Computer modeling: molecular mechanics

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Computer modeling:

molecular mechanics

- why inspection of structures is not enough - molecular mechanics

1 Oct 2009 Week 5, Thurs

A. R. Leach (2001) Molecular Modelling: Principles and Applications, 2

^nd

ed., Prentice Hall; Chapter 4

‘Empirical Force Field Models: Molecular Mechanics’. pages 165-173 only.

J. A. Brannigan, J. J. Wilkinson (2002) Protein engineering 20 years on. Nature Rev. Mol. Cell Biol. 3, 964-970

H. W. Hellinga (1998) Computational protein engineering. Nature Struct. Biol. 5, 525-527.

Thursday, 1 October 2009

Qualitative protein engineering:

molecular graphics

Use qualitative rules through inspection using computer graphics

- Stabilize proteins: add metal-binding sites to crosslink the folded form

Muheim et al. (1993) Ruthenium-mediated protein cross-linking and stabilization J. Am.

Chem. Soc. 115, 5312.

- Expand substrate range: to accept both NADPH and NADH

Wilks & Holbrook (1991) Alteration of enzyme specificity and catalysis by protein engineering. Curr. Opin. Biotechnol. 2, 561.

- Increase activity of insulin: prevent crystallization by disrupting protein- protein interface

Brange et al. (1988) Monomeric insulins obtained by protein engineering and their medical implications, Nature 333, 679.

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Difficulties. 1. Precisely how to improve your target property

• Incomplete understanding of what changes are needed:

how protein structure determines key properties (stability, selectivity, reaction rate)

• Incomplete understanding of how to create needed changes: Precise positioning is hard with only 20 amino acid substitutions; need multiple substitutions to precisely position functional groups.

• Many possibilities: ~90,000 ways to place two cysteines, each having three rotamers, in a 100-residue protein,

Difficulties. 2. Do not damage other properties

!

Activity

Efficiency Specificity

Stability Turnover frequency (k_cat)

Temperature profile Specific activity (kat/kg, U/mg)

pH profile

Space-time yield

Product inhibition

Byproduct/ingredient inhibition Producibility/expression yield

Temperature stability pH stability

Ingredient/byproduct stability

Solvent stability

Substrate range

Substrate specificity (K_m, k_cat/K_m) Substrate regioselectivity and enantioselectivity

Substrate conversion (%), yield

6 5 4 3 2 1 1 2 3 4 5 6

laundry detergents⁸. Their versatility allows their use in many applications, including processes to degrade natural polymers such as starch, cellulose and proteins, as well as for the REGIOSELECTIVE or ENANTIOSELECTIVE

synthesis of asymmetric chemicals.

Global industrial enzyme sales were esti- mated at $2.3 billion in 2003 REF. 15.The main profits were divided among detergents ($789 million), food applications ($634 million), agriculture/feed ($376 million), textile processing ($237 million), and pulp/paper, leather and other applications including enzymes for the production of fine and bulk chemicals ($222 million). In the face of soar- ing energy costs, dwindling fossil resources, environmental pollution and a globalized economy, the large-scale use of biotechnology instead of, or to complement, traditional industrial production processes, particularly in the chemical sector, is viewed as both an opportunity and a necessity. In the future, novel biotechnological applications should boost the market for industrial enzymes.

White biotechnology

The notion that environmentally sound, commercially viable biotechnological processes can take their respected place in a global industrial environment has been acknowledged for some time¹⁶. Companies from Europe, Canada, Japan, South Africa and the USA reported on their experiences in

processes as diverse as the production of acryl- amide (Mitsubishi Rayon, Japan) and the use of enzymes in oil-well completion (British Petroleum Exploration, UK). Currently, the movement towards implementing sustainable technologies and processes is gaining momentum, particularly in Europe.

‘Industrial’ or WHITE BIOTECHNOLOGY is currently a buzzword in the European bio- business community. The term was coined in 2003 by the European Association for Bioindustries (EuropaBio), based on a case study report, and it denotes all industrially harnessed bio-based processes that are not covered by the RED BIOTECHNOLOGY (medical) or GREEN BIOTECHNOLOGY (plant) labels¹⁷. White biotechnology has its roots in ancient human history and its products are increasingly part of everyday life, from vitamins, medicines, biofuel and bioplastics to enzymes in detergents or dairy and bakery products. It is the belief of industrial promoters, analysts and policy makers that white biotechnology has the potential to impact industrial production processes on a global scale. The main long-term applications of white biotechnology will be focused on replacing fossil fuels with renewable resources (biomass conversion), replacing conventional processes with bioprocesses (including metabolic engineering) and creating new high-value bioprod- ucts, including nutraceuticals, performance chemicals and bioactives.

Besides the involvement of the food, feed, detergent and politically heavily- promoted biofuel industries, it is the globally operating chemical and pharmaceutical industries that are riding this ‘third wave of biotechnology’¹⁸ (a term illustrating the chronology of developments in which red and green biotechnology come first and sec- ond, respectively). As industries face increas- ing low-cost competition, particularly from East Asia, and political pressure to reduce their environmental impact and resource consumption to improve sustainability, it is felt that there is a strong need for smart and innovative technologies, processes and products to remain competitive.

