A Piecewise Design Approach to Engineering a Miniature ACE2 Mimic to Bind SARS-CoV-2

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues its global spread, the exploration of novel therapeutic and diagnostic strategies is still needed. The virus enters host cells by binding the angiotensin-converting enzyme 2 (ACE2) receptor through the spike protein. Here, we develop an engineered, small, stable, and catalytically inactive version of ACE2, termed miniature ACE2 (mACE2), designed to bind the spike protein with high affinity. Employing a magnetic nanoparticle-based assay, we harnessed the strong binding affinity of mACE2 to develop a sensitive and specific platform for the detection or neutralization of SARS-CoV-2. Our findings highlight the potential of engineered mACE2 as a valuable tool in the fight against SARS-CoV-2. The success of developing such a small reagent based on a piecewise molecular design serves as a proof-of-concept approach for the rapid deployment of such agents to diagnose and fight other viral diseases.

Rational design of MIPs for the detection of Myxovirus resistance protein A (MxA), a biomarker for viral infection

Accurate diagnosis is crucial for effective patient care and the containment of antimicrobial resistance outbreaks. The intricate challenge of distinguishing bacterial from viral infections, coupled with limited diagnostic tools and overlapping symptoms has driven the utilization of molecular imprinting techniques. This study focuses on developing cost-effective, chemically stable antibody analogs for the interferon-induced protein myxovirus resistance protein A (MxA). MxA is an intracellular, cytoplasmic GTPase having activity against a wide range of viruses and serves as a distinctive biomarker for viral infections. We utilized computational design to guide the polymer assembly, centering on epitope imprinting to target MxA-specific regions crucial for interaction. Molecular docking calculations, alongside a pioneering multi-monomer simultaneous docking (MMSD) protocol, efficiently elucidate cooperativity during pre-polymerization. Monomer binding affinity scores, such as for APTMS, exhibited notable increase, ranging from -3.11 to -13.03 kcal/mol across various MMSD combinations compared to a maximum of -2.78 kcal/mol in single monomer docking, highlighting the capacity of MMSD in elucidating crucial monomer-monomer interactions. This computational approach provides a theoretical alternative to labor-intensive experimental optimization, streamlining the development process for synthetic receptors. Simulations reveal unique interactions enhancing MIP-peptide complementarity, yielding optimized receptors selectively binding to MxA epitopes. The obtained MIPs demonstrated a maximum adsorption capacity of approximately 12 mg/g and captured 1.6 times more epitope and 2.6 times more epitope containing MxA protein than corresponding NIPs. A proof-of-concept study demonstrates MxA protein binding to synthetic receptors, highlighting the potential of MIPs, analogous to antibodies, in overcoming current diagnostic challenges for precise detection of viral infection.

Multi-strategy orthogonal enhancement and analysis of aldo-keto reductase thermal stability

Given their outstanding efficiency and selectivity, enzymes are integral in various domains such as drug synthesis, the food industry, and environmental management. However, the inherent instability of natural enzymes limits their widespread industrial application. In this study, we underscore the efficacy of enhancing protein thermal stability through comprehensive protein design strategies, encompassing elements such as the free energy of protein folding, internal forces within proteins, and the overall structural design. We also demonstrate the efficiency and precision of combinatorial screening in the thermal stability design of aldo-keto reductase (AKR7-2-1). In our research, three single-point mutations and five combinatorial mutations were strategically introduced into AKR7-2-1, using multiple computational techniques. Notably, the E12I/S235I mutant showed a significant increase of 25.4 °C in its melting temperature (Tm). Furthermore, the optimal mutant, E12V/S235I, maintained 80 % of its activity while realizing a 16.8 °C elevation in Tm. Remarkably, its half-life at 50 °C was increased to twenty times that of the wild type. Structural analysis indicates that this enhanced thermal stability primarily arises from reduced oscillation in the loop region and increased internal hydrogen bonding. The promising results achieved with AKR7-2-1 demonstrate that our strategy could serve as a valuable reference for enhancing the thermal stability of other industrial enzymes.

Computational design and engineering of self-assembling multivalent microproteins with therapeutic potential against SARS-CoV-2

Multivalent drugs targeting homo-oligomeric viral surface proteins, such as the SARS-CoV-2 trimeric spike (S) protein, have the potential to elicit more potent and broad-spectrum therapeutic responses than monovalent drugs by synergistically engaging multiple binding sites on viral targets. However, rational design and engineering of nanoscale multivalent protein drugs are still lacking. Here, we developed a computational approach to engineer self-assembling trivalent microproteins that simultaneously bind to the three receptor binding domains (RBDs) of the S protein. This approach involves four steps: structure-guided linker design, molecular simulation evaluation of self-assembly, experimental validation of self-assembly state, and functional testing. Using this approach, we first designed trivalent constructs of the microprotein miniACE2 (MP) with different trimerization scaffolds and linkers, and found that one of the constructs (MP-5ff) showed high trimerization efficiency, good conformational homogeneity, and strong antiviral neutralizing activity. With its trimerization unit (5ff), we then engineered a trivalent nanobody (Tr67) that exhibited potent and broad neutralizing activity against the dominant Omicron variants, including XBB.1 and XBB.1.5. Cryo-EM complex structure confirmed that Tr67 stably binds to all three RBDs of the Omicron S protein in a synergistic form, locking them in the "3-RBD-up" conformation that could block human receptor (ACE2) binding and potentially facilitate immune clearance. Therefore, our approach provides an effective strategy for engineering potent protein drugs against SARS-CoV-2 and other deadly coronaviruses.

