Alteration of enzyme specificity by computational loop remodeling and design

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Insertions and deletions in protein evolution and engineering

Protein evolution or engineering studies are traditionally focused on amino acid substitutions and the way these contribute to fitness. Meanwhile, the insertion and deletion of amino acids is often overlooked, despite being one of the most common sources of genetic variation. Recent methodological advances and successful engineering stories have demonstrated that the time is ripe for greater emphasis on these mutations and their understudied effects. This review highlights the evolutionary importance and biotechnological relevance of insertions and deletions (indels). We provide a comprehensive overview of approaches that can be employed to include indels in random, (semi)-rational or computational protein engineering pipelines. Furthermore, we discuss the tolerance to indels at the structural level, address how domain indels can link the function of unrelated proteins, and feature studies that illustrate the surprising and intriguing potential of frameshift mutations.

LoopGrafter: a web tool for transplanting dynamical loops for protein engineering

The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses.

Accurate positioning of functional residues with robotics-inspired computational protein design

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.

Computer-aided engineering of a branching sucrase for the glucodiversification of a tetrasaccharide precursor of S. flexneri antigenic oligosaccharides

Enzyme engineering approaches have allowed to extend the collection of enzymatic tools available for synthetic purposes. However, controlling the regioselectivity of the reaction remains challenging, in particular when dealing with carbohydrates bearing numerous reactive hydroxyl groups as substrates. Here, we used a computer-aided design framework to engineer the active site of a sucrose-active [Formula: see text]-transglucosylase for the 1,2-cis-glucosylation of a lightly protected chemically synthesized tetrasaccharide, a common precursor for the synthesis of serotype-specific S. flexneri O-antigen fragments. By targeting 27 amino acid positions of the acceptor binding subsites of a GH70 branching sucrase, we used a RosettaDesign-based approach to propose 49 mutants containing up to 15 mutations scattered over the active site. Upon experimental evaluation, these mutants were found to produce up to six distinct pentasaccharides, whereas only two were synthesized by the parental enzyme. Interestingly, we showed that by introducing specific mutations in the active site of a same enzyme scaffold, it is possible to control the regiospecificity of the 1,2-cis glucosylation of the tetrasaccharide acceptor and produce a unique diversity of pentasaccharide bricks. This work offers novel opportunities for the development of highly convergent chemo-enzymatic routes toward S. flexneri haptens.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

Enhancement of the Isomerization Activity and Thermostability of Cellobiose 2-Epimerase from Caldicellulosiruptor saccharolyticus by Exchange of a Flexible Loop

Cellobiose 2-epimerase (CE) offers a promising enzymatic approach to produce lactulose. However, its application is limited by the unsatisfactory isomerization activity and thermostability. Our study attempted to optimize the catalytic performances of CEs by flexible loop exchange, for which four mutants were constructed using CsCE (CE from Caldicellulosiruptor saccharolyticus) as a template. As a result, all mutants maintained the same catalytic directions as the templates. Mutant RmC displayed a 2.2- and 1.34-fold increase in the isomerization activity and catalytic efficiency, respectively. According to the results of molecular dynamics (MD) simulations, it was revealed that the loop exchange in RmC enlarged the entrance of the active site for substrate binding and benefited proton transfer involved in the isomerization process. Besides, the t_1/2 of mutant StC at 70 °C was increased from 29.07 to 38.29 h, owing to the abundance of rigid residues (proline) within the flexible loop of StC. Our work demonstrated that the isomerization activity and thermostability of CEs were closely related to the flexible loop surrounding the active site, which provides a new perspective to engineer CEs for higher lactulose production.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Optimizing the Specificity Window of Biomolecular Receptors Using Structure-Switching and Allostery

