Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.

IPRO+/-: Computational Protein Design Tool Allowing for Insertions and Deletions.

Insertions and deletions (indels) in protein sequences alter the residue spacing along the polypeptide backbone and consequently open up possibilities for tuning protein function in a way that is inaccessible by amino acid substitution alone. We describe an optimization-based computational protein redesign approach centered around predicting beneficial combinations of indels along with substitutions and also obtain putative substrate-docked structures for these protein variants. This modified algorithmic capability would be of interest for enzyme engineering and broadly inform other protein design tasks. We highlight this capability by (1) identifying active variants of a bacterial thioesterase enzyme ('TesA) with experimental corroboration, (2) recapitulating existing active TEM-1 ß-Lactamase sequences of different sizes, and (3) identifying shorter 4-Coumarate:CoA ligases with enhanced in vitro activities toward non-native substrates. A separate PyRosetta-based open-source tool, Indel-Maker (http://www.maranasgroup.com/software.htm), has also been created to construct computational models of user-defined protein variants with specific indels and substitutions.

Computational Redesign of Acyl-ACP Thioesterase with Improved Selectivity toward Medium-Chain-Length Fatty Acids.

Enzyme and metabolic engineering offer the potential to develop biocatalysts for converting natural resources into a wide range of chemicals. To broaden the scope of potential products beyond natural metabolites, methods of engineering enzymes to accept alternative substrates and/or perform novel chemistries must be developed. DNA synthesis can create large libraries of enzyme-coding sequences, but most biochemistries lack a simple assay to screen for promising enzyme variants. Our solution to this challenge is structure-guided mutagenesis in which optimization algorithms select the best sequences from libraries based on specified criteria (i.e. binding selectivity). Here, we demonstrate this approach by identifying medium-chain (C6-C12) acyl-ACP thioesterases through structure-guided mutagenesis. Medium-chain fatty acids, products of thioesterase-catalyzed hydrolysis, are limited in natural abundance compared to long-chain fatty acids; the limited supply leads to high costs of C6-C10 oleochemicals such as fatty alcohols, amines, and esters. Here, we applied computational tools to tune substrate binding to the highly-active 'TesA thioesterase in Escherichia coli. We used the IPRO algorithm to design thioesterase variants with enhanced C12- or C8-specificity while maintaining high activity. After four rounds of structure-guided mutagenesis, we identified three thioesterases with enhanced production of dodecanoic acid (C12) and twenty-seven thioesterases with enhanced production of octanoic acid (C8). The top variants reached up to 49% C12 and 50% C8 while exceeding native levels of total free fatty acids. A comparably sized library created by random mutagenesis failed to identify promising mutants. The chain length-preference of 'TesA and the best mutant were confirmed in vitro using acyl-CoA substrates. Molecular dynamics simulations, confirmed by resolved crystal structures, of 'TesA variants suggest that hydrophobic forces govern 'TesA substrate specificity. We expect that the design rules we uncovered and the thioesterase variants identified will be useful to metabolic engineering projects aimed at sustainable production of medium-chain oleochemicals.

The Iterative Protein Redesign and Optimization (IPRO) suite of programs.

Proteins are an important class of biomolecules with applications spanning across biotechnology and medicine. In many cases, native proteins must be redesigned to improve various performance metrics by changing their amino acid sequences. Algorithms can help sharpen protein library design by focusing the library on sequences that optimize computationally accessible proxies. The Iterative Protein Redesign and Optimization (IPRO) suite of programs offers an integrated environment for (1) altering protein binding affinity and specificity, (2) grafting a binding pocket into an existing protein scaffold, (3) predicting an antibody's tertiary structure based on its sequence, (4) enhancing enzymatic activity, and (5) assessing the structure and binding energetics for a specific mutant. This manuscript provides an overview of the methods involved in IPRO, input language terminology, algorithmic details, software implementation specifics and application highlights. IPRO can be downloaded at http://maranas.che.psu.edu.

Methane oxidation by anaerobic archaea for conversion to liquid fuels.

Given the recent increases in natural gas reserves and associated drawbacks of current gas-to-liquids technologies, the development of a bioconversion process to directly convert methane to liquid fuels would generate considerable industrial interest. Several clades of anaerobic methanotrophic archaea (ANME) are capable of performing anaerobic oxidation of methane (AOM). AOM carried out by ANME offers carbon efficiency advantages over aerobic oxidation by conserving the entire carbon flux without losing one out of three carbon atoms to carbon dioxide. This review highlights the recent advances in understanding the key enzymes involved in AOM (i.e., methyl-coenzyme M reductase), the ecological niches of a number of ANME, the putative metabolic pathways for AOM, and the syntrophic consortia that they typically form.

OptZyme: computational enzyme redesign using transition state analogues.

OptZyme is a new computational procedure for designing improved enzymatic activity (i.e., kcat or kcat/KM) with a novel substrate. The key concept is to use transition state analogue compounds, which are known for many reactions, as proxies for the typically unknown transition state structures. Mutations that minimize the interaction energy of the enzyme with its transition state analogue, rather than with its substrate, are identified that lower the transition state formation energy barrier. Using Escherichia coli ß-glucuronidase as a benchmark system, we confirm that KM correlates (R(2) = 0.960) with the computed interaction energy between the enzyme and the para-nitrophenyl- ß, D-glucuronide substrate, kcat/KM correlates (R(2) = 0.864) with the interaction energy of the transition state analogue, 1,5-glucarolactone, and kcat correlates (R(2) = 0.854) with a weighted combination of interaction energies with the substrate and transition state analogue. OptZyme is subsequently used to identify mutants with improved KM, kcat, and kcat/KM for a new substrate, para-nitrophenyl- ß, D-galactoside. Differences between the three libraries reveal structural differences that underpin improving KM, kcat, or kcat/KM. Mutants predicted to enhance the activity for para-nitrophenyl- ß, D-galactoside directly or indirectly create hydrogen bonds with the altered sugar ring conformation or its substituents, namely H162S, L361G, W549R, and N550S.

Recent advances in computational protein design.

Proteins are the molecules cells primarily rely on for catalysis, recognition, signaling, defense, locomotion, and structural integrity. Engineering proteins for improved function or new applications is a fast-growing segment of biotechnology and biomedicine. Experimental efforts based on the screening of large mutant libraries have led to many successes but they do not provide quantitative design principles and/or insight into the structural features that underpin the desired function. The computational de novo design of proteins promises to bridge this gap; however, it requires reliable structure prediction, provisions for protein stability, and accurate descriptions of inter-molecule interactions. Studies that successfully meet all these criteria are beginning to emerge including the design of an O(2)-binding protein and a novel enzyme that catalyzes a Diels-Alder reaction.