Biology subjects

Shanbhag, A. P.

Publications and source records attributed to Shanbhag, A. P..

3 recordsLinked to original sources

bioRxiv2023

Reducing the vicissitudes of heterologous prochiral substrate catalysis by alcohol dehydrogenases through machine learning algorithms

Alcohol dehydrogenases (ADHs) encompass three distinctive superfamilies: medium-chain dehydrogenase (MDRs), short-chain dehydrogenase (SDRs), and iron-containing alcohol dehydrogenases. MDRs and SDRs have pivotal roles in converting pro-pharmaceutical ketones into chiral alcohols, serving as active pharmaceutical intermediate catalysts in industries. Nevertheless, the pursuit of aligning enzymes to a one enzyme-one substrate paradigm is intricate and resource-intensive. This study undertook the catalysis of 284 antibacterial ketone intermediates, yielding distinct patterns for both MDRs and SDRs. We endeavored to leverage machine learning to discriminate between compounds subjected to the influence of these superfamilies. To this end, we curated a dataset comprising 33 features, encompassing 4 descriptors for each compound. Subsequently, an ensemble of machine learning techniques, including Partial Least Squares (PLS) regression, k-Nearest Neighbors (kNN) regression, and Support Vector Machine (SVM) regression, was harnessed. Furthermore, the assimilation of Principal Component Analysis (PCA) augmented precision and accuracy, refining compound classification and intensifying differentiation and demarcation within diverse compound classes. As such, this classification strategy emerged as a potent approach for discerning substrates amenable to diverse alcohol dehydrogenases, thereby mitigating the reliance on high-throughput screening in identifying the optimal enzyme for specific substrate preferences.

biochemistry↗

bioRxiv2021

Steps to (s)-stereoisomers: Rationalization of a standard Short-chain dehydrogenase for the reduction of multiple pharmaceutical ketone intermediates

Short-chain dehydrogenases/reductases (SDRs) are a convenient class of enzymes used to synthesize enantiopure alcohols. Several studies describe native or engineered SDRs for converting substrates of interest using cost and time-intensive high-throughput approaches. The classification of SDRs is based on chain length and cofactor binding site. Of these, the shorter Classical and the longer Extended enzymes participate in ketoreduction. However, comparative analysis of various modelled SDRs reveals a length independent conserved N-terminal Rossmann fold and a variable C-terminus region for both types. The general hypothesis is that the latter domain influences the enzymes flexibility that may affect the observed promiscuity of the enzyme. We have used a machine learning algorithm on this flexible domain to build a rationale to screen promiscuous candidates. We have built a data set consisting of physicochemical properties derived from the amino-acid composition of enzymes to select closely associated promiscuous mesophilic enzymes. The resulting in vitro studies on pro-pharmaceutical substrates illustrate a direct correlation between the C-terminal lid-loop structure, enzyme melting temperature and the turnover number. We present a walkthrough for exploring promiscuous SDRs for catalyzing enantiopure alcohols of industrial importance.

biochemistry↗

bioRxiv2019

Maximization of non-nitrogenous metabolite production in E. coli using population systems biology

The E. coli metabolome is an interconnected set of enzymes that has measurable kinetic parameters ascribed for the production of most of its metabolites. Flux Balance Analysis (FBA) or Ordinary Differential Equation (ODE) models are used to increase product yield using defined media. However, they either give a range (FBA) or exact amount (ODE) of metabolite yield which isnt true as the transcriptome diversity of individual cells isnt considered. We formulate the metabolic-behaviour of individual cells by using a POpulation SYstems-Biology ALgorithm (POSYBAL) which predicts multiple-gene knockouts for increasing industrially relevant metabolites. We validate this prediction for producing isobutanol (Heterogenous metabolite) and shikimate (Homogenous metabolite) where, the product-yield was increased by 40 times (~2000 ppm) and 42 times (~3000 ppm) respectively. Also, we introduce a nitrogen-swap in standard media to its low-nitrogen counterpart during post-growth phase to redistribute flux towards non-nitrogenous pathways for increasing overall product-yield. Further, our model shows growth-phase diversity in bacterial population even under normal glucose-uptake, portraying a real-world scenario of diverse and robust environment thus, making it evolutionarily favourable to threats such as anti-bacterial attack.

bioengineering↗