The covalent complex of Jo-In results from a long-lived, non-covalent intermediate state with near-native structure.

Covalent protein complexes have been used to assemble enzymes in large scaffolds for biotechnology purposes. Although the catalytic mechanism of the covalent linking of such proteins is well known, the recognition and overall structural mechanisms driving the association are far less understood but could help further functional engineering of these complexes. Here, we study the Jo-In complex by NMR spectroscopy and molecular modelling. We characterize a transient non-covalent complex, with structural elements close to those in the final covalent complex. Using site specific mutagenesis, we further show that this non-covalent association is essential for the covalent complex to form.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase.

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ ß-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Positive multistate protein design.

MOTIVATION: Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. RESULTS: We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. AVAILABILITY AND IMPLEMENTATION: https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein Design with Deep Learning.

Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.

A Comparative Study to Decipher the Structural and Dynamics Determinants Underlying the Activity and Thermal Stability of GH-11 Xylanases.

With the growing need for renewable sources of energy, the interest for enzymes capable of biomass degradation has been increasing. In this paper, we consider two different xylanases from the GH-11 family: the particularly active GH-11 xylanase from Neocallimastix patriciarum, NpXyn11A, and the hyper-thermostable mutant of the environmentally isolated GH-11 xylanase, EvXyn11TS. Our aim is to identify the molecular determinants underlying the enhanced capacities of these two enzymes to ultimately graft the abilities of one on the other. Molecular dynamics simulations of the respective free-enzymes and enzyme-xylohexaose complexes were carried out at temperatures of 300, 340, and 500 K. An in-depth analysis of these MD simulations showed how differences in dynamics influence the activity and stability of these two enzymes and allowed us to study and understand in greater depth the molecular and structural basis of these two systems. In light of the results presented in this paper, the thumb region and the larger substrate binding cleft of NpXyn11A seem to play a major role on the activity of this enzyme. Its lower thermal stability may instead be caused by the higher flexibility of certain regions located further from the active site. Regions such as the N-ter, the loops located in the fingers region, the palm loop, and the helix loop seem to be less stable than in the hyper-thermostable EvXyn11TS. By identifying molecular regions that are critical for the stability of these enzymes, this study allowed us to identify promising targets for engineering GH-11 xylanases. Eventually, we identify NpXyn11A as the ideal host for grafting the thermostabilizing traits of EvXyn11TS.

A structural homology approach for computational protein design with flexible backbone.

MOTIVATION: Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. RESULTS: We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. AVAILABILITY AND IMPLEMENTATION: Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rational design of adjuvants targeting the C-type lectin Mincle.

The advances in subunit vaccines development have intensified the search for potent adjuvants, particularly adjuvants inducing cell-mediated immune responses. Identification of the C-type lectin Mincle as one of the receptors underlying the remarkable immunogenicity of the mycobacterial cell wall, via recognition of trehalose-6,6'-dimycolate (TDM), has opened avenues for the rational design of such molecules. Using a combination of chemical synthesis, biological evaluation, molecular dynamics simulations, and protein mutagenesis, we gained insight into the molecular bases of glycolipid recognition by Mincle. Unexpectedly, the fine structure of the fatty acids was found to play a key role in the binding of a glycolipid to the carbohydrate recognition domain of the lectin. Glucose and mannose esterified at O-6 by a synthetic α-ramified 32-carbon fatty acid showed agonist activity similar to that of TDM, despite their much simpler structure. Moreover, they were seen to stimulate proinflammatory cytokine production in primary human and murine cells in a Mincle-dependent fashion. Finally, they were found to induce strong Th1 and Th17 immune responses in vivo in immunization experiments in mice and conferred protection in a murine model of Mycobacterium tuberculosis infection. Here we describe the rational development of new molecules with powerful adjuvant properties.

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Motivation: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Results: Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. Availability and implementation: as open-source Python/C++ code at sourcesup.renater.fr/projects/easy-jayz. Supplementary information: Supplementary data are available at Bioinformatics online.

Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems.