To ensure maximum specificity (i.e., minimize cross-reactivity with structurally similar analogues of the desired target), most bioassays invoke "stringency", the careful tuning of the conditions employed (e.g., pH, ionic strength, or temperature). Willingness to control assay conditions will fall, however, as quantitative, single-step biosensors begin to replace multistep analytical processes. This is especially true for sensors deployed in vivo, where the tuning of such parameters is not just inconvenient but impossible. In response, we describe here the rational adaptation of two strategies employed by nature to tune the affinity of biomolecular receptors so as to optimize the placement of their specificity "windows" without the need to alter measurement conditions: structure-switching and allosteric control. We quantitatively validate these approaches using two distinct, DNA-based receptors: a simple, linear-chain DNA suitable for detecting a complementary DNA strand and a structurally complex DNA aptamer used for the detection of a small-molecule drug. Using these models, we show that, without altering assay conditions, structure-switching and allostery can tune the concentration range over which a receptor achieves optimal specificity over orders of magnitude, thus optimally matching the specificity window with the range of target concentrations expected to be seen in a given application.

ProDCoNN: Protein design using a convolutional neural network

Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C _α atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets.

Attempts to develop an enzyme converting DHIV to KIV

Dihydroxy-acid dehydratase (DHAD) catalyzes the dehydration of R-2,3-dihydroxyisovalerate (DHIV) to 2-ketoisovalerate (KIV) using an Fe-S cluster as a cofactor, which is sensitive to oxidation and expensive to synthesize. In contrast, sugar acid dehydratases catalyze the same chemical reactions using a magnesium ion. Here, we attempted to substitute the high-cost DHAD with a cost-efficient engineered sugar acid dehydratase using computational protein design (CPD). First, we tried without success to modify the binding pocket of a sugar acid dehydratase to accommodate the smaller, more hydrophobic DHIV. Then, we used a chemically activated substrate analog to react with sugar acid dehydratases or other enolase superfamily enzymes. Mandelate racemase from Pseudomonas putida (PpManR) and the putative sugar acid dehydratase from Salmonella typhimurium (StPutD) showed beta-elimination activity towards chlorolactate (CLD). CPD combined with medium-throughput selection improved the PpManR kcat/KM for CLD by four-fold. However, these enzyme variants did not show dehydration activity towards DHIV. Lastly, assuming phosphorylation could also be a good activation mechanism, we found that mevalonate-3-kinase (M3K) from Picrophilus torridus (PtM3K) exhibited adenosine triphosphate (ATP) hydrolysis activity when mixed with DHIV, indicating phosphorylation activity towards DHIV. Engineering PpManR or StPutD to accept 3-phospho-DHIV as a substrate was performed, but no variants with the desired activity were obtained.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

Ultrahigh specificity in a network of computationally designed protein-interaction pairs

Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity.

The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.

An NMR Confirmation for Increased Folded State Entropy Following Loop Truncation

Previous studies conducted on flexible loop regions in proteins revealed that the energetic consequences of changing loop length predominantly arise from the entropic cost of ordering a loop during folding. However, in an earlier study of human acylphosphatase (hmAcP) using experimental and computational approaches, we showed that thermodynamic stabilization upon loop truncation can be attributed mainly to the increased entropy of the folded state. Here, using 15N NMR spectroscopy, we studied the effect of loop truncation on hmAcP backbone dynamics on the picosecond-nanosecond timescale with the aim of confirming the effect of folded state entropy on protein stability. NMR-relaxation-derived N-H squared generalized order parameters reveal that loop truncation results in a significant increase in protein conformational flexibility. Comparison of these results with previously acquired all-atom molecular dynamics simulation, analyzed here in terms of squared generalized NMR order parameters, demonstrates general agreement between the two methods. The NMR study not only provides direct evidence for the enhanced conformational entropy of the folded state of hmAcP upon loop truncation but also gives a quantitative measure of the observed effects.