The McKinsey consultancy predicts that, by 2010, biotechnology could be applied in the production of between 10% and 20% of all chemicals sold (amounting to a value of $160 billion) and that up to 60% of all fine chemicals (medium-volume products used as intermediates in the manufacturing of products such as pharmaceuticals, flavours, fragrances, agro-chemicals and detergents) might be produced using biotechnology¹⁷. Even for the traditional mainstay of the chemical industries, the polymer market (typical bulk-volume products), McKinsey predicts that biotechnology might account for up to 10% of the output.

The European Chemical Industry Council and EuropaBio have responded to a call from the European Commission, which asked for suggestions for ten central technological plat- forms, by proposing a ‘European Technology Platform for Sustainable Chemistry’. White biotechnology will be one of three pillars of the platform, together with materials sci- ence, and reaction and process design. As a corollary, this triggered the nomination of sustainable chemistry as one of the thematic priorities in the European Union’s seventh framework research programme, with the aim of strengthening the scientific and technological bases of European industry and encouraging its international competitive- ness. Clearly, this joint European initiative does not come without ‘stimulating’ prec- edent from forward-looking international peers in the United States or Japan, and leading European stakeholders (among them DSM¹⁹, Degussa, BASF, Henkel, Novozymes and Genencor) have urged policy makers to act to ensure a favourable competitive position. The promises of industrial biotechnology cannot be realized without coordinated and well-funded research and development (R&D) efforts, a supportive and guid- ing political framework and early public involvement to pre-empt concerns over new Figure 1 | Multi-parameter footprint analysis. This figure illustrates the ideal biocatalyst concept. Each

enzyme candidate from the metagenome is ranked, from low (rating of 1) to high (rating of 6) using a specific set of criteria, to produce a multi-parameter fingerprint (shown in yellow). Criteria include in vitro enzyme activity, efficiency, specificity and stability. This decision matrix reveals the strengths and weaknesses of every candidate enzyme, so that the most promising candidate enzymes from diverse enzyme libraries can be selected for further process development by re-screening, protein engineering or directed evolution methods. kat, catalytic reaction rate; k_cat, catalytic constant; K_m, Michaelis constant; U, unit.

F O C U S O N M E T A G E N O M I C S

Lorenz & Eck (2005) Metagenomics and industrial applications. Nature Rev. Microbiol. 3, 510.

Engineering to improve one property must not damage another property;

need to have some ideas about molecular basis of all key properties.

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Exercise

• Using PyMol and the x-ray crystal structure of lactate dehydrogenase, use qualitative protein design to suggest specific mutations to make the following changes:

a) expand the substrate range to include 3-phenyl pyruvate

b) increase the thermostability of LDH

c) increase the reaction rate with pyruvate 10-fold

• Identify the limitations of qualitative design in each case.

Grade (out of 5)

5 2.5

0

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Computer modeling for protein engineering

• More accurate than inspection of structures

• Can balance competing forces

• Can keep track of many simultaneous interactions

• Can test many, many possibilities

Molecular mechanics: Incorrect theory yields reliable structures

• Ignore electrons! Calculate energy of

molecules based on positions of nuclei only.

• Accurate results: Calculated structures and energies match experiments.

• Transferable: A force field developed for

one set of molecules give reliable results

for other, similar, molecules.

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Components of Molecular Mechanics

• Force field = the mechanical model used to represent molecules in molecular mechanics calculations. It consists of:

1) a set of equations

2) a set of atom types to describe the molecule, and

3) a set of parameters (constants) that relate energy to internal coordinates

Energies of different structures

Note: All parameters used are finalized (Quality = 4).!

Iteration 14: Minimization terminated normally because the gradient norm is less than the minimum gradient norm!

Stretch: 0.1577!

Bend: 0.2943!

Stretch-Bend: 0.0547!

Torsion: 0.0075!

Non-1,4 VDW: -0.4054!

1,4 VDW: 2.0653!

Total: 2.1742!

Note: All parameters used are finalized (Quality = 4).

Iteration 51: Minimization terminated normally because the gradient norm is less than the minimum gradient norm

Stretch: 0.1675 Bend: 0.6051 Stretch-Bend: 0.0727 Torsion: 0.4535 Non-1,4 VDW: -0.3814 1,4 VDW: 2.1239

Total: 3.0412

Steric energy = energy relative to a hypothetical strainless molecule where all bond lengths, angles, torsions, and nonbonded interactions are at their optimum values.

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FoldX: a force field for protein stability

• Will my planned amino acid substitution yield a more stable or less stable protein?

Example: repair 1shg.pdb cd to directory that contains FoldX add files: list.txt, 1shg.pdb, repair.txt to that directory

Threonine:FoldX romas$ ./Foldx.mac - runfile repair.txt

<TITLE>FOLDX_runscript;

<JOBSTART>#;

<PDBS>#;

<BATCH>list.txt;

<COMMANDS>FOLDX_commandfile;

<RepairPDB>#;

<END>#;

<OPTIONS>FOLDX_optionfile;

<Temperature>298;

<R>#;

<pH>7;

<IonStrength>0.050;

<water>-CRYSTAL;

<metal>-CRYSTAL;

<VdWDesign>2;

<OutPDB>true;

<pdb_hydrogens>false;

<END>#;

<JOBEND>#;

<ENDFILE>#;

Computer modeling: molecular mechanics