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The Interaction between Base Compositional Heterogeneity and Among-Site Rate Variation in Models of Molecular Evolution

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International Scholarly Research Notices 2012年第1期2013卷

作者： Nathan C. Sheffield Program in Computational Biology and Bioinformatics Institute for Genome Sciences and Policy Duke University 101 Science Drive Box 3382 Durham NC 27708 USAduke.edu

Many commonly used models of molecular evolution assume homogeneous nucleotide frequencies. A deviation from this assumption has been shown to cause problems for phylogenetic inference. However, some claim that only extreme heterogeneity affects phylogenetic accuracy and suggest that violations of other model assumptions, such as variable rates among sites, are more problematic. In order to explore the interaction between compositional heterogeneity and variable rates among sites, I reanalyzed 3 real heterogeneous datasets using several models. My Bayesian inference recovers accurate topologies under variable rates-among-sites models, but fails under some models that account for compositional heterogeneity. I also ran simulations and found that accounting for rates among sites improves topology accuracy in compositionally heterogeneous data. This indicates that in some cases, models accounting for among-site rate variation can improve outcomes for data that violates the assumption of compositional homogeneity.

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Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis

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Cell 2025年

作者： Bo Li James Elsten-Brown Miao Li Enbo Zhu Zhe Li Yuning Chen Elliot Kang Feiyang Ma Jennifer Chiang Yan-Ruide Li Yichen Zhu Jie Huang Audrey Fung Quentin Scarborough Robin Cadd Jin J. Zhou Arnold I. Chin Matteo Pellegrini Lili Yang Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA Department of Materials Science and Engineering University of California Los Angeles Los Angeles CA 90095 USA Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA USA Department of Urology University of California Los Angeles Los Angeles CA 90095 USA Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA 90095 USA Institute for Quantitative and Computational Biosciences-The Collaboratory University of California Los Angeles Los Angeles CA 90095 USA Department of Bioengineering University of California Los Angeles Los Angeles CA 90095 USA Goodman-Luskin Microbiome Center University of California Los Angeles Los Angeles CA 90095 USA Molecular Biology Institute University of California Los Angeles Los Angeles CA 90095 USA Parker Institute for Cancer Immunotherapy University of California Los Angeles Los Angeles CA 90095 USA

Identifying additional immune checkpoints hindering antitumor T cell responses is key to the development of next-generation cancer immunotherapies. Here, we report the induction of serotonin transporter (SERT), a regulator of serotonin levels and physiological functions in the brain and peripheral tissues, in tumor-infiltrating CD8 T cells. Inhibition of SERT using selective serotonin reuptake inhibitors (SSRIs), the most widely prescribed antidepressants, significantly suppressed tumor growth and enhanced T cell antitumor immunity in various mouse syngeneic and human xenograft tumor models. Importantly, SSRI treatment exhibited significant therapeutic synergy with programmed cell death protein 1 (PD-1) blockade, and clinical data correlation studies negatively associated intratumoral SERT expression with patient survival in a range of cancers. Mechanistically, SERT functions as a negative-feedback regulator inhibiting CD8 T cell reactivities by depleting intratumoral T cell-autocrine serotonin. These findings highlight the significance of the intratumoral serotonin axis and identify SERT as an immune checkpoint, positioning SSRIs as promising candidates for cancer immunotherapy.

关键词： immune checkpoint cancer immunotherapy serotonin transporter serotonin SSRIs antidepressants T cell antitumor immunity intratumoral serotonin axis PD-1 blockade tumor microenvironment

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28 GBshape: a genome browser database for DNA shape annotations

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Journal of Biomolecular Structure and Dynamics 2015年第SUPP 1期33卷 16页

作者： Tsu-Pei Chiu[a][*] Lin Yang[a] Tianyin Zhou[a] Bradley J. Main[a] Stephen C.J. Parker[b] Sergey V. Nuzhdin[a] Thomas D. Tullius[c] Remo Rohs[a] [a] Molecular and Computational Biology Program Department of Biological Sciences University of Southern California Los Angeles CA 90089 USA[b] Departments of Computational Medicine and Bioinformatics and Human Genetics University of Michigan Ann Arbor MI 48109 USA[c] Department of Chemistry and Program in Bioinformatics Boston University Boston MA 02215 USA

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Capturing the Mechanical Unfolding Pathway of a Large Protein with Coiled‐Coil Probes†

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Angewandte Chemie 2014年第49期126卷

作者： Qing Li Zackary N. Scholl Piotr E. Marszalek Department of Mechanical Engineering and Material Sciences Duke University Durham NC 27708 (USA) Program in Computational Biology and Bioinformatics Duke University Durham NC 27708 (USA)

