1. Transient Protein-Protein Interactions (TPPI)
Presented By:
Muhammad Shoaib Amjad
11 arid 3758
PhD Botany
1st Semester

2. Contents Protein-protein interactions Transient protein interaction
Transient protein and drug interaction
Experimental techniques used for detecting TPPIs
List of databases
Summary
References

3. Protein-Protein Interaction (PPI)
Interaction of two proteins
Play an essential role in the proper functioning of living cells
The forces responsible for these interactions include
Electrostatic forces
Hydrogen bonds
Van der waals forces
Hydrophobic effects
Protein–protein interactions occur when two or more proteins bind together
The understanding of these interactions will provide the clues to their biological function

4. Types of Potien-Protien interactions
PPIs can be classified on the bases of
Composition
Homo and hetero-oligomeric complexes
Affinity
Non-obligate and obligate complexes
Stability/Life time
Transient and permanent

6. Transient and Permanent
Stable
Irreversible
Strong
Long life
Electrostatic force
Example: α/β tubulin dimer and many enzyme-inhibitor complexes

7. Transiet protein-protein interactions (TPPI)
TPPIs are involved in many biological processes:
Signal transduction
Protein complexes or molecular machinery
Protein carrier
Protein modifications (phosphorylation)
Hormone receptor binding
Allostery of enzymes
Inhibition of proteases
Correction of misfold protein by chaperones
TPPIs help to decipher the molecular mechanisms underlying the biological functions, and enhance the approaches for drug discovery
For example, signals from the exterior of a cell are mediated to the inside of that cell by protein-protein interactions of the signalling molecules
In protein complex, members are linked by non-covalent interactions, they often activate or inhibit other members.
a protein may be carrying another protein, for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins, a type of protein that moves other protein molecules into the nucleus by binding to a specific recognition sequence
protein kinase will add a phosphate to a target protein

8. Transient protein and drug interaction
Transient interactions might be important in
drug mechanisms in two ways: the drugs that
act on TPPIs
act transiently on their multiple targets
A cancer-related example for the former type
of drugs is nutlins

9. Transient protein and drug interaction
Nutlins
MDM2
p53
Tumor suppressed

10. Experimental Techniques
Yeast two-hybrid screens
Mass spectrometry
Intracellular localization of proteins with fluorescence markers

11. The Yeast Two-Hybrid System

12. Yeast Two-hybrid
Researchers insert a gene in yeast for a "bait" protein alongside DNA for half of an "activator" protein.
The other half of the activator DNA is then inserted alongside DNA for random "prey" proteins.
The yeast cells are then grown up and the proteins are allowed to interact.
If bait and prey proteins bind, the two halves of the activator protein be close enough to work together to turn on another yeast gene that turns the cell blue, signaling a match.

13. How does it work?
Uses yeast as a model for eukaryotic protein interactions
A library is screened or a protein is characterized using a bait construct
Interactions are identified by the transcription of reporter genes
Positives are selected using differential media

14. DNA-Binding
Domain
Bait Protein
Prey Protein
Transcription
Activating
Region
DNA-Binding Site
Reporter Gene

16. What is the yeast two-hybrid system used for?
Identifies novel protein-protein interactions
Can identify protein cascades
Identifies mutations that affect protein-protein binding
Can identify interfering proteins in known interactions

17. Steps to Screen a Library
Create the Bait Plasmid Construct from the gene of interest and the DNA binding domain of Gal4 or LexA or other suitable domain
Transform with the bait construct a yeast strain lacking the promoter for the reporter genes and select for transformed yeast
Transform the yeast again with the library plasmids
Select for interaction

18. Sequence analysis Isolate plasmid from yeast and transform E. coli
Purify plasmid from E. coli and sequence
Blast sequence against database for known proteins or construct a possible protein sequence from the DNA sequence and compare to other proteins

19. Sample Plasmid
From Golemis Lab Homepage

20. Reporter Genes LacZ reporter - Blue/White Screening
HIS3 reporter - Screen on His+ media
LEU2 reporter - Screen on Leu+ media
ADE2 reporter - Screen on Ade+ media
URA3 reporter - Screen on Ura+ media

21. False Positives
False positives are the largest problem with the yeast two-hybrid system
Can be caused by:
Non-specific binding of the prey
Ability to induce transcription without interaction with the bait (Majority of false positives)

22. Elimination of False Positives
Sequence Analysis
Plasmid Loss Assays
Retransformation of both strain with bait plasmid and strain without bait plasmid
Test for interaction with an unrelated protein as bait
Two (or more) step selections

23. Advantages
Immediate availability of the cloned gene of the interacting protein
Only a single plasmid construction is required
Interactions are detected in vivo
Weak, transient interactions can be detected
Can accumulate a weak signal over time

24. Examples of Uses of the Yeast Two-Hybrid System
Identification of caspase substrates
Interaction of Calmodulin and L-Isoaspartyl Methyltransferase
Genetic characterization of mutations in E2F1
Peptide hormone-receptor interactions
Pha-4 interactions in C. elegans

25. The Study of Protein-protein Interaction by Mass Spectrometry
Direct analysis of large protein complexes (DALPC). In the
flow diagram, the rectangles represent a strong cation exchange
(SCX) and a reversed-phase (RP) liquid chromatography column.
Typically, a denatured protein complex is digested with trypsin. The
acidified peptide mixture is loaded onto the SCX column. A discrete
fraction of peptides is displaced from the SCX column to the RP
column. This fraction is eluted from the RP column into the mass
spectrometer. This iterative process is repeated, obtaining the
fragmentation patterns of peptides in the original peptide mixture.
The program SEQUEST is used to correlate the tandem mass
spectra of fragmented peptides to amino acid sequences using
nucleotide databases 6
. The filtered outputs from the program are
used to identify the proteins in the original protein complex.

