Computational Design Strategy Research Articles

Structure-Based Discovery of Allosteric Inhibitors Targeting a New Druggable Site in the Respiratory Syncytial Virus Polymerase.

Respiratory syncytial virus (RSV) is a major cause of severe lower respiratory infections for which effective treatment options remain limited. Herein, we employed a computational structure-based design strategy aimed at identifying potential targets for a new class of allosteric inhibitors. Our investigation led to the discovery of a previously undisclosed allosteric binding site within the RSV polymerase, the large (L) protein. This discovery was achieved through a combination of virtual screening and molecular dynamics simulations. Subsequently, we identified two inhibitors, 6a and 10b, which both exhibited promising antiviral activity in the low micromolar range. Resistance profiling revealed a distinctive pattern in how RSV evaded treatment with this class of inhibitors. This pattern strongly suggested that this class of small molecules was targeting a new binding site in the RSV L protein, aligning with the computational predictions made in our study. This study paves the way for the development of more potent inhibitors for combating RSV infections by targeting a new druggable pocket within the RdRp which does not overlap with previously known resistance sites.

Automated optimization of the solubility of a hyper-stable α-amylase.

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

A Computational and Chemical Design Strategy for Manipulating Glycan-Protein Recognition.

Glycans are complex biomolecules that encode rich information and regulate various biological processes, such as fertilization, host-pathogen binding, and immune recognition, through interactions with glycan-binding proteins. A key driving force for glycan-protein recognition is the interaction between the π electron density of aromatic amino acid side chains and polarized C─H groups of the pyranose (termed the CH-π interaction). However, the relatively weak binding affinity between glycans and proteins has hindered the application of glycan detection and imaging. Here, computational modeling and molecular dynamics simulations are employed to design a chemical strategy that enhances the CH-π interaction between glycans and proteins by genetically incorporating electron-rich tryptophan derivatives into a lectin PhoSL, which specifically recognizes core fucosylated N-linked glycans. This significantly enhances the binding affinity of PhoSL with the core fucose ligand and enables sensitive detection and imaging of core fucosylated glycans in vitro and in xenograft tumors in mice. Further, the study showed that this strategy is applicable to improve the binding affinity of GafD lectin for N-acetylglucosamine-containing glycans. The approach thus provides a general and effective way to manipulate glycan-protein recognition for glycoscience applications.

A general computational design strategy for stabilizing viral class I fusion proteins

Many pathogenic viruses rely on class I fusion proteins to fuse their viral membrane with the host cell membrane. To drive the fusion process, class I fusion proteins undergo an irreversible conformational change from a metastable prefusion state to an energetically more stable postfusion state. Mounting evidence underscores that antibodies targeting the prefusion conformation are the most potent, making it a compelling vaccine candidate. Here, we establish a computational design protocol that stabilizes the prefusion state while destabilizing the postfusion conformation. With this protocol, we stabilize the fusion proteins of the RSV, hMPV, and SARS-CoV-2 viruses, testing fewer than a handful of designs. The solved structures of these designed proteins from all three viruses evidence the atomic accuracy of our approach. Furthermore, the humoral response of the redesigned RSV F protein compares to that of the recently approved vaccine in a mouse model. While the parallel design of two conformations allows the identification of energetically sub-optimal positions for one conformation, our protocol also reveals diverse molecular strategies for stabilization. Given the clinical significance of viruses using class I fusion proteins, our algorithm can substantially contribute to vaccine development by reducing the time and resources needed to optimize these immunogens.

Deepening the synergistic role of additive manufacturing and computational strategies in jewellery

This study investigates the synergy between additive manufacturing (AM) technologies and computational design strategies in jewellery and how that synergy can be successfully exploited to extend innovation in that field further. A case study called Ecdysis, a bioinspired jewellery collection, is presented. A dedicated computational algorithm has been developed and is described in detail. This algorithm allows for the exploitation of the shape and functional complexity dimensions allowed by AM and the control of the printability of the generated concept. Shape and functional complexity are exploited to mimic the beauty and dynamism of snakes’ slithering mechanism. At the same time, starting from the developed algorithm, multiple digital models and physical prototypes have been fabricated, leveraging material extrusion, vat photopolymerisation, and powder bed fusion processes. This further development step of the collection thus confirms the versatility of both the proposed approach and AM technologies for jewellery. Therefore, the paper demonstrates how unique wearing experiences can be created and how uniqueness can be simultaneously preserved and democratised in jewellery by deepening the synergy between AM technologies and computational strategies.

