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MOST+: A de novo motif finding approach combining genomic sequence and heterogeneous genome-wide signatures

BMC Genomics 2015年第7期16卷 S13-S13页

作者： Zhang, Yizhe He, Yupeng Zheng, Guangyong Wei, Chaochun Shanghai Jiao Tong University School of Life Sciences and Biotechnology 800 Dongchuan Road Shanghai 200240 China Shanghai Center for Bioinformation Technology 1278 Keyuan Road Pudong District Shanghai 201203 China University of California Bioinformatics and Systems Biology Program San Diego 9500 Gilman Drive La Jolla 92093 CA United States Shanghai Institutes for Biological Sciences Chinese Academy of Sciences CAS-MPG Partner Institute for Computational Biology 320 Yueyang Road Shanghai 200031 China Duke University Program in Computational Biology and Bioinformatics Durham 27708 NC United States

Background: Motifs are regulatory elements that will activate or inhibit the expression of related genes when proteins (such as transcription factors, TFs) bind to them. Therefore, motif finding is important to understand the mechanisms of gene regulation. De novo discovery of regulatory elements, like transcription factor binding sites (TFBSs), has long been a major challenge to gain insight on mechanisms of gene regulation. Recent advances in experimental profiling of genome-wide signals such as histone modifications and DNase I hypersensitivity sites allow scientists to develop better computational methods to enhance motif discovery. However, existing methods for motif finding suffer from high false positive rates and slow speed, and it's difficult to evaluate the performance of these methods systematically. Result: Here we present MOST+, a motif finder integrating genomic sequences and genome-wide signals such as intensity and shape features from histone modification marks and DNase I hypersensitivity sites, to improve the prediction accuracy. MOST+ can detect motifs from a large input sequence of about 100 Mbs within a few minutes. Systematic comparison method has been established and MOST+ has been compared with existing methods. Conclusion: MOST+ is a fast and accurate de novo method for motif finding by integrating genomic sequence and experimental signals as clues. © 2015 Zhang et al.;licensee BioMed Central Ltd.

关键词： ChIP-seq De novo Motif finding DNase I hypersensitivity sites Epigenetics Suffix tree

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PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion

arXiv

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arXiv 2024年

作者： Tang, Sophia Zhang, Yinuo Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Peptide therapeutics, a major class of medicines, have achieved remarkable success across diseases such as diabetes and cancer, with landmark examples such as GLP-1 receptor agonists revolutionizing the treatment of type-2 diabetes and obesity. Despite their success, designing peptides that satisfy multiple conflicting objectives, such as target binding affinity, solubility, and membrane permeability, remains a major challenge. Classical drug development and target structure-based design are ineffective for such tasks, as they fail to optimize global functional properties critical for therapeutic efficacy. Existing generative frameworks are largely limited to continuous spaces, unconditioned outputs, or single-objective guidance, making them unsuitable for discrete sequence optimization across multiple properties. To address this, we present PepTune, a multi-objective discrete diffusion model for the simultaneous generation and optimization of therapeutic peptide SMILES. Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures valid peptide structures with bond-dependent masking schedules and penalty-based objectives. To guide the diffusion process, we propose a Monte Carlo Tree Search (MCTS)-based strategy that balances exploration and exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates classifier-based rewards with search-tree expansion, overcoming gradient estimation challenges and data sparsity. Using PepTune, we generate diverse, chemically modified peptides optimized for multiple therapeutic properties, including target binding affinity, membrane permeability, solubility, hemolysis, and non-fouling for various disease-relevant targets. In total, our results demonstrate that MCTS-guided masked discrete diffusion is a powerful and modular approach for multi-objective sequence design in discrete state spaces. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

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Connecting polymer collapse and the onset of jamming

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Physical Review E 2024年第3期109卷 034406-034406页

