Outcome of a Workshop on Applications of Protein Models in Biomedical Research

Schwede, Torsten; Šali, Andrej; Honig, Barry; Levitt, Michael; Berman, Helen M.; Jones, David T.; Brenner, Steven E.; Burley, S.K.; Das, Rhiju; Dokholyan, Nikolay V.; Dunbrack, Roland L.; Fidelis, Krzysztof; Fiser, András; Godzik, Adam; Huang, Yuanpeng Janet; Humblet, Christine; Jacobson, Matthew P.; Joachimiak, Andrzej; Krystek, Stanley R.; Kortemme, Tanja; Kryshtafovych, Andriy; Montelione, Gaetano T.; Moult, John; Murray, Diana; Sánchez, Roberto; Sosnick, Tobin R.; Standley, Daron M.; Stouch, Terry R.; Vajda, Sándor; Vásquez, Maximiliano; Westbrook, John D.; Wilson, Ian A.

doi:10.1016/j.str.2008.12.014

Cited by 140 publications

(125 citation statements)

References 100 publications

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“…The variant found here (G->A) results in a missense mutation in the gene, replacing a glycine residue with serine at amino acid 141. This position is predicted to occur within an alpha-helical membrane spanning region of the protein where the polar alcohol side group of serine could impact the protein side chain interactions within the alpha-helix and thus secondary and tertiary structure of the protein based on sequence homology with other highly conserved Eukaryotes for which the structure is experimentally known (Schwede et al, 2009). …”

Section: Resultsmentioning

confidence: 99%

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing

Morris

Zhu

et al. 2017

Cell Reports

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Summary A number of mitochondrial diseases arise from Single Nucleotide Variant (SNV) accumulation in multiple mitochondria. Here we present a method for identification of variants present at the single mitochondrion level in individual mouse and human neuronal cells allowing for extremely high resolution study of mitochondrial mutation dynamics. We identified extensive heteroplasmy between individual mitochondrion, along with three high confidence variants in mouse and one in human that were present in multiple mitochondria across cells. The pattern of variation revealed by single mitochondrion data shows surprisingly pervasive levels of heteroplasmy in inbred mice. Distribution of SNV loci suggests inheritance of variants across generations resulting in Poisson jackpot lines with large SNV load. Comparison of human and mouse variants suggests that the two species might employ distinct modes of somatic segregation. Single mitochondrion resolution revealed mitochondria mutational dynamics that we hypothesize to affect risk probabilities for mutations reaching disease thresholds.

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Section: Resultsmentioning

confidence: 99%

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing

Morris

Zhu

et al. 2017

Cell Reports

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“…Over the past few decades, there has been continuous progress and improvement in protein homology modeling methods [69, 70]. Protein homology models are now routinely used in various bio-medical applications, including drug discovery [71]. Similar to proteins, homologous RNAs also frequently share similar tertiary structures, and therefore can be modelled using homology methods.…”

Section: Progress and Challenges In Rna 3d Modeling Methodsmentioning

confidence: 99%

Progress and Current Challenges in Modeling Large RNAs

Somarowthu

2016

Journal of Molecular Biology

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Recent breakthroughs in next-generation sequencing technologies have led to the discovery of several classes of non-coding RNAs (ncRNAs). It is now apparent that RNA molecules are not just carriers of genetic information, but are also key players in many cellular processes. While there has been a rapid increase in the number of ncRNA sequences deposited in various databases over the past decade, the biological functions of these ncRNAs are largely not well understood. Similar to proteins, RNA molecules carry out a function by forming specific three-dimensional structures. Understanding the function of a particular RNA therefore requires a detailed knowledge of its structure. However, determining experimental structures of RNA is extremely challenging. In fact, RNA-only structures represent just 1% of the total structures deposited in the PDB. Thus, computational methods that predict three-dimensional RNA structures are in high demand. Computational models can provide valuable insights into structure-function relationships in ncRNAs, and can aid in the development of functional hypotheses and experimental designs. In recent years, a set of diverse RNA structure prediction tools have become available, which differ in computational time, input data and accuracy. This review discusses the recent progress and challenges in RNA structure prediction methods.

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“…Although none of these tools were specifically developed for studying the structural effects of protein sequence mutations, they can be readily applied for this purpose [163]. For example, protein structure prediction tools have been used to model protein variants given a homologous protein structure [164,165].…”

Section: Computational Toolsmentioning

confidence: 99%

Bioinformatics and variability in drug response: a protein structural perspective

Lahti

Tang

Capriotti

et al. 2012

J. R. Soc. Interface.

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Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk -benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein -drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein -drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

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Outcome of a Workshop on Applications of Protein Models in Biomedical Research

Cited by 140 publications

References 100 publications

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing

Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing

Progress and Current Challenges in Modeling Large RNAs

Bioinformatics and variability in drug response: a protein structural perspective

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