Computational Design and Fabrication Strategy for Topology Optimization of Spiral Staircase Using Metal Wire Arc Additive Manufacturing

The study presents the integration of Topology Optimization (TO) as a design strategy and Wire Arc Additive Manufacturing (WAAM) as a fabrication strategy. Although TO is well-known in the discipline, a gap in implementing this technique in architectural design due to fabrication limitations remains. In this research, an integrated framework is proposed to obtain a design where both TO and WAAM printing are implemented simultaneously. This framework is divided into five main phases, (1) checking suitability, (2) preprocessing, (3) processing, (4) post-processing, and (5) printing. The framework is then applied to the design of a topology-optimized spiral staircase and studied for fabrication using WAAM metal 3D printing. The results conclude that avoiding the usual standardization of design can be realized by implementing a framework that takes the fabrication strategies into consideration throughout the design process.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure.

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

Novel computational and drug design strategies for the inhibition of human T-cell leukemia virus 1-associated lymphoma by Astilbin derivatives

Human T-cell leukemia virus 1 (HTLV-1) associated lymphoma is a devastating malignancy triggered by HTLV-1 infections. We employeda comprehensive drug design and computational strategy in this work to explore the inhibitory activitiesof Astilbin derivatives against HTLV-1-associated lymphoma. We evaluated the stability, binding affinities, and various computational analysis of Astilbin derivatives against target proteins, such as HTLV-1 main protease and HTLV-1 capsid protein. The root mean square deviation (RMSD), root mean square fluctuation, radius of gyration, hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM) were applied to characterize these protein–ligand interactions further. Ligand-03 and ligand-04 exhibited notable binding affinity to HTLV-1 capsid protein, while ligand-05 displayed high binding affinity to HTLV-1 protease. MD simulation analysis revealed that ligand-03, bound to HTLV-1 capsid protein, demonstrated enhanced stability with lower RMSD values and fewer conformational changes, suggesting a promising binding orientation. Ligand-04, despite stable binding, exhibited increased structural deviations, making it less suitable. Ligand-05 demonstrated stable binding to HTLV-1 protease throughout the simulation period at 100 nanoseconds. Hydrogen bond analysis indicated that ligand-05 formed persistent hydrogen bonds with significantresidues, contributing to its stability. PCA highlighted ligand-03's more remarkable conformational changes, while DCCM showed ligand-05's distinct dynamics, indicating its different behavior in the complex. Furthermore, binding free energy calculations supported the favorable interactions of ligand-03 and ligand-04 with HTLV-1 capsid protein, while ligand-05 showed weaker interactions with HTLV-1 protease. Molecular electrostatic potential and frontier molecular orbital analyses provided insights into these compounds’ charge distribution and stability. In conclusion, this research found Astilbin derivatives as potential inhibitors of HTLV-1-associated lymphoma. Future attempts at drug development will benefit from the steady interaction landscape provided by Ligand-03, Ligand-04 and Ligand-05, which showed the most attractive binding profile with the target protein. These results open up new opportunities for innovative drug development, and more experimental testing should be done between Astilbin derivatives and HTLV-1-associated lymphoma. Communicated by Ramaswamy H. Sarma

Enhancing the thermostability and activity of glycosyltransferase UGT76G1 via computational design

The diterpene glycosyltransferase UGT76G1, derived from Stevia rebaudiana, plays a pivotal role in the biosynthesis of rebaudioside A, a natural sugar substitute. Nevertheless, its potential for industrial application is limited by certain enzymatic characteristics, notably thermostability. To enhance the thermostability and enzymatic activity, we employed a computational design strategy, merging stabilizing mutation scanning with a Rosetta-based protein design protocol. Compared to UGT76G1, the designed variant 76_4 exhibited a 9 °C increase in apparent Tm, a 2.55-fold increase rebaudioside A production capacity, and a substantial 11% reduction in the undesirable byproduct rebaudioside I. Variant 76_7 also showed a 1.91-fold enhancement rebaudioside A production capacity, which was maintained up to 55 °C, while the wild-type lost most of its activity. These results underscore the efficacy of structure-based design in introducing multiple mutations simultaneously, which significantly improves the enzymatic properties of UGT76G1. This strategy provides a method for the development of efficient, thermostable enzymes for industrial applications.