作者： Alex T. Grigas Aliza Fisher Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering and 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 Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Previous studies have shown that the interiors of proteins are densely packed, reaching packing fractions that are as large as those found for static packings of individual amino-acid-shaped particles. How can the interiors of proteins take on such high packing fractions given that amino acids are connected by peptide bonds and many amino acids are hydrophobic with attractive interactions? We investigate this question by comparing the structural and mechanical properties of collapsed attractive disk-shaped bead-spring polymers to those of three reference systems: static packings of repulsive disks, of attractive disks, and of repulsive disk-shaped bead-spring polymers. We show that the attractive systems quenched to temperatures below the glass transition T≪Tg and static packings of both repulsive disks and bead-spring polymers possess similar interior packing fractions. Previous studies have shown that static packings of repulsive disks are isostatic at jamming onset, i.e., the number of interparticle contacts Nc matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymer packings are hypostatic at jamming onset (i.e., with fewer contacts than degrees of freedom) but are effectively isostatic when including stabilizing quartic modes, which give rise to quartic scaling of the potential energy with displacements along these modes. While attractive disk and polymer packings are often considered hyperstatic with excess contacts over the isostatic number, we identify a definition for interparticle contacts for which they can also be considered as effectively isostatic. As a result, we show that the mechanical properties (e.g., scaling of the potential energy with excess contact number and low-frequency contribution to the density of vibrational modes) of weakly attractive disk and polymer packings are similar to those of isostatic repulsive disk and polymer packings. Our results demonstrate that static

关键词： Jamming Polymer folding Protein folding Single polymer chains

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The mTORC1 complex in pre-osteoblasts regulates whole-body energy metabolism independently of osteocalcin

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Bone Research 2021年第1期9卷 109-125页

作者： Pawanrat Tangseefa Sally K.Martin Peck Yin Chin James Breen Chui Yan Mah Paul A.Baldock Gary A.Wittert Amanda J.Page Christopher G.Proud Stephen Fitter Andrew C.W.Zannettino Adelaide Medical School Faculty of Health and Medical ScienceUniversity of AdelaideAdelaide5005 SAAustralia Cancer Program Precision Medicine ThemeSouth Australian Health and Medical Research InstituteAdelaide5000 SAAustralia Research Centre for Reproductive Health School of Paediatrics and Reproductive HealthUniversity of AdelaideAdelaide5005 SAAustralia Computational&Systems Biology program Precision Medicine ThemeSouth Australian Health and Medical Research InstituteAdelaide5000 SAAustralia Robinson Research Institute University of AdelaideAdelaideSA 5005Australia Bioinformatics Hub University of AdelaideAdelaideSA 5005Australia Freemasons Foundation Centre for Men’s Health University of AdelaideAdelaide5005 SAAustralia Skeletal Metabolism Group Garvan Institute of Medical ResearchSydney2010 NSWAustralia Nutrition Diabetes&Metabolism ProgramLifelong Health ThemeSouth Australian Health and Medical Research InstituteAdelaide5000 SAAustralia School of Biological Sciences University of AdelaideAdelaide5005 SAAustralia Central Adelaide Local Health Network AdelaideSAAustralia

Overnutrition causes hyperactivation of mTORC1-dependent negative feedback loops leading to the downregulation of insulin signaling and development of insulin *** osteoblasts(OBs),insulin signaling plays a crucial role in the control of systemic glucose *** utilized mice with conditional deletion of Rptor to investigate how the loss of mTORC1 function in OB affects glucose metabolism under normal and overnutrition dietary *** to the controls,chow-fed Rptorob−/−mice had substantially less fat mass and exhibited adipocyte ***,upon feeding with high-fat diet,mice with pre-and post-natal deletion of Rptor in OBs were protected from diet-induced obesity and exhibited improved glucose metabolism with lower fasting glucose and insulin levels,increased glucose tolerance and insulin *** leanness and resistance to weight gain was not attributable to changes in food intake,physical activity or lipid absorption but instead was due to increased energy expenditure and greater whole-body substrate ***-seq revealed an increase in glycolysis and skeletal insulin signaling pathways,which correlated with the potentiation of insulin signaling and increased insulin-dependent glucose uptake in Rptorknockout ***,these findings point to a critical role for the mTORC1 complex in the skeletal regulation of wholebody glucose metabolism and the skeletal development of insulin resistance.

