Biomass valorization plays a significant role in the production of biofuels and various value-added biochemicals, in addition to lowering greenhouse gas emissions. In terms of biorefining methods, hydrothermal (HT) and biological techniques have demonstrated the capability of valorizing biomass raw materials to yield value added end-products. An inter-disciplinary bio-economical approach is capable of optimizing biomass’s total potential in terms of environmental perspective and circular bioeconomy standpoint. The aim of this review is to provide an in-depth overview of combinatorial HT and biological techniques to maximize biomass value, which includes biological valorization following HT pretreatment and HT valorization of lignocellulosic substrates emanating from biocatalytic hydrolysis/anaerobic digestion and/or pretreated food waste for the ultimate yield of biogas/biochar and biocrude. In this study, we discuss recent advances regarding HT and biological treatment conditions, synergies between the two technologies, and optimal performance. Additionally, energy balances and economic feasibility assessments of alternative integrated solutions reported in previous studies are compared. Furthermore, we conclude by discussing the challenges and opportunities involved in integrating HT and biologicals methods toward complete biomass utilization.

Algal biomass is a prospective feedstock for the eco-sustainable production of many different products with added value, such as meals, feeds, and fuels. The remaining biomass from the algae can be used as raw material and can be transformed into useful secondary products after the important macromolecules have been removed. By optimizing algal biomass hydrolysate utilizing microbial fermentation, several studies demonstrated the generation of bioenergy (bioalcohol, biogas, and biohydrogen) and biochemicals (organic acids and biopolymers). Since the harvest and maintenance of sustainable algal cultivation incur considerable energy and economical prowess, developing products from algae remains a challenge to be countered in commercial applications. This is a typical bottleneck issue when processing algae for fuels or chemicals at the pilot scale. Implementation of integrated algae biorefinery methods can substantially reduce the cost of production and energy consumption. An algae-based green economy can be financially more viable and utilizable, especially for countries with weaker economies. This review’s goal is to examine the implementation of integrated biorefineries for the recovery of bioproducts generated from algae and potential applications. In this context, the life cycle analysis and business elements of a unified algal biorefinery are also addressed.

Context: Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis (‘ classic type functions ’ of proteins) or a signal transduction (‘ non-classic’ ), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (ΔG _binding , K _d ) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ΔG _binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX etc.) that directly compute ΔG _binding without involving intermediate structural descriptors. Methods: Here we present EnCPdock ( www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ΔG _binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and, renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional Complementarity Plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr _fmax ) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.