Proteomics Informatics Protein Characterization II Protein Interactions Week
- Slides: 60
Proteomics Informatics – Protein Characterization II: Protein Interactions (Week 12)
Discovering New Protein Interactions with Affinity Capture Mass Spectrometry E A A C D B Digestion Mass spectrometry Identification F
Affinity Capture Optimization Screen Cell extraction More / better quality interactions + Filtration Lysate clearance/ Batch Binding SDS-PAGE Binding/Washing/Eluting Hakhverdyan, et. al. , "Rapid Optimized Screening of the Cellular Interactome", Nature Methods 2015.
Affinity Capture Optimization Screen Hakhverdyan, et. al. , "Rapid Optimized Screening of the Cellular Interactome", Nature Methods 2015.
Analysis of Non-Covalent Protein Complexes Taverner et al. , Acc Chem Res 2008
Non-Covalent Protein Complexes Schreiber et al. , Nature 2011
Molecular Architecture of the NPC Over 20 different extraction and washing conditions ~ 10 years or art. (41 pullouts are shown) Actual model Alber F. et al. Nature (450) 683 -694. 2007 Alber F. et al. Nature (450) 695 -700. 2007
Interaction Map of Histone Deacetylaces Joshi et al. Molecular Systems Biology 9: 672
Protein Complexes – specific/non-specific binding Sowa et al. , Cell 2009
Protein Complexes – specific/non-specific binding Choi et al. , Nature Methods 2010
Protein Complexes – specific/non-specific binding Tackett et al. JPR 2005
Interaction Partners by Chemical Cross-Linking Protein Complex Chemical Cross-Linking Cross-Linked Protein Complex Enzymatic Digestion MS Proteolytic Peptides Isolation Peptides Fragmentation MS/MS M/Z
Protein Crosslinking by Formaldehyde ~1% w/v Fal 20 – 60 min ~0. 3% w/v Fal 5 – 20 min 1/100 the volume La. Cava
Protein Crosslinking by Formaldehyde RED: Formaldehyde crosslinking BLACK: No crosslinking SCORE: Log Ion Current / Log protein abundance
Interaction Sites by Chemical Cross-Linking Protein Complex Chemical Cross-Linking Cross-Linked Protein Complex Enzymatic Digestion MS Proteolytic Peptides Isolation Peptides Fragmentation MS/MS M/Z
Cross-linking protein n peptides with reactive groups (n-1)n/2 potential ways to cross-link peptides pairwise + many additional uninformative forms Protein A + Ig. G heavy chain 990 possible peptide pairs Yeast NPC 106 possible peptide pairs
Cross-linking Mass spectrometers have a limited dynamic range and it therefore important to limit the number of possible reactions not to dilute the cross-linked peptides. For identification of a cross-linked peptide pair, both peptides have to be sufficiently long and required to give informative fragmentation. High mass accuracy MS/MS is recommended because the spectrum will be a mixture of fragment ions from two peptides. Because the cross-linked peptides are often large, CAD is not ideal, but instead ETD is recommended.
