IU Indianapolis ScholarWorks :: Browsing by Author "Dunker, A. Keith"

Browsing by Author "Dunker, A. Keith"

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2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research
(BMC, 2010-11-29) Yang, Jack Y.; Niemierko, Andrzej; Bajcsy, Ruzena; Xu, Dong; Athey, Brian D.; Zhang, Aidong; Ersoy, Okan K.; Li, Guo-zheng; Borodovsky, Mark; Zhang, Joe C.; Arabnia, Hamid R.; Deng, Youping; Dunker, A. Keith; Liu, Yunlong; Ghafoor, Arif; Medicine, School of Medicine
Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine.
A Comparative Experimental and Computational Study on the Nature of the Pangolin-CoV and COVID-19 Omicron
(MDPI, 2024-07-09) Wei, Lai; Song, Lihua; Dunker, A. Keith; Foster, James A.; Uversky, Vladimir N.; Goh, Gerard Kian-Meng; Biochemistry and Molecular Biology, School of Medicine
The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.
A Study on the Nature of SARS-CoV-2 Using the Shell Disorder Models: Reproducibility, Evolution, Spread, and Attenuation
(MDPI, 2022-09-23) Goh, Gerard Kian-Meng; Dunker, A. Keith; Foster, James A.; Uversky, Vladimir N.; Biochemistry and Molecular Biology, School of Medicine
The basic tenets of the shell disorder model (SDM) as applied to COVID-19 are that the harder outer shell of the virus shell (lower PID-percentage of intrinsic disorder-of the membrane protein M, PIDM) and higher flexibility of the inner shell (higher PID of the nucleocapsid protein N, PIDN) are correlated with the contagiousness and virulence, respectively. M protects the virion from the anti-microbial enzymes in the saliva and mucus. N disorder is associated with the rapid replication of the virus. SDM predictions are supported by two experimental observations. The first observation demonstrated lesser and greater presence of the Omicron particles in the lungs and bronchial tissues, respectively, as there is a greater level of mucus in the bronchi. The other observation revealed that there are lower viral loads in 2017-pangolin-CoV, which is predicted to have similarly low PIDN as Omicron. The abnormally hard M, which is very rarely seen in coronaviruses, arose from the fecal-oral behaviors of pangolins via exposure to buried feces. Pangolins provide an environment for coronavirus (CoV) attenuation, which is seen in Omicron. Phylogenetic study using M shows that COVID-19-related bat-CoVs from Laos and Omicron are clustered in close proximity to pangolin-CoVs, which suggests the recurrence of interspecies transmissions. Hard M may have implications for long COVID-19, with immune systems having difficulty degrading viral proteins/particles.
Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms
(Springer (Biomed Central Ltd.), 2014) Yang, Jack Y.; Dunker, A. Keith; Liu, Jun S.; Qin, Xiang; Arabnia, Hamid R.; Yang, William; Niemierko, Andrzej; Chen, Zhongxue; Luo, Zuojie; Wang, Liangjiang; Liu, Yunlong; Xu, Dong; Deng, Youping; Tong, Weida; Yang, Mary Qu; Department of Biochemistry and Molecular Biology, IU School of Medicine
Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.
AI for infectious disease modelling and therapeutics
(World Scientific, 2020-11) Alterovitz, Gil; Alterovitz, Wei-Lun; Cassell, Gail H.; Zhang, Lixin; Dunker, A. Keith; Biochemistry and Molecular Biology, School of Medicine
AI for infectious disease modelling and therapeutics is an emerging area that leverages new computational approaches and data in this area. Genomics, proteomics, biomedical literature, social media, and other resources are proving to be critical tools to help understand and solve complicated issues ranging from understanding the process of infection, diagnosis and discovery of the precise molecular details, to developing possible interventions and safety profiling of possible treatments.
An assignment of intrinsically disordered regions of proteins based on NMR structures
(Elsevier, 2013) Ota, Motonori; Koike, Ryotaro; Amemiya, Takayuki; Tenno, Takeshi; Romero, Pedro R.; Hiroaki, Hidekazu; Dunker, A. Keith; Fukuchi, Satoshi; Center for Computational Biology and Bioinformatics, School of Medicine
Intrinsically disordered proteins (IDPs) do not adopt stable three-dimensional structures in physiological conditions, yet these proteins play crucial roles in biological phenomena. In most cases, intrinsic disorder manifests itself in segments or domains of an IDP, called intrinsically disordered regions (IDRs), but fully disordered IDPs also exist. Although IDRs can be detected as missing residues in protein structures determined by X-ray crystallography, no protocol has been developed to identify IDRs from structures obtained by Nuclear Magnetic Resonance (NMR). Here, we propose a computational method to assign IDRs based on NMR structures. We compared missing residues of X-ray structures with residue-wise deviations of NMR structures for identical proteins, and derived a threshold deviation that gives the best correlation of ordered and disordered regions of both structures. The obtained threshold of 3.2Å was applied to proteins whose structures were only determined by NMR, and the resulting IDRs were analyzed and compared to those of X-ray structures with no NMR counterpart in terms of sequence length, IDR fraction, protein function, cellular location, and amino acid composition, all of which suggest distinct characteristics. The structural knowledge of IDPs is still inadequate compared with that of structured proteins. Our method can collect and utilize IDRs from structures determined by NMR, potentially enhancing the understanding of IDPs.