Computational protein design (CPD) aims to predict amino acid sequences that fold to specific structures and perform desired functions. CPD depends on a rotamer library, an energy function, and an algorithm to search the sequence/conformation space. Variable neighborhood search (VNS) with cost function networks is a powerful framework that can provide tight upper bounds on the global minimum energy. We propose a new CPD heuristic based on VNS in which a subset of the solution space (a "neighborhood") is explored, whose size is gradually increased with a dedicated probabilistic heuristic. The algorithm was tested on 99 protein designs with fixed backbones involving nine proteins from the SH2, SH3, and PDZ families. The number of mutating positions was 20, 30, or all of the amino acids, while the rest of the protein explored side-chain rotamers. VNS was more successful than Monte Carlo (MC), replica-exchange MC, and a heuristic steepest-descent energy minimization, providing solutions with equal or lower best energies in most cases. For complete protein redesign, it gave solutions that were 2.5 to 11.2 kcal/mol lower in energy than those obtained with the other approaches. VNS is implemented in the toulbar2 software. It could be very helpful for large and/or complex design problems.

Fast search algorithms for computational protein design.

One of the main challenges in computational protein design (CPD) is the huge size of the protein sequence and conformational space that has to be computationally explored. Recently, we showed that state-of-the-art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow speeding up provable rigid backbone protein design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well-established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of A* to produce conformations in increasing order of energy, we defined new A* strategies combining CFN lower bounds, with new side-chain positioning-based branching scheme. Beyond the speedups obtained in the new A*-CFN combination, this novel branching scheme enables a much faster enumeration of suboptimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/ A* combination inside Osprey* and make it possible to solve larger CPD problems with provable algorithms.

A new framework for computational protein design through cost function network optimization.

MOTIVATION: The main challenge for structure-based computational protein design (CPD) remains the combinatorial nature of the search space. Even in its simplest fixed-backbone formulation, CPD encompasses a computationally difficult NP-hard problem that prevents the exact exploration of complex systems defining large sequence-conformation spaces. RESULTS: We present here a CPD framework, based on cost function network (CFN) solving, a recent exact combinatorial optimization technique, to efficiently handle highly complex combinatorial spaces encountered in various protein design problems. We show that the CFN-based approach is able to solve optimality a variety of complex designs that could often not be solved using a usual CPD-dedicated tool or state-of-the-art exact operations research tools. Beyond the identification of the optimal solution, the global minimum-energy conformation, the CFN-based method is also able to quickly enumerate large ensembles of suboptimal solutions of interest to rationally build experimental enzyme mutant libraries. AVAILABILITY: The combined pipeline used to generate energetic models (based on a patched version of the open source solver Osprey 2.0), the conversion to CFN models (based on Perl scripts) and CFN solving (based on the open source solver toulbar2) are all available at http://genoweb.toulouse.inra.fr/~tschiex/CPD

Essential role of amino acid position 226 in oligosaccharide elongation by amylosucrase from Neisseria polysaccharea.

Amylosucrase from Neisseria polysaccharea is a remarkable transglucosylase that synthesizes an insoluble amylose-like polymer from sole substrate sucrose. One particular amino acid, Arg226, was proposed from molecular modeling studies to play an important role in the formation of the active site topology and in the accessibility of ligands to the catalytic site. The systematic mutation of this Arg residue by all 19 other possible amino acids revealed that all single-mutants had a higher activity on sucrose compared to the wild-type enzyme. An extensive kinetic investigation showed that catalytic efficiencies are greatly impacted by the presence of natural acceptors in the reaction media, their chain length and the nature of the amino acid at position 226. Compared to the wild-type enzyme, the R226N mutant showed a 10-fold enhancement in the catalytic efficiency and a nearly twofold higher production of an insoluble amylose-like polymer that can be of interest for biotechnological applications.

Structural investigation of the thermostability and product specificity of amylosucrase from the bacterium Deinococcus geothermalis.

