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Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

Since the beginning of oil exploration, whole ecosystems have been affected by accidents and bad practices involving petroleum compounds. In this sense, bioremediation stands out as the cheapest and most eco-friendly alternatives to reverse the damage done in oil-impacted areas. However, more efforts must be made to engineer enzymes that could be used in the bioremediation process. Interestingly, a recent work described that α-amylase, one of the most evolutionary conserved enzymes, was able to promiscuously degrade n-alkanes, a class of molecules abundant in the petroleum admixture. Considering that α-amylase is expressed in almost all known organisms, and employed in numerous biotechnological processes, using it can be a great leap toward more efficient applications of enzyme or microorganism-consortia bioremediation approaches. In this work, we employed a strict computational approach to design new α-amylase mutants with potentially enhanced catalytic efficiency toward n-alkanes. Using in silico techniques, such as molecular docking, molecular dynamics, metadynamics, and residue-residue interaction networks, we generated mutants potentially more efficient for degrading n-alkanes, L183Y, and N314A. Our results indicate that the new mutants have an increased binding rate for tetradecane, the longest n-alkane previously tested, which can reside in the catalytic center for more extended periods. Additionally, molecular dynamics and network analysis showed that the new mutations have no negative impact on protein structure than the WT. Our results aid in solidifying this enzyme as one more tool in the petroleum bioremediation toolbox.

Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X‐ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.