关键词： metabolism mTORC1 homeostasis

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Deciphering sequence and structure features of disease-causing small insertions and deletions

Deciphering sequence and structure features of disease-causi...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Xinjun Zhang Huiying Zhao Yadong Wang Guohua Wang Yaoqi Zhou Yunlong Liu School of Informatics and Computing Indiana UniversityBloomingtonIN47408USA Center for Computational Biology and Bioinformatics Indiana University School of MedicineIndianapolisIN 46202USA Bioinformatics program School of InformaticsIndiana University Purdue University IndianapolisIndianapolisIN 46202USA School of Computer Sciences and Technology Harbin Institute of Technology Department of Medical and Molecular Genetics Indiana University School of MedicineIndianapolisIN 46202USA

Background:Small insertions and deletions(INDELs) compose of the second largest category of genetic variants(next to single nucleotide polymorphism) in the human genome, which accounts for 18%of all the variants *** from the 1,000 Genome Project indicated an estimate of 1 million INDELs per human *** to SNPs(Single Nucleotide Polymorphisms) and large structural variations,INDELs are of significant interest due to their potentials in affecting gene molecular functions and therefore cause *** of its functional importance,lacking a highly accurate bioinformatics tool for INDEL classifications has become a major obstacle in understanding the molecular functions. Methods:In this study,we develop a bioinformatics tool for INDEL *** constructed a training dataset that includes all the disease-causing and neutral INDELs documented in the Human Genome Mutation Database(HGMD) and 1,000 Genome *** the two sets of INDELs,we systematically investigated a series of features on their potentials in disrupting both protein structures and pre-mRNA *** then designed a predictor to best distinguish the disease-causing and neutral INDELs using machine-learning technique. Results:In this study,we focused on the disease relevance of the INDELs in the exonic regions,which include 25,923 and 4,643 disease-causing and neutral INDELs,*** found that the disease-causing INDELs more likely occur in predicted structured regions and neutral INDELs are more likely in unstructured,intrinsically disordered *** also found that INDEL sites matched more to homologous sequences are more like *** RNA processing, we found disease-causing INDELs are twice likely to disrupt binding sites of RNA-binding proteins. We also found that disease-causing INDELs tend to be longer,and be closer to the splice *** addition,the genomic loci for disease-causing INDELs are more evolutionarily conserved,and tend to locat

关键词： insertion deletions INDELs genetic variants next generation sequencing

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The Genomic Landscape of Pediatric Myelodysplastic Syndromes

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BLOOD 2016年第22期128卷 956-956页

作者： Schwartz, Jason R. Walsh, Michael P. Ma, Jing Lamprecht, Tamara Wang, Shuoguo Wu, Gang Easton, John Ribeiro, Raul C. Klco, Jeffery M. Department of Oncology St. Jude Children's Research Hospital Memphis TN Department of Pathology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology and Bioinformatics St. Jude Children's Research Hospital Memphis TN Department of Oncology and Global Medicine International Outreach Program St. Jude Children's Research Hospital Memphis TN

956

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Comparative Genomics of Human Adenovirus Types 7,14 and 55 Emergent and Re-emergent in China:New Epidemic Threats

Comparative Genomics of Human Adenovirus Types 7,14 and 55 E...

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第五届武汉现代病毒学国际研讨会

作者： Qiwei Zhang Donald Seto Suhui Zhao Bin Cao Changbing Wang Bing Zhu Yi Chen Changwen Ke Lirong Zou Jie Zhou Shuping Jing Zetao Cheng Chengsong Wan School of Public Health and Tropical Medicine Southern Medical University Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason UniversityManassasVAUSA Department of Infectious Diseases and Clinical Microbiology Beijing Chao-Yang Hospital Central Laboratory Guangzhou Women and Children's Medical Center Guangdong Provincial Center for Diseases Control and Prevention

Human adenovirus types 7,14 and 55(HAdV-B7,-B14 and -B55) caused several acute respiratory disease(ARD) outbreaks in America,Europe and Asia recently,including in *** genome of HAdV-B14 firstly isolated in China by us in 2010,is highly similar with those causing ARD outbreaks in US during 2006-2009,with 15 mismatches,3 deletions, 7 insertions and 99.92%identity,all belonging to the same genotype HAdV-B14p1 by

关键词： Comparative Genomics of Human Adenovirus Types 7,14 and 55 Emergent and Re-emergent in China:New Epidemic Threats

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computational approaches to understanding protein aggregation in neurodegeneration

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Journal of Molecular Cell biology 2014年第2期8卷 104-115页

作者： Rachel L. Redler David Shirvanyants Onur Dagliyan Feng Ding Doo Nam Kim Pradeep Kota Elizabeth A. Proctor Srinivas Ramachandran Arpit Tandon Nikolay V. Dokholyan Department of Biochemistry and Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Program in Cellular and Molecular Biophysics University of North Carolina at Chapel Hill Chapel Hill NC USA Curriculum in Bioinformatics and Computational Biology University of North Carolina at Chapel Hill Chapel Hill NC USA Center for Computational and Systems Biology University of NorthCarolina at Chapel Hill chapel Hill NC USA Department of Physics and Astronomy Ctemson University Ctemson SC USA Cellular Growth Mechanisms Section Laboratory of Cell and Developmental Signaling Frederick National Laboratory for Cancer Research Frederick MD USA Division of Basic Sciences Fred Hutchison Cancer Research Center Seatt{e WA USA

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer＇s disease, Parkinson＇s disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggre- gation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute signifi- cantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. computational methods, particularly those combine both aU-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

关键词： protein aggregation molecular dynamics protein folding neurodegeneration

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Compressed diffusion

arXiv

引用

arXiv 2019年

作者： Gigante, Scott Stanley, Jay S. Vu, Ngan van Dijk, David Moon, Kevin R. Wolf, Guy Krishnaswamy, Smita Computational Biology and Bioinformatics Program Department of Computer Science Department of Genetics Yale University New HavenCT United States Department of Mathematics and Statistics Utah State University LoganUT United States Department of Mathematics and Statistics Université de Montréal MontéalQC Canada

Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most kernel methods, its implementation requires a heavy computational load, reaching up to cubic complexity in the number of data points. This limits its usability in modern data analysis. Here, we present a new approach to computing the diffusion geometry, and related embeddings, from a compressed diffusion process between data regions rather than data points. Our construction is based on an adaptation of the previously proposed measure-based Gaussian correlation (MGC) kernel that robustly captures the local geometry around data points. We use this MGC kernel to efficiently compress diffusion relations from pointwise to data region resolution. Finally, a spectral embedding of the data regions provides coordinates that are used to interpolate and approximate the pointwise diffusion map embedding of data. We analyze theoretical connections between our construction and the original diffusion geometry of diffusion maps, and demonstrate the utility of our method in analyzing big datasets, where it outperforms competing approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

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Current Opinion in Structural biology 2023年 78卷 102517页

作者： Banerjee, Anupam Saha, Satyaki Tvedt, Nathan C. Yang, Lee-Wei Bahar, Ivet Computational and Systems Biology University of Pittsburgh School of Medicine Pittsburgh 15261 PA United States Computational and Applied Mathematics and Statistics The College of William and Mary Williamsburg 23185 VA United States Institute of Bioinformatics and Structural Biology and PhD Program in Biomedical Artificial Intelligence National Tsing Hua University Hsinchu 300044 Taiwan Physics Division National Center for Theoretical Sciences Taipei 106319 Taiwan

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities. © 2022 The Authors

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