Improvement of α-amino Ester Hydrolase Stability via Computational Protein Design

Amino ester hydrolases (AEHs) are capable of rapid synthesis of cephalexin but suffer from rapid deactivation even at low temperatures. Previous efforts to engineer AEH have generated several improved variants but have been limited in scope in part due to limitations in activity assay throughput for β-lactam synthesis reactions. Rational design of 'whole variants' was explored to rapidly improve AEH thermostability by mutating between 3-15% of residues. Most variants were found to be inactive due to a mutated calcium binding site, the function of which has not previously been described. Four active variants, all with improved melting temperatures, were characterized in terms of synthesis and hydrolysis activity, melting temperature, and deactivation at 25°C. Two variants were found to have improved total turnover numbers relative to the initial AEH variant; however, a clear tradeoff exists between improved stability and overall activity of each variant.

A sweeter future: Using protein language models for exploring sweeter brazzein homologs

With growing concerns over the health impact of sugar, brazzein offers a viable alternative due to its sweetness, thermostability, and low risk profile. Here, we demonstrated the ability of protein language models to design new brazzein homologs with improved thermostability and potentially higher sweetness, resulting in new diverse optimized amino acid sequences that improve structural and functional features beyond what conventional methods could achieve. This innovative approach resulted in the identification of unexpected mutations, thereby generating new possibilities for protein engineering. To facilitate the characterization of the brazzein mutants, a simplified procedure was developed for expressing and analyzing related proteins. This process involved an efficient purification method using Lactococcus lactis (L. lactis), a generally recognized as safe (GRAS) bacterium, as well as taste receptor assays to evaluate sweetness. The study successfully demonstrated the potential of computational design in producing a more heat-resistant and potentially more palatable brazzein variant, V23.

Identification, characterization, and engineering of glycosylation in thrombolyticsa

Cardiovascular diseases, such as myocardial infarction, ischemic stroke, and pulmonary embolism, are the most common causes of disability and death worldwide. Blood clot hydrolysis by thrombolytic enzymes and thrombectomy are key clinical interventions. The most widely used thrombolytic enzyme is alteplase, which has been used in clinical practice since 1986. Another clinically used thrombolytic protein is tenecteplase, which has modified epitopes and engineered glycosylation sites, suggesting that carbohydrate modification in thrombolytic enzymes is a viable strategy for their improvement. This comprehensive review summarizes current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modification of glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of this engineering strategy. Outstanding combinatorics originating from multiple glycosylation sites and the vast variety of covalently attached glycan species can be addressed by directed evolution or rational design. Directed evolution pipelines would benefit from more efficient cell-free expression and high-throughput screening assays, while rational design must employ structure prediction by machine learning and in silico characterization by supercomputing. Perspectives on challenges and opportunities for improvement of thrombolytic enzymes by engineering and evolution of protein glycosylation are provided.

Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops)

Antibodies and T-cell receptors have been a subject of much interest due to their central role in the immune system and their potential applications in several biotechnological and medical applications from cancer therapy to vaccine development. A unique feature of these two lymphocyte receptors is their ability to bind a huge variety of different (pathogen) targets. This ability stems from six short loops in the binding domain that have hypervariable sequence due to genetic recombination mechanism. Particularly one of these loops, the third complementarity determining region (CDR3), has the highest sequence variability and a dominant role in binding the target. However, it has also been proven the most difficult to be modeled structurally, which is vitally important for downstream tasks such as binding prediction. This difficulty stems from its variability in sequence that both reduces the possibility of finding homologues and introduces unique structural features in the loop. We present here a general protocol for modeling such loops in antibodies and T-cell receptors. We also discuss the difficulties in loop modeling and the advantages and limitations of different modeling methods.

Reassembly Design of Ferritin Cages

The ferritin family is distributed in nearly all organisms and protects them from iron-induced oxidative damage. Besides, its highly symmetrical structure and biochemical features make it an appealing material for biotechnological applications, such as building blocks for multidimensional assembly, templates for nano-reactors, and scaffolds for encapsulation and delivery of nutrients and drugs. Moreover, it is of great significance to construct ferritin variants with different properties, size, and shape to further broaden its application. In this chapter, we present a routine process of the ferritin redesign and the characterization method of the protein structure to provide a feasible scheme.

A computational design of a programmable biological processor

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

Prevention of hospital pathogen biofilm formation by antimicrobial peptide KWI18

This study investigated the antimicrobial and antibiofilm activity of KWI18, a new synthetic peptide. KWI18 was tested against planktonic cells and Pseudomonas aeruginosa and Candida parapsilosis biofilms. Time-kill and synergism assays were performed. Sorbitol, ergosterol, lipid peroxidation, and protein oxidation assays were used to gain insight into the mechanism of action of the peptide. Toxicity was evaluated against erythrocytes and Galleria mellonella. KWI18 showed antimicrobial activity, with minimum inhibitory concentration (MIC) values ranging from 0.5 to 10 μM. KWI18 at 10 × MIC reduced P. aeruginosa and C. parapsilosis biofilm formation and cell viability. Time-kill assays revealed that KWI18 inhibited the growth of P. aeruginosa in 4 h and that of C. parapsilosis in 6 h. The mechanism of action was related to ergosterol as well as induction of oxidative damage in cells and biofilms. Furthermore, KWI18 demonstrated low toxicity to erythrocytes and G. mellonella. KWI18 proved to be an effective antibiofilm agent, opening opportunities for the development of new antimicrobials.