Antibodies V 1 VDJ Recombination V 2……Vn D 1…Dn J 1 J 2 …Jn Variable heavychain domain CDR 1 CDR 2 CDR 3 (Fingerprint) Somatic hypermutation CDR 1 CDR 2 CDR 3
An MS-based Approach for Antibody Discovery Sequence Database With Paired Light-heavy Chain Single Cell PCR B cell Affinity Selection Sanger. AGTCCGATCGGATCC Seq GTCCGATCGGATCCA Sorting AGTCCGATCGGATCCCC ~500 Sequences Serum Ig. G HIV Carrier HIV-binding Ig. G Affinity Selection Spectra HIV-binding Ig. Gs Digest /MS Scheid J, Mouquet H*, Ueberheide T*, Diskin R*, et al. Science, 2011
HIV Antibodies J. F. Scheid et al, “Sequence and structural convergence of broad and potent HIV antibodies that mimic CD 4 binding”, Science, 333 (2011) 1633 -1637
A Functional Ig. G Requires Paired Light and Heavy Chains V V L C H C L H 1 CH 2 = Light CH 3 Standard Ig. G Heavy
Cloning Single-Chain Llama Antibodies
Single-Chain Ig. G from Llama • Atypical single-chain Ig. G antibody produced in camelid family (e. g. llama) • Retain high affinity for antigen without light chain • Antigen binding domain can be cloned and expressed to make “Nanobodies”: - Extremely Cheap & Unlimited Amounts - Tiny (~15 k. Da) , Fold well & Stable in Solution - Easily Engineered for Special Needs V Na HH no b CH 2 od y CH 3 Single-chain Ig. G Standard Ig. G
New MS-based Nanobody Discovery
New MS-based Nanobody Discovery
DNA Library Construction Trim Read 1: 301 bp Overlap: ~200 bp Read 2: 301 bp Trim 1 5 10 -14 30 -34 250 -299 150 -199 50 -59 30 -34 10 -14 5 1 50 -59 Read 2 Quality Read 1 Quality
DNA Library Construction Trim Read 1: 301 bp Overlap: ~200 bp Merging of reads Trim Read 2: 301 bp Merged read length Merged read quality 250 -299 150 -199 50 -59 30 -34 10 -14 5 1
Identifying peptides
Identifying full-length sequences from peptides Nanobody Primary Sequences with CDR Regions Annotated 1. CDR regions are identified based on approximate position in the sequence and the presence of specific leading and trailing amino acids. 2. Nanobody sequences ranked based on: MS coverage and length of individual CDR regions with CDR 3 carrying highest weight; overall coverage including scaffold region; HT-Seq counts. 3. Nanobody sequences grouped by CDR 3. One sequence is assigned to a group where its hamming distance to an existing member is 1. Mapping Annotated Nanobody Sequences with MS coverage Ranking Ranked Nanobody Lists Grouping Ranked Nanobody Groups Identified Peptides
Identifying full-length sequences from peptides
Nanobody Production Scheme Sequence of Discovered Nanobody Candidates Gene synthesis & Codon optimization Expression Vector Cloning MAQVQLVESGGGLVQAGGSLRLSCVASGR TFSGYAMGWFRQTPGREREAVAAITWSAH STYYSDSVKDRFTISIDNTRNTGYLQMNS LKPEDTAVYYCTVRHGTWFTTSRYWTDWG QGTQVTVS ~ $100 / sequence Transformation One-Step Purification ~ 2 mg / 1 L E. coli Expression
Application of Anti-GFP Nanobodies in Immunofluorescence Microscopy GFP Homemade Nanobody
Creating Super-high-affinity Reagent Against GFP: Clone A KD = 0. 7 n. M Clone B KD = 16 n. M Nano GFP Overlay Super-high-affinity KD = 0. 03 n. M
HIV-1 Lipid Bilayer gp 120 gp 41 MA RT IN PR NC CA MA CA NC p 6 Genome gp 120 vpu gag RNA Particle 5’ LTR PR vif pol RT vpr IN 9, 200 nucleotides gp 41 nef env tat rev 3’ LTR
Random Insertion of 5 Amino Acids in Proviral DNA Clone Kan r + Kan r Pme. I Site R 7/3 Digestion & Ligation Random insertion of 5 amino acids (Pme. I) within specific viral coding region
Fitness Landscape of Targeted Viral Segment Day 1 Day 3 Day 6
Specific and Non-Specific Interactors I-DIRT = Isotopic Differentiation of Interactions as Random or Targeted 3 x. FLAG Tagged HIV-1 WT HIV-1 Infection Light Heavy (13 C labeled Lys, Arg) 1: 1 Mix Immunoisolation MS Lys Arg (+6 daltons) Modified from Tackett AJ et al. , J Proteome Res. (2005) 4, 1752 -6.
