A microfluidic cell capture device was designed, fabricated, evaluated by numerical simulations and validated experimentally. The cell capture device was designed with a minimal footprint compartment comprising internal micropillars with the goal to obtain a compact, integrated bioanalytical system. The design of the device was accomplished by computational fluid dynamics (CFD) simulations. Various microdevice designs were rapidly prototyped in poly-dimethylsiloxane using conventional soft lithograpy technique applying micropatterned SU-8 epoxy based negative photoresist as moulding replica. The numerically modeled flow characteristics of the cell capture device were experimentally validated by tracing and microscopic recording the flow trajectories using yeast cells. Finally, we give some perspectives on how CFD modeling can be used in the early stage of microfluidics-based cell capture device development.
Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.
- Keywords
- Biocatalyst, Catalytic efficiency, Computational enzyme design, Enzyme biotechnologies, Protein dynamics, Protein engineering, Software, Solubility, Stability,
- MeSH
- Biocatalysis MeSH
- Biotechnology * MeSH
- Enzymes genetics metabolism MeSH
- Mutagenesis MeSH
- Protein Engineering MeSH
- Directed Molecular Evolution * MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- Names of Substances
- Enzymes MeSH
Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.
- MeSH
- Biotechnology * MeSH
- Internet * MeSH
- Solubility MeSH
- Computational Biology MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
Rational enzyme design presents a major challenge that has not been overcome by computational approaches. One of the key challenges is the difficulty in assessing the magnitude of the maximum possible catalytic activity. In an attempt to overcome this challenge, we introduce a strategy that takes an active enzyme (assuming that its activity is close to the maximum possible activity), design mutations that reduce the catalytic activity, and then try to restore that catalysis by mutating other residues. Here we take as a test case the enzyme haloalkane dehalogenase (DhlA), with a 1,2-dichloroethane substrate. We start by demonstrating our ability to reproduce the results of single mutations. Next, we design mutations that reduce the enzyme activity and finally design double mutations that are aimed at restoring the activity. Using the computational predictions as a guide, we conduct an experimental study that confirms our prediction in one case and leads to inconclusive results in another case with 1,2-dichloroethane as substrate. Interestingly, one of our predicted double mutants catalyzes dehalogenation of 1,2-dibromoethane more efficiently than the wild-type enzyme.
- Keywords
- EVB, dehalogenase, enzyme design, nucleophilic substitution, transient kinetics,
- MeSH
- Models, Chemical * MeSH
- Ethylene Dichlorides chemistry MeSH
- Hydrolases chemistry MeSH
- Catalytic Domain MeSH
- Models, Molecular * MeSH
- Computer Simulation * MeSH
- Substrate Specificity MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Names of Substances
- Ethylene Dichlorides MeSH
- ethylene dichloride MeSH Browser
- haloalkane dehalogenase MeSH Browser
- Hydrolases MeSH
The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science.
- Keywords
- AlphaFold, Computational protein design, Nobel prize, Protein engineering, Protein structure prediction,
- MeSH
- Biocatalysis MeSH
- Protein Conformation MeSH
- Protein Engineering MeSH
- Proteins * chemistry metabolism MeSH
- Machine Learning MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Proteins * MeSH
There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.
- MeSH
- Point Mutation genetics physiology MeSH
- Databases, Genetic MeSH
- Lyases chemistry genetics metabolism MeSH
- Models, Molecular MeSH
- Computer Simulation MeSH
- Protein Engineering methods MeSH
- Enzyme Stability genetics MeSH
- Temperature MeSH
- Computational Biology methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Lyases MeSH
BACKGROUND: Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very costly, challenging and time taking route in the recent times. METHODS: The review summarized about the modern approaches of computational aided drug discovery to developing a novel inhibitor within a short period of time and less cost. RESULTS: The outcome suggests on the premise of reported information that the computational drug discovery is a powerful technology to design a defensive and fruitful therapeutic agents to combat HIV-1 infection and recover the lifespan of suffering one. CONCLUSION: Based on survey of the reported information, we concluded that the current computational approaches is highly supportive in the progress of drug discovery and controlling the viral infection.