The folding behaviors and mechanisms of large multidomain proteins have remained largely uncharacterized, primarily because of the lack of appropriate research methods. To address these limitations, novel mechanical folding probes have been developed that are based on antiparallel coiled‐coil polypeptides. Such probes can be conveniently inserted at the DNA level, at different positions within the protein of interest where they minimally disturb the host protein structure. During single‐molecule force spectroscopy measurements, the forced unfolding of the probe captures the progress of the unfolding front through the host protein structure. This novel approach allows unfolding pathways of large proteins to be directly identified. As an example, this probe was used in a large multidomain protein with ten identical ankyrin repeats, and the unfolding pathway, its direction, and the order of sequential unfolding were unequivocally and precisely determined. This development facilitates the examination of the folding pathways of large proteins, which are predominant in the proteasomes of all organisms, but have thus far eluded study because of the technical limitations encountered when using traditional techniques.

关键词： Coiled‐Coil‐Strukturen Einzelmolekülspektroskopie Multidomänenproteine Proteinfaltung Proteinstrukturen

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Estimating Classification Confidence Using Kernel Densities

arXiv

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arXiv 2022年

作者： Salamon, Peter Salamon, David Cantu, V. Adrian An, Michelle Perry, Tyler Edwards, Robert A. Segall, Anca M. The Department of Mathematics and Statistics The Computational Science Research Center The Viral Information Institute San Diego State University San DiegoCA92182 United States The Department of Mathematics and Statistics San Diego State University San DiegoCA92182 United States The Computational Science Research Center The Department of Biology San Diego State University San DiegoCA92182 United States The Perelman School of Medicine The University of Pennsylvania 425 Johnson Pavilion 3610 Hamilton Walk PhiladelphiaPA19104 United States The Computational Science Research Center San Diego State University San DiegoCA92182 United States Google 1600 Amphitheater Parkway Mountain ViewCA94043 United States The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States Helix 9875 Towne Center Drive San DiegoCA92121 United States The Department of Biology The Computational Science Research Center The Viral Information Institute The Bioinformatics and Medical Informatics Program San Diego State University San DiegoCA92182 United States

This paper investigates the post-hoc calibration of confidence for "exploratory" machine learning classification problems. The difficulty in these problems stems from the continuing desire to push the boundaries of which categories have enough examples to generalize from when curating datasets, and confusion regarding the validity of those categories. We argue that for such problems the "one-versus-all" approach (top-label calibration) must be used rather than the "calibrate-the-full-response-matrix" approach advocated elsewhere in the literature. We introduce and test four new algorithms designed to handle the idiosyncrasies of category-specific confidence estimation. Chief among these methods is the use of kernel density ratios for confidence calibration including a novel, bulletproof algorithm for choosing the bandwidth. We test our claims and explore the limits of calibration on a bioinformatics application (PhANNs) [1], [2] as well as the classic MNIST benchmark [3]. Finally, our analysis argues that post-hoc calibration should always be performed, should be based only on the test dataset, and should be sanity-checked visually. Copyright © 2022, The Authors. All rights reserved.

关键词： Calibration

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Theoretical Insights into Co2 Electroreduction on Single and Dual Heteroatom-Doped Diamonds

SSRN

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SSRN 2023年

作者： Injongkol, Yuwanda Zhang, Rui-Qin Montoya, Alejandro Rungrotmongkol, Thanyada Jungsuttiwong, Siriporn Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand City University of Hong Kong Hong Kong School of Chemical and Biomolecular Engineering The University of Sydney SydneyNSW2006 Australia Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand Department of Chemistry Faculty of Science Ubon Ratchathani University Ubon Ratchathani34190 Thailand

We employed Periodic Density Functional Theory (DFT) to investigate the catalytic activity of single and double dopant of boron (B), nitrogen (N), and phosphorous (P) anchored on diamond surfaces for the CO2 reduction reaction (CO2RR) by analyzing reaction energy profiles. Our findings show that the double-doped catalyst exhibits lower energy barriers in CO2RR compared to the single dopants. Specifically, when considering BB and NN configurations, there is a thermodynamic preference towards the formation of formic acid (HCOOH) with overpotentials of 0.40 and 0.09 V, respectively. The co-doped catalyst comprising B and N (BN) demonstrates a tendency towards the formation of HCOOH without requiring any applied overpotentials. Notably, BN outperforms other catalysts, occupying the top position on the volcano plot, indicating the lowest limiting potential (UL), remarkable thermal stability, and the ability to suppress the competing hydrogen evolution reaction (HER). This research provides valuable insights into the product differentiation in the electroreduction of CO2. © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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General framework for the mechanical response of metallic glasses during strain-rate-dependent uniaxial compression