Fitness Landscape of HIV with random 15 bp insertions in ENV
HIV interactome
Limitation of Light Microscopy 300 nm 3 nm
Fluorescent Imaging with One Nanometer Accuracy (FIONA) CCD image of a single Cy 3 molecule: Width ~ 250 nm Center is localized within width/(S/N)2 ~ N N = total # photon (for N ~ 104 center within ~ 1. 3 nm) Y ax is s X axi Yildiz et al, Science 2003. Paul Selvin
Limitation of Light Microscopy 3 nm 3 nm 3 nm
Limitation of Light Microscopy 3 nm 3 nm 3 nm
Limitation of Light Microscopy 3 nm 3 nm 3 nm
Limitation of Light Microscopy 3 nm 3 nm 3 nm
Limitation of Light Microscopy 20 nm 20 nm 20 nm
Super-Resolution Localization Microscopy Using two lasers for interchangeable activation and excitation of probes PALM: Photo. Activation Localization Microscopy Using fluorescence proteins (m. EOS, etc) Betzig, 2006 Science STORM: STochastic Optical Reconstruction Microscopy Using doubly labeled (Cy 3 -Cy 5) Ab Bates, 2007 Science Huang, Annu. Rev. Biochem, 2009
Molecular Organization of the Intercalated Disc Saffitz, Heart Rhythm (2009)
Molecular Organization of the Intercalated Disc Plakophilin-2 (PKP 2) Desmosome Connexin 43 (Cx 43) Gap junctions What is the interaction map of ID proteins? Agullo-Pascual E, Reid DA, Keegan S, Sidhu M, Fenyö D, Rothenberg E, Delmar M. "Super-resolution fluorescence microscopy of the cardiac connexome reveals plakophilin -2 inside the connexin 43 plaque“, Cardiovasc Res. 2013
Regular Microscopy v. Super-Resolution Cx 43 PKP 2
Regular Microscopy v. Super-Resolution Cx 43 PKP 2
Regular Microscopy v. Super-Resolution Cx 43 PKP 2
What Do We Mean by Colocalization?
Characterization of Cx 43 Clusters Scale =200 nm Two distinct size populations corresponding to hemichannels and full channels. Predominantly circular
Cx 43 -PKP 2 Overlap Analysis % p 0 10 erla ov 50% lap r e ov Cx 43 A correlation between overlap and Cx 43 cluster area
Effect Ank. G Silencing on Cx 43 lap % 0 10 r ve o rlap ve %o 50 Ank. G Sil Ank. G silencing results in increase of Cx 43 cluster size and loss of circularity.
Monte-Carlo Simulations
Monte-Carlo Simulations Experiment Simulation Cx 43 Experiment Simulation PKP 2
Is the Observed Overlap Random? Experiment Ank. G Silencing Experiment Colocalization Area Untreated Cx 43 Area Uniform Experiment Non-uniform Ank. G Silencing Colocalization Area Untreated Cx 43 Area
Proteomics Informatics – Protein Characterization II: Protein Interactions (Week 12)
- Comparative proteomics kit ii western blot module
- Sac spectrogram
- Carmelego
- Prionproteine
- Yosin hitomi
- Yosin hitomi
- History of proteomics
- Protein binding interactions
- Protein binding interactions
- Hits renewable sort
- Dna-protein interactions
- Protein binding interactions
- Week by week plans for documenting children's development
- Direct characterization definition
- Direct characterization example
- Protein purification and characterization techniques
- Protein-protein docking
- Carrier vs channel proteins
- George mason university health informatics
- History of pharmacy informatics
- Belarusian university of informatics and radioelectronics
- Health informatics ryerson
- Python for informatics
- Informatics
- Goals of nursing informatics
- Social informatics definition
- Health informatics in saudi arabia
- Python for informatics: exploring information
- Health informatics
- Vector calculus introduction
- Informatics 43 uci
- Journal of american medical informatics association
- Health informatics courses uk
- History of pharmacy informatics
- Pitt health informatics
- Python for informatics
- Mason life program
- Metastructures of nursing informatics
- School of computing and informatics
- Observational health data sciences and informatics
- Hong kong olympiad in informatics
- Chapter 26 documentation and informatics
- John von neumann faculty of informatics
- Python for informatics
- Poc informatics systems
- Informatics vce
- In4matx 43 uci
- Health informatics career framework
- Biomedical informatics definition
- School of computing and informatics
- Pharmacy informatics definition
- Supply chain informatics
- Polyu surveying
- Nursing informatics and healthcare policy
- Activity slides
- What is informatics
- Shortliffe
- Career skills in health informatics chapter 2
- Nursing informatics theories, models and frameworks
- Ucsd nlp
- Supply chain informatics