Novel computational and drug design strategies for inhibition of human papillomavirus-associated cervical cancer and DNA polymerase theta receptor by Apigenin derivatives

The present study deals with the advanced in-silico analyses of several Apigenin derivatives to explore human papillomavirus-associated cervical cancer and DNA polymerase theta inhibitor properties by molecular docking, molecular dynamics, QSAR, drug-likeness, PCA, a dynamic cross-correlation matrix and quantum calculation properties. The initial literature study revealed the potent antimicrobial and anticancer properties of Apigenin, prompting the selection of its potential derivatives to investigate their abilities as inhibitors of human papillomavirus-associated cervical cancer and DNA polymerase theta. In silico molecular docking was employed to streamline the findings, revealing promising energy-binding interactions between all Apigenin derivatives and the targeted proteins. Notably, Apigenin 4′-O-Rhamnoside and Apigenin-4′-Alpha-l-Rhamnoside demonstrated higher potency against the HPV45 oncoprotein E7 (PDB ID 2EWL), while Apigenin and Apigenin 5-O-Beta-d-Glucopyranoside exhibited significant binding energy against the L1 protein in humans. Similarly, a binding affinity range of − 7.5 kcal/mol to − 8.8 kcal/mol was achieved against DNA polymerase theta, indicating the potential of Apigenin derivatives to inhibit this enzyme (PDB ID 8E23). This finding was further validated through molecular dynamic simulation for 100 ns, analyzing parameters such as RMSD, RMSF, SASA, H-bond, and RoG profiles. The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADMET, pharmacokinetics, and drug-likeness properties, fulfilling all the necessary criteria. QSAR, PCA, dynamic cross-correlation matrix, and quantum calculations were conducted, yielding satisfactory outcomes. Since this study utilized in silico computational approaches and obtained outstanding results, further validation is crucial. Therefore, additional wet-lab experiments should be conducted under in vivo and in vitro conditions to confirm the findings.

Computationally Designed AMPs with Antibacterial and Antibiofilm Activity against MDR Acinetobacter baumannii.

The discovery of new antimicrobials is necessary to combat multidrug-resistant (MDR) bacteria, especially those that infect wounds and form prodigious biofilms, such as Acinetobacter baumannii. Antimicrobial peptides (AMPs) are a promising class of new therapeutics against drug-resistant bacteria, including gram-negatives. Here, we utilized a computational AMP design strategy combining database filtering technology plus positional analysis to design a series of novel peptides, named HRZN, designed to be active against A. baumannii. All of the HRZN peptides we synthesized exhibited antimicrobial activity against three MDR A. baumannii strains with HRZN-15 being the most active (MIC 4 µg/mL). This peptide also inhibited and eradicated biofilm of A. baumannii strain AB5075 at 8 and 16 µg/mL, which is highly effective. HRZN-15 permeabilized and depolarized the membrane of AB5075 rapidly, as demonstrated by the killing kinetics. HRZN 13 and 14 peptides had little to no hemolysis activity against human red blood cells, whereas HRZN-15, -16, and -17 peptides demonstrated more significant hemolytic activity. HRZN-15 also demonstrated toxicity to waxworms. Further modification of HRZN-15 could result in a new peptide with an improved toxicity profile. Overall, we successfully designed a set of new AMPs that demonstrated activity against MDR A. baumannii using a computational approach.

A prediction of the thermodynamic, thermophysical, and mechanical properties of CrTaO4 from first principles

AbstractIt is reported that the self‐forming CrTaO4 oxide scale can protect refractory high‐entropy alloys from oxidation, superior to Cr2O3. In this paper, the phase stability, mechanical, and thermal properties of three polymorphous phases of CrTaO4 are systematically investigated from first‐principles density functional theory calculations. The mechanical properties predicted using the strain–energy methods indicated that all three phases are mechanically stable. The temperature dependence of elastic constants and polycrystalline moduli of three phases demonstrated the thermal softening as temperature increase. The Helmholtz‐free energies as a function of volume and temperature are derived from phonon dispersions within the quasi‐harmonic approximation at six strained volumes. The calculated apparent bulk coefficients of thermal expansion of these three phases are evaluated, the highest value approximately 13.4× 10−6 K−1 within a temperature range of 500–2000 K for the rutile I41md phase. The lattice thermal conductivity calculated by the Debye–Callaway model suggested that the rutile type I41md phase has the lowest value of approximately 2.1 W/m/K at 1800 K. The other two phases, C2/m and P2/c, exhibit higher values due to relatively lower Grüneisen parameters and larger phonon velocities. The melting point of CrTaO4 is predicted to be between 1975 and 2449 K using ab initio molecular dynamics simulations. This work provides a comprehensive theoretical understanding of the thermodynamic, mechanical, and thermal properties for the new material CrTaO4 and serves as an example of a viable computational design strategy for improved oxidation resistance of refractory alloys at high temperatures.