Amylosucrases are sucrose-utilizing α-transglucosidases that naturally catalyze the synthesis of α-glucans, linked exclusively through α1,4-linkages. Side products and in particular sucrose isomers such as turanose and trehalulose are also produced by these enzymes. Here, we report the first structural and biophysical characterization of the most thermostable amylosucrase identified so far, the amylosucrase from Deinoccocus geothermalis (DgAS). The three-dimensional structure revealed a homodimeric quaternary organization, never reported before for other amylosucrases. A sequence signature of dimerization was identified from the analysis of the dimer interface and sequence alignments. By rigidifying the DgAS structure, the quaternary organization is likely to participate in the enhanced thermal stability of the protein. Amylosucrase specificity with respect to sucrose isomer formation (turanose or trehalulose) was also investigated. We report the first structures of the amylosucrases from Deinococcus geothermalis and Neisseria polysaccharea in complex with turanose. In the amylosucrase from N. polysaccharea (NpAS), key residues were found to force the fructosyl moiety to bind in an open state with the O3' ideally positioned to explain the preferential formation of turanose by NpAS. Such residues are either not present or not similarly placed in DgAS. As a consequence, DgAS binds the furanoid tautomers of fructose through a weak network of interactions to enable turanose formation. Such topology at subsite +1 is likely favoring other possible fructose binding modes in agreement with the higher amount of trehalulose formed by DgAS. Our findings help to understand the inter-relationships between amylosucrase structure, flexibility, function, and stability and provide new insight for amylosucrase design.

Crystal structures of bacterial peptidoglycan amidase AmpD and an unprecedented activation mechanism.

AmpD is a cytoplasmic peptidoglycan (PG) amidase involved in bacterial cell-wall recycling and in induction of ß-lactamase, a key enzyme of ß-lactam antibiotic resistance. AmpD belongs to the amidase_2 family that includes zinc-dependent amidases and the peptidoglycan-recognition proteins (PGRPs), highly conserved pattern-recognition molecules of the immune system. Crystal structures of Citrobacter freundii AmpD were solved in this study for the apoenzyme, for the holoenzyme at two different pH values, and for the complex with the reaction products, providing insights into the PG recognition and the catalytic process. These structures are significantly different compared with the previously reported NMR structure for the same protein. The NMR structure does not possess an accessible active site and shows the protein in what is proposed herein as an inactive "closed" conformation. The transition of the protein from this inactive conformation to the active "open" conformation, as seen in the x-ray structures, was studied by targeted molecular dynamics simulations, which revealed large conformational rearrangements (as much as 17 Å) in four specific regions representing one-third of the entire protein. It is proposed that the large conformational change that would take the inactive NMR structure to the active x-ray structure represents an unprecedented mechanism for activation of AmpD. Analysis is presented to argue that this activation mechanism might be representative of a regulatory process for other intracellular members of the bacterial amidase_2 family of enzymes.

Applying pairwise combinations of amino acid mutations for sorting out highly efficient glucosylation tools for chemo-enzymatic synthesis of bacterial oligosaccharides.

Iterative saturation mutagenesis and combinatorial active site saturation focused on vicinal amino acids were used to alter the acceptor specificity of amylosucrase from Neisseria polysaccharea , a sucrose-utilizing α-transglucosidase, and sort out improved variants. From the screening of three semirational sublibraries accounting in total for 20,000 variants, we report here the isolation of three double mutants of N. polysaccharea amylosucrase displaying a spectacular specificity enhancement toward both sucrose, the donor substrate, and the allyl 2-acetamido-2-deoxy-α-D-glucopyranoside acceptor as compared to the wild-type enzyme. Such levels of activity improvement have never been reported before for this class of carbohydrate-active enzymes. X-ray structure of the best performing enzymes supported by molecular dynamics simulations showed local rigidity of the -1 subsite as well as flexibility of loops involved in active site topology, which both account for the enhanced catalytic performances of the mutants. The study well illustrates the importance of taking into account the local conformation of catalytic residues as well as protein dynamics during the catalytic process, when designing enzyme libraries.

Computational Design of Miniprotein Binders.

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

An integrated approach reveals how lipo-chitooligosaccharides interact with the lysin motif receptor-like kinase MtLYR3.