Colloid-like solution behavior of computationally designed coiled coil bundlemers

The use of isotropic potential models of simple colloids for describing complex protein-protein interactions is a topic of ongoing debate in the biophysical community. This contention stems from the unavailability of synthetic protein-like model particles that are amenable to systematic experimental characterization. In this article, we test the utility of colloidal theory to capture the solution structure, interactions and dynamics of novel globular protein-mimicking, computationally designed peptide assemblies called bundlemers that are programmable model systems at the intersection of colloids and proteins. Small-angle neutron scattering (SANS) measurements of semi-dilute bundlemer solutions in low and high ionic strength solution indicate that bundlemers interact locally via repulsive interactions that can be described by a screened repulsive potential. We also present neutron spin echo (NSE) spectroscopy results that show high-Q freely-diffusive dynamics of bundlemers. Importantly, formation of clusters due to short-range attractive, inter-bundlemer interactions is observed in SANS even at dilute bundlemer concentrations, which is indicative of the complexity of the bundlemer charged surface. The similarities and differences between bundlemers and simple colloidal as well as complex protein-protein interactions is discussed in detail.

Computational Identification and Design of Complementary β-Strand Sequences

The ß-sheet is a regular secondary structure element which consists of linear segments called ß-strands. They are involved in many important biological processes, and some are known to be related to serious diseases such as neurologic disorders and amyloidosis. The self-assembly of ß-sheet peptides also has practical applications in material sciences since they can be building blocks of repeated nanostructures. Therefore, computational algorithms for identification of ß-sheet formation can offer useful insight into the mechanism of disease-prone protein segments and the construction of biocompatible nanomaterials. Despite the recent advances in structure-based methods for the assessment of atomic interactions, identifying amyloidogenic peptides has proven to be extremely difficult since they are structurally very flexible. Thus, an alternative strategy is required to describe ß-sheet formation. It has been hypothesized and observed that there are certain amino acid propensities between ß-strand pairs. Based on this hypothesis, a database search algorithm, B-SIDER, is developed for the identification and design of ß-sheet forming sequences. Given a target sequence, the algorithm identifies exact or partial matches from the structure database and constructs a position-specific score matrix. The score matrix can be utilized to design novel sequences that can form a ß-sheet specifically with the target.

Computational Design of Structured and Functional Peptide Macrocycles

Structure-based computational design methods have been developed to create proteins in silico with diverse shapes and sizes that accurately fold in vitro, from 7-residue macrocycles to megadalton-scale self-assembling nanomaterials. Precise control over protein shape has further enabled design and optimization of functional therapeutic proteins, including agonists, antagonists, enzymes, and vaccines. Computational design of functional peptides of smaller size presents a persistent challenge, with few successful examples to date. Herein we describe validated general methods for computational design of peptides using the Rosetta molecular modeling suite and discuss outstanding challenges and future directions.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Anion solvation enhanced by positive supercharging mutations preserves thermal stability of an antibody in a wide pH range

Proteins function through interactions with other molecules. In protein engineering, scientists often engineer proteins by mutating their amino acid sequences on the protein surface to improve various physicochemical properties. "Supercharging" is a method to design proteins by mutating surface residues with charged amino acids. Previous studies demonstrated that supercharging mutations conferred better thermal resistance, solubility, and cell penetration to proteins. Likewise, antibodies recognize antigens through the antigen-binding site on the surface. The genetic and structural diversity of antibodies leads to high specificity and affinity toward antigens, enabling antibodies to be versatile tools in various applications. When assessing therapeutic antibodies, surface charge is an important factor to consider because the isoelectric point plays a role in protein clearance inside the body. In this study, we explored how supercharging mutations affect physicochemical properties of antibodies. Starting from a crystal structure of an antibody with the net charge of -4, we computationally designed a supercharged variant possessing the net charge of +10. The positive-supercharged antibody exhibited marginal improvement in thermal stability, but the secondary structure and the binding affinity to the antigen (net charge of +8) were preserved. We also used physicochemical measurements and molecular dynamics simulations to analyze the effects of supercharging mutations in sodium phosphate buffer with different pH and ion concentrations, which revealed preferential solvation of phosphate ions to the supercharged surface relative to the wild-type surface. These results suggest that supercharging would be a useful approach to preserving thermal stability of antibodies in a wide range of pH, which may enable further diversification of antibody repertoires beyond natural evolution.

Designing of a Novel Fusion Protein Vaccine Candidate Against Human Visceral Leishmaniasis (VL) Using Immunoinformatics and Structural Approaches

Leishmaniasis is caused by an obligate intracellular protozoan parasite. The clinical forms of leishmaniasis differ from cutaneous leishmaniasis, mucocutaneous leishmaniasis and visceral leishmaniasis (VL) which depend on the parasite species and the host’s immune responses. There are significant challenges to the available anti-leishmanial drug therapy, particularly in severe forms of disease, and the rise of drug resistance has made it more difficult. Currently, no licensed vaccines have been introduced to the market for the control and elimination of VL. A potential target for use in candidate vaccines against leishmaniasis has been shown to be leishmania Kinetoplastid membrane protein-11 (KMP-11) antigen. In this study, we chose KMP-11 antigen as target antigen in our vaccine construct. In addition, B-type flagellin (fliC) was used as an adjuvant for enhancing vaccine immunogenicity. The GSGSGSGSGSG linker was applied to link the KMP-11 antigen and fliC (KMP-11-fliC) to construct our fusion protein. Bioinformatics approaches such as; 3D homology modeling, CTL, B-cell, MHC class I and II epitopes prediction, allergenicity, antigenicity evaluations, molecular docking, fast simulations of flexibility of docked complex and in silico cloning were employed to analysis and evaluation of various properties of the designed fusion construct. Computational results showed that our engineered structure has the potential for proper stimulation of cellular and humoral immune responses against VL. Consequently, it could be proposed as a candidate vaccine against VL according to these data and after verifying the efficacy of the candidate vaccine through in vivo and in vitro immunological tests.

