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David H. Mathews, M.D., Ph.D.

Contact Information

Phone Numbers

Office: (585) 275-1734

Fax: (585) 275-6007

Research Labs

Faculty Appointments

Biography

Research

Our understanding of the role of RNA in cellular processes has expanded enormously over the last two decades. Originally, RNA was understood to participate in protein expression as a carrier of genetic information (mRNA) and as an adapter molecule (tRNA) for reading the code. Then RNA was discovered to catalyze reactions, including self-splicing, phosphodiester bond cleavage, and peptide bond formation. RNA is now known to play functions in diverse cellular processes, such as development, immunity, RNA editing and modification, and post-transcriptional gene regulation. RNA is also an important player in many diseases, including Prader-Willi, b-thalassemia, and myotonic dystrophy. RNA sequences can be evolved in vitro to catalyze many reactions that are not part of the natural repertoire. Antisense and RNAi can be used to modulate gene expression.

Research in the Mathews lab spans the fields of Computational Biology and Bioinformatics. We are interested in predicting RNA structure and we develop computational tools for targeting RNA with pharmaceuticals and for using RNA as a pharmaceutical (Mathews et al., 1999a).

In collaboration with Doug Turner (University of Rochester) and Michael Zuker (RPI), we have developed software that predicts secondary structure, i.e. the canonical base pairs (Mathews et al., 2004; Mathews et al., 1999b). On average, 73% of base pairs are correctly predicted in a set of diverse sequences with known structures. This accuracy can be improved by constraining the structure prediction using data derived from experiments.

We have also developed software that uses a partition function to predict base pairing probabilities (Mathews, 2004). Using this algorithm, secondary structures can be color annotated according to pairing probability to graphically demonstrate both high probability pairs and low probability pairs that are, on average, not as accurate.

Finally, we are developing methods to predict a secondary structure common to multiple sequences (Mathews & Turner, 2002). The accuracy of structure predictions is dramatically improved by using the information contained in multiple sequences. For example, for a set of poorly predicted 5S rRNA sequences, the average accuracy of base pair prediction improves from 47.8% to 86.4% when the structure common to two sequences is determined.

Credentials

Education

1994
BS | University of Rochester
Physics

2002
PhD | University of Rochester
Chemistry

2003
MD | Univ Rochester Sch Med/Dent
Medicine

Publications

Journal Articles

6/1/2018
Spasic A, Berger KD, Chen JL, Seetin MG, Turner DH, Mathews DH. "Improving RNA nearest neighbor parameters for helices by going beyond the two-state model." Nucleic acids research.. 2018 Jun 1; 46(10):4883-4892.

5/2018
Spasic A, Kennedy SD, Needham L, Manoharan M, Kierzek R, Turner DH, Mathews D. "Molecular dynamics correctly models the unusual major conformation of the GAGU RNA internal loop and with NMR reveals an unusual minor conformation." RNA.. 2018 May 0; 24(5):656-672. Epub 2018 Feb 06.

4/10/2018
Berger KD, Kennedy SD, Schroeder SJ, Znosko BM, Sun H, Mathews DH, Turner DH. "Surprising Sequence Effects on GU Closure of Symmetric 2 × 2 Nucleotide RNA Internal Loops." Biochemistry.. 2018 Apr 10; 57(14):2121-2131. Epub 2018 Mar 23.

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