Engineering Improved Antiphosphotyrosine Antibodies Based on an Immunoconvergent Binding Motif

Phosphotyrosine (pY) is one of the most highly studied posttranslational modifications that is responsible for tightly regulating many signaling pathways in eukaryotes. Pan-specific pY antibodies have emerged as powerful tools for understanding the role of these modifications. Nevertheless, structures have not been reported for pan-specific pY antibodies, greatly impeding the further development of tools for integrating this ubiquitous posttranslational modification using structure-guided designs. Here, we present the first crystal structures of two widely utilized pan-specific pY antibodies, PY20 and 4G10. The two antibodies, although developed independently from animal immunizations, have surprisingly similar modes of recognition of the phosphate group, implicating a generic binding structure among pan-specific pY antibodies. Sequence alignments revealed that many pY binding residues are predominant in the mouse V germline genes, which consequently led to the convergent antibodies. On the basis of the convergent structure, we designed a phage display library by lengthening the CDR-L3 loop with the aid of computational modeling. Panning with this library resulted in a series of 4G10 variants with 4 to 11-fold improvements in pY binding affinities. The crystal structure of one improved variant showed remarkable superposition to the computational model, where the lengthened CDR-L3 loop creates an additional hydrogen bond indirectly bound to the phosphate group via a water molecule. The engineered variants exhibited superior performance in Western blot and immunofluorescence.

Rossmann-Fold Methyltransferases: Taking a "β-Turn" around Their Cofactor, S-Adenosylmethionine

Methyltransferases (MTases) are superfamilies of enzymes that catalyze the transfer of a methyl group from S-adenosylmethionine (SAM), a nucleoside-based cofactor, to a wide variety of substrates such as DNA, RNA, proteins, small molecules, and lipids. Depending upon their structural features, the MTases can be further classified into different classes; we consider exclusively the largest class of MTases, the Rossmann-fold MTases. It has been shown that the nucleoside cofactor-binding Rossmann enzymes, particularly the nicotinamide adenine dinucleotide (NAD)-, flavin adenine dinucleotide (FAD)-, and SAM-binding MTases enzymes, share common binding motifs that include a Gly-rich loop region that interacts with the cofactor and a highly conserved acidic residue (Asp/Glu) that interacts with the ribose moiety of the cofactor. Here, we observe that the Gly-rich loop region of the Rossmann MTases adapts a specific type II' β-turn in the proximity of the cofactor (<4 Å), and it appears to be a key feature of these superfamilies. Additionally, we demonstrate that the conservation of this β-turn could play a critical role in the enzyme-cofactor interaction, thereby shedding new light on the structural conformation of the Gly-rich loop region from Rossmann MTases.

Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation

An accurate energy function is an essential component of biomolecular structural modeling and design. The comparison of differently derived energy functions enables analysis of the strengths and weaknesses of each energy function and provides independent benchmarks for evaluating improvements within a given energy function. We compared the molecular mechanics Amber empirical energy function to two versions of the Rosetta energy function (talaris2014 and REF2015) in decoy discrimination and loop modeling tests. In decoy discrimination tests, both Rosetta and Amber (ff14SBonlySC) energy functions performed well in scoring the native state as the lowest energy conformation in many cases, but several false minima were found in with both talaris2014 and Amber ff14SBonlySC scoring functions. The current default version of the Rosetta energy function, REF2015, which is parametrized on both small molecule and macromolecular benchmark sets to improve decoy discrimination, performs significantly better than talaris2014, highlighting the improvements made to the Rosetta scoring approach. There are no cases in Rosetta REF2015, and 8/140 cases in Amber, where a false minimum is found that is absent in the alternative landscape. In loop modeling tests, Amber ff14SBonlySC and REF2015 perform equivalently, although false minima are detected in several cases for both. The balance between dihedral, electrostatic, solvation and hydrogen bonding scores contribute to the existence of false minima. To take advantage of the semi-orthogonal nature of the Rosetta and Amber energy functions, we developed a technique that combines Amber and Rosetta conformational rankings to predict the most near-native model for a given protein. This algorithm improves upon predictions from either energy function in isolation and should aid in model selection for structure evaluation and loop modeling tasks.

