Biological polymers, studied with statistical and computational
Optimizing gene expression with mRNA free energy modeling and algorithms
The same protein sequence can be encoded by many different mRNA
sequences because of the degeneracy of the genetic code.
Variations in mRNA sequence can play an important role in regulating
protein expression level; for example, synonymous mutations may alter human
susceptibility to diseases.
We seek to understand the RNA features controlling gene expression.
Analyzing protein expression experiments performed by the Northeast Structural Genomics (NeSG)
Consortium, we find that mRNA folding effects dominate in the head
region (initial 16 codons), while codon usage dominates in the tail
(the remainder). Head and tail have similar overall influence.
[Supported by a grant from the National Institutes of Health (GM106372)]
Binding and Splicing mRNA
The famous double helical shape of DNA arises when
the bases of the two complementary strands pair.
Base-pairing is also the key to allowing special sites of mRNA
to be recognized and we have developed a tool
efficiently calculate free energies to optimally BIND olIGOs (short pieces) of RNA
to long RNA, important for mRNA splicing, siRNA, miRNA, etc.
To predict mRNA splice sites, we have used physical
chemical models (Finding with Binding) and a novel statistical
method (Primary Sequence Ranking).
[Supported by a grant from the National Institutes of Health (GM080690)]
Improving RNA Pseudoknot Models and Algorithms
Rhodopsin, the optically active molecule in our eyes, changes shape
when it absorbs a photon. This is the fastest photochemical reaction
known --- 200 femtoseconds, less than the time it takes light to cross
the width of your hair. Rhodopsin is a highly efficient system with
very little noise which is one of the reasons we see when our rod cells
are stimulated by only a few photons. I have been developing quantum
mechanical models to study how absorption of light creates compact,
coherent excitations called solitons and how these may induce the
molecular shape change.
RNA is more than an intermediary between DNA and proteins, it also
modulates gene expression and catalyzes certain reactions.
Complementary base pairing condenses RNA into complex, compact shapes.
Hairpin or tree-like structures (Fig a) emerge most often, but occasionally the more
complicated pseudoknot fold (Fig b) appears.
Pseudoknots are not common, but have amazing functionality when they do appear,
catalyzing reactions as enzymes or performing other gene regulation functions.
For example, the core of most catalytic RNAs is the interesting pseudoknot fold.
Pseudoknots cannot be predicted using traditional RNA folding algorithms.
Aalberts and his students have been improving models of pseudoknot
structures and have been computing how abundant pseudoknots are.
[Supported by a grant from the National Science Foundation (MCB 0641995)]
The figures depict the molecular backbone and the base pairs.
[Supported with a Cottrell College Science Award by the Research
Visualizing RNA base pairing probabilities with RNAbow diagrams,
Daniel P. Aalberts and William K. Jannen
RNA 19, 475-478 (2013)
[RNAbows web server]
Free Energy Cost of Stretching mRNA Hairpin Loops Inhibits Small RNA
Yuzhong Meng and Daniel P. Aalberts,
Biophysical Journal, 104, 482-487 (2013)
Loop Entropy Assists Tertiary Order: Loopy Stabilization of
Daniel P. Aalberts,
Entropy , 13, 1958-1966 (2011)
A Two Length Scale Polymer Theory for RNA Loop Free Energies and
Daniel P. Aalberts and Nagarajan Nandagopal '09,
RNA, 16, 1350-1355 (2010)
Quantifying Optimal Accuracy of
Local Primary Sequence Bioinformatics Methods,
Daniel P. Aalberts, Eric G. Daub '04, and Jesse W. Dill '04,
Bioinformatics 21 3347-3351 (2005).
Primary Sequence Rank web server]
Asymmetry in RNA Pseudoknots: Observation and Theory,
Daniel P. Aalberts and Nathan O. Hodas '04,
Nucleic Acids Res. 33, 2210-2214 (2005).
Efficient Computation of Optimal Oligo-RNA Binding,
Nathan O. Hodas '04 and Daniel P. Aalberts,
Nucleic Acids Res. 32, 6636-6642 (2004).
BINDIGO web server]
Finding with Binding: Thermodynamic Modeling of Donor
Splice Site Recognition in pre-mRNA,
Jeffrey A. Garland '03 and Daniel P. Aalberts,
Phys. Rev. E 69, 041903 (2004).
Single-Strand Stacking Free Energy from DNA Beacon Kinetics,
Daniel P. Aalberts, John M. Parman '02, and Noel L. Goddard,
Biophys. J, 84, 3212 (2003).
A Vision for Ultrafast Photoisomerization,
Daniel P. Aalberts and Hans F. Stabenau '02,
Physica A doi:10.1016/j.physa.2010.02.016 (2010).
Quantum Coherent Dynamics of Molecules: A Simple Scenario for
Ultrafast Photoisomerization, Daniel P. Aalberts, Lucas du Croo
de Jongh, Brian F. Gerke '99, Wim van Saarloos,
Phys Rev A 61, 040701 (R) (2000).
Towards Understanding the Ultrafast Dynamics of Rhodopsin,
Daniel P. Aalberts, Fernando L. J. Vos, and Wim van Saarloos, Pure
and Applied Chemistry 69, 2099 (1997).
The Su-Schrieffer-Heeger Model Applied to Chains of Finite Length,
Fernando L.J. Vos, Daniel P. Aalberts, and Wim van Saarloos,
Phys. Rev. B 53, 14922 (1996).
A Simple Method for Calculating the Speed of Sound in
One-Dimensional Tight-Binding Models: Application to the
Su-Schrieffer-Heeger Model, Fernando L.J. Vos, Daniel P.
Aalberts, and Wim van Saarloos, Phys. Rev. B 53,
Electrophoretic Mobility of Asymmetric Reptating Polymers,
Daniel P. Aalberts, Phys. Rev. Lett. 75, 4544 (1995).
Reptation in a Weak Driving Field, Daniel P. Aalberts and
J.M.J. van Leeuwen, Physica A 236, 220 (1997).
Dynamic Symmetry Breaking in a Model of Polymer Reptation,
Daniel P. Aalberts and J.M.J. van Leeuwen,
Electrophoresis 17, 1003 (1996).
Bill Jannen '09 [Stony Brook
Computer Science PhD student]