Validation of discrete time-to-event prediction models in the presence of competing risks

Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.     

An in silico analysis of deleterious single nucleotide polymorphisms and molecular dynamics simulation of disease linked mutations in genes responsible for neurodegenerative disorder

Abstract

Mutation in two genes deglycase gene (DJ-1) and retromer complex component gene (VPS35) are linked with neurodegenerative disorder such as Parkinson's disease, Huntington's disease, and Alzheimer's disease. DJ-1 gene located at 1p36 chromosomal position and involved in PD pathogenesis through many pathways including mitochondrial dysfunction and oxidative injury. VPS35 gene located at 16q13-q21 chromosomal position and the two pathways, the Wnt signaling pathway, and retromer-mediated DMT1 missorting are proposed for basis of VPS35 related PD. The study focuses on identifying most deleterious SNPs through computational analysis. Result obtained from various bioinformatics tools shows that D149A is most deleterious in DJ-1 and A54W, R365H, and V717M are most deleterious in VPS35. To understand the functionality of protein comparative modeling of DJ-1 and VPS35 native and mutants was done by MODELLER. The generated structures are validated by two web servers–ProSa and RAMPAGE. Molecular dynamic simulation (MDS) analysis done for the most validated structures to know the functional and structural nature of native and mutants protein of DJ-1 and VPS35. Native structure of DJ-1 and VPS35 show more flexibility through MDS analysis. DJ-1 D149A mutant structures become more compact which shows the structural perturbation and loss of DJ-1 protein function which in turn are probable cause for PD. A54W, R365H, and V717M mutant protein of VPS35 also shows compactness which cause structure perturbation and absence of retromer function which likely to be linked to PD pathogenesis. This in silico study may provide a new insight for fundamental molecular mechanism involved in Parkinson’s disease.

Communicated by Ramaswamy H. Sarma     

Biophysical insights into the binding characteristics of bovine serum albumin with dipyridamole and the influence of molecular interaction with β cyclodextrin

Abstract

The binding characteristic of anti-platelet drug dipyridamole has been investigated with a transport protein, serum albumin. A multi-spectroscopic approach has been employed, and the results were well supported by in silico molecular docking and simulation studies. The fluorescence quenching of serum albumin at three different temperatures revealed that the mechanism involved is static and the binding constant of the interaction was found to be of the order of 10⁴ M^?1. The reaction was found to be spontaneous and involved hydrophobic interactions. Synchronous, 3D fluorescence and CD spectroscopy indicated a change in conformation of bovine serum albumin (BSA) on interaction with DP. Using site-selective markers, the binding site of DP was found to be in subdomain IB. Molecular docking studies further corroborated these results. Molecular dynamic (MD) simulations showed lower RMSD values on interaction, suggesting the existence of a stable complex between DP and BSA. Furthermore, since β-Cyclodextrin (βCD) is used to improve the solubility of DP in ophthalmic solutions, therefore, the effect of (βCD) on the interaction of BSA and DP was also studied, and it was found that in the presence of βCD, the binding constant for BSA-DP interaction decreased. The present study is an attempt to characterize the transport of DP and to improve its bioavailability, consequently helping in dosage design to achieve optimum therapeutic levels.

Communicated by Ramaswamy H. Sarma     

C1188D mutation abolishes specific recognition between MLL1-CXXC domain and CpG site by inducing conformational switch of flexible N-terminal

Mixed lineage leukemia protein (MLL1 protein) recognizes the CpG site via its CXXC domain and is frequently associated with leukemia. The specific recognition is abolished by C1188D mutation, which also prevents MLL-related leukemia. In this paper, multiple molecular dynamic (MD) simulations were performed to investigate the mechanism of recognition and influences of C1188D mutation. Started from fully dissociated DNA and MLL1-CXXC domain, remarkably, the center of mass (COM) of MLL1-CXXC domain quickly concentrates on the vicinity of the CpG site in all 53 short MD simulations. Extended simulations of the wild type showed that the native complex formed in 500 ns among 4 of 53 simulations. In contrast, the C1188D mutant COM distributed broadly around the DNA and the native complex was not observed in any of the extended simulations. Simulations on the apo MLL1-CXXC domain further suggest that the wild type protein remained predominantly in an open form that closely resembles its structure in the native complex whereas C1188D mutant formed predominantly compact structures in which the N- terminal bends to D1188. This conformational switch hinders the formation of encounter complex, thus abolishes the recognition. Our study also provides clues to the study mechanism of recognition, by the CXXC domain from proteins like DNA methyltransferase and ten-eleven translocation enzymes.     

Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation

Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the correct model structure to quantify the complex biological mechanism poses a severe challenge for the construction of kinetic models, while the lack of data due to the time-consuming experiments greatly impedes applications of most data-driven models. This study proposes the use of an innovative hybrid modelling approach that consists of a simple kinetic model to govern the overall process dynamic trajectory and a data-driven model to estimate mismatch between the kinetic equations and the real process. An advanced automatic model structure identification strategy is adopted to simultaneously identify the most physically probable kinetic model structure and minimum number of data-driven model parameters that can accurately represent multiple data sets over a broad spectrum of process operating conditions. Through this hybrid modelling and automatic structure identification framework, a highly accurate mathematical model was constructed to simulate and optimise an algal lutein production process. Performance of this hybrid model for long-term predictive modelling, optimisation, and online self-calibration is demonstrated and thoroughly discussed, indicating its significant potential for future industrial application.     

A dynamic model linking cell growth to intracellular metabolism and extracellular by-product accumulation

Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1-antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by-products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α-ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.     

When size does matter: organelle size influences the properties of transport mediated by molecular motors

Background

Organelle transport is driven by the action of molecular motors. In this work, we studied the dynamics of organelles of different sizes with the aim of understanding the complex relation between organelle motion and microenvironment.

Methods

We used single particle tracking to obtain trajectories of melanosomes (pigmented organelles in Xenopus laevis melanophores). In response to certain hormones, melanosomes disperse in the cytoplasm or aggregate in the perinuclear region by the combined action of microtubule and actin motors.

Results and conclusions

Melanosome trajectories followed an anomalous diffusion model in which the anomalous diffusion exponent (α) provided information regarding the trajectories' topography and thus of the processes causing it. During aggregation, the directionality of big organelles was higher than that of small organelles and did not depend on the presence of either actin or intermediate filaments (IF). Depolymerization of IF significantly reduced α values of small organelles during aggregation but slightly affect their directionality during dispersion.

General significance

Our results could be interpreted considering that the number of copies of active motors increases with organelle size. Transport of big organelles was not influenced by actin or IF during aggregation showing that these organelles are moved processively by the collective action of dynein motors. Also, we found that intermediate filaments enhance the directionality of small organelles suggesting that this network keeps organelles close to the tracks allowing their efficient reattachment. The higher directionality of small organelles during dispersion could be explained considering the better performance of kinesin-2 vs. dynein at the single molecule level.     

Cellulose-bound Peptide Arrays: Preparation and Applications

Spatial organization of metabolic enzymes may represent a general cellular mechanism to regulate metabolic flux. One recent example of this type of cellular phenomenon is the purinosome, a newly discovered multi-enzyme metabolic assembly that includes all of the enzymes within the de novo purine biosynthetic pathway. Our understanding of the components and regulation of purinosomes has significantly grown in recent years. This paper reviews the purine de novo biosynthesis pathway and its regulation, and presents the evidence supporting the purinosome assembly and disassembly processes under the control of G-protein-coupled receptor (GPCR) signaling. This paper also discusses the implications of purinosome and GPCR regulation in drug discovery.