Highly active enzymes by automated combinatorial backbone assembly and sequence design

Automated design of enzymes with wild-type-like catalytic properties has been a long-standing but elusive goal. Here, we present a general, automated method for enzyme design through combinatorial backbone assembly. Starting from a set of homologous yet structurally diverse enzyme structures, the method assembles new backbone combinations and uses Rosetta to optimize the amino acid sequence, while conserving key catalytic residues. We apply this method to two unrelated enzyme families with TIM-barrel folds, glycoside hydrolase 10 (GH10) xylanases and phosphotriesterase-like lactonases (PLLs), designing 43 and 34 proteins, respectively. Twenty-one GH10 and seven PLL designs are active, including designs derived from templates with <25% sequence identity. Moreover, four designs are as active as natural enzymes in these families. Atomic accuracy in a high-activity GH10 design is further confirmed by crystallographic analysis. Thus, combinatorial-backbone assembly and design may be used to generate stable, active, and structurally diverse enzymes with altered selectivity or activity.

Computationally designed enzymes often show lower activity or stability than their natural counterparts. Here, the authors present an evolution-inspired method for automated enzyme design, creating stable enzymes with accurate active site architectures and wild-type-like activities.

Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein

With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.

Enzyme engineering: reaching the maximal catalytic efficiency peak

The practical need for highly efficient enzymes presents new challenges in enzyme engineering, in particular, the need to improve catalytic turnover (k_cat) or efficiency (k_cat/K_M) by several orders of magnitude. However, optimizing catalysis demands navigation through complex and rugged fitness landscapes, with optimization trajectories often leading to strong diminishing returns and dead-ends. When no further improvements are observed in library screens or selections, it remains unclear whether the maximal catalytic efficiency of the enzyme (the catalytic 'fitness peak') has been reached; or perhaps, an alternative combination of mutations exists that could yield additional improvements. Here, we discuss fundamental aspects of the process of catalytic optimization, and offer practical solutions with respect to overcoming optimization plateaus.

Specificity of peptidases secreted by filamentous fungi

Peptidases are enzymes that cleave peptide bonds, yielding proteins and peptides. Enzymes in this class also perform several other functions, regulating the activation or inactivation of target substrates via proteolysis. Owing to these functions, peptidases have been extensively used in industrial and biotechnological applications. Given their potential functions, it is important to optimize the use of these enzymes, which requires determination of the specificity of each peptidase. The peptidase specificity must be taken into account in choosing a peptidase to catalyze the available protein source within the desired application. The specificity of a peptidase defines the profile of enzyme–substrate interactions, and for this the catalytic site and the arrangement of the amino acid residues involved in peptide bond cleavage need to be known. The catalytic sites of peptidases may be composed of several subsites that interact with amino acid residues for proteolysis. Filamentous fungi produce peptidases with varying specificity, and here we provide a review of those reported to date and their potential applications.

Computationally optimized deimmunization libraries yield highly mutated enzymes with low immunogenicity and enhanced activity

Therapeutic proteins of wide-ranging function hold great promise for treating disease, but immune surveillance of these macromolecules can drive an antidrug immune response that compromises efficacy and even undermines safety. To eliminate widespread T-cell epitopes in any biotherapeutic and thereby mitigate this key source of detrimental immune recognition, we developed a Pareto optimal deimmunization library design algorithm that optimizes protein libraries to account for the simultaneous effects of combinations of mutations on both molecular function and epitope content. Active variants identified by high-throughput screening are thus inherently likely to be deimmunized. Functional screening of an optimized 10-site library (1,536 variants) of P99 β-lactamase (P99βL), a component of ADEPT cancer therapies, revealed that the population possessed high overall fitness, and comprehensive analysis of peptide-MHC II immunoreactivity showed the population possessed lower average immunogenic potential than the wild-type enzyme. Although similar functional screening of an optimized 30-site library (2.15 × 10⁹ variants) revealed reduced population-wide fitness, numerous individual variants were found to have activity and stability better than the wild type despite bearing 13 or more deimmunizing mutations per enzyme. The immunogenic potential of one highly active and stable 14-mutation variant was assessed further using ex vivo cellular immunoassays, and the variant was found to silence T-cell activation in seven of the eight blood donors who responded strongly to wild-type P99βL. In summary, our multiobjective library-design process readily identified large and mutually compatible sets of epitope-deleting mutations and produced highly active but aggressively deimmunized constructs in only one round of library screening.

