Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. TB mainly affects the lungs, and its etiological agent is Mycobacterium tuberculosis (Mtb). In 2022, more than 10 million people were infected worldwide, and 1.3 million were children. The current study considered the in-silico and machine learning (ML) approaches to explore the potential anti-TB molecules from the SelleckChem database against Enoyl-Acyl Carrier Protein Reductase (InhA). Initially, the entire database of ∼ 119000 molecules was sorted out through drug-likeness. Further, the molecular docking study was conducted to reduce the chemical space. The standard TB drug molecule's binding energy was considered a threshold, and molecules found with lower affinity were removed for further analyses. Finally, the molecules were checked for the pharmacokinetic and toxicity studies, and compounds found to have acceptable pharmacokinetic parameters and were non-toxic were considered as final promising molecules for InhA. The above approach further evaluated five molecules for ML-based toxicity and synthetic accessibility assessment. Not a single molecule was found toxic and each of them was revealed as easy to synthesise. The complex between InhA and proposed and standard molecules was considered for molecular dynamics simulation. Several statistical parameters showed the stability between InhA and the proposed molecule. The high binding affinity was also found for each of the molecules towards InhA using the MM-GBSA approach. Hence, the above approaches and findings exposed the potentiality of the proposed molecules against InhA.

Energy balance and life cycle assessments in producing microalgae biodiesel via a continuous microalgal-bacterial photobioreactor loaded with wastewater.

Life cycle assessments of microalgal cultivation systems are often conducted to evaluate the sustainability and feasibility factors of the entire production chain. Unlike widely reported conventional microalgal cultivation systems, the present work adopted a microalgal-bacterial cultivation approach which was upscaled into a pilot-scale continuous photobioreactor for microalgal biomass production into biodiesel from wastewater resources. A multiple cradle-to-cradle system ranging from microalgal biomass-to-lipid-to-biodiesel was evaluated to provide insights into the energy demand of each processes making up the microalgae-to-biodiesel value chain system. Energy feasibility studies revealed positive NER values (4.95-8.38) for producing microalgal biomass but deficit values for microalgal-to-biodiesel (0.14-0.23), stemming from the high energy input requirements in the downstream processes for converting biomass into lipid and biodiesel accounting to 88-90% of the cumulative energy demand. Although the energy balance for microalgae-to-biodiesel is in the deficits, it is comparable with other reported biodiesel production case studies (0.12-0.40). Nevertheless, the approach to using microalgal-bacterial cultivation system has improved the overall energy efficiency especially in the upstream processes compared to conventional microalgal cultivation systems. Energy life cycle assessments with other microalgal based biofuel systems also proposed effective measures in increasing the energy feasibility either by utilizing the residual biomass and less energy demanding downstream extraction processes from microalgal biomass. The microalgal-bacterial cultivation system is anticipated to offer both environmental and economic prospects for upscaling by effectively exploiting the low-cost nutrients from wastewaters via bioconversion into valuable microalgal biomass and biodiesel.

Fundamental alteration of cellular biochemicals from attached microalgae onto palm kernel expeller waste upon optimizing the growth environment in forming adhesion complex.

Changing the growth environment for microalgae can overall lead to the fundamental alteration in cellular biochemicals whilst attaching onto palm kernel expeller (PKE) waste to form adhesion complex in easing harvesting at stationary growth phase. This study had initially optimized the PKE dosage, light intensity and photoperiod in maximizing the attached microalgal productivity being attained at 0.72 g/g day. Lipid content increased progressively from pH 3 to pH 11, with the highest value observed at pH 11. Meanwhile, in terms of protein and carbohydrate contents, the highest values were obtained by cultivation medium of pH 5 with 9.92 g and 17.72 g, respectively followed by pH 7 with 9.16 g and 16.36 g, respectively. Moreover, the findings also suggested that the low pH mediums utilized polar interactions in the formation of complexes between PKE and microalgae, whereas at higher pH levels, the non-polar interactions became more significant. The work of attachment was thermodynamically favourable towards the attachment formation with values greater than zero which was also aligned with the microscopic surface topography, i.e., revealing a clustering pattern of microalgae colonizing the PKE surface. These findings contribute to comprehensive understanding of optimizing growth condition and harvesting strategy of attached microalgae in attaining the cellular biochemical components, facilitating the development of efficient and sustainable bioresource utilization.