Recombinant expression of computationally designed peptide-bundlemers in Escherichia coli

Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.

Computational design of highly stable and soluble alcohol dehydrogenase for NADPH regeneration

Nicotinamide adenine dinucleotide phosphate (NADPH), as a well-known cofactor, is widely used in the most of enzymatic redox reactions, playing an important role in industrial catalysis. However, the absence of a comparable method for efficient NADP+ to NADPH cofactor regeneration radically impairs efficient green chemical synthesis. Alcohol dehydrogenase (ADH) enzymes, allowing the in situ regeneration of the redox cofactor NADPH with high specific activity and easy by-product separation process, are provided with great industrial application potential and research attention. Accordingly, herein a NADP+-specific ADH from Clostridium beijerinckii was selected to be engineered for cofactor recycle, using an automated algorithm named Protein Repair One-stop Shop (PROSS). The mutant CbADH-6M (S24P/G182A/G196A/H222D/S250E/S254R) exhibited a favorable soluble and highly active expression with an activity of 46.3 U/mL, which was 16 times higher than the wild type (2.9 U/mL), and a more stable protein conformation with an enhanced thermal stability: Δ T 1 / 2 60 min =  + 3.6 °C (temperature of 50% inactivation after incubation for 60 min). Furthermore, the activity of CbADH-6M was up-graded to 2401.8 U/mL by high cell density fermentation strategy using recombinant Escherichia coli, demonstrating its industrial potential. Finally, the superb efficiency for NADPH regeneration of the mutant enzyme was testified in the synthesis of some fine chiral aromatic alcohols coupling with another ADH from Lactobacillus kefir (LkADH).

GRAPE, a greedy accumulated strategy for computational protein engineering

Nature harbors fascinating enzymatic catalysts with high efficiency, chemo-, regio- and stereoselectivity. However, the insufficient stability of the enzymes often prevents their widespread utilization for industrial processes. Not content with the finite repertoire of naturally occurring enzymes, protein engineering holds promises to extend the applications of the improved enzymes with desired physical and catalytic properties. Herein, we devised a computational strategy (greedy accumulated strategy for protein engineering, GRAPE) to enhance the thermostability of enzymes. Through scanning of all point mutations of the structural and evolutionary consensus analysis, a library containing fewer than 100 mutations was established for characterization. After preliminary experimental verification, effective mutations are clustered in a multidimensional physical property space and then accumulated via the greedy algorithm to produce the final designed enzyme. Using the recently reported IsPETase from Ideonella sakaiensis that decomposes PET under ambient temperatures as a starting point, we adopted the GRAPE strategy to come up with a DuraPETase (T_M=77°C, raised by 31°C) which showed drastically enhanced degradation performance (300-fold) on semicrystalline PET films at 40°C.

A phenylalanine dynamic switch controls the interfacial activation of Rhizopus chinensis lipase

The catalytic mechanism of most lipases involves a step called "interfacial activation" which significantly increases lipases activity beyond the critical micellar concentration (CMC) of substrate. In the present study, Rhizopus chinensis lipase (RCL) was used as a research model to explore the mechanism of lipase interfacial activation beyond the CMC. Molecular dynamic (MD) simulations indicated the open- and closed-lid transitions and revealed that Phe113 was the critical site for RCL activation by its dynamic flipping. Such putative switch affecting interfacial activation has not been reported in lipase so far. The function of Phe113 was subsequently verified by mutation experiments. The F113W mutant increases the lipase catalytic efficiency (1.9 s^-1·μM^-1) to 280% at the optimum temperature (40 °C) and pH 8.5 with the addition of 0.12 μg protein in the 200 μL reaction system. MD simulations indicated that the fast flipping rate from the closed to the open state, the high open state proportion, and the exposure of the catalytic triad are the main reasons for the lipase activation. The mutual corroboration of simulations and site-directed mutagenesis results revealed the vital role of Phe113 in the lipase activation.

A metabolic and physiological design study of Pseudomonas putida KT2440 capable of anaerobic respiration

BACKGROUND

Pseudomonas putida KT2440 is a metabolically versatile, HV1-certified, genetically accessible, and thus interesting microbial chassis for biotechnological applications. However, its obligate aerobic nature hampers production of oxygen sensitive products and drives up costs in large scale fermentation. The inability to perform anaerobic fermentation has been attributed to insufficient ATP production and an inability to produce pyrimidines under these conditions. Addressing these bottlenecks enabled growth under micro-oxic conditions but does not lead to growth or survival under anoxic conditions.

RESULTS

Here, a data-driven approach was used to develop a rational design for a P. putida KT2440 derivative strain capable of anaerobic respiration. To come to the design, data derived from a genome comparison of 1628 Pseudomonas strains was combined with genome-scale metabolic modelling simulations and a transcriptome dataset of 47 samples representing 14 environmental conditions from the facultative anaerobe Pseudomonas aeruginosa.

CONCLUSIONS

The results indicate that the implementation of anaerobic respiration in P. putida KT2440 would require at least 49 additional genes of known function, at least 8 genes encoding proteins of unknown function, and 3 externally added vitamins.