Dynamics of the active site loops in catalyzing aminoacylation reaction in seryl and histidyl tRNA synthetases

Aminoacylation reaction is the first step of protein biosynthesis. The catalytic reorganization at the active site of aminoacyl tRNA synthetases (aaRSs) is driven by the loop motions. There remain lacunae of understanding concerning the catalytic loop dynamics in aaRSs. We analyzed the functional loop dynamics in seryl tRNA synthetase from Methanopyrus kandleri (^mkSerRS) and histidyl tRNA synthetases from Thermus thermophilus (^ttHisRS), respectively, using molecular dynamics. Results confirm that the motif 2 loop and other active site loops are flexible spots within the catalytic domain. Catalytic residues of the loops form a network of interaction with the substrates to form a reactive state. The loops undergo transitions between closed state and open state and the relaxation of the constituent residues occurs in femtosecond to nanosecond time scale. Order parameters are higher for constituent catalytic residues which form a specific network of interaction with the substrates to form a reactive state compared to the Gly residues within the loop. The development of interaction is supported from mutation studies where the catalytic domain with mutated loop exhibits unfavorable binding energy with the substrates. During the open-close motion of the loops, the catalytic residues make relaxation by ultrafast librational motion as well as fast diffusive motion and subsequently relax rather slowly via slower diffusive motion. The Gly residues act as a hinge to facilitate the loop closing and opening by their faster relaxation behavior. The role of bound water is analyzed by comparing implicit solvent-based and explicit solvent-based simulations. Loops fail to form catalytically competent geometry in absence of water. The present result, for the first time reveals the nature of the active site loop dynamics in aaRS and their influence on catalysis.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Computational protein design with backbone plasticity

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops.

Computational design of variants for cephalosporin C acylase from Pseudomonas strain N176 with improved stability and activity

In this report, redesigning cephalosporin C acylase from the Pseudomonas strain N176 revealed that the loss of stability owing to the introduced mutations at the active site can be recovered by repacking the nearby hydrophobic core regions. Starting from a quadruple mutant M31βF/H57βS/V68βA/H70βS, whose decrease in stability is largely owing to the mutation V68βA at the active site, we employed a computational enzyme design strategy that integrated design both at hydrophobic core regions for stability enhancement and at the active site for activity improvement. Single-point mutations L154βF, Y167βF, L180βF and their combinations L154βF/L180βF and L154βF/Y167βF/L180βF were found to display improved stability and activity. The two-point mutant L154βF/L180βF increased the protein melting temperature (T _m) by 11.7 °C and the catalytic efficiency V _max/K _m by 57 % compared with the values of the starting quadruple mutant. The catalytic efficiency of the resulting sixfold mutant M31βF/H57βS/V68βA/H70βS/L154βF/L180βF is recovered to become comparable to that of the triple mutant M31βF/H57βS/H70βS, but with a higher T _m. Further experiments showed that single-point mutations L154βF, L180βF, and their combination contribute no stability enhancement to the triple mutant M31βF/H57βS/H70βS. These results verify that the lost stability because of mutation V68βA at the active site was recovered by introducing mutations L154βF and L180βF at hydrophobic core regions. Importantly, mutation V68βA in the six-residue mutant provides more space to accommodate the bulky side chain of cephalosporin C, which could help in designing cephalosporin C acylase mutants with higher activities and the practical one-step enzymatic route to prepare 7-aminocephalosporanic acid at industrial-scale levels.