26. Databases

28. ELM is a computational biology resource for investigating candidate functional sites in eukarytic proteins. Functional sites which fit to the description "linear motif" are currently specified as patterns using Regular Expression rules. To improve the predictive power, context-based rules and logical filters are being developed and applied to reduce the amount of false positives.
The current version of the ELM server provides core functionality including filtering by cell compartment, phylogeny, globular domain clash (using the SMART/Pfam databases) and structure. In addition, both the known ELM instances and any positionally conserved matches in sequences similar to ELM instance sequences are identified and displayed (seeELM instance mapper). Although the ELM resource contains a large collection of functional site motifs, the current set of motifs is not exhaustive.

29. Our group is affiliated with the Departments of Environmental Health and Biomedical Engineering,University of Cincinnati College of Medicine, as well as with the Division of Biomedical Informatics,Children's Hospital Research Foundation (see links below). Our research in the fields ofbioinformatics and computational biology is driven by both methodological developments and applications, spanning a wide range of projects that deal with various data streams and problems arising in the context of genomics, proteomics and biomedical research. Some examples includeprotein structure and function prediction, protein-protein interactions and the recognition of functional sites in proteins, gene (genome) annotation, and the identification of predictive fingerprints of disease states, especially in the context of large SNP genotyping. We rely heavily onpattern recognition and machine learning approaches to develop improved and/or tailored methods for the above applications. In collaboration with biologists, biochemists and physicians, we are applying standard and our own methods to a particular problem at hand.

30. The AMS tool allows for identification of PTM (post-translational modification) sites in proteins. The local sequence preferences of short segments around PTM residues are described here as SVM based LFM (linear functional motifs). We train support vector machine (SVM) for each type of PTM separately on proteins of the Swiss-Prot database (version 42.0) using two datasets of short sequence segments: positives and negatives. The set of positives is created from experimentally annotated sequence segments (9-amino acids long) from proteins known to include PTM site. The dataset of negative cases is build from short sequence segments not annotated in Swiss-Prot database to include any type of PTM site. These two datasets are then projected as sets of points into a single multidimensional space. For this purpose we used generic projection methods of short segments and additional combinations of these. The SVM constructs the separation plane in the multidimensional space between the sets of positives and negatives.
The identification of PTM sites in a query protein is performed as follows. The query protein is divided into overlapping sequence segments. Two search procedures can be used to annotate these segments: identity scan or SVM classification. The first method identifies those segments that are identical in terms of sequence to any positive instance from the database. The second method uses SVM to classify the query protein’s segments into two groups: potential PTM sites and negatives. The list of PTM sites found in the protein by any projection method is then returned to the user.
The performance of SVM models is described by the recall R and the precision P. The recall R value measures the percentage of correct predictions (the probability of correct prediction), whereas precision P gives the percentage of observed positives that are correctly predicted (the measure of the reliability of positive instances prediction). These measures of accuracy are calculated separately for each type of PTM using the leave-one-out procedure. The typical recall value is around 30%, and the precision P is over 70% for majority of PTM.

31. Summary
The components of transient complexes associate and dissociate rapidly while transiently interacting with each other to function dynamically in crucial regulatory and signaling pathways.
The identification and analysis of these complexes have become more manageable with the emerging sensitive and high-resolution experimental techniques accompanied by the high-throughput computational methods.

32. As the coverage of these techniques increase, they can provide a good template to understand and design new transient complexes.
An example for such advanced techniques is, PRISM, which uses available transient interactions as a template set and searches structural and evolutionary similarities between the template set and the target proteins to be predicted.

33. References
James R. Perkins, I. Diboun, B.H. Dessailly, J. G. Lees and C. Orengo Transient Protein- Protein Interactions: Structural Functional And Network Properties. Cell:
Saliha E. A. O., H. B. Engin, A. Gursoy and O. Keskin Transient Protein- Protein Interactions. Protein Interaction Designed And selection: 1-14
Costel C. D., K. Deinhardt, G. Zhang, H. S. Cardasis and M. V. C.A. Neubert Identifying transient protein–protein interactions in EphB2 signaling by blue native PAGE and mass spectrometry Proteomics J:11, 4514–4528
Lakshmipuram S. S., R. M. Bhaskara, J. Sharma and N. Srinivasan Roles of residues in the interface of transient protein-protein complexes before complexation. Scientific reports: 334.

34. THANK YOU