Computational design of noncanonical amino acid-based thioether staples at N/C-terminal domains of multi-modular pullulanase for thermostabilization in enzyme catalysis

Enzyme thermostabilization is considered a critical and often obligatory step in biosynthesis, because thermostability is a significant property of enzymes that can be used to evaluate their feasibility for industrial applications. However, conventional strategies for thermostabilizing enzymes generally introduce non-covalent interactions and/or natural covalent bonds caused by natural amino acid substitutions, and the trade-off between the activity and stability of enzymes remains a challenge. Here, we developed a computationally guided strategy for constructing thioether staples by incorporating noncanonical amino acid (ncAA) into the more flexible N/C-terminal domains of the multi-modular pullulanase from Bacillus thermoleovorans (BtPul) to enhance its thermostability. First, potential thioether staples located in the N/C-terminal domains of BtPul were predicted using RosettaMatch. Next, eight variants involving stable thioether staples were precisely predicted using FoldX and Rosetta ddg_monomer. Six positive variants were obtained, of which T73(O2beY)-171C had a 157% longer half-life at 70 °C and an increase of 7.0 °C in T m, when compared with the wild-type (WT). T73(O2beY)-171C/T126F/A72R exhibited an even more improved thermostability, with a 211% increase in half-life at 70 °C and a 44% enhancement in enzyme activity compared with the WT, which was attributed to further optimization of the local interaction network. This work introduces and validates an efficient strategy for enhancing the thermostability and activity of multi-modular enzymes.

Using Molecular Dynamics in the Study of Molecularly Imprinted Polymers

Molecular dynamics (MD) simulations of prepolymerization mixtures can provide detailed insights concerning the molecular-level mechanisms underlying the performance of molecularly imprinted polymers (MIPs) and can be used for the in silico screening of candidate polymer systems. Here, we describe the use of MD simulations of all-atom, all-component MIP prepolymerization mixtures and procedures for the evaluation of the simulation data using the Amber simulation software suite.

Challenges on optimization of 3D-printed bone scaffolds

Advances in biomaterials and the need for patient-specific bone scaffolds require modern manufacturing approaches in addition to a design strategy. Hybrid materials such as those with functionally graded properties are highly needed in tissue replacement and repair. However, their constituents, proportions, sizes, configurations and their connection to each other are a challenge to manufacturing. On the other hand, various bone defect sizes and sites require a cost-effective readily adaptive manufacturing technique to provide components (scaffolds) matching with the anatomical shape of the bone defect. Additive manufacturing or three-dimensional (3D) printing is capable of fabricating functional physical components with or without porosity by depositing the materials layer-by-layer using 3D computer models. Therefore, it facilitates the production of advanced bone scaffolds with the feasibility of making changes to the model. This review paper first discusses the development of a computer-aided-design (CAD) approach for the manufacture of bone scaffolds, from the anatomical data acquisition to the final model. It also provides information on the optimization of scaffold's internal architecture, advanced materials, and process parameters to achieve the best biomimetic performance. Furthermore, the review paper describes the advantages and limitations of 3D printing technologies applied to the production of bone tissue scaffolds.

Computation-aided engineering of starch-debranching pullulanase from Bacillus thermoleovorans for enhanced thermostability

Pullulanases are widely used in food, medicine, and other industries because they specifically hydrolyze α-1,6-glycosidic linkages in starch and oligosaccharides. In addition, high-temperature thermostable pullulanase has multiple advantages, including decreasing saccharification solution viscosity accompanied with enhanced mass transfer and reducing microbial contamination in starch hydrolysis. However, thermophilic pullulanase availability remains limited. Additionally, most do not meet starch-manufacturing requirements due to weak thermostability. Here, we developed a computation-aided strategy to engineer the thermophilic pullulanase from Bacillus thermoleovorans. First, three computational design predictors (FoldX, I-Mutant 3.0, and dDFIRE) were combined to predict stability changes introduced by mutations. After excluding conserved and catalytic sites, 17 mutants were identified. After further experimental verification, we confirmed six positive mutants. Among them, the G692M mutant had the highest thermostability improvement, with 3.8 °C increased T_m and 2.1-fold longer half-life than the wild type at 70 °C. We then characterized the mechanism underlying increased thermostability, such as rigidity enhancement, closer conformation, and strengthened motion correlation using root mean square fluctuation (RMSF), principal component analysis (PCA), dynamic cross-correlation map (DCCM), and free energy landscape (FEL) analysis. KEY POINTS: • A computation-aided strategy was developed to engineer pullulanase thermostability. • Seventeen mutants were identified by combining three computational design predictors. • The G692M mutant was obtained with increased T_mand half-life at 70 °C.

A Three-Dimensional Model of Human Lung Airway Tree to Study Therapeutics Delivery in the Lungs

Surfactant instillation into the lungs is used to treat several respiratory disorders such as neonatal respiratory distress syndrome (NRDS). The success of the treatments significantly depends on the uniformity of distribution of the instilled surfactant in airways. This is challenging to directly evaluate due to the inaccessibility of lung airways and great difficulty with imaging them. To tackle this problem, we developed a 3D physical model of human lung airway tree. Using a defined set of principles, we first generated computational models of eight generations of neonates' tracheobronchial tree comprising the conducting zone airways. Similar to native lungs, these models contained continuously-branching airways that rotated in the 3D space and reduced in size with increase in the generation number. Then, we used additive manufacturing to generate physical airway tree models that precisely replicated the computational designs. We demonstrated the utility of the physical models to study surfactant delivery in the lungs and showed the effect of orientation of the airway tree in the gravitational field on the distribution of instilled surfactant between the left and right lungs and within each lung. Our 3D lung airway tree model offers a novel tool for quantitative studies of therapeutics delivery.

Significant expansion and red-shifting of fluorescent protein chromophore determined through computational design and genetic code expansion

ABSTRACT

Fluorescent proteins (FPs) with emission wavelengths in the far-red and infrared regions of the spectrum provide powerful tools for deep-tissue and super-resolution imaging. The development of red-shifted FPs has evoked widespread interest and continuous engineering efforts. In this article, based on a computational design and genetic code expansion, we report a rational approach to significantly expand and red-shift the chromophore of green fluorescent protein (GFP). We applied computational calculations to predict the excitation and emission wavelengths of a FP chromophore harboring unnatural amino acids (UAA) and identify in silico an appropriate UAA, 2-amino-3-(6-hydroxynaphthalen-2-yl)propanoic acid (naphthol-Ala). Our methodology allowed us to formulate a GFP variant (cpsfGFP-66-Naphthol-Ala) with red-shifted absorbance and emission spectral maxima exceeding 60 and 130 nm, respectively, compared to those of GFP. The GFP chromophore is formed through autocatalytic post-translational modification to generate a planar 4-(p-hydroxybenzylidene)-5-imidazolinone chromophore. We solved the crystal structure of cpsfGFP-66-naphthol-Ala at 1.3 Å resolution and demonstrated the formation of a much larger conjugated π-system when the phenol group is replaced by naphthol. These results explain the significant red-shifting of the excitation and emission spectra of cpsfGFP-66-naphthol-Ala.

GRAPHICAL ABSTRACT

Tailored Mesoporous Silica Nanoparticles for Controlled Drug Delivery: Platform Fabrication, Targeted Delivery, and Computational Design and Analysis

Mesoporous silica nanoparticles (MSNs) are exceptionally promising drug carriers for controlled drug delivery systems because their morphology, pore structure, pore volume and pore size can be well tailored to obtain certain drug release profiles. Moreover, they possess the ability to specifically transport and deliver anti-cancer drugs when targeting molecules are properly grafted onto their surface. MSNs based drug delivery systems have the potential to revolutionize cancer therapy. This review provides a comprehensive overview of the fabrication, modification of MSNs and their applications in tumour-targeted delivery. In addition, the characterization and analysis of MSNs with computer aided strategies were described. The existing issues and future prospective concerning the applications of MSNs as drug carriers for controlled drug delivery systems were discussed.

Creation of artificial protein-protein interactions using α-helices as interfaces

Designing novel protein-protein interactions (PPIs) with high affinity is a challenging task. Directed evolution, a combination of randomization of the gene for the protein of interest and selection using a display technique, is one of the most powerful tools for producing a protein binder. However, the selected proteins often bind to the target protein at an undesired surface. More problematically, some selected proteins bind to their targets even though they are unfolded. Current state-of-the-art computational design methods have successfully created novel protein binders. These computational methods have optimized the non-covalent interactions at interfaces and thus produced artificial protein complexes. However, to date there are only a limited number of successful examples of computationally designed de novo PPIs. De novo design of coiled-coil proteins has been extensively performed and, therefore, a large amount of knowledge of the sequence-structure relationship of coiled-coil proteins has been accumulated. Taking advantage of this knowledge, de novo design of inter-helical interactions has been used to produce artificial PPIs. Here, we review recent progress in the in silico design and rational design of de novo PPIs and the use of α-helices as interfaces.

A Computational Library Design Protocol for Rapid Improvement of Protein Stability: FRESCO

The ability to stabilize enzymes and other proteins has wide-ranging applications. Most protocols for enhancing enzyme stability require multiple rounds of high-throughput screening of mutant libraries and provide only modest improvements of stability. Here, we describe a computational library design protocol that can increase enzyme stability by 20-35 °C with little experimental screening, typically fewer than 200 variants. This protocol, termed FRESCO, scans the entire protein structure to identify stabilizing disulfide bonds and point mutations, explores their effect by molecular dynamics simulations, and provides mutant libraries with variants that have a good chance (>10%) to exhibit enhanced stability. After experimental verification, the most effective mutations are combined to produce highly robust enzymes.

Hierarchical design of artificial proteins and complexes toward synthetic structural biology

In multiscale structural biology, synthetic approaches are important to demonstrate biophysical principles and mechanisms underlying the structure, function, and action of bio-nanomachines. A central goal of "synthetic structural biology" is the design and construction of artificial proteins and protein complexes as desired. In this paper, I review recent remarkable progress of an array of approaches for hierarchical design of artificial proteins and complexes that signpost the path forward toward synthetic structural biology as an emerging interdisciplinary field. Topics covered include combinatorial and protein-engineering approaches for directed evolution of artificial binding proteins and membrane proteins, binary code strategy for structural and functional de novo proteins, protein nanobuilding block strategy for constructing nano-architectures, protein-metal-organic frameworks for 3D protein complex crystals, and rational and computational approaches for design/creation of artificial proteins and complexes, novel protein folds, ideal/optimized protein structures, novel binding proteins for targeted therapeutics, and self-assembling nanomaterials. Protein designers and engineers look toward a bright future in synthetic structural biology for the next generation of biophysics and biotechnology.

Harnessing self-assembled peptide nanoparticles in epitope vaccine design

Vaccination has been one of the most successful breakthroughs in medical history. In recent years, epitope-based subunit vaccines have been introduced as a safer alternative to traditional vaccines. However, they suffer from limited immunogenicity. Nanotechnology has shown value in solving this issue. Different kinds of nanovaccines have been employed, among which virus-like nanoparticles (VLPs) and self-assembled peptide nanoparticles (SAPNs) seem very promising. Recently, SAPNs have attracted special interest due to their unique properties, including molecular specificity, biodegradability, and biocompatibility. They also resemble pathogens in terms of their size. Their multivalency allows an orderly repetitive display of antigens on their surface, which induces a stronger immune response than single immunogens. In vaccine design, SAPN self-adjuvanticity is regarded an outstanding advantage, since the use of toxic adjuvants is no longer required. SAPNs are usually composed of helical or β-sheet secondary structures and are tailored from natural peptides or de novo structures. Flexibility in subunit selection opens the door to a wide variety of molecules with different characteristics. SAPN engineering is an emerging area, and more novel structures are expected to be generated in the future, particularly with the rapid progress in related computational tools. The aim of this review is to provide a state-of-the-art overview of self-assembled peptide nanoparticles and their use in vaccine design in recent studies. Additionally, principles for their design and the application of computational approaches to vaccine design are summarized.