Statistical analysis of structural determinants for protein-DNA-binding specificity

DNA-binding proteins play critical roles in biological processes including gene expression, DNA packaging and DNA repair. They bind to DNA target sequences with different degrees of binding specificity, ranging from highly specific (HS) to nonspecific (NS). Alterations of DNA-binding specificity, due to either genetic variation or somatic mutations, can lead to various diseases. In this study, a comparative analysis of protein-DNA complex structures was carried out to investigate the structural features that contribute to binding specificity. Protein-DNA complexes were grouped into three general classes based on degrees of binding specificity: HS, multispecific (MS), and NS. Our results show a clear trend of structural features among the three classes, including amino acid binding propensities, simple and complex hydrogen bonds, major/minor groove and base contacts, and DNA shape. We found that aspartate is enriched in HS DNA binding proteins and predominately binds to a cytosine through a single hydrogen bond or two consecutive cytosines through bidentate hydrogen bonds. Aromatic residues, histidine and tyrosine, are highly enriched in the HS and MS groups and may contribute to specific binding through different mechanisms. To further investigate the role of protein flexibility in specific protein-DNA recognition, we analyzed the conformational changes between the bound and unbound states of DNA-binding proteins and structural variations. The results indicate that HS and MS DNA-binding domains have larger conformational changes upon DNA-binding and larger degree of flexibility in both bound and unbound states. Proteins 2016; 84:1147-1161. © 2016 Wiley Periodicals, Inc.

Shortening a loop can increase protein native state entropy

Protein loops are essential structural elements that influence not only function but also protein stability and folding rates. It was recently reported that shortening a loop in the AcP protein may increase its native state conformational entropy. This effect on the entropy of the folded state can be much larger than the lower entropic penalty of ordering a shorter loop upon folding, and can therefore result in a more pronounced stabilization than predicted by polymer model for loop closure entropy. In this study, which aims at generalizing the effect of loop length shortening on native state dynamics, we use all-atom molecular dynamics simulations to study how gradual shortening a very long or solvent-exposed loop region in four different proteins can affect their stability. For two proteins, AcP and Ubc7, we show an increase in native state entropy in addition to the known effect of the loop length on the unfolded state entropy. However, for two permutants of SH3 domain, shortening a loop results only with the expected change in the entropy of the unfolded state, which nicely reproduces the observed experimental stabilization. Here, we show that an increase in the native state entropy following loop shortening is not unique to the AcP protein, yet nor is it a general rule that applies to all proteins following the truncation of any loop. This modification of the loop length on the folded state and on the unfolded state may result with a greater effect on protein stability.

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.

Designing new protein–ligand interactions has tremendous potential for engineering sensitive biosensors for diagnostics or new enzymes useful in biotechnology, but these applications are extremely challenging, both because of inaccuracies of the energy functions used in modeling and design, and because protein active and binding sites are highly sensitive to subtle changes in structure. Here we describe a new method that addresses the second problem and couples changes in the structure of the protein backbone and of the amino acid side chains, the amino acid sequence, and the conformation of the ligand and its orientation in the binding site. We show that our method improvements significantly increase the accuracy of designing protein–ligand interactions compared to current state-of-the-art design methods. We assess these improvements in two important tests: the first predicts mutations that change ligand-binding preferences in enzymes, and the second predicts protein sequences that bind a given ligand. In these tests, subtle conformational changes made in our model are essential to recapitulate both the results from engineering experiments and the sequence diversity occurring in natural protein families. These results therefore shed light on the mechanisms of how new protein functions might have emerged and can be engineered in the laboratory.

Computational strategies for the design of new enzymatic functions

In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and Retro-Aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies.

Computational design and experimental verification of a symmetric protein homodimer

Homodimers are the most common type of protein assembly in nature and have distinct features compared with heterodimers and higher order oligomers. Understanding homodimer interactions at the atomic level is critical both for elucidating their biological mechanisms of action and for accurate modeling of complexes of unknown structure. Computation-based design of novel protein-protein interfaces can serve as a bottom-up method to further our understanding of protein interactions. Previous studies have demonstrated that the de novo design of homodimers can be achieved to atomic-level accuracy by β-strand assembly or through metal-mediated interactions. Here, we report the design and experimental characterization of a α-helix-mediated homodimer with C2 symmetry based on a monomeric Drosophila engrailed homeodomain scaffold. A solution NMR structure shows that the homodimer exhibits parallel helical packing similar to the design model. Because the mutations leading to dimer formation resulted in poor thermostability of the system, design success was facilitated by the introduction of independent thermostabilizing mutations into the scaffold. This two-step design approach, function and stabilization, is likely to be generally applicable, especially if the desired scaffold is of low thermostability.

