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High fitness paths can connect proteins with low sequence overlap

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arXiv 2024年

作者： Kantroo, Pranav Wagner, Günter P. Machta, Benjamin B. Computational Biology and Bioinformatics Program Yale University New HavenCT06520 United States Emeritus Department of Ecology and Evolutionary Biology Yale University New HavenCT06520 United States Department of Evolutionary Biology University of Vienna Djerassi Platz 1 ViennaA-1030 Austria Hagler Institute for Advanced Studies Texas A&M College StationTX77843 United States Department of Physics Yale University New HavenCT06520 United States Quantitative Biology Institute Yale University New HavenCT06520 United States

The structure and function of a protein are determined by its amino acid sequence. While random mutations change a protein’s sequence, evolutionary forces shape its structural fold and biological activity. Studies have shown that neutral networks can connect a local region of sequence space by single residue mutations that preserve viability. However, the larger-scale connectedness of protein morphospace remains poorly understood. Recent advances in artificial intelligence have enabled us to computationally predict a protein’s structure and quantify its functional plausibility. Here we build on these tools to develop an algorithm that generates viable paths between distantly related extant protein pairs. The intermediate sequences in these paths differ by single residue changes over subsequent steps – substitutions, insertions and deletions are admissible moves. Their fitness is evaluated using the protein language model ESM2, and maintained as high as possible subject to the constraints of the traversal. We document the qualitative variation across paths generated between progressively divergent protein pairs, some of which do not even acquire the same structural fold. The ease of interpolating between two sequences could be used as a proxy for the likelihood of homology between them. © 2024, CC BY.

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Continuous Spatiotemporal Transformers

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arXiv 2023年

作者： de Oliveira Fonseca, Antonio Henrique Zappala, Emanuele Caro, Josue Ortega van Dijk, David Interdepartmental Neuroscience Program Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Neuroscience Yale University New HavenCT United States Wu Tsai Institute Yale University New HavenCT United States Yale University New HavenCT United States Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT United States

Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of transformers in modeling continuous dynamical systems is that they are fundamentally discrete time and space models and thus have no guarantees regarding continuous sampling. To address this challenge, we present the Continuous Spatiotemporal Transformer (CST), a new transformer architecture that is designed for modeling of continuous systems. This new framework guarantees a continuous and smooth output via optimization in Sobolev space. We benchmark CST against traditional transformers as well as other spatiotemporal dynamics modeling methods and achieve superior performance in a number of tasks on synthetic and real systems, including learning brain dynamics from calcium imaging data. © 2023, CC BY-NC-SA.

关键词： Sobolev spaces

Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling

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arXiv 2024年

作者： Liu, Zhuoyi Grigas, Alex T. Sumner, Jacob Knab, Edward Davis, Caitlin M. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs Nr that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, Nr/N 0.08, where N is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo. © 2024, CC BY.

关键词： Conformations

Estimating Causal Effects using a Multi-task Deep Ensemble

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arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

Hypostatic jammed packings of frictionless nonspherical particles

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Physical Review E 2018年第1期97卷 012909-012909页

作者： Kyle VanderWerf Weiwei Jin Mark D. Shattuck Corey S. O'Hern Department of Physics Yale University New Haven Connecticut 06520 USA Department of Mechanics and Engineering Science Peking University Beijing 100871 China Department of Mechanical Engineering & Materials Science 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 Applied Physics Yale University New Haven Connecticut 06520 USA Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

We perform computational studies of static packings of a variety of nonspherical particles including circulo-lines, circulo-polygons, ellipses, asymmetric dimers, dumbbells, and others to determine which shapes form packings with fewer contacts than degrees of freedom (hypostatic packings) and which have equal numbers of contacts and degrees of freedom (isostatic packings), and to understand why hypostatic packings of nonspherical particles can be mechanically stable despite having fewer contacts than that predicted from naive constraint counting. To generate highly accurate force- and torque-balanced packings of circulo-lines and cir-polygons, we developed an interparticle potential that gives continuous forces and torques as a function of the particle coordinates. We show that the packing fraction and coordination number at jamming onset obey a masterlike form for all of the nonspherical particle packings we studied when plotted versus the particle asphericity A, which is proportional to the ratio of the squared perimeter to the area of the particle. Further, the eigenvalue spectra of the dynamical matrix for packings of different particle shapes collapse when plotted at the same A. For hypostatic packings of nonspherical particles, we verify that the number of “quartic” modes along which the potential energy increases as the fourth power of the perturbation amplitude matches the number of missing contacts relative to the isostatic value. We show that the fourth derivatives of the total potential energy in the directions of the quartic modes remain nonzero as the pressure of the packings is decreased to zero. In addition, we calculate the principal curvatures of the inequality constraints for each contact in circulo-line packings and identify specific types of contacts with inequality constraints that possess convex curvature. These contacts can constrain multiple degrees of freedom and allow hypostatic packings of nonspherical particles to be mechanically stable.

关键词： Granular materials Granular packing Jamming

Plant scent and plant-insect interactions—Review and outlook from a macroevolutionary perspective

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植物分类学报 2023年第3期61卷 465-486页

作者： Orlando Schwery Breanna N.Sipley Mariana P.Braga Yan Yang Roberto Rebollo Pengjuan Zu New Mexico Consortium 4200 W.Jemez RdSuite 301Los AlamosNM 87544USA Department of Biological Sciences University of Idaho875 Perimeter Dr.MS 3051MoscowID 83844USA Department of Biological Sciences Southeastern Louisiana University2400 N.Oak StHammondLA 70402USA Program for Bioinformatics and Computational Biology University of IdahoMoscowID 83844USA Department of Biology Washington University in St.LouisSt.LouisMO 63130USA Department of Ecology Swedish University of Agricultural SciencesUppsalaSweden Helsinki Life Science Institute University of HelsinkiHelsinkiFinland Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants Ministry of EducationCollege of ForestryHainan UniversityHaikou 570228P.R.China Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Department of Environmental Systems Science ETH ZurichSchmelzbergstrasse 9CH-8092 ZurichSwitzerland Swiss Federal Institute of Aquatic Science and Technology(Eawag) Seestrasse 79CH-6047 KastanienbaumSwitzerland

The astonishing diversity of plants and insects and their entangled interactions are cornerstones in terrestrial ***-occurring with species diversity is the diversity of plant secondary metabolites(PSMs).So far,their estimated number is more than 200 000 compounds,which are not directly involved in plant growth and development but play important roles in helping plants handle their environment including the mediation of plant-insect ***,we use plant volatile organic compounds(VOCs),a key olfactory communication channel that mediates plant-insect interactions,as a showcase of *** spite of the cumulative knowledge of the functional,ecological,and microevolutionary roles of VOCs,we still lack a macroevolutionary understanding of how they evolved with plant-insect interactions and contributed to species diversity throughout the long coevolutionary history of plants and *** first review the literature to summarize the current state-of-the-art research on this *** then present various relevant types of phylogenetic methods suitable to answer macroevolutionary questions on plant VOCs and suggest future directions for employing phylogenetic approaches in studying plant VOCs and plant-insect ***,we found that current studies in this field are still very limited in their macroevolutionary ***,with the fast-growing development of metabolome analysis techniques and phylogenetic methods,it is becoming increasingly feasible to integrate the advances of these two *** highlight promising approaches to generate new testable hypotheses and gain a mechanistic understanding of the macroevolutionary roles of chemical communication in plant-insect interactions.

