These slides are based on our presentation at CASP14 on Day 3, Dec 3, 2020, in the Satellite session of the data-assisted modeling category. Modified to add some more explanation.

1. CASP14 – Data Assisted
Target Modeling
Group: KiharaLab
Daipayan Sarkar, Charles Christoffer, Genki Terashi,
Yuki Kagaya, Jacob Verburgt, Sai Raghavendra Maddhuri
Venkata Subramanya, Aashish Jain, Tunde Aderinwale,
Xiao Wang, and Daisuke Kihara
Purdue University
We thank to:
Guy Montelione
Andriy Kryshtafovych
CASP14 Organizers & Assessors
http://kiharalab.org
Twitter: @kiharalab

2. Context
• This slides are based on our presentation at CASP14 on Day 3, Dec 3,
2020, in the Satellite session of the data-assisted modeling category,
which was held in the afternoon. Modified to add some more
explanation.
• By the time of the meeting, only one data-assisted target, N1088, has
a solved structure.
• Our group did well in this modeling, and this was used to explain our
procedure of modeling.

3. Performance of NMR data assisted target refinement (N1088)
(See also Assessors slides for Data-assisted modeling when made available)
https://predictioncenter.org/casp14/

4. Question from Organizer: NMR Data used for
selecting models or sampling?
We used it for Both
1. From our structure modeling pipeline, AttentiveDist, we
selected one starting model according to the agreement
of the NOESY data
2. Ran MD-based refinements using the NOESY data as
constraints with tolerance of 2, 4, 10 Å, respecitvely.
Also ran another MD refinement without NOESY
constraints but with flat-bottom restraints to keep the
structures to the starting model
3. Structures in MD frames clustered and selected
manually

5. NMR data assisted target refinement (N1088)
Initial model selection NMR data-guided MD Final model selection
Flat-bottom (FB) harmonic
restraints using Colvars
NMR distance restraints (NOESY
spectrum ) using Colvars
Initial equilibration MD using CHARMM36m in NAMD v2.14
Production MD (FB): 100
ns x 3 replica = 300 ns
Production MD (NMR): 25 ns
per constraint satisfaction
tolerance (2, 4, 10 Å) = 75 ns
Percentage constraints satisfied and
Ranksum (within 2 Å)
Initial 56.36 %
Final 57.46 %
Sidechain
conformational
change
between initial
and final
models.
PyRosetta
A model selected
by agreement of
constraints
AttentiveDist (bioRxiv)

6. Constraint Satisfaction in
Model 1, Model 2, & Model5
0 Å: 22.59%
2 Å: 57.46%
0 Å: 28.92%
2 Å: 58.77%
0 Å: 28.95%
2 Å: 61.84%
Model 1 structure was selected because it maintains good conformation yet increased the number of satisfied constraints.
Model 2 and 5 are selected solely because they have maximum number of NOESY constraints satisfied.
Satisfied Constraints
within 0 and 2 Å

7. Difficulty
• The NOESY data seem to include signals from different conformations
and noise.
• Thus, forcing a model to satisfy many constraints corrupted the
structure.
• Separating signals for different conformations and noise is
challenging.