1,720,999 research outputs found
Thermodynamic analysis of enzyme enantioselectivity: a statistical approach by means of new differential HybridMIF descriptors
No full textThe study of relationships between substrate structure and enzyme stereoselectivity was approached by means of a new molecular descriptor: the “differential Hybrid Molecular Interaction Field” (dH-MIF). The descriptor was conceived with the purpose of combining enthalpic and entropic information related to enzyme–enantiomer interactions. The dH-MIFs were developed based on experimental data previously published by the group of Karl Hult on the enantioselectivity of the W104A mutant of lipase B from Candida antarctica, which is endowed with an enlarged stereoselectivity pocket. Because of the increased conformational freedom of substrates, the entropic contribution to enantiodiscrimination is particularly relevant in kinetic resolution of alcohols catalyzed by this enzyme. By combining molecular dynamic simulations and GRID analysis the new dH-MIF descriptors proved to be able to extract both enthalpic and entropic information from models of the tetrahedral intermediates of enantiomers
Modelling and Predicting Enzyme Enantioselectivity: the Aid of Computational Methods for the Rational use of Lipase B from Candida antarctica
No full textLipase B from Candida antarctica (CaLB) is one of the most largely employed biocatalysts
for the synthesis of chiral fine chemicals. The successful application of this enzyme has also been
promoted by advanced computational methods able to simulate enantiodiscrimination at molecular and
energy level. Quantitative prediction of enantioselectivity remains a challenging task, affordable by
means of sophisticated and rigorous QM/MM methods or by hybrid methods that combine molecular
mechanics with experimental data and regression analysis. Most of the methods reported in the
literature aim to predict CaLB enantiopreference and to understand the structural basis of
enantiodiscrimination. Various experimental problems, such as resolution of alcohols, amines and carboxylic acids,
solvent effect, entropic contribution of substrates, are expected to receive beneficial indications from novel advanced
computational methods. However, the choice of the appropriate strategy is crucial for success in solving specific problems
within a realistic time frame and with a convenient computational cost. In order to be competitive with experimental work,
the rational and computational approach should be ideally within a high throughput scheme. Therefore, automation of
computational procedures, software and scoring steps represents a new emerging and promising perspective to make the
planning of biotransformation more effective and rational
An integrated platform for automatic design and screening of virtual mutants based on 3D-QSAR analysis
No full texttAn innovative application of 3D-QSAR methodology to the rational design of enzymes is here reported.The introduction of amidase activity inside the scaffold of lipase B from Candida antarctica (CaLB) wasstudied and 3D-QSAR models were constructed to correlate the structures of a set of CaLB mutants withtheir experimentally measured activities. Properties, like hydrophilicity, hydrophobicity and hydrogenbonding capability of the enzyme active site were computed by means of the GRID method and theoutput was used as molecular descriptors. Correlations with experimental behavior of the catalysts werecalculated by means of partial least square regression (PLS). The analysis of the QSAR model fully exploitsfundamental knowledge while avoiding conceptual biases. Rationales for driving enzyme engineeringare disclosed and a priori evaluation of new virtual candidate mutants becomes feasible. On that respect,the whole procedure for production of virtual mutants and scoring of their activity was automated withina workflow constructed by means of the modeFRONTIER package. The method allows for the automatedconstruction and scoring of each mutant in 2 h on a normal workstation
Molecular descriptors for the structural analysis of enzyme active sites
No full textEnzymes are increasingly used to perform a range of chemical reactions. These catalysts from nature are sustainable, selective, and efficient and offer a variety of benefits such as environmentally friendly manufacturing processes, reduced use of solvents, lower energy requirement, high atom efficiency, and reduced cost. However, natural biocatalysts are often not optimally suited for industrial applications. To boost the use of enzymes in industrial processes, it is important to expand the range of reactions catalyzed by enzymes and to improve their properties for industrial applications. Traditionally, in the past, new enzymes for desired reactions were obtained by tedious and time-consuming screening of microbial cultures, often following enrichment and isolation of new cultures. Due to the genomics revolution, massive sequencing combined with appropriate use of databases and efficient predictive bioinformatics tools have the potential to replace the current laborious screening approaches. The technological advances in the field offer an array of tools, which nowadays still have to express their full applicative potential. Actually, time-consuming, expensive, and investment-intensive screening in the laboratory is expected to be replaced by in silico screening using computer programs, ranking, design, and automated DNA synthesis, thus allowing a much shorter time from process idea to feasibility judgment with considerable savings on research costs.
To fully exploit the enormous developments in life sciences, technologies and information must be used according to more effective and integrated strategies so that designing, developing, and applying new and better enzymes for industrial processes become a faster and more effective practice. The achievement of this goal is of crucial importance for the technological and economic competitiveness of industrial biotechnological processes. During the last 40 years, rigorous quantum mechanics (QM)-based computational methodologies have been developed and applied for the investigation of the physical-chemical features, thermodynamic parameters, and electrostatic contributions of enzyme active site in order to fully understand the source of the catalytic power of enzymes. QM simulations result to be very expensive in terms of computational power required because of the system definition with its high level of theory. Therefore, the enzyme system is usually QM defined just in its catalytic machinery or in a limited portion of the enzyme corresponding to the active site, while the remaining part of the system is defined with the molecular mechanics (MM) theory level [1]. While the oversimplification of the former methods makes quantitative predictions unfeasible, the latter are definitely too much time consuming to be attractive as predicting tools, and above all, they often still provide unsatisfactory quantitative accuracy .