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Physical Review Materials 2023年第10期7卷 105604-105604页

作者： Weiwei Jin Amit Datye Udo D. Schwarz Mark D. Shattuck Corey S. O'Hern Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Department of Chemical and Environmental Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Experimental data on compressive strength σmax versus strain rate ɛ̇eng for metallic glasses undergoing uniaxial compression show varying strain rate sensitivity. For some metallic glasses, σmax decreases with increasing ɛ̇eng, while for others, σmax increases with increasing ɛ̇eng, and for certain alloys σmax versus ɛ̇eng is nonmonotonic. To understand their strain rate sensitivity, we conduct molecular dynamics simulations of metallic glasses undergoing uniaxial compression at finite strain rates and coupled to heat baths with a range of temperatures T0 and damping parameters b. In the T0→0 and b→0 limits, we find that the compressive strength σmax versus temperature T obeys a “chevron-shaped” scaling relation. In the low-strain-rate regime, σmax decreases linearly with increasing T, whereas σmax grows as a power law with decreasing T in the high-strain-rate regime. For T0>0, σmax(T) deviates from the scaling curve at low strain rates, but σmax(T) rejoins the scaling curve as the strain rate increases. Enhanced dissipation reduces compression-induced heating, which causes σmax(T) to deviate from the b→0 scaling behavior for intermediate strain rates, but σmax(T) converges to the high-strain-rate power-law scaling behavior at sufficiently high strain rates. Determining σmax(T) as a function of b and T0 provides a general framework for explaining the strain rate sensitivity of metallic glasses under compression.

关键词： Compressive strength Mechanical deformation Amorphous materials Metallic glasses Molecular dynamics

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Causes and Consequences of Apparent Timescaling Across All Estimated Evolutionary Rates

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Ecology, Evolution, and Systematics 1000年第1期52卷 587-609页

作者： Luke J. Harmon Matthew W. Pennell L. Francisco Henao-Diaz Jonathan Rolland Breanna N. Sipley Josef C. Uyeda 1Institute for Bioinformatics and Evolutionary Studies (IBEST) and Department of Biological Sciences University of Idaho Moscow Idaho 83844 USA email: lukeh@uidaho.edu 2Department of Zoology and Biodiversity Research Centre University of British Columbia Vancouver British Columbia V6T 1Z4 Canada 3Laboratoire Evolution et Diversité Biologique CNRS UMR5174 Université Toulouse III–Paul Sabatier 31062 Toulouse France 4Program for Bioinformatics and Computational Biology University of Idaho Moscow Idaho 83844 USA 5Department of Biological Sciences Virginia Tech University Blacksburg Virginia 24061 USA

Evolutionary rates play a central role in connecting micro- and macroevolution. All evolutionary rate estimates, including rates of molecular evolution, trait evolution, and lineage diversification, share a similar scaling pattern with time: The highest rates are those measured over the shortest time interval. This creates a disconnect between micro- and macroevolution, although the pattern is the opposite of what some might expect: Patterns of change over short timescales predict that evolution has tremendous potential to create variation and that potential is barely tapped by macroevolution. In this review, we discuss this shared scaling pattern across evolutionary rates. We break down possible explanations for scaling into two categories, estimation error and model misspecification, and discuss how both apply to each type of rate. We also discuss the consequences of this ubiquitous pattern, which can lead to unexpected results when comparing ratesover different timescales. Finally, after addressing purely statistical concerns, we explore a few possibilities for a shared unifying explanation across the three types of rates that results from a failure to fully understand and account for how biological processes scale over time.

关键词： speciation extinction genetics phenotypes microevolution macroevolution

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DOMMINO 2.0: Integrating structurally resolved protein-, RNA-, and DNA-mediated Macromolecular interactions

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Database 2016年第2016期2016卷 bav114页

作者： Kuang, Xingyan Dhroso, Andi Han, Jing Ginger Shyu, Chi-Ren Korkin, Dmitry Informatics Institute University of Missouri Columbia MO United States Department of Computer Science Bioinformatics and Computational Biology Program Worcester Polytechnic Institute Worcester MA United States Department of Electrical and Computer Engineering United States Department of Computer Science University of Missouri Columbia MO United States Ohio State University Comprehensive Cancer Center Columbus OH United States

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction's mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein-protein interactions or protein-DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N-termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary intera

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How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2023年 9卷 e45312页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods: We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001

关键词： artificial intelligence chatbot ChatGPT conversational agent education technology generative pre-trained transformer GPT machine learning medical education MedQA natural language processing NLP

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