and effort-based decision-making (14–16, 29–32).
We propose that these mPFC-based cognitive processes may provide a neurobiological foundation
for dominance-associated personality traits, such
as perseverance or competitive drive.
One important parameter for the cost-benefit
computation in a social confrontation is the history of winning. With the in vivo optogenetic LTP
and LTD experiments, we provide evidence that
synapses in the MDT-dmPFC pathway may encode winning history. Whereas earlier work on
the winner effect in fish was mostly focused on
hormonal changes after repeated winning (33),
our results reveal that the synaptic plasticity
mechanism in the MDT-dmPFC circuit plays a
key role in the winner effect in mammals. Moreover, we discovered a generalized form of the
winner effect, where dominance transfers from
one contest type to another through a shared
neural circuit mechanism. Previous studies of
the winner effect were restricted to the impact of
winning on the same behavior paradigm (33).
However, given that animals are dealing with
different forms of competition in setting up the
social hierarchy, the generalized winner effect
that we describe here is of high evolutionary
importance—for example, it may allow a monkey
that succeeds in fighting for bananas earlier to
occupy a more comfortable resting spot later.
Such reciprocal reinforcement between winning
in different behavioral paradigms would help to
accelerate the establishment of a stable dominance hierarchy (Fig. 5J). It may also have important implications in cognitive training for
competitive games. Considering that an excess
or lack of dominance drive is associated with many
personality disorders and mental problems, our
results might shed light on the treatment of
these psychiatric diseases.
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We thank Q. Li, H. Kessels, H. Li, and W. Li for critical comments on
the manuscript; D. Anderson for stimulating discussions that led to
the idea of the warm spot test; J. Zhu and K. Yuan for assistance
in experiments; K. Deisseroth and Z. Qiu for AAV-ChR2 constructs;
B. Roth for AAV-hM4D and AAV-hM3D constructs; C. Li and X. Gu
for advice on analysis of tetrode recording data; and X. Xu for
Matlab code for behavior annotation. This work was supported by
grants from the Ministry of Science and Technology of China
(2011CBA00400 and 2016YFA0501000), the National Natural
Science Foundation of China (91432108, 31225010, and 81527901),
and the Strategic Priority Research Program (B) of the Chinese
Academy of Sciences (XDB02030004) to H.H. All the data
necessary to understand and assess the conclusions of this
manuscript are available in the supplementary materials.
Computer codes are archived at www.dropbox.com/sh/
7pthozsrzj4vxr0/AACMa W9a8_5ITDumIJw_6Z_pa?dl=0. T.Z. and
H.Z. conducted most optogenetic and behavioral experiments
and designed the experiments with H.H. T.Z. performed in vivo
tetrode recording with the help of Y.C. and Z. Y. and conducted
optogenetic LTP and LTD experiments. Z.F. performed the warm
spot test and dominance transfer experiments. F. W. and H.L.
participated in tube test and viral injection experiments. L.Z.,
L.L., Y.Z., and Z. W. participated in analysis of in vivo tetrode
recordings. H.H. conceived the project and wrote the manuscript
with the help of T.Z. and H.Z.
Materials and Methods
Figs. S1 to S12
Movies S1 to S5
18 February 2017; accepted 9 June 2017
Global analysis of protein folding
using massively parallel design,
synthesis, and testing
Gabriel J. Rocklin,1 Tamuka M. Chidyausiku,1,2 Inna Goreshnik,1 Alex Ford,1,2
Scott Houliston,3,4 Alexander Lemak,3 Lauren Carter,1 Rashmi Ravichandran,1
Vikram K. Mulligan,1 Aaron Chevalier,1 Cheryl H. Arrowsmith,3,4,5 David Baker1,6*
Proteins fold into unique native structures stabilized by thousands of weak interactions
that collectively overcome the entropic cost of folding. Although these forces are
“encoded” in the thousands of known protein structures, “decoding” them is challenging
because of the complexity of natural proteins that have evolved for function, not
stability. We combined computational protein design, next-generation gene synthesis,
and a high-throughput protease susceptibility assay to measure folding and stability for
more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point
mutants, and 30,000 negative control sequences. This analysis identified more than 2500
stable designed proteins in four basic folds—a number sufficient to enable us to
systematically examine how sequence determines folding and stability in uncharted
protein space. Iteration between design and experiment increased the design success rate
from 6% to 47%, produced stable proteins unlike those found in nature for topologies
where design was initially unsuccessful, and revealed subtle contributions to stability as
designs became increasingly optimized. Our approach achieves the long-standing goal
of a tight feedback cycle between computation and experiment and has the potential to
transform computational protein design into a data-driven science.
The key challenge to achieving a quantita- tive understanding of the sequence deter- minants of protein folding is to accurately and efficiently model the balance among the many energy terms that contribute to the
free energy of folding (1–3). Minimal protein
domains (30 to 50 amino acids in length), such
as the villin headpiece and WW domain, are
commonly used to investigate this balance because
they are the simplest protein folds found in nature
(4). The primary experimental approach used to
investigate this balance has been mutagenesis
(5–12), but the results are context-dependent and
do not provide a global view of the contributions
to stability. Molecular dynamics simulations on
minimal proteins have also been used to study
folding (13–15), but these do not reveal which
interactions specify and stabilize the native struc-
ture, and in general they cannot determine whether
a given sequence will fold into a stable structure.
168 14 JULY 2017 • VOL 357 ISSUE 6347 sciencemag.org SCIENCE
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