Changes in predicted protein disorder tendency may contribute to disease risk
(Springer Nature, 2011) Hu, Yang; Liu, Yunlong; Jung, Jeesun; Dunker, A. Keith; Wang, Yadong; Medical and Molecular Genetics, School of Medicine
Background: Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort. Results: Using the exonic single nucleotide variations (SNVs) identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk. Conclusions: After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.
Characterization of intrinsically disordered regions in proteins informed by human genetic diversity
(PLOS, 2022-03-11) Ahmed, Shehab S.; Rifat, Zaara T.; Lohia, Ruchi; Campbell, Arthur J.; Dunker, A. Keith; Rahman, M. Sohel; Iqbal, Sumaiya; Biochemistry and Molecular Biology, School of Medicine
All proteomes contain both proteins and polypeptide segments that don't form a defined three-dimensional structure yet are biologically active-called intrinsically disordered proteins and regions (IDPs and IDRs). Most of these IDPs/IDRs lack useful functional annotation limiting our understanding of their importance for organism fitness. Here we characterized IDRs using protein sequence annotations of functional sites and regions available in the UniProt knowledgebase ("UniProt features": active site, ligand-binding pocket, regions mediating protein-protein interactions, etc.). By measuring the statistical enrichment of twenty-five UniProt features in 981 IDRs of 561 human proteins, we identified eight features that are commonly located in IDRs. We then collected the genetic variant data from the general population and patient-based databases and evaluated the prevalence of population and pathogenic variations in IDPs/IDRs. We observed that some IDRs tolerate 2 to 12-times more single amino acid-substituting missense mutations than synonymous changes in the general population. However, we also found that 37% of all germline pathogenic mutations are located in disordered regions of 96 proteins. Based on the observed-to-expected frequency of mutations, we categorized 34 IDRs in 20 proteins (DDX3X, KIT, RB1, etc.) as intolerant to mutation. Finally, using statistical analysis and a machine learning approach, we demonstrate that mutation-intolerant IDRs carry a distinct signature of functional features. Our study presents a novel approach to assign functional importance to IDRs by leveraging the wealth of available genetic data, which will aid in a deeper understating of the role of IDRs in biological processes and disease mechanisms.
Classification of Intrinsically Disordered Regions and Proteins
(American Chemical Society, 2014-07-09) van der Lee, Robin; Buljan, Marija; Lang, Benjamin; Weatheritt, Robert J.; Daughdrill, Gary W.; Dunker, A. Keith; Fuxreiter, Monika; Gough, Julian; Gsponer, Joerg; Jones, David T.; Kim, Philip M.; Kriwacki, Richard W.; Oldfield, Christopher J.; Pappu, Rohit V.; Tompa, Peter; Uversky, Vladimir N.; Wright, Peter E.; Babu, M. Madan; Department of Biochemistry & Molecular Biology, IU School of Medicine
Comparing NMR and X-ray protein structure: Lindemann-like parameters and NMR disorder
(Taylor & Francis, 2017) Faraggi, Eshel; Dunker, A. Keith; Sussman, Joel L.; Klockowski, Andrzej; Biochemistry and Molecular Biology, School of Medicine
Disordered protein chains and segments are fast becoming a major pathway for our understanding of biological function, especially in more evolved species. However, the standard definition of disordered residues: the inability to constrain them in X-ray derived structures, is not easily applied to NMR derived structures. We carry out a statistical comparison between proteins whose structure was resolved using NMR and using X-ray protocols. We start by establishing a connection between these two protocols for obtaining protein structure. We find a close statistical correspondence between NMR and X-ray structures if fluctuations inherent to the NMR protocol are taken into account. Intuitively this tends to lend support to the validity of both NMR and X-ray protocols in deriving biomolecular models that correspond to in vivo conditions. We then establish Lindemann-like parameters for NMR derived structures and examine what order/disorder cutoffs for these parameters are most consistent with X-ray data and how consistent are they. Finally, we find critical value of for the best correspondence between X-ray and NMR derived order/disorder assignment, judged by maximizing the Matthews correlation, and a critical value if a balance between false positive and false negative prediction is sought. We examine a few non-conforming cases, and examine the origin of the structure derived in X-ray. This study could help in assigning meaningful disorder from NMR experiments.

Browsing by Author "Dunker, A. Keith"

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