Prediction of strain engineerings that amplify recombinant protein secretion through the machine learning approach MaLPHAS.

This article presents a discussion of the process of precision fermentation (PF), describing the history of the space, the expected 70% growth over the next 5 years, various applications of precision fermented products, and the markets available to be disrupted by the technology. A range of prokaryotic and eukaryotic host organisms used for PF are described, with the advantages, disadvantages and applications of each. The process of setting up PF and strain engineering is described, as well as various ways that computational analysis and design techniques can be employed to assist PF engineering. The article then describes the design and implementation of a machine learning method, machine learning predictions having amplified secretion (MaLPHAS) to predict strain engineerings, which optimise the secretion of a recombinant protein. This approach showed an in silico cross-validated R 2 accuracy on the training data of up to 46.6% and in an in vitro test on a Komagataella phaffii strain, identified one gene engineering out of five predicted, which was shown to double the secretion of a heterologous protein and outperform three of the best-known edits from the literature for improving secretion in K. phaffii.

Long-term oncologic outcomes of positive surgical margins following robot-assisted partial nephrectomy.

BACKGROUND: Previous reports on positive surgical margin (PSM) after robot-assisted partial nephrectomy (RAPN) have reached inconsistent conclusions as to the impact of a PSM on oncologic outcomes. We sought to determine the effect of PSM on long-term cancer recurrence and survival outcomes. METHODS: We queried our renal oncology database for patients having undergone RAPN and compared recurrence-free survival (RFS) and overall survival (OS) between patients with PSM and negative surgical margin (NSM). Kaplan-Meier analysis was also performed for RFS and OS for PSM versus NSM. RESULTS: Of the 432 patients who underwent RAPN we identified 29 (6.7%) patients with PSM and 403 (93.3%) patients with NSM. Median follow-up for the overall cohort was 45.1 months. Three of the 29 patients with PSM and fourteen of the 403 patients with NSM had disease recurrence (P=0.09). RFS at 24, 48, and 72 months was 95.8%, 90%, and 85.5% for patients with NSM and 96.6%, 86.6%, and 80.4% for patients with PSM, respectively (log-rank P value =0.382). OS at 24, 48, and 72 months was 98%, 93.1%, and 89.7% for patients with NSM and 96.3%, 91.2%, and 85.2% for patients with PSM, respectively (log-rank P value =0.584). CONCLUSIONS: While PSM are relatively uncommon, their presence still serves as a potential risk factor for worse oncologic outcomes. In instances of PSM, immediate secondary intervention is most likely unnecessary and more attentive long-term clinical follow-up, especially in patients with high-risk features, may be more advisable.

A Multi-Institutional Analysis of the Effect of Positive Surgical Margins Following Robot-Assisted Partial Nephrectomy on Oncologic Outcomes.

Objective: To determine the effect of positive surgical margins (PSMs) on oncologic outcomes following robot-assisted partial nephrectomy (RAPN) and to identify factors that increase the likelihood of adverse oncologic outcomes. Methods: A multi-institutional database of patients who underwent RAPN with complete follow-up data was used to compare recurrence-free survival (RFS) and overall survival (OS) between 42 (5.1%) patients with a PSM and 797 (94.9%) patients with a negative surgical margin. Analysis was performed with univariable and multivariable Cox proportional hazard regression models adjusting for confounding variables. A Kaplan-Meier method was used to evaluate the relationship between PSM and oncologic outcomes (RFS and OS), and the equality of the curves was assessed using a log-rank test. Results: The rate of PSM was 5.1%. RFS at 12, 24, and 36 months was 97.8%, 95.2%, and 92.9%. OS at 12, 24, and 36 months was 98.6%, 97.7%, and 93.3%. PSM was not associated with worse RFS in both univariable and multivariable analyses (hazard ratio [HR] = 1.43; 95% confidence interval [CI] = 0.37, 5.55; p = 0.607). Factors associated with worse RFS include pT3a upstaging (HR = 4.97; 95% CI = 1.63, 15.12; p = 0.005), a higher Charlson comorbidity index (HR = 1.68; 95% CI = 1.20, 2.34; p = 0.002); and advanced clinical stage (cT1a vs cT1b, HR = 4.22; 95% CI = 1.84, 9.68; p = 0.001 vs cT2a, HR = 14.09; 95% CI = 3.85, 51.53; p < 0.001). PSM was not associated with worse OS in both univariable and multivariable analyses (HR = 0.87; 95% CI = 0.26, 2.94; p = 0.821). Higher R.E.N.A.L. nephrometry score was found to be associated with worse OS (HR = 1.26; 95% CI = 1.01, 1.57; p = 0.041). Conclusions: Given the absence of association between PSM and worse oncologic outcomes, patients with PSM following RAPN should be carefully monitored for recurrence rather than undergo immediate secondary intervention. As advanced clinical stage (cT1b, cT2a) and pathologic upstaging (pT3a) were independently associated with disease recurrence, their presence may warrant more attentive postoperative surveillance.

EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.

EzMol is a molecular visualization Web server in the form of a software wizard, located at http://www.sbg.bio.ic.ac.uk/ezmol/. It is designed for easy and rapid image manipulation and display of protein molecules, and is intended for users who need to quickly produce high-resolution images of protein molecules but do not have the time or inclination to use a software molecular visualization system. EzMol allows the upload of molecular structure files in PDB format to generate a Web page including a representation of the structure that the user can manipulate. EzMol provides intuitive options for chain display, adjusting the color/transparency of residues, side chains and protein surfaces, and for adding labels to residues. The final adjusted protein image can then be downloaded as a high-resolution image. There are a range of applications for rapid protein display, including the illustration of specific areas of a protein structure and the rapid prototyping of images.

Comparison of perioperative and functional outcomes of robotic partial nephrectomy for cT1a vs cT1b renal masses.

OBJECTIVE: To compare perioperative and functional outcomes of patients with cT1a or cT1b renal masses undergoing robotic partial nephrectomy (RPN) in a large multi-institutional study PATIENTS AND METHODS: The present retrospective Institutional Review Board-approved multi-institutional study utilised a prospectively maintained database to identify patients undergoing RPN by six surgeons for a solitary cT1a (n = 1 307) or cT1b (n = 377) renal mass from 2006 to 2016. Perioperative and renal function outcomes at discharge and at a median follow-up of 12.2 months were compared in univariable and multivariable regression analyses adjusting for surgeon performing the procedure and date of surgery. RESULTS: In univariable analysis, cT1b masses were associated with longer operative time (190.0 vs 159.0 min, P < 0.001), longer warm ischaemia time (18.8 vs 15.0 min, P < 0.001), higher estimated blood loss (150.0 vs 100.0 mL, P < 0.001), more intraoperative complications (5.6% vs 2.4%, P = 0.034), and more surgical postoperative complications (10.1% vs 5.7%, P =0.002). Results were similar in multivariable analysis with additional findings including more overall postoperative complications (odds ratio 1.55, P = 0.015) and longer length of stay (P < 0.001) associated with cT1b masses. There were no differences in the risk of progression of chronic kidney disease stage at 12.2 months, positive surgical margins, or major postoperative complications. CONCLUSIONS: Although our study shows a longer operative time, longer warm ischemia time, and higher complication rate for patients undergoing RPN for cT1b renal masses, the magnitude of these differences is small. RPN should be considered for cT1b lesions when anatomical and spatial location allow for a feasible procedure.

Thienopyrimidinone Based Sirtuin-2 (SIRT2)-Selective Inhibitors Bind in the Ligand Induced Selectivity Pocket.

Sirtuins (SIRTs) are NAD-dependent deacylases, known to be involved in a variety of pathophysiological processes and thus remain promising therapeutic targets for further validation. Previously, we reported a novel thienopyrimidinone SIRT2 inhibitor with good potency and excellent selectivity for SIRT2. Herein, we report an extensive SAR study of this chemical series and identify the key pharmacophoric elements and physiochemical properties that underpin the excellent activity observed. New analogues have been identified with submicromolar SIRT2 inhibtory activity and good to excellent SIRT2 subtype-selectivity. Importantly, we report a cocrystal structure of one of our compounds (29c) bound to SIRT2. This reveals our series to induce the formation of a previously reported selectivity pocket but to bind in an inverted fashion to what might be intuitively expected. We believe these findings will contribute significantly to an understanding of the mechanism of action of SIRT2 inhibitors and to the identification of refined, second generation inhibitors.

