scholarly journals Embracing enzyme promiscuity with activity-based compressed biosensing

2022 ◽  
Author(s):  
Brandon Alexander Holt ◽  
Hong Seo Lim ◽  
Melanie Su ◽  
McKenzie Tuttle ◽  
Haley Liakakos ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Classification Accuracy ◽  
In Silico ◽  
Peptide Libraries ◽  
Complex Mixtures ◽  
Enzyme Promiscuity ◽  
Genome Scale ◽  
Selection Of

Genome-scale activity-based profiling of proteases requires identifying substrates that are specific to each individual protease. However, this process becomes increasingly difficult as the number of target proteases increases because most substrates are promiscuously cleaved by multiple proteases. We introduce a method - Substrate Libraries for Compressed sensing of Enzymes (SLICE) - for selecting complementary sets of promiscuous substrates to compile libraries that classify complex protease samples (1) without requiring deconvolution of the compressed signals and (2) without the use of highly specific substrates. SLICE ranks substrate libraries according to two features: substrate orthogonality and protease coverage. To quantify these features, we design a compression score that was predictive of classification accuracy across 140 in silico libraries (Pearson r = 0.71) and 55 in vitro libraries (Pearson r = 0.55) of protease substrates. We demonstrate that a library comprising only two protease substrates selected with SLICE can accurately classify twenty complex mixtures of 11 enzymes with perfect accuracy. We envision that SLICE will enable the selection of peptide libraries that capture information from hundreds of enzymes while using fewer substrates for applications such as the design of activity-based sensors for imaging and diagnostics.

Genome scale metabolic models and analysis for evaluating probiotic potentials

2020 ◽  
Vol 48 (4) ◽  
pp. 1309-1321
Author(s):  
Yoon-Mi Choi ◽  
Yi Qing Lee ◽  
Hyun-Seob Song ◽  
Dong-Yup Lee
Keyword(s):  
In Silico ◽  
Metabolic Characteristics ◽  
Knowledge Based ◽  
Main Challenge ◽  
Metabolic States ◽  
Probiotic Strains ◽  
Metabolic Models ◽  
Genome Scale

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

scholarly journals In silico ADME in drug design – enhancing the impact

ADMET & DMPK ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 15 ◽  
Author(s):  
Susanne Winiwarter ◽  
Ernst Ahlberg ◽  
Edmund Watson ◽  
Ioana Oprisiu ◽  
Mickael Mogemark ◽  
...  
Keyword(s):  
In Silico ◽  
In Vitro Assays ◽  
Lead Compounds ◽  
Human Dose ◽  
Pharmacokinetic Properties ◽  
In Silico Predictions ◽  
In Silico Models ◽  
The Impact ◽  
Selection Of

<p>Each year the pharmaceutical industry makes thousands of compounds, many of which do not meet the desired efficacy or pharmacokinetic properties, describing the absorption, distribution, metabolism and excretion (ADME) behavior. Parameters such as lipophilicity, solubility and metabolic stability can be measured in high throughput in vitro assays. However, a compound needs to be synthesized in order to be tested. In silico models for these endpoints exist, although with varying quality. Such models can be used before synthesis and, together with a potency estimation, influence the decision to make a compound. In practice, it appears that often only one or two predicted properties are considered prior to synthesis, usually including a prediction of lipophilicity. While it is important to use all information when deciding which compound to make, it is somewhat challenging to combine multiple predictions unambiguously. This work investigates the possibility of combining in silico ADME predictions to define the minimum required potency for a specified human dose with sufficient confidence. Using a set of drug discovery compounds,in silico predictions were utilized to compare the relative ranking based on minimum potency calculation with the outcomes from the selection of lead compounds. The approach was also tested on a set of marketed drugs and the influence of the input parameters investigated.</p>

scholarly journals GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

2019 ◽  
Author(s):  
Khushboo Borah ◽  
Jacque-Lucca Kearney ◽  
Ruma Banerjee ◽  
Pankaj Vats ◽  
Huihai Wu ◽  
...  
Keyword(s):  
Amino Acids ◽  
Metabolic Network ◽  
In Silico ◽  
Metabolic Pathways ◽  
Mycobacterium Leprae ◽  
Human Pathogen ◽  
Intracellular Growth ◽  
A Genome ◽  
Genome Scale

AbstractLeprosy, caused by Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogen’s obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis (DPA) to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.Author SummaryMycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth.

The Circulating Cell-free Genome Atlas (CCGA) Study: Size selection of cell-free DNA (cfDNA) fragments.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3103-3103 ◽  
Author(s):  
Darya Filippova ◽  
Matthew H. Larson ◽  
M. Cyrus Maher ◽  
Robert Calef ◽  
Monica Pimentel ◽  
...  
Keyword(s):  
In Silico ◽  
Potential Effect ◽  
Classification Performance ◽  
Size Selection ◽  
Increased Sensitivity ◽  
Cancer Types ◽  
N 93 ◽  
Tumor Types ◽  
Selection Of

3103 Background: Detection of somatic copy number aberrations in individuals with cancer via cfDNA whole-genome sequencing (WGS) is challenging at low tumor fractions. Given that tumor-derived cfDNA fragments are shorter than those from healthy tissues, this exploratory analysis evaluated the potential effect of size selection on the ability to detect cancer. Methods: CCGA WGS libraries were in silico and in vitro size selected to estimate the change in tumor fraction by tumor types (breast, lung, and colorectal [CRC]) and stage (I-III vs IV). In silico analyses used clinically evaluable training set samples with WGS assay results (n = 1422: 560 non-cancer [NC], 862 cancer [C] stages I-IV); classification (cancer/non-cancer) performance was estimated using fragments within the 90-150 bp range. In vitro analyses used a subset of samples (n = 93: 28 NC, 65 C stages I-IV), including C cases sampled within a range of tumor fractions; tumor fraction was also measured at each progressive removal of maximum-length fragments (intervals of 10 bp: 150 bp down to 50 bp). Results: In silico and in vitro analyses, respectively, resulted in median 2.00±0.58-fold (at 6.91±2.64X depth) and 2.00±0.52-fold (at 23±4.45X depth) increases, in overall tumor fraction (compared to non-size-selected 36X depth). This was consistent across tumor types ( in silico: 1.78±0.73 breast, 2.00±0.58 CRC, 2.00±0.41 lung; in vitro: 2.00±0.82 breast, 2.51±0.52 CRC, 2.53±0.94 lung) and stages ( in silico: 2.00±0.74 I-III, 1.78±0.52 IV; in vitro: 2.00±0.55 I-III, 1.68±0.29 IV). Tumor fraction increased with initial fragment length titrations, but not following size selection to shorter lengths ( < 140 bp). Classifier trained on in silico size-selected data had increased sensitivity at 98% specificity compared to those trained on non-size-selected data (p < 1e-5). Conclusions: In silico and in vitro size selection consistently increased tumor fraction across cancer types and stages, and this increase was maximized by tuning the length range of size selection. Relative to full-depth data, classification performance improved significantly. These data suggest that size selection targeting cfDNA under 140 bp may enhance cfDNA-based cancer detection. Clinical trial information: NCT02889978.

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