Building from Scrap: Computational Design and Robotic Fabrication Strategies for Spatial Reciprocal Structures from Plate-shaped Wooden Production Waste

Building from Scrap: Computational Design and Robotic Fabrication Strategies for Spatial Reciprocal Structures from Plate-shaped Wooden Production Waste

Optimal design of Multiresonant Layered Acoustic Metamaterials (MLAM) via a homogenization approach

Broadband sound attenuation at low frequency ranges (below 500 Hz) has been a challenge in the acoustics field which cannot be solved, via conventional materials, unless impractical amounts of mass are employed. Multiresonant Layered Acoustic Metamaterials (MLAM) offer exceptional attenuating properties at lower frequencies, through novel coupled resonances mechanisms, in a layered configuration that make them amenable for large-scale manufacturing. To show the potential capabilities of MLAM, a novel computational design strategy has been developed to optimize the metamaterials’ performance in terms of their Sound Transmission Loss (STL). First, a multiscale homogenization framework specifically derived for MLAM allows an accurate and extremely fast evaluation of their STL response to normal-incidence acoustic waves in the frequency range of interest. Then, the MLAM design is parameterized into a set of relevant geometric features, which are optimized by means of an optimization scheme based on standard genetic algorithms combined with the homogenization model. The results demonstrate how this design strategy is a powerful tool to obtain optimal MLAM panel designs subject to constraints imposed by the application, for instance, in terms of weight or thickness of the panel, or the manufacturing process (e.g. geometric tolerances).

Novel computational and drug design strategies for inhibition of monkeypox virus and Babesia microti: molecular docking, molecular dynamic simulation and drug design approach by natural compounds.

The alarming increase in tick-borne pathogens such as human Babesia microti is an existential threat to global public health. It is a protozoan parasitic infection transmitted by numerous species of the genus Babesia. Second, monkeypox has recently emerged as a public health crisis, and the virus has spread around the world in the post-COVID-19 period with a very rapid transmission rate. These two novel pathogens are a new concern for human health globally and have become a significant obstacle to the development of modern medicine and the economy of the whole world. Currently, there are no approved drugs for the treatment of this disease. So, this research gap encourages us to find a potential inhibitor from a natural source. In this study, a series of natural plant-based biomolecules were subjected to in-depth computational investigation to find the most potent inhibitors targeting major pathogenic proteins responsible for the diseases caused by these two pathogens. Among them, most of the selected natural compounds are predicted to bind tightly to the targeted proteins that are crucial for the replication of these novel pathogens. Moreover, all the molecules have outstanding ADMET properties such as high aqueous solubility, a higher human gastrointestinal absorption rate, and a lack of any carcinogenic or hepatotoxic effects; most of them followed Lipinski's rule. Finally, the stability of the compounds was determined by molecular dynamics simulations (MDs) for 100 ns. During MDs, we observed that the mentioned compounds have exceptional stability against selected pathogens. These advanced computational strategies reported that 11 lead compounds, including dieckol and amentoflavone, exhibited high potency, excellent drug-like properties, and no toxicity. These compounds demonstrated strong binding affinities to the target enzymes, especially dieckol, which displayed superior stability during molecular dynamics simulations. The MM/PBSA method confirmed the favorable binding energies of amentoflavone and dieckol. However, further in vitro and in vivo studies are necessary to validate their efficacy. Our research highlights the role of Dieckol and Amentoflavone as promising candidates for inhibiting both monkeypox and Babesia microti, demonstrating their multifaceted roles in the control of these pathogens.

Design and delivery of national housing in the UAE: an alternative approach

The provision of national housing to citizens in the United Arab Emirates (UAE) is considered a crucial topic. Over the past four decades, the process of developing national housing has emerged into multiple housing programs and schemes, all with the same aim of offering affordable and high-quality housing to citizens, in addition to meeting the needs of local families regarding spatial configurations while maintaining cultural values. However, despite all these efforts, the question has always remained: are the offered housing practices suited for family needs, socioeconomic trends, and environmental challenges? This study aims to offer an alternative approach for the design and delivery of national housing practices in the UAE. The proposed process is structured based on the following ethos: first, a conceptual approach for design flexibility toward offering customization while maintaining contextual and cultural qualities for inhabitants; second, a computational design strategy for facade optimization that illustrates the significance of incorporating environmentally conscious design strategies in response to local climatic conditions toward enhancing overall building performance; and third, a hybrid production model that relies on a prefabricated building approach that combines precast concrete systems with 3D printing technology. The efforts described in this article represent a significant phase of an ongoing research endeavor that explores how technological capacities could help rethink national housing in the UAE.

