Protein Engineering

1. Protein design, principles and
examples

2. • Protein design has many applications not only in
biotechnology but also in basic science.
• It uses our current knowledge in structural biology to
predict, by computer simulations, an amino acid sequence
that would produce a protein with targeted properties.
• As in other examples of synthetic biology, this approach
allows the testing of many hypotheses in biology.
• The recent development of automated computational
methods to design proteins has enabled proteins to be
designed that are very different from any known ones.
• Moreover, some of those methods mostly rely on a physical
description of atomic interactions, which allows the
designed sequences not to be biased towards known
proteins.

3. • Protein design remains an important problem in computational
structural biology.
• Current computational protein design methods largely use physics-
based methods, which make use of information from a single protein
structure.
• This is despite the fact that multiple structures of many protein folds
are now readily available in the PDB.
• While ensemble protein design methods can use multiple protein
structures, they treat each structure independently.
• Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which
learns global protein backbone movements directly from multiple
protein structures.
• FlexiBaL-GP uses the machine learning method of Gaussian Process
Latent Variable Models to learn a lower dimensional representation
of the protein coordinates that best represent backbone movements.

4. • Protein Designer.
• Protein Designer is a web-based software tool for creating and
manipulating Protein Language diagrams, available at 
http://biocad.ncl.ac.uk/protein-designer/
• A screenshot is shown in Figure 2. (Note: At present, Protein
Designer requires the Google Chrome or Chromium desktop
browser.)
• The user can create a protein backbone (arbitrary region) by right
clicking on the blank canvas.
• An unlimited number of resizable backbone lines are supported.
• The sidebar allows the user to select a glyph from the glyph set,
which can then be placed on the canvas or attached to a protein
backbone.

6. • The structured protein region glyph, in turn, has its
own backbone attachment points for adding site glyphs
to the top or bottom.
• Once completed, designs can be exported, using the
button located in the top right, into Scalable Vector
Graphic (SVG13) images.
• The SVG can also be converted to PDF by the browser’s
print dialogue, and either form imported into
compatible illustration or presentation software.
• Protein Designer’s simple interface allows fast layout of
designs using Protein Language.

7. • Protein Designer uses a modular system of drawing rules to render
SVG.
• New glyphs can be defined as geometrical rules using the SVG
commands for path drawing: move to, line to, and close path (ref 13
Section 8.3).
• This allows users the option of contributing new glyphs to the
language as SVG geometry definitions, which can be incorporated
into the Protein Designer code.
• The architecture of Protein Designer allows new glyphs and sets of
glyphs to be added easily, which we hope will facilitate the
development of a standard visual protein language.
• Example A: Protease Sensor.
• Figure 3 shows a Protein Language diagram representing a protease-
based sensor.

8. • This protein device consists of regions encoding two
colors of fluorescent proteins with a disordered region
between them.
• Inside the disordered region is a protein cleavage site.
• This sensor exhibits fluorescent resonance energy
transfer (FRET) between the two fluorescent protein
domains, which is abolished when the protein is
cleaved.
• The FRET signal is enhanced through a noncovalent
binding: an intra molecular “helper interaction.”

10. • Example B: Light-Inducible Protein Membrane Localization.
• Figure 4 shows a Protein Language diagram general,
reversible system for regulated recruitment in eukaryotes.
• artificial transcription factor.
• The estrogen receptor region is used to add inducible
response to an artificial transcription factor.
• This design incorporates three structured protein regions:
• a DNA binding domain, the estrogen receptor, and a
eukaryotic activation domain.
• Each domain’s function is described by site glyphs for
binding and localization.

13. • Visual depictions have always been an
important tool in the design of biological
systems.
•  community-supported open standard that
helps to effectively integrate engineered
proteins into the design of biological systems.

14. • PPT-DB: the protein property prediction and testing
database
• The protein property prediction and testing database (PPT-
DB) is a database housing nearly 30 carefully curated
databases, each of which contains commonly predicted
protein property information.
• These properties include both
•  structural (i.e. Secondary structure, contact order,
disulfide pairing) and
• dynamic (i.e. order parameters, B-factors, folding rates)
features that have been measured, derived or tabulated from
a variety of sources.

15. • PPTDB is designed to serve two purposes.
• First it is intended to serve as a centralized, up-to-date, freely
downloadable and easily queried repository of predictable or
‘derived’ protein property data.
• In this role, PPT-DB can serve as a one-stop, fully standardized
repository for developers to obtain the required training, testing and
validation data needed for almost any kind of protein property
prediction program they may wish to create.
• The second role that PPT-DB can play is as a tool for homology
based protein property prediction.
• Users may query PPT-DB with a sequence of interest and have a
specific property predicted using a sequence similarity search against
PPT-DB’s extensive collection of proteins with known properties.
• PPT-DB exploits the well-known fact that protein structure and
dynamic properties are highly conserved between homologous
proteins.

