Understanding and reshaping cellular behaviors with synthetic gene networks requires the ability to sense and respond to changes in the intracellular environment. Intracellular proteins are involved in almost all cellular processes, and thus can provide important information about changes in cellular conditions such as infections, mutations, or disease states. Here we report the design of a modular platform for intrabody-based protein sensing-actuation devices with transcriptional output triggered by detection of intracellular proteins in mammalian cells. We demonstrate reporter activation response (fluorescence, apoptotic gene) to proteins involved in hepatitis C virus (HCV) infection, human immunodeficiency virus (HIV) infection, and Huntington’s disease, and show sensor-based interference with HIV-1 downregulation of HLA-I in infected T cells. Our method provides a means to link varying cellular conditions with robust control of cellular behavior for scientific and therapeutic applications.Synthetic biology principles are often used to design circuits that tune gene expression in response to changes in intracellular environments. Here the authors design a modular platform for intracellular protein sensing devices with transcriptional output.

Engineering modular intracellular protein sensor-actuator devices

Small molecules and their impact in drug discovery: a perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory.

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19.

Typical bioprocess comprises of different unit operations wherein a near optimal environment is required for cells to grow, divide, and synthesize the desired product. However, bioprocess control caters to unique challenges that arise due to non-linearity, variability, and complexity of biotech processes. This article presents a review of modern control strategies employed in bioprocessing. Conventional control strategies (open loop, closed loop) along with modern control schemes such as fuzzy logic, model predictive control, adaptive control and neural network-based control are illustrated, and their effectiveness is highlighted. Furthermore, it is elucidated that bioprocess control is more than just automation, and includes aspects such as system architecture, software applications, hardware, and interfaces, all of which are optimized and compiled as per demand. This needs to be accomplished while keeping process requirement, production cost, market value of product, regulatory constraints, and data acquisition requirements in our purview. This article aims to offer an overview of the current best practices in bioprocess control, monitoring, and automation.

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Bioprocess Control: Current Progress and Future Perspectives.

ApoE is the major lipid and cholesterol carrier in the CNS. There are three major human polymorphisms, apoE2, apoE3, and apoE4, and the genetic expression of APOE4 is one of the most influential risk factors for the development of late-onset Alzheimer's disease (AD). Neuroinflammation has become the third hallmark of AD, together with Amyloid-β plaques and neurofibrillary tangles of hyperphosphorylated aggregated tau protein. This review aims to broadly and extensively describe the differential aspects concerning apoE. Starting from the evolution of apoE to how APOE's single-nucleotide polymorphisms affect its structure, function, and involvement during health and disease. This review reflects on how APOE's polymorphisms impact critical aspects of AD pathology, such as the neuroinflammatory response, particularly the effect of APOE on astrocytic and microglial function and microglial dynamics, synaptic function, amyloid-β load, tau pathology, autophagy, and cell-cell communication. We discuss influential factors affecting AD pathology combined with the APOE genotype, such as sex, age, diet, physical exercise, current therapies and clinical trials in the AD field. The impact of the APOE genotype in other neurodegenerative diseases characterized by overt inflammation, e.g., alpha- synucleinopathies and Parkinson's disease, traumatic brain injury, stroke, amyotrophic lateral sclerosis, and multiple sclerosis, is also addressed. Therefore, this review gathers the most relevant findings related to the APOE genotype up to date and its implications on AD and CNS pathologies to provide a deeper understanding of the knowledge in the APOE field. 

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APOE in the bullseye of neurodegenerative diseases: impact of the APOE genotype in Alzheimer’s disease pathology and brain diseases

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

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Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Post-translational modifications: Regulators of neurodegenerative proteinopathies.

Protein S-sulfhydration: Unraveling the prospective of hydrogen sulfide in the brain, vasculature and neurological manifestations

Recent advancements and growing attention about free radicals (ROS) and redox signaling enable the scientific fraternity to consider their involvement in the pathophysiology of inflammatory diseases, metabolic disorders, and neurological defects. Free radicals increase the concentration of reactive oxygen and nitrogen species in the biological system through different endogenous sources and thus increased the overall oxidative stress. An increase in oxidative stress causes cell death through different signaling mechanisms such as mitochondrial impairment, cell-cycle arrest, DNA damage response, inflammation, negative regulation of protein, and lipid peroxidation. Thus, an appropriate balance between free radicals and antioxidants becomes crucial to maintain physiological function. Since the 1brain requires high oxygen for its functioning, it is highly vulnerable to free radical generation and enhanced ROS in the brain adversely affects axonal regeneration and synaptic plasticity, which results in neuronal cell death. In addition, increased ROS in the brain alters various signaling pathways such as apoptosis, autophagy, inflammation and microglial activation, DNA damage response, and cell-cycle arrest, leading to memory and learning defects. Mounting evidence suggests the potential involvement of micro-RNAs, circular-RNAs, natural and dietary compounds, synthetic inhibitors, and heat-shock proteins as therapeutic agents to combat neurological diseases. Herein, we explain the mechanism of free radical generation and its role in mitochondrial, protein, and lipid peroxidation biology. Further, we discuss the negative role of free radicals in synaptic plasticity and axonal regeneration through the modulation of various signaling molecules and also in the involvement of free radicals in various neurological diseases and their potential therapeutic approaches.

Free radical biology in neurological manifestations: mechanisms to therapeutics interventions.

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine. 

Mehar Sahu

Papers

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Post-translational modifications: Regulators of neurodegenerative proteinopathies.

Protein S-sulfhydration: Unraveling the prospective of hydrogen sulfide in the brain, vasculature and neurological manifestations

Free radical biology in neurological manifestations: mechanisms to therapeutics interventions.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis.