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Andrew F. Neuwald, PhD

Academic Title:

Adjunct Professor

Primary Appointment:

Biochemistry and Molecular Biology

Location:

Health Sciences Facility III, 670 West Baltimore St, Baltimore 21201

Fax:

(410) 706-1481

Education and Training

  • University of Wisconsin-Eau Claire, B.S., Medical Technology, 1978    
  • University of Wisconsin-Eau Claire, M.S., Biology, 1983                       
  • University of Iowa, Ph.D., Microbiology, 1987               
  • Washington University, M.S., Computer Science, 1992
  • Post Doctoral Fellowships: Molecular Microbiology, Washington University, 1989; Statistics & Computational Biology, the National Center for Biotechnology Information, 1997

Biosketch

Rigorous statistical methods are required to distinguish between subtle biological information and random noise within protein sequence data.  Dr. Neuwald and collaborators Jun S. Liu (Harvard University) and C.S. Lawrence (Brown University) were the first to address this problem using Bayesian Markov chain Monte Carlo sampling—an approach specifically designed for such a statistical and algorithmic challenge.  They were also the first to develop genetic algorithm-based Bayesian multiple sequence alignment strategies. Dr. Neuwald has applied these programs to various biological problems. One important early discovery was that Barth syndrome, an inherited cardiomypathic disorder, is due to an acyltransferase deficiency. This led to clinical confirmation and to potential treatments for this disease. Likewise, using these approaches, he structurally and functionally defined the AAA+ class of chaperone-like ATPases, which led to many follow up studies and more than 1,250 citations.  His other studies led to the discovery of unanticipated protein internal repeats, including subtle β-propeller-like repeats in UV-damaged DNA-binding protein and HEAT repeats in certain chromosome condensation components, the latter leading to the discovery (in collaboration withTatsuya Hirano at Cold Spring Harbor Laboratory) of a new vertebrate condensin component.

Dr. Neuwald was also the first to formulate (in collaboration with Jun S. Liu) and implement Bayesian strategies to infer likely determinants of underlying biochemical functions based on correlations in protein sequences. He has applied this approach to P-loop GTPases, protein kinases, N-acetyltransferases, AAