DASH extensions (Immunology)
T cell – APC binding

Protein-RNA interactions

DASH

BCR/TCR modeling

Antibody– Antigen binding

DASH Extension 1: protein-RNA interactions

Example: protein-RNA interactions from DASH?
DASH hits boundto RNA

Query

Query superimposedon hits

Overalldistribution ofRNA on queryOverall distribution of nucleotides on Regnase-1

NucleotideabundantNucleotide-scarceIs this asymmetric distribution a coincidence?
Not acoincidence!

180

High
RNA binding
propensityLow

RNA-binding Regnase-1-DASH hits are all ribosome components
DASH Score
3732

PDB ID5OQL_P
5WLC_SL

Protein Chains
C,R,T,Z,a,d,r,w,0
L9,LE,LU,NB,NC,NE, SA,

Complex
Pre-Ribosome
Processome

SC, SF, SI

23
20

4A2I_V
2YKR_W

LK,LRibosome
Ribosome

What are these components?
Cleaving rRNA (utp24)

4A2I_V: ribosome

5OQL_P: Pre-ribosome

5WLC_SL: Processome
2YKR_W: ribosome

UTP24: a Regnase-1 homolog in ribosomes

WD40

US8UTP24
Pre-rRNA

US4Nop1

Helicase may be required for utp24-mediated rRNA cleavage

5’Utp24helicase

3’Functional similarity:
Regnase-1 also helicase to unwind target RNAs
UPF1 unwinds in the 5’ to 3’ direction

Nupf1 5’3’NN5’upf1NN

5’3’Reg1 bound to RNA but
unable to cleave stem

Reg1-upf1 interaction
activates helicase,unwinding stem

upf1
NReg1 cleaves unwoundstem

Therefore, Reg1 expected to cleave on 3’ side of stemDASH Extension 2: BCR/TCR modeling

MSAs encode structural information
Traditional AB modeling is Slow

Approach used by Rosetta,
Schrodinger, etc. (thousands of hours!)

Adding a sequence to an
existing MSA is fast (<1 sec)!

Repertoire Builder: MAFFTDASH MSA + MAFFT v7
10,000 models in 30 min!

Repertoire Builder uses MSA-based approach
A. Prepare template MSAs

B. CDR template MSAs binned by lengthRepertoire Builder workflow

Repertoire Builder uses MSA-based approach
A. Prepare template MSAs

B. CDR templateMSAsbinned by length
How important
are MSAs?

Good MSAs are crucial

Good MSAs are crucial

Repertoire Builder Accuracy
BCR
CDR-H3 ERROR

Repertoire Buildererror was lower thanother tested methods.

-log(p)TCR
CDR-B3 ERROR-log(p)Extension to Antibody-Antigen docking
Build 3D models
Sequences
Antibody

3D modelsRepertoire Builder
Forte, Spanner, Scwrl

Antigen

Extension to Antibody-Antigen docking
Predict initial paratope and epitope

3D models
Antibody

Initialpredictions
Paratope

Antigen

Epitope

Separate features

Extension to Antibody-Antigen docking
Dock antibody and antigen
Initial epitope
prediction

Patches

PosesHex

Extension to Antibody-Antigen docking
Prepare improved features and predict final epitope

Pose classification
Improvedfeatures
False

TrueFinalepitope

Significant improvement over Hex samplingSignificant improvement over Hex scoringQuantitative improvement over ClusProQuantitative improvement over ClusPro
Docking performance comparison between Adapt and ClusPro
Total query

Successfulquery

Total models

Successfulmodels

Adapt

430

156 (36.28%)11218

343 (3.06%)

ClusPro430

166 (38.60%)

11218

227 (2.02%)

Distribution of three quality classes among successful models
Successfulmodels

AcceptableMediumHigh

Adapt

343

306/343(89.21%)35/343(10.20%)2/343(0.58%)

ClusPro227

186/227(81.94%)

39/227(17.18%)2/227(0.88%)

Adapt poses show low overlap with ClusPro
Adapt

ClusProAntibody-specific epitope prediction
Initial AUC .76
Final AUC .80

Extension to TCR-epitope-MHC modelinghttps://sysimm.org/immune-scape/

TCRepitope

ImmuneScape is the first
automated TCR-epitope-MHC
modeling server of its kind

MHC
Li et al. . Meth Mol Biol (2019)

Extension to T cell-APC cell modeling
Antigen presenting
cell (APC)

Peptide-MHC

TCR

T cellAcknowledgements
Standley Lab◆ John Rozewicki◆ Kazutaka Katoh◆ Songling Li◆ Ana Davila◆ Zichang Xu◆ Floris van Eerdan

Collaborators◆ Sho Yaasaki◆ Shuhei Sakakibara◆ Osamu Takeuchi◆ Ai Tachikawa◆ Eliane Piaggio◆ Thomas Boehm