Modeling and Design of Peptidomimetics to Modulate Protein-Protein Interactions

We describe a modular approach to identify and inhibit protein-protein interactions (PPIs) that are mediated by protein secondary and tertiary structures with rationally designed peptidomimetics. Our analysis begins with entries of high-resolution complexes in the Protein Data Bank and utilizes conformational sampling, scoring, and design capabilities of advanced biomolecular modeling software to develop peptidomimetics.

Coiled-Coil Design: Updated and Upgraded

α-Helical coiled coils are ubiquitous protein-folding and protein-interaction domains in which two or more α-helical chains come together to form bundles. Through a combination of bioinformatics analysis of many thousands of natural coiled-coil sequences and structures, plus empirical protein engineering and design studies, there is now a deep understanding of the sequence-to-structure relationships for this class of protein architecture. This has led to considerable success in rational design and what might be termed in biro de novo design of simple coiled coils, which include homo- and hetero-meric parallel dimers, trimers and tetramers. In turn, these provide a toolkit for directing the assembly of both natural proteins and more complex designs in protein engineering, materials science and synthetic biology. Moving on, the increased and improved use of computational design is allowing access to coiled-coil structures that are rare or even not observed in nature, for example α-helical barrels, which comprise five or more α-helices and have central channels into which different functions may be ported. This chapter reviews all of these advances, outlining improvements in our knowledge of the fundamentals of coiled-coil folding and assembly, and highlighting new coiled coil-based materials and applications that this new understanding is opening up. Despite considerable progress, however, challenges remain in coiled-coil design, and the next decade promises to be as productive and exciting as the last.

Computational Sequence Design with R2oDNA Designer

Recently developed DNA assembly methods have enabled the rapid and simultaneous assembly of multiple parts to create complex synthetic gene circuits. A number of groups have proposed the use of computationally designed orthogonal spacer sequences to guide the ordered assembly of parts using overlap-directed or homologous recombination-based methods. This approach is particularly useful for assembling multiple parts with repetitive elements. Orthogonal spacer sequences (sometimes called UNSs-unique nucleotide sequences) also have a number of other potential uses including in the design of synthetic promoters regulated by novel regulatory elements.

Combined and Iterative Use of Computational Design and Directed Evolution for Protein-Ligand Binding Design

The advantages of computational design and directed evolution are complementary, and only through combined and iterative use of both approaches, a daunting task such as protein-ligand interaction design, can be achieved efficiently. Here, we describe a systematic strategy to combine structure-guided computational design, iterative site saturation mutagenesis, and yeast two-hybrid system (Y2H)-based phenotypic screening to engineer novel and orthogonal interactions between synthetic ligands and human estrogen receptor α (hERα) for the development of novel gene switches.

Oxytetracycline recovery from aqueous media using computationally designed molecularly imprinted polymers

Polymers for recovery/removal of the antimicrobial agent oxytetracycline (OTC) from aqueous media were developed with use of computational design and molecular imprinting. 2-Hydroxyethyl methacrylate, 2-acrylamide-2-methylpropane sulfonic acid (AMPS), and mixtures of the two were chosen according to their predicted affinity for OTC and evaluated as functional monomers in molecularly imprinted polymers and nonimprinted polymers. Two levels of AMPS were tested. After bulk polymerization, the polymers were crushed into particles (200-1000 μm). Pressurized liquid extraction was implemented for template removal with a low amount of methanol (less than 20 mL in each extraction) and a few extractions (12-18 for each polymer) in a short period (20 min per extraction). Particle size distribution, microporous structure, and capacity to rebind OTC from aqueous media were evaluated. Adsorption isotherms obtained from OTC solutions (30-110 mg L(-1)) revealed that the polymers prepared with AMPS had the highest affinity for OTC. The uptake capacity depended on the ionic strength as follows: purified water > saline solution (0.9 % NaCl) > seawater (3.5 % NaCl). Polymer particles containing AMPS as a functional monomer showed a remarkable ability to clean water contaminated with OTC. The usefulness of the stationary phase developed for molecularly imprinted solid-phase extraction was also demonstrated. Graphical Abstract Selection of functional monomers by molecular modeling renders polymer networks suitable for removal of pollutants from contaminated aqueous environments, under either dynamic or static conditions.

Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids

Multinuclear metal ion clusters, coordinated by proteins, catalyze various critical biological redox reactions, including water oxidation in photosynthesis, and nitrogen fixation. Designed metalloproteins featuring synthetic metal clusters would aid in the design of bio-inspired catalysts for various applications in synthetic biology. The design of metal ion-binding sites in a protein chain requires geometrically constrained and accurate placement of several (between three and six) polar and/or charged amino acid side chains for every metal ion, making the design problem very challenging to address. Here, we describe a general computational method to redesign oligomeric interfaces of symmetric proteins for the purpose of creating novel multinuclear metalloproteins with tunable geometries, electrochemical environments, and metal cofactor stability via first and second-shell interactions. The method requires a target symmetric organometallic cofactor whose coordinating ligands resemble the side chains of a natural or unnatural amino acid and a library of oligomeric protein structures featuring the same symmetry as the target cofactor. Geometric interface matches between target cofactor and scaffold are determined using a program that we call symmetric protein recursive ion-cofactor sampler (SyPRIS). First, the amino acid-bound organometallic cofactor model is built and symmetrically aligned to the axes of symmetry of each scaffold. Depending on the symmetry, rigid body and inverse rotameric degrees of freedom of the cofactor model are then simultaneously sampled to locate scaffold backbone constellations that are geometrically poised to incorporate the cofactor. Optionally, backbone remodeling of loops can be performed if no perfect matches are identified. Finally, the identities of spatially proximal neighbor residues of the cofactor are optimized using Rosetta Design. Selected designs can then be produced in the laboratory using genetically incorporated unnatural amino acid technology and tested experimentally for structure and catalytic activity.

Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments

The ability to de novo design proteins that can bind small molecules has wide implications for synthetic biology and medicine. Combining computational protein design with the high-throughput screening of mutagenic libraries of computationally designed proteins is emerging as a general approach for creating binding proteins with programmable binding modes, affinities, and selectivities. The computational step enables the creation of a binding site in a protein that otherwise does not (measurably) bind the intended ligand, and targeted mutagenic screening allows for validation and refinement of the computational model as well as provides orders-of-magnitude increases in the binding affinity. Deep sequencing of mutagenic libraries can provide insights into the mutagenic binding landscape and enable further affinity improvements. Moreover, in such a combined computational-experimental approach where the binding mode is preprogrammed and iteratively refined, selectivity can be achieved (and modulated) by the placement of specified amino acid side chain groups around the ligand in defined orientations. Here, we describe the experimental aspects of a combined computational-experimental approach for designing-using the software suite Rosetta-proteins that bind a small molecule of choice and engineering, using fluorescence-activated cell sorting and high-throughput yeast surface display, high affinity and ligand selectivity. We illustrated the utility of this approach by performing the design of a selective digoxigenin (DIG)-binding protein that, after affinity maturation, binds DIG with picomolar affinity and high selectivity over structurally related steroids.

Rosetta and the Design of Ligand Binding Sites

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand. Starting with a protein structure and the structure of the ligand, Rosetta can optimize both the placement of the ligand in the pocket and the identity and conformation of the surrounding sidechains, yielding proteins that bind the target compound.

Computational approaches to metabolic engineering utilizing systems biology and synthetic biology

Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.

Computational design of allosteric ribozymes as molecular biosensors

Nucleic acids have proven to be a very suitable medium for engineering various nanostructures and devices. While synthetic DNAs are commonly used for self-assembly of nanostructures and devices in vitro, functional RNAs, such as ribozymes, are employed both in vitro and in vivo. Allosteric ribozymes have applications in molecular computing, biosensoring, high-throughput screening arrays, exogenous control of gene expression, and others. They switch on and off their catalytic function as a result of a conformational change induced by ligand binding. Designer ribozymes are engineered to respond to different effectors by in vitro selection, rational and computational design methods. Here, I present diverse computational methods for designing allosteric ribozymes with various logic functions that sense oligonucleotides or small molecules. These methods yield the desired ribozyme sequences within minutes in contrast to the in vitro selection methods, which require weeks. Methods for synthesis and biochemical testing of ribozymes are also discussed.

Stabilization of cyclohexanone monooxygenase by a computationally designed disulfide bond spanning only one residue

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Cyclohexanone monooxygenase was stabilized by an in silico designed disulfide bond.

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Stabilizing disulfide bonds were successfully designed based on a model structure.

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The half-life at 30 °C was increased 12-fold for the mutant enzyme.

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The apparent melting point was increased by 6 °C for the mutant enzyme.

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The most stabilizing disulfide bond spans only one residue.

Enzyme stability is an important parameter in biocatalytic applications, and there is a strong need for efficient methods to generate robust enzymes. We investigated whether stabilizing disulfide bonds can be computationally designed based on a model structure. In our approach, unlike in previous disulfide engineering studies, short bonds spanning only a few residues were included. We used cyclohexanone monooxygenase (CHMO), a Baeyer–Villiger monooxygenase (BVMO) from Acinetobacter sp. NCIMB9871 as the target enzyme. This enzyme has been the prototype BVMO for many biocatalytic studies even though it is notoriously labile. After creating a small library of mutant enzymes with introduced cysteine pairs and subsequent screening for improved thermostability, three stabilizing disulfide bonds were identified. The introduced disulfide bonds are all within 12 Å of each other, suggesting this particular region is critical for unfolding. This study shows that stabilizing disulfide bonds do not have to span many residues, as the most stabilizing disulfide bond, L323C–A325C, spans only one residue while it stabilizes the enzyme, as shown by a 6 °C increase in its apparent melting temperature.

Computational design of short-chain dehydrogenase Gox2181 for altered coenzyme specificity

Short-chain dehydrogenase Gox2181 from Gluconobacter oxydans catalyzes the reduction of 2,3-pentanedione by using NADH as the physiological electron donor. To realize its synthetic biological application for coenzyme recycling use, computational design and site-directed mutagenesis have been used to engineer Gox2181 to utilize not only NADH but also NADPH as the electron donor. Single and double mutations at residues Q20 and D43 were made in a recombinant expression system that corresponded to Gox2181-D43Q and Gox2181-Q20R&D43Q, respectively. The design of mutant Q20R not only resolved the hydrogen bond interaction and electrostatic interaction between R and 2'-phosphate of NADPH, but also could enhance the binding with 2'-phophated of NADPH by combining with D43Q. Molecular dynamics simulation has been carried out to testify the hydrogen bond interactions between mutation sites and 2'-phosphate of NADPH. Steady-state turnover measurement results indicated that Gox2181-D43Q could use both NADH and NADPH as its coenzyme, and so could Gox2181-Q20R&D43Q. Meanwhile, compared to the wild-type enzyme, Gox2181-D43Q exhibited dramatically reduced enzymatic activity while Gox2181-Q20R&D43Q successfully retained the majority of enzymatic activity.