Emerging Vaccine Technologies

Vaccination has proven to be an invaluable means of preventing infectious diseases by reducing both incidence of disease and mortality. However, vaccines have not been effectively developed for many diseases including HIV-1, hepatitis C virus (HCV), tuberculosis and malaria, among others. The emergence of new technologies with a growing understanding of host-pathogen interactions and immunity may lead to efficacious vaccines against pathogens, previously thought impossible.

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.

Computational design of a red fluorophore ligase for site-specific protein labeling in living cells

Chemical fluorophores offer tremendous size and photophysical advantages over fluorescent proteins but are much more challenging to target to specific cellular proteins. Here, we used Rosetta-based computation to design a fluorophore ligase that accepts the red dye resorufin, starting from Escherichia coli lipoic acid ligase. X-ray crystallography showed that the design closely matched the experimental structure. Resorufin ligase catalyzed the site-specific and covalent attachment of resorufin to various cellular proteins genetically fused to a 13-aa recognition peptide in multiple mammalian cell lines and in primary cultured neurons. We used resorufin ligase to perform superresolution imaging of the intermediate filament protein vimentin by stimulated emission depletion and electron microscopies. This work illustrates the power of Rosetta for major redesign of enzyme specificity and introduces a tool for minimally invasive, highly specific imaging of cellular proteins by both conventional and superresolution microscopies.

Structural and dynamic changes associated with beneficial engineered single-amino-acid deletion mutations in enhanced green fluorescent protein

Single-amino-acid deletions are a common part of the natural evolutionary landscape but are rarely sampled during protein engineering owing to limited and prejudiced molecular understanding of mutations that shorten the protein backbone. Single-amino-acid deletion variants of enhanced green fluorescent protein (EGFP) have been identified by directed evolution with the beneficial effect of imparting increased cellular fluorescence. Biophysical characterization revealed that increased functional protein production and not changes to the fluorescence parameters was the mechanism that was likely to be responsible. The structure EGFP(D190Δ) containing a deletion within a loop revealed propagated changes only after the deleted residue. The structure of EGFP(A227Δ) revealed that a `flipping' mechanism was used to adjust for residue deletion at the end of a β-strand, with amino acids C-terminal to the deletion site repositioning to take the place of the deleted amino acid. In both variants new networks of short-range and long-range interactions are generated while maintaining the integrity of the hydrophobic core. Both deletion variants also displayed significant local and long-range changes in dynamics, as evident by changes in B factors compared with EGFP. Rather than being detrimental, deletion mutations can introduce beneficial structural effects through altering core protein properties, folding and dynamics, as well as function.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

A highly efficient cocaine-detoxifying enzyme obtained by computational design

Compared to naturally occurring enzymes, computationally designed enzymes are usually much less efficient, with their catalytic activities being more than six orders of magnitude below the diffusion limit. Here we use a two-step computational design approach, combined with experimental work, to design a highly efficient cocaine hydrolising enzyme. We engineer E30-6 from human butyrylcholinesterase (BChE), which is specific for cocaine hydrolysis, and obtain a much higher catalytic efficiency for cocaine conversion than for conversion of the natural BChE substrate, acetylcholine (ACh). The catalytic efficiency of E30-6 for cocaine hydrolysis is comparable to that of the most efficient known naturally-occurring hydrolytic enzyme, acetylcholinesterase, the catalytic activity of which approaches the diffusion limit. We further show that E30-6 can protect mice from a subsequently administered lethal dose of cocaine, suggesting the enzyme may have therapeutic potential in the setting of cocaine detoxification or cocaine abuse.