Recent advances in computer sciences have led to sophisticated and refined molecular descriptors able to describe quantitatively the features of target molecules and macromolecules
ENZYMATIC CATALYSIS FOR POLYCONDENSATION: POTENTIAL IMPACT AND TECHNOLOGICAL BARRIERS
No full textENZYMATIC CATALYSIS FOR POLYCONDENSATION:
POTENTIAL IMPACT AND TECHNOLOGICAL BARRIERS
Alessandro Pellis1, Livia Corici2, Valerio Ferrario1, Cynthia Ebert1 and Lucia Gardossi1*
1Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Trieste, Piazzale Europa 1, 34127 Trieste, Italy
2SPRIN S.p.A., via Flavia 23/1, 34148 Trieste, Italy
e-mail: [email protected]
1.Introduction
The extraordinary catalytic potential of enzymes and lipases in particular in polyesters synthesis has been reported in the last two decades.[1] Enzymes are selective bio-catalysts that enable the minimization of protection/deprotection strategies so that monomers with functionalities can be used while avoiding branching. The benefits coming from the use of enzymes in polycondensation reactions are also related to their sustainability and high efficiency at mild conditions: toxic metal catalysts can be avoided and processes can be carried out at temperatures below 80°C. Although the Mn of products attainable by enzymatic polycondensation is in most cases below 10.000, the technology can be used in the production of pre-polymers or in combination with chemical or thermal polymerization. Thanks to the mild reaction conditions, the enzymatic approach to polycondensation is complementary to the chemical synthesis providing a route for the introduction of functional groups inside the polymeric chain with the aim of production of “reactive” polyesters. However, the wide array of enzymatic polyester synthesis described in the scientific literature at laboratory scale are currently not exploited at industrial scale, especially because of low biocatalyst efficiency under process conditions. Recyclability, stability in the viscous conditions of polymerization process and under stirring are the main problems investigated by the “Laboratory of Applied and Computational Biocatalysis” of the University of Trieste. Results achieved in our recent studies will be presented, along with specific enzymatic and synthetic methodologies that can be now used in the enzymatic polycondensation of bio-based polyols and diacids.
2.Results and discussion
The reactions were performed using a robust immobilized enzymes suspended in the monomers, without addition of solvent. A specific immobilization method has been developed for preventing the release of the enzyme during the polycondensation into the polymeric product.[2] Lipase B from Candida antarctica (CALB) was used as biocatalyst
A three-dimensional and multivariate statistical analysis of serine-hydrolases active sites.
No full textBacillus subtilis lipase A – lipase or esterase?
Full text linkThe question how to distinguish between lipases and esterases is about as old as the definition of the sub-classification is. Many different criteria have been proposed to this end, all indicative but not decisive. Here the activity of lipases in dry organic solvents as criterion is probed on a minimal α/β hydrolase fold enzyme, the Bacillus subtilis lipase A (BSLA) and compared to Candida antarctica lipase B (CALB), a proven lipase. Both hydrolases show activity in dry solvents and this proves BSLA to be a lipase. Overall, this demonstrates the value of this additional parameter to distinguish between lipases and esterases. Lipases tend to be active in dry organic solvents while esterases are not active under these circumstances
BioGPS descriptors for rational engineering of enzyme promiscuity and structure-based bioinformatic analysis
No full textA new bioinformatic methodology was developed founded on the Unsupervised Pattern Cognition Analysis of GRID-based BioGPS descriptors (Global Positioning System in Biological Space). The procedure relies entirely on three-dimensional structure analysis of enzymes and does not stem from sequence or structure alignment. The BioGPS descriptors account for chemical, geometrical and physical-chemical features of enzymes and are able to describe comprehensively the active site of enzymes in terms of ‘‘pre-organized environment’’ able to stabilize the transition state of a given reaction. The efficiency of this new bioinformatic strategy was demonstrated by the consistent clustering of four different Ser hydrolases classes, which are characterized by the same active site organization but able to catalyze different reactions. The method was
validated by considering, as a case study, the engineering of amidase activity into the scaffold of a lipase. The BioGPS tool predicted correctly the properties of lipase variants, as demonstrated by the projection of mutants inside the BioGPS ‘‘roadmap’’
A Three- Dimensional Quantitative Structure Activity Relationship (3D-QSAR) Model for Predicting the Enantioselectivity for Candida Antarctica Lipase B
No full textComputational techniques involving molecular modeling coupled with multivariate statistical analysis were used to evaluate and predict quantitatively the enantioselectivity of lipase B from Candida antarctica (CALB). In order to allow the mathematical and statistical processing of the experimental data largely available in the literature (namely enantiomeric ratio E), a novel class of GRID-based molecular descriptors was developed (differential molecular interaction fields or DMIFs). These descriptors proved to be efficient in providing the structural information needed for computing the regression model. Multivariate statistical methods based on PLS (partial least square – projection to latent structures), were used for the analysis of data available from the literature and for the construction of the first three-dimensional quanititative structure-activity relationship (3D-QSAR) model able to predict the enantioselectivity of CALB. Our results indicate that the model is statisticall
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