N-acetylglucosamine containing compounds acting as pathogenic or symbiotic signals are perceived by plant-specific Lysin Motif Receptor-Like Kinases (LysM-RLKs). The molecular mechanisms of this perception are not fully understood, notably those of lipo-chitooligosaccharides (LCOs) produced during root endosymbioses with nitrogen-fixing bacteria or arbuscular mycorrhizal fungi. In Medicago truncatula, we previously identified the LysM-RLK LYR3 (MtLYR3) as a specific LCO-binding protein. We also showed that the absence of LCO binding to LYR3 of the non-mycorrhizal Lupinus angustifolius, (LanLYR3), was related to LysM3, which differs from that of MtLYR3 by several amino acids and, particularly, by a critical tyrosine residue absent in LanLYR3. Here, we aimed to define the LCO binding site of MtLYR3 by using molecular modelling and simulation approaches, combined with site-directed mutagenesis and LCO binding experiments. 3D models of MtLYR3 and LanLYR3 ectodomains were built, and homology modelling and molecular dynamics (MD) simulations were performed. Molecular docking and MD simulation on the LysM3 identified potential key residues for LCO binding. We highlighted by steered MD simulations that in addition to the critical tyrosine, two other residues were important for LCO binding in MtLYR3. Substitution of these residues in LanLYR3-LysM3 by those of MtLYR3-LysM3 allowed the recovery of high-affinity LCO binding in experimental radioligand-binding assays. An analysis of selective constraints revealed that the critical tyrosine has experienced positive selection pressure and is absent in some LYR3 proteins. These findings now pave the way to uncover the functional significance of this specific evolutionary pattern.

A mixed molecular modeling-robotics approach to investigate lipase large molecular motions.

Large-scale conformational rearrangement of a lid subdomain is a key event in the interfacial activation of many lipases. We present herein a study in which the large-scale "open-to-closed" movement of Burkholderia cepacia lipase lid has been simulated at the atomic level using a hybrid computational method. The two-stage approach combines path-planning algorithms originating from robotics and molecular mechanics methods. In the first stage, a path-planning approach is used to compute continuous and geometrically feasible pathways between two protein conformational states. Then, an energy minimization procedure using classical molecular mechanics is applied to intermediate conformations in the path. The main advantage of such a combination of methods is that only geometrically feasible solutions are prompted for energy calculation in explicit solvent, which allows the atomic-scale description of the transition pathway between two extreme conformations of B. cepacia lipase (BCL; open and closed states) within very short computing times (a few hours on a desktop computer). Of interest, computed pathways enable the description of intermediate conformations along the "open-to-closed" conformational transition of BCL lid and the identification of bottlenecks during the lid closing. Furthermore, consideration of the solvent effect when computing the transition energy profiles provides valuable information regarding the feasibility and the spontaneity of the movement under the influence of the solvent environment. This new hybrid computational method turned out to be well-suited for investigating at an atomistic level large-scale conformational motion and at a qualitative level, the solvent effect on the energy profiles associated with the global motion.

Enantioselectivity of Candida rugosa lipases (Lip1, Lip3, and Lip4) towards 2-bromo phenylacetic acid octyl esters controlled by a single amino acid.

Enantiomer discrimination by enzymes is a very accurate mechanism, which often involves few amino acids located at the active site. Lipase isoforms from Candida rugosa are very good enzymatic models to study this phenomenon as they display high sequence homology (>80%) and their enantioselectivity is often pointed out. In the present work, we investigated three lipases from C. rugosa (Lip1, Lip3, and Lip4, respectively) towards the resolution of 2-bromo-arylacetic acid esters, an important class of chemical intermediates in the pharmaceutical industry. All exhibited a high enantioselectivity, with Lip4 preferring the R-enantiomer (E-value = 15), while Lip1 and Lip3 showed an S-enantioselectivity >200. A combination of sequence and structure analysis of the three C. rugosa lipases suggested that position 296 could play a role in S- or R-enantiomer preference of C. rugosa lipases. This led to the construction by site-directed mutagenesis of Lip1 and Lip4 variants in which position 296 was, respectively, exchanged by a Gly, Ala, Leu, or Phe amino acid. Screening of these variants for their enantioselectivity toward 2-bromo phenyl acetic acid octyl esters revealed that steric hindrance of the amino acid residue introduced at position 296 controls both the enantiopreference and the enantioselectivity value of the lipase: bulkier is the amino acid at position 296, larger is the selectivity towards the S-enantiomer. To investigate further these observations at an atomic level, we carried out a preliminary modeling study of the tetrahedral intermediates formed by Lip1 and Lip4 with the (R, S)-2-bromo-phenylacetic acid octyl ester enantiomers that provides some insight regarding the determinants responsible for lipase enantiodiscrimination.

Molecular flexibility in computational protein design: an algorithmic perspective.

Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.