Computational Systems Design of Low-Cost Lightweight Robots

With the increased demand for customisation, developing task-specific robots for industrial and personal applications has become essential. Collaborative robots are often preferred over conventional industrial robots in human-centred production environments. However, fixed architecture robots lack the ability to adapt to changing user demands, while modular, reconfigurable robots provide a quick and affordable alternative. Standardised robot modules often derive their characteristics from conventional industrial robots, making them expensive and bulky and potentially limiting their wider adoption. To address this issue, the current work proposes a top-down multidisciplinary computational design strategy emphasising the low cost and lightweight attributes of modular robots within two consecutive optimisation problems. The first step employs an informed search strategy to explore the design space of robot modules to identify a low-cost robot architecture and controller. The second step employs dynamics-informed structural optimisation to reduce the robot’s net weight. The proposed methodology is demonstrated on a set of example requirements, illustrating that (1) the robot modules allow exploring non-intuitive robot architectures, (2) the structural mass of the resulting robot is 16 % lower compared to a robot designed using conventional aluminium tubes, and (3) the designed modules ensure the physical feasibility of the robots produced.

De novo protein design by inversion of the AlphaFold structure prediction network.

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in computational design strategies, de novo design of protein structures remains challenging, given the vast size of the sequence-structure space. AlphaFold2 (AF2), a state-of-the-art neural network architecture, achieved remarkable accuracy in predicting protein structures from amino acid sequences. This raises the question whether AF2 has learned the principles of protein folding sufficiently for de novo design. Here, we sought to answer this question by inverting the AF2 network, using the prediction weight set and a loss function to bias the generated sequences to adopt a target fold. Initial design trials resulted in de novo designs with an overrepresentation of hydrophobic residues on the protein surface compared to their natural protein family, requiring additional surface optimization. In silico validation of the designs showed protein structures with the correct fold, a hydrophilic surface and a densely packed hydrophobic core. In vitro validation showed that seven out of 39 designs were folded and stable in solution with high melting temperatures. In summary, our design workflow solely based on AF2 does not seem to fully capture basic principles of de novo protein design, as observed in the protein surface's hydrophobic vs. hydrophilic patterning. However, with minimal post-design intervention, these pipelines generated viable sequences as assessed experimental characterization. Thus such pipelines show the potential to contribute to solving outstanding challenges in de novo protein design. This article is protected by copyright. All rights reserved.

A computationally designed ACE2 decoy has broad efficacy against SARS-CoV-2 omicron variants and related viruses in vitro and in vivo

SARS-CoV-2, especially B.1.1.529/omicron and its sublineages, continues to mutate to evade monoclonal antibodies and antibodies elicited by vaccination. Affinity-enhanced soluble ACE2 (sACE2) is an alternative strategy that works by binding the SARS-CoV-2 S protein, acting as a ‘decoy’ to block the interaction between the S and human ACE2. Using a computational design strategy, we designed an affinity-enhanced ACE2 decoy, FLIF, that exhibited tight binding to SARS-CoV-2 delta and omicron variants. Our computationally calculated absolute binding free energies (ABFE) between sACE2:SARS-CoV-2 S proteins and their variants showed excellent agreement to binding experiments. FLIF displayed robust therapeutic utility against a broad range of SARS-CoV-2 variants and sarbecoviruses, and neutralized omicron BA.5 in vitro and in vivo. Furthermore, we directly compared the in vivo therapeutic efficacy of wild-type ACE2 (non-affinity enhanced ACE2) against FLIF. A few wild-type sACE2 decoys have shown to be effective against early circulating variants such as Wuhan in vivo. Our data suggest that moving forward, affinity-enhanced ACE2 decoys like FLIF may be required to combat evolving SARS-CoV-2 variants. The approach described herein emphasizes how computational methods have become sufficiently accurate for the design of therapeutics against viral protein targets. Affinity-enhanced ACE2 decoys remain highly effective at neutralizing omicron subvariants.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters.

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD1 exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD1-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD1. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD1 and the redesigned 1a8uD1-M8 were devised from scratch. More importantly, the designed 1a8uD1-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.