Rice University Research Repository
The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.
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RACER-m leverages structural features for sparse T cell specificity prediction
(AAAS, 2024) Wang, Ailun; Lin, Xingcheng; Chau, Kevin Ng; Onuchic, José N.; Levine, Herbert; George, Jason T.; Center for Theoretical Biological Physics
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
Audio misinformation encoding via an on-phone sub-terahertz metasurface
(Optica Publishing Group, 2024) Shaikhanov, Zhambyl; Al-Madi, Mahmoud; Chen, Hou-Tong; Chang, Chun-Chieh; Addamane, Sadhvikas; Mittleman, Daniel M.; Knightly, Edward W.
We demonstrate a wireless security application to protect the weakest link in phone-to-phone communication, using a terahertz metasurface. To our knowledge, this is the first example of an eavesdropping countermeasure in which the attacker is actively misled.
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2
(National Academy of Sciences, 2024) Guan, Xingyue; Tang, Qian-Yuan; Ren, Weitong; Chen, Mingchen; Wang, Wei; Wolynes, Peter G.; Li, Wenfei; Center for Theoretical Biological Physics
Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.
Quantum simulation of an extended Dicke model with a magnetic solid
(Springer Nature, 2024) Marquez Peraca, Nicolas; Li, Xinwei; Moya, Jaime M.; Hayashida, Kenji; Kim, Dasom; Ma, Xiaoxuan; Neubauer, Kelly J.; Fallas Padilla, Diego; Huang, Chien-Lung; Dai, Pengcheng; Nevidomskyy, Andriy H.; Pu, Han; Morosan, Emilia; Cao, Shixun; Bamba, Motoaki; Kono, Junichiro
The Dicke model describes the cooperative interaction of an ensemble of two-level atoms with a single-mode photonic field and exhibits a quantum phase transition as a function of light–matter coupling strength. Extending this model by incorporating short-range atom–atom interactions makes the problem intractable but is expected to produce new physical phenomena and phases. Here, we simulate such an extended Dicke model using a crystal of ErFeO3, where the role of atoms (photons) is played by Er3+ spins (Fe3+ magnons). Through terahertz spectroscopy and magnetocaloric effect measurements as a function of temperature and magnetic field, we demonstrated the existence of a novel atomically ordered phase in addition to the superradiant and normal phases that are expected from the standard Dicke model. Further, we elucidated the nature of the phase boundaries in the temperature–magnetic-field phase diagram, identifying both first-order and second-order phase transitions. These results lay the foundation for studying multiatomic quantum optics models using well-characterized many-body solid-state systems.
Measurement of multijet azimuthal correlations and determination of the strong coupling in proton-proton collisions at $$\sqrt{s}=13\,\text {Te}\hspace{-.08em}\text {V} $$
(Springer Nature, 2024) CMS Collaboration
A measurement is presented of a ratio observable that provides a measure of the azimuthal correlations among jets with large transverse momentum $$p_{\textrm{T}}$$. This observable is measured in multijet events over the range of $$p_{\textrm{T}} = 360$$–$$3170\,\text {Ge}\hspace{-.08em}\text {V} $$based on data collected by the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13$$\,\text {Te}\hspace{-.08em}\text {V}$$, corresponding to an integrated luminosity of 134$$\,\text {fb}^{-1}$$. The results are compared with predictions from Monte Carlo parton-shower event generator simulations, as well as with fixed-order perturbative quantum chromodynamics (pQCD) predictions at next-to-leading-order (NLO) accuracy obtained with different parton distribution functions (PDFs) and corrected for nonperturbative and electroweak effects. Data and theory agree within uncertainties. From the comparison of the measured observable with the pQCD prediction obtained with the NNPDF3.1 NLO PDFs, the strong coupling at the Z boson mass scale is $$\alpha _\textrm{S} (m_{{\textrm{Z}}}) =0.1177 \pm 0.0013\, \text {(exp)} _{-0.0073}^{+0.0116} \,\text {(theo)} = 0.1177_{-0.0074}^{+0.0117}$$, where the total uncertainty is dominated by the scale dependence of the fixed-order predictions. A test of the running of $$\alpha _\textrm{S}$$in the $$\,\text {Te}\hspace{-.08em}\text {V}$$region shows no deviation from the expected NLO pQCD behaviour.