGDH]) is dependent on availability of NAD+. We tested the hypothesis that there is intra-tumoral variability in PGE₂ content, as well as in levels and activity of 15-PGDH, in human CRC liver metastases (CRCLM). To understand possible underlying mechanisms, we investigated the relationship between hypoxia, 15-PGDH and PGE₂ in human CRC cells in vitro. Methods: Tissue from the periphery and centre of 20 human CRCLM was analysed for PGE₂ levels, 15-PGDH and cyclooxygenase (COX)-2 expression, 15-PGDH activity, and NAD+/NADH levels. EMT of LIM1863 human CRC cells was induced by transforming growth factor (TGF) β. Results: PGE₂ levels were significantly higher in the centre of CRCLM compared with peripheral tissue (P = 0.04). There were increased levels of 15-PGDH protein in the centre of CRCLM associated with reduced 15-PGDH activity and low NAD+/NADH levels. There was no significant heterogeneity in COX-2 protein expression. NAD+ availability controlled 15-PGDH activity in human CRC cells in vitro. Hypoxia induced 15-PGDH expression in human CRC cells and promoted EMT, in a similar manner to PGE₂. Combined 15-PGDH expression and loss of membranous E-cadherin (EMT biomarker) were present in the centre of human CRCLM in vivo.Conclusions: There is significant intra-tumoral heterogeneity in PGE₂ content, 15-PGDH activity and NAD+ availability in human CRCLM. Tumour micro-environment (including hypoxia)-driven differences in PGE₂ metabolism should be targeted for novel treatment of advanced CRC

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

There was no funding support for this study. Disclosure. The authors declare no conflict of interest.Peer reviewedPublisher PD

Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.</p> <p>Results</p> <p>To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date.</p> <p>Conclusions</p> <p>The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.</p