16. • Predictions derived from PPT-DB’s similarity
searches are typically 85–95% correct
• (for categorical predictions, such as secondary
structure) or exhibit correlations of >0.80 (for
numeric predictions, such as accessible surface
area).
• This performance is 10–20% better than what is
typically obtained from standard ‘ab initio’
predictions.
• its contents are available at http://www.pptdb.ca

17. • PROPERTY PREDICTION USING PPT-DB
• One of the most useful and important applications of PPT-DB
lies in its ability to help predict protein properties through
homology-based property mapping.
• Just as sequence searches through GenBank and SwissProt
allow evolutionary relationships or functional annotations to be
made for newly sequenced proteins,
• so too it is possible to use sequence searches through PPT-
DB to accurately predict both structural and dynamic properties
of proteins.
• Previous studies have shown that homology-based property
prediction can significantly outperform the best ab initio
(neural net, SVM or HMM) prediction methods
• —>if the query is sufficiently similar to a protein in the
database

19. • Homology modelling method refers to:
• Use a protein with a known structure that has
homology to an unknown structure protein as
a template, and apply bioinformatics methods
• to predict its three-dimensional structure
based on the primary sequence through
computer simulation and calculation.

20. • At Alfa Chemistry, homologous modeling can be carried
out in the following eight steps:
• 1. Search for the template of the structural model
• The modelling method assumes that two homologous
proteins share the same skeleton.
• When building a model for the protein to be predicted,
a template based on the structure of the homologous
protein is built.
• The template is a protein with a known structure, and
the sequence of the protein is very similar to the target
protein.

21. • 2. Sequence alignment
• The sequence of the target protein is aligned with the sequence of
the template protein, so that the amino acid residues of the target
protein match those of the template protein.
• Template sequence and target sequence similarity should > 30
• Moreover, insertion and deletion operations are allowed in the
comparison.
• 3. Build the skeleton.
• We apply the atomic coordinates of the template structure to the
target protein, and only copy the coordinates of the matching
residues.
• The main chain atom position is adjusted to make the bone structure
conform to the principle of stereochemistry.
• In general, this step is designed to construct the main chain structure
of the target protein.

22. • 4. Build the ring area
• Our experts create the ring area based on known ring
structure or predict it from scratch based on the principles
of quantum chemistry.
• 5. Construct the side chain of the target protein.
• The coordinates of the same residues in the template are
directly used as the residue coordinates of the target
protein.
• However, for residues with incomplete matching, the side
chain conformations are different and further prediction is
required.
• In general, the prediction of side chain coordinates is
achieved by employing empirical data with known
structures.

23. • 6. Construct the loop of the target protein
• In the step 2 of sequence alignment, regions for
loops which often correspond to loops between
secondary structure elements may be added.
• For loop regions, additional models need to be
established.
• We usually apply an empirical method to find an
optimal loop region from a protein with a known
structure, and build its structure by copying its
structure data.

24. • 7. Optimize the model
• A preliminary structural model is established for the target
protein through the above process.
• However, there may besome incompatible spatial
coordinates in this model.
• Therefore, improvements and optimizations are required
such as using molecular dynamics, simulated annealing and
other methods to optimize the structure.
• For example, the energy is minimized to find the lowest
energy point, that is, the stable conformation for the side
chain optimization.
• In addition, we use simulated annealing and molecular
dynamics methods to eliminate the unreasonable contact
between atoms in the three-dimensional model obtained
through the above process.

25. • 8. Model evaluation
• The results of protein structure prediction need to be verified. Alfa
Chemistry applies a common evaluation standard called RMSD,
which represents the root-mean-square deviation of the
corresponding atoms between the target protein and the template
protein.
• Features of Services
• High reliability
• Automation and fast
• High structural similarity
• Application of Our Services
• Understand the structural properties of the target protein
• Study the interaction between the target and drug
• Analyze its 'structure-function' relationship
• Discovery of potential therapeutic drugs for a disease

26. • What is the proteome?
• Proteins are biological molecules made up of building blocks called
amino acids.
• Proteins are essential to life, with structural, metabolic, transport,
immune, signaling and regulatory functions among many other
roles.
• Data analysis in proteomics
• Proteomic studies, particularly those employing high-throughput
technologies, can generate huge amounts of data.
• In addition to the sheer quantity of data produced, proteomic data
analysis can also be relatively complex for certain techniques such
as shotgun MS.
• Adding to this complexity is the range of bioinformatics tools
available for proteomic analyses

27. • Proteomic studies often require multiple data
processing and analysis steps that need to be
performed in a specific sequence.
• To address this need, researchers are increasingly
assembling the needed scripts, tools and software into
customized proteomic analysis pipelines suited to their
particular research questions.
• Applications of proteomics
• The applications of proteomics are incredibly
numerous and varied.
• The table below lists just some of these applications
and provides links to examples of studies using these
approaches.