Alexander Serganov

Dr. Alexander Serganov

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Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2013 in Cell
Verified
Journal Article
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Alexander SerganovAlexander Serganov
Alexander Serganov
ENEvgeny Nudler
Evgeny Nudler

1 December 2004 in Chemistry & Biology
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Journal Article
Closed access
Alexander SerganovAlexander Serganov
Alexander Serganov
YYYu-Ren Yuan
Yu-Ren Yuan
OPOlga Pikovskaya
Olga Pikovskaya
APAnna Polonskaia
Anna Polonskaia
LMLucy Malinina
Lucy Malinina
DPDinshaw J. Patel
Dinshaw J. Patel

11 September 2007 in Nature Reviews Genetics
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Journal Article
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Alexander SerganovAlexander Serganov
Alexander Serganov
DPDinshaw J. Patel
Dinshaw J. Patel

21 May 2006 in Nature
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Journal Article
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Alexander SerganovAlexander Serganov
Alexander Serganov
APAnna Polonskaia
Anna Polonskaia
APAnh Tuân Phan
Anh Tuân Phan
RBRonald R. Breaker
Ronald R. Breaker
DPDinshaw J. Patel
Dinshaw J. Patel
Genes are commonly turned on or off by protein factors that respond to cellular signals. The recent discovery of riboswitches, regulatory elements within some messenger RNAs, proved that RNA can also detect essential metabolites and control genes. Two structural studies throw new light on the riboswitch system. Serganov et al. use X-ray diffraction to establish the three-dimensional structure of a riboswitch from Escherichia coli bound to its target, a vitamin B1 derivative. These findings reveal how RNA folds to form a precise pocket for its target and how the antibiotic pyrithiamine acts by tricking the riboswitch. This suggests a new drug design strategy for antibacterials and antifungals targeting riboswitches. Montange and Batey have solved the structure of a bacterial riboswitch RNA bound to S-adenosyl methionine. Its complex folded structure reveals how ligand binding leads structural changes that prevent further transcription. Riboswitches are metabolite-sensing RNAs, typically located in the non-coding portions of messenger RNAs, that control the synthesis of metabolite-related proteins1,2,3. Here we describe a 2.05 Å crystal structure of a riboswitch domain from the Escherichia coli thiM mRNA4 that responds to the coenzyme thiamine pyrophosphate (TPP). TPP is an active form of vitamin B1, an essential participant in many protein-catalysed reactions5. Organisms from all three domains of life6,7,8,9, including bacteria, plants and fungi, use TPP-sensing riboswitches to control genes responsible for importing or synthesizing thiamine and its phosphorylated derivatives, making this riboswitch class the most widely distributed member of the metabolite-sensing RNA regulatory system. The structure reveals a complex folded RNA in which one subdomain forms an intercalation pocket for the 4-amino-5-hydroxymethyl-2-methylpyrimidine moiety of TPP, whereas another subdomain forms a wider pocket that uses bivalent metal ions and water molecules to make bridging contacts to the pyrophosphate moiety of the ligand. The two pockets are positioned to function as a molecular measuring device that recognizes TPP in an extended conformation. The central thiazole moiety is not recognized by the RNA, which explains why the antimicrobial compound pyrithiamine pyrophosphate targets this riboswitch and downregulates the expression of thiamine metabolic genes. Both the natural ligand and its drug-like analogue stabilize secondary and tertiary structure elements that are harnessed by the riboswitch to modulate the synthesis of the proteins coded by the mRNA. In addition, this structure provides insight into how folded RNAs can form precision binding pockets that rival those formed by protein genetic factors.

23 February 2012 in RNA
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JCJosé Almeida Cruz
José Almeida Cruz
MBMarc-Frédérick Blanchet
Marc-Frédérick Blanchet
MBMichal Boniecki
Michal Boniecki
JBJanusz M. Bujnicki
Janusz M. Bujnicki
SCShi-Jie Chen
Shi-Jie Chen
EWEric Westhof
Eric Westhof
We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises.

25 January 2009 in Nature
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Journal Article
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Alexander SerganovAlexander Serganov
Alexander Serganov
LHLili Huang
Lili Huang
DPDinshaw J. Patel
Dinshaw J. Patel