Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein–protein and protein–nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

The cellular milieu is a complex and crowded aqueous solution. Macromolecular crowding effects are commonly studied in vitro using crowding agents. The aim of the present study was to evaluate the effects, if any, of macromolecular synthetic crowding agents on the apparent steady-state kinetic parameters (K _m , k _cat , and k _cat /K _m ) of Mycobacterium tuberculosis 2-trans-enoyl-ACP (CoA) reductase (InhA). Negligible effects on InhA activity were observed for ficoll 70, ficoll 400 and dextran 70. A complex effect was observed for PEG 6000. Glucose and sucrose showed, respectively, no effect on InhA activity and decreased k _cat /K _m for NADH and k _cat for 2-trans-dodecenoyl-CoA. Molecular dynamics results suggest that InhA adopts a more compact conformer in sucrose solution. The effects of the crowding agents on the energy (E _a and E _η ), enthalpy (∆H ^# ), entropy (∆S ^# ), and Gibbs free energy (∆G ^# ) of activation were determined. The ∆G ^# values for all crowding agents were similar to buffer, suggesting that excluded volume effects did not facilitate stable activated ES ^# complex formation. Nonlinear Arrhenius plot for PEG 6000 suggests that “soft” interactions play a role in crowding effects. The results on InhA do not unequivocally meet the criteria for crowding effect due to exclude volume only.

Information regarding pathways through voids in biomolecules and their roles in ligand transport is critical to our understanding of the function of many biomolecules. Recently, the advent of high-throughput molecular dynamics simulations has enabled the study of these pathways, and of rare transport events. However, the scale and intricacy of the data produced requires dedicated tools in order to conduct analyses efficiently and without excessive demand on users. To fill this gap, we developed the TransportTools, which allows the investigation of pathways and their utilization across large, simulated datasets. TransportTools also facilitates the development of custom-made analyses. TransportTools is implemented in Python3 and distributed as pip and conda packages. The source code is available at https://github.com/labbit-eu/transport_tools. Data are available in a repository and can be accessed via a link: https://doi.org/10.5281/zenodo.5642954. Supplementary data are available at Bioinformatics online.

ABCG46 of the legume Medicago truncatula is an ABC-type transporter responsible for highly selective translocation of the phenylpropanoids, 4-coumarate, and liquiritigenin, over the plasma membrane. To investigate molecular determinants of the observed substrate selectivity, we applied a combination of phylogenetic and biochemical analyses, AlphaFold2 structure prediction, molecular dynamics simulations, and mutagenesis. We discovered an unusually narrow transient access path to the central cavity of MtABCG46 that constitutes an initial filter responsible for the selective translocation of phenylpropanoids through a lipid bilayer. Furthermore, we identified remote residue F562 as pivotal for maintaining the stability of this filter. The determination of individual amino acids that impact the selective transport of specialized metabolites may provide new opportunities associated with ABCGs being of interest, in many biological scenarios.

An aqueous environment is vital for life as we know it, and water is essential for nearly all biochemical processes at the molecular level. Proteins utilize water molecules in various ways. Consequently, proteins must transport water molecules across their internal network of tunnels to reach the desired action sites, either within them or by functioning as molecular pipes to control cellular osmotic pressure. Despite water playing a crucial role in enzymatic activity and stability, its transport has been largely overlooked, with studies primarily focusing on water transport across membrane proteins. The transport of molecules through a protein's tunnel network is challenging to study experimentally, making molecular dynamics simulations the most popular approach for investigating such events. In this study, we focused on the transport of water molecules across three different α/β-hydrolases: haloalkane dehalogenase, epoxide hydrolase, and lipase. Using a 5 μs adaptive simulation per system, we observed that only a few tunnels were responsible for the majority of water transport in dehalogenase, in contrast to a higher diversity of tunnels in other enzymes. Interestingly, water molecules could traverse narrow tunnels with subangstrom bottlenecks, which is surprising given the commonly accepted water molecule radius of 1.4 Å. Our analysis of the transport events in such narrow tunnels revealed a markedly increased number of hydrogen bonds formed between the water molecules and protein, likely compensating for the steric penalty of the process. Overall, these commonly disregarded narrow tunnels accounted for ∼20% of the total water transport observed, emphasizing the need to surpass the standard geometrical limits on the functional tunnels to properly account for the relevant transport processes. Finally, we demonstrated how the obtained insights could be applied to explain the differences in a mutant of the human soluble epoxide hydrolase associated with a higher incidence of ischemic stroke.