Biofuels

Regarding the limited resources for fossil fuels and increasing global energy demands, greenhouse gas emissions, and climate change, there is a need to find alternative energy sources that are sustainable, environmentally friendly, renewable, and economically viable. In the last several decades, interest in second-generation bioethanol production from non-food lignocellulosic biomass in the form of organic residues rapidly increased because of its abundance, renewability, and low cost. Bioethanol production fits into the strategy of a circular economy and zero waste plans, and using ethanol as an alternative fuel gives the world economy a chance to become independent of the petrochemical industry, providing energy security and environmental safety. However, the conversion of biomass into ethanol is a challenging and multi-stage process because of the variation in the biochemical composition of biomass and the recalcitrance of lignin, the aromatic component of lignocellulose. Therefore, the commercial production of cellulosic ethanol has not yet become well-received commercially, being hampered by high research and production costs, and substantial effort is needed to make it more widespread and profitable. This review summarises the state of the art in bioethanol production from lignocellulosic biomass, highlights the most challenging steps of the process, including pretreatment stages required to fragment biomass components and further enzymatic hydrolysis and fermentation, presents the most recent technological advances to overcome the challenges and high costs, and discusses future perspectives of second-generation biorefineries.

Plant cell walls are composed of a heterogeneous mixture of polysaccharides that require several different enzymes to degrade. These enzymes are important for a variety of biotechnological processes, from biofuel production to food processing. Several classical mannanolytic enzyme functions of glycoside hydrolases (GH), such as β-mannanase, β-mannosidase and α-galactosidase activities, are helpful for efficient mannan hydrolysis. In this light, we bring three enzymes into the model of mannan degradation that have received little or no attention. By linking their three-dimensional structures and substrate specificities, we have predicted the interactions and cooperativity of these novel enzymes with classical mannanolytic enzymes for efficient mannan hydrolysis. The novel exo-β-1,4-mannobiohydrolases are indispensable for the production of mannobiose from the terminal ends of mannans, this product being the preferred product for short-chain mannooligosaccharides (MOS)-specific β-mannosidases. Second, the side-chain cleaving enzymes, acetyl mannan esterases (AcME), remove acetyl decorations on mannan that would have hindered backbone cleaving enzymes, while the backbone cleaving enzymes liberate MOS, which are preferred substrates of the debranching and sidechain cleaving enzymes. The nonhydrolytic expansins and swollenins disrupt the crystalline regions of the biomass, improving their accessibility for AcME and GH activities. Finally, lytic polysaccharide monooxygenases have also been implicated in promoting the degradation of lignocellulosic biomass or mannan degradation by classical mannanolytic enzymes, possibly by disrupting adsorbed mannan residues. Modelling effective enzymatic mannan degradation has implications for improving the saccharification of biomass for the synthesis of value-added and upcycling of lignocellulosic wastes.

The steady increase in human population and a rising standard of living heighten global demand for energy. Fossil fuels account for more than three-quarters of energy production, releasing enormous amounts of carbon dioxide (CO2) that drive climate change effects as well as contributing to severe air pollution in many countries. Hence, drastic reduction of CO2 emissions, especially from fossil fuels, is essential to tackle anthropogenic climate change. To reduce CO2 emissions and to cope with the ever-growing demand for energy, it is essential to develop renewable energy sources, of which biofuels will form an important contribution. In this Essay, liquid biofuels from first to fourth generation are discussed in detail alongside their industrial development and policy implications, with a focus on the transport sector as a complementary solution to other environmentally friendly technologies, such as electric cars.

IntroductionTranscription factors (TFs) activate transcriptional initiation by binding specific signal molecules (SMs), yet designing TFs to precisely target non-natural SMs remains challenging.ObjectivesUsing transcriptional activator BmoR as an example, a machine-learning based model named BT to predict three crucial residue regions (CRRs) was generated. This study aimed to achieve BmoR with strict SM orthogonality (SSO).MethodsRandom Forest Algorithm was used to generate a model BT that pinpointed CRRs. The BmoR-SM complexes in the prediction dataset of Model BT were batch-simulated using a computational pipeline via Discovery Studio. Semi-rational engineering of the residues in the CRRs generated BmoR mutants with SSO, validated through MicroScale Thermophoresis (MST) affinity assays. The SSO-enabled BmoR-based biosensor was used to screen microbial overproducers for 3-L fed-batch fermentation.ResultsThe transcription activation effects of 245 TF-SM complexes were experimentally verified, providing the training and test dataset to generate a machine-learning based Model BT with 88.5 % accuracy. The binding between 5,700 BmoR mutants and four SMs was simulated by Discovery Studio, generating 22,800 complexes to output BmoR-SM hydrogen bond (BSH) counts. BSH counts combined with supplementary parameters to form a prediction dataset. The CRRs containing totally 36 residues were successfully predicted by Model BT. The CRRs were semi-rational modified to obtain BmoR mutants with SSO. The SSO-enabled BmoR-based biosensor effectively screened a strain yielding 12.6 g/L isopentanol.ConclusionBy demonstrating the dominant role of the BSH counts in TF-SM interactions and establishing a machine learning-guided framework for TF evolution, this work advances rational design principles for engineering TFs with precise molecular recognition, offering broad applications in synthetic biology and metabolic engineering.