Incorporating Virtual Reactions into a Logic-based Ligand-based Virtual Screening Method to Discover New Leads.

The use of virtual screening has become increasingly central to the drug development pipeline, with ligand-based virtual screening used to screen databases of compounds to predict their bioactivity against a target. These databases can only represent a small fraction of chemical space, and this paper describes a method of exploring synthetic space by applying virtual reactions to promising compounds within a database, and generating focussed libraries of predicted derivatives. A ligand-based virtual screening tool Investigational Novel Drug Discovery by Example (INDDEx) is used as the basis for a system of virtual reactions. The use of virtual reactions is estimated to open up a potential space of 1.21×1012 potential molecules. A de novo design algorithm known as Partial Logical-Rule Reactant Selection (PLoRRS) is introduced and incorporated into the INDDEx methodology. PLoRRS uses logical rules from the INDDEx model to select reactants for the de novo generation of potentially active products. The PLoRRS method is found to increase significantly the likelihood of retrieving molecules similar to known actives with a p-value of 0.016. Case studies demonstrate that the virtual reactions produce molecules highly similar to known actives, including known blockbuster drugs.

The discovery of a highly selective 5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidin-4(3H)-one SIRT2 inhibitor that is neuroprotective in an in vitro Parkinson's disease model.

Sirtuins, NAD(+) -dependent histone deacetylases (HDACs), have recently emerged as potential therapeutic targets for the treatment of a variety of diseases. The discovery of potent and isoform-selective inhibitors of this enzyme family should provide chemical tools to help determine the roles of these targets and validate their therapeutic value. Herein, we report the discovery of a novel class of highly selective SIRT2 inhibitors, identified by pharmacophore screening. We report the identification and validation of 3-((2-methoxynaphthalen-1-yl)methyl)-7-((pyridin-3-ylmethyl)amino)-5,6,7,8-tetrahydrobenzo[4,5]thieno[2,3-d]pyrimidin-4(3H)-one (ICL-SIRT078), a substrate-competitive SIRT2 inhibitor with a Ki value of 0.62 ± 0.15 µM and more than 50-fold selectivity against SIRT1, 3 and 5. Treatment of MCF-7 breast cancer cells with ICL-SIRT078 results in hyperacetylation of α-tubulin, an established SIRT2 biomarker, at doses comparable with the biochemical IC50 data, while suppressing MCF-7 proliferation at higher concentrations. In concordance with the recent reports that suggest SIRT2 inhibition is a potential strategy for the treatment of Parkinson's disease, we find that compound ICL-SIRT078 has a significant neuroprotective effect in a lactacystin-induced model of Parkinsonian neuronal cell death in the N27 cell line. These results encourage further investigation into the effects of ICL-SIRT078, or an optimised derivative thereof, as a candidate neuroprotective agent in in vivo models of Parkinson's disease.

Assessment of a rule-based virtual screening technology (INDDEx) on a benchmark data set.

The Investigational Novel Drug Discovery by Example (INDDEx) package has been developed to find active compounds by linking activity to chemical substructure and to guide the process of further drug development. INDDEx is a machine-learning technique, based on forming qualitative logical rules about substructural features of active molecules, weighting the rules to form a quantitative model, and then using the model to screen a molecular database. INDDEx is shown to be able to learn from multiple active compounds and to be useful for scaffold-hopping when performing virtual screening, giving high retrieval rates even when learning from a small number of compounds. Across the data sets tested, at 1% of the data, INDDEx was found to have average enrichment factors of 69.2, 82.7, and 90.4 when learning from 2, 4, and 8 active ligands, respectively. At 0.1% of the data, INDDEx had average enrichment factors of 492, 631, and 707 when learning from 2, 4, and 8 active ligands, respectively. Excluding all ligands with more than 0.5 Tanimoto Maximum Common Substructure, INDDEx had average enrichment factors at 1% of 52.3, 63.6, and 66.9 when learning from 2, 4, and 8 active ligands, respectively. The performance of INDDEx is compared with that of eHiTS LASSO, PharmaGist, and DOCK.