- Keywords
- AIDS, HIV-1 life cycle, anti-HIV, biological targets, computational drug discovery, drugs, virtual screening.,
- MeSH
- HIV Infections drug therapy MeSH
- HIV-1 drug effects MeSH
- Anti-HIV Agents chemistry MeSH
- Humans MeSH
- Drug Discovery * MeSH
- Computer Simulation * MeSH
- Drug Design * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- Names of Substances
- Anti-HIV Agents MeSH
Peptides that form transmembrane barrel-stave pores are potential alternative therapeutics for bacterial infections and cancer. However, their optimization for clinical translation is hampered by a lack of sequence-function understanding. Recently, we have de novo designed the first synthetic barrel-stave pore-forming antimicrobial peptide with an identified function of all residues. Here, we systematically mutate the peptide to improve pore-forming ability in anticipation of enhanced activity. Using computer simulations, supported by liposome leakage and atomic force microscopy experiments, we find that pore-forming ability, while critical, is not the limiting factor for improving activity in the submicromolar range. Affinity for bacterial and cancer cell membranes needs to be optimized simultaneously. Optimized peptides more effectively killed antibiotic-resistant ESKAPEE bacteria at submicromolar concentrations, showing low cytotoxicity to human cells and skin model. Peptides showed systemic anti-infective activity in a preclinical mouse model of Acinetobacter baumannii infection. We also demonstrate peptide optimization for pH-dependent antimicrobial and anticancer activity.
- MeSH
- Acinetobacter baumannii drug effects MeSH
- Anti-Bacterial Agents pharmacology chemistry chemical synthesis MeSH
- Antimicrobial Peptides chemistry pharmacology chemical synthesis MeSH
- Antimicrobial Cationic Peptides pharmacology chemistry chemical synthesis MeSH
- Humans MeSH
- Microbial Sensitivity Tests MeSH
- Mice MeSH
- Cell Line, Tumor MeSH
- Antineoplastic Agents * pharmacology chemistry chemical synthesis MeSH
- Drug Design * MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Anti-Bacterial Agents MeSH
- Antimicrobial Peptides MeSH
- Antimicrobial Cationic Peptides MeSH
- Antineoplastic Agents * MeSH
Molecular electronics promises the ultimate level of miniaturization of computers and other machines as organic molecules are the smallest known physical objects with nontrivial structure and function. But despite the plethora of molecular switches, memories, and motors developed during the almost 50-years long history of molecular electronics, mass production of molecular computers is still an elusive goal. This is mostly due to the lack of scalable nanofabrication methods capable of rapidly producing complex structures (similar to silicon chips or living cells) with atomic precision and a small number of defects. Living nature solves this problem by using linear polymer templates encoding large volumes of structural information into sequence of hydrogen bonded end groups which can be efficiently replicated and which can drive assembly of other molecular components into complex supramolecular structures. In this paper, we propose a nanofabrication method based on a class of photosensitive polymers inspired by these natural principles, which can operate in concert with UV photolithography used for fabrication of current microelectronic processors. We believe that such a method will enable a smooth transition from silicon toward molecular nanoelectronics and photonics. To demonstrate its feasibility, we performed a computational screening of candidate molecules that can selectively bind and therefore allow the deterministic assembly of molecular components. In the process, we unearthed trends and design principles applicable beyond the immediate scope of our proposed nanofabrication method, e.g., to biologically relevant DNA analogues and molecular recognition within hydrogen-bonded systems.
- Keywords
- DNA analogue, ab initio calculations, computational screening, hydrogen bonded system, molecular electronics, nanofabrication, self-assembly,
- Publication type
- Journal Article MeSH
Soft glucocorticoids are compounds that are biotransformed to inactive and non-toxic metabolites and have fewer side effects than traditional glucocorticoids. A new class of 17β-carboxamide steroids has been recently introduced by our group. In this study, local anti-inflammatory activity of these derivatives was evaluated by use of the croton oil-induced ear edema test. Glucocorticoids with the highest maximal edema inhibition (MEI) were pointed out, and the systemic side effects of those with the lowest EC50 values were significantly lower in comparison to dexamethasone. A 3D-QSAR model was created and employed for the design of 27 compounds. By use of the sequential combination of ligand-based and structure-based virtual screening, three compounds were selected from the ChEMBL library and used as a starting point for the design of 15 derivatives. Molecular docking analysis of the designed derivatives with the highest predicted MEI and relative glucocorticoid receptor binding affinity (20, 22, 24-1, 25-1, 27, VS7, VS13, and VS14) confirmed the presence of interactions with the glucocorticoid receptor that are important for the activity.
- Keywords
- Anti-inflammatory activity, QSAR, Rational drug design,
- MeSH
- Anti-Inflammatory Agents adverse effects chemistry pharmacology MeSH
- Computer-Aided Design * MeSH
- Edema chemically induced drug therapy MeSH
- Glucocorticoids adverse effects chemistry pharmacology MeSH
- Croton Oil MeSH
- Rats MeSH
- Quantitative Structure-Activity Relationship MeSH
- Molecular Conformation MeSH
- Ear Diseases chemically induced drug therapy MeSH
- Rats, Wistar MeSH
- Drug Design MeSH
- Molecular Docking Simulation MeSH
- Dose-Response Relationship, Drug MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Male MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Anti-Inflammatory Agents MeSH
- Glucocorticoids MeSH
- Croton Oil MeSH