关键词： coevolution

Integrating yeast chemical genomics and mammalian cell pathway analysis

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中国药理学报（英文版） 2019年第9期40卷 1245-1255页

作者： Fu-lai Zhou Sheena C Li Yue Zhu Wan-jing Guo Li-jun Shao Justin Nelson Scott Simpkins De-hua Yang Qing Liu Yoko Yashiroda Jin-biao Xu Yao-yue Fan Jian-min Yue Minoru Yoshida Tian Xia Chad L Myers Charles Boone Ming-wei Wang The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of Sciences(CAS)Shanghai 201203China University of Chinese Academy of Sciences Beijing 100049China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Bioinformatics and Computational Biology Program University of Minnesota-Twin CitiesMinneapolisMinnesota 55455USA The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research Shanghai Institute of Materia MedicaChinese Academy of Sciences(CAS)Shanghai 201203China The State Key Laboratory of Drug Research Shanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Department of Biology The University of TokyoBunkyo-kuTokyo 1138657Japan Collaborative Research for Innovative Microbiology The University of TokyoBunkyo-kuTokyo 1138657Japan Department of Electronics and Information Engineering Huazhong University of Science and TechnologyWuhan 430074China RIKEN Center for Sustainable Resource Science WakoSaitama 3510198Japan Donnelly Centre and Department of Molecular Genetics University of TorontoOntario M5S 3E1Canada

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces ***,we use yeast as a surrogate system for studying compounds that are active against metazoan ***-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target *** discover compounds that have the potential to function like therapeutic agents with known targets,we also analyzed a reference library of approved *** uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/β-catenin signal transduction were further examined in mammalian cells,and new modulators with specific modes of action were *** analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

关键词： yeast chemical genomics tubulin cytoskeleton assembly autophagy Wnt/β-catenin signaling pathway

相关性和一致性:这对相仿概念和测量方法的回顾与阐明（英文）

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上海精神医学 2016年第2期28卷 115-120页

作者： Liu JY Tang W Chen GQ Lu Y Feng CY Tu XM Department of Biostatistics and Computational Biology University of Rochester Rochester NY USA Department of Biostatistics & Bioinformatics School of Public Health and Tropical Medicine Tulane University New Orleans LA USA VA Cooperative Studies Program Palo Alto Coordinating Center VA Palo Alto Health Care System Palo Alto CA USA Department of Biomedical Data Science Stanford University Stanford CA USA

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以... 详细信息

一致性(agreement)和相关性(correlation)是两个广泛使用的概念,用来评估变量之间的关联。虽然二者相似且相关,但是它们代表关联完全不同的概念。评估变量之间的一致性假设变量测量的是相同的结构,而在变量测量完全不同的结构时也可以评估它们之间的相关性。这种概念上的差异就要求使用不同的统计方法,并且当评估一致性或相关性时,统计方法根据数据的分布和研究者的兴趣可能会有所不同。例如,Pearson相关性,作为评估连续变量之间相关性的一种普遍测量方法,只有用于符合线性关系的变量时才能提供有用的信息;当用于不符合线性关系的变量时就无法提供准确信息甚至会产生误导。同样地,内部相关性,作为一种评估连续变量之间一致性的常用方法,如果一致性不好的实质正好是研究兴趣所在,那么该测量就不能为研究者提供充分的信息。本报告回顾了一致性和相关性的概念,并讨论了几种常用方法在应用中的差异。

关键词：积差相关性内部一致性 Kendall's tau 非线性相关 Pearson's相关性 Spearman's rho

Graph Fourier MMD for Signals on Graphs

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Graph Fourier MMD for Signals on Graphs

International Conference on Sampling Theory and Applications (SampTA)

作者： Samuel Leone Aarthi Venkat Guillaume Huguet Alexander Tong Guy Wolf Smita Krishnaswamy Applied Math Program New Haven Connecticut Computational Biology & Bioinformatics Program Yale University New Haven Connecticut Dept. of Math. & Stat Mila - Quebec AI Institute Université de Montréal Montreal Canada Dept. of Comp. Sci. & Oper. Res. Mila-Quebec AI Institute Université de Montréal Montreal Canada Department of Computer Science Department of Genetics Yale University New Haven Connecticut

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes the difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called gene localization score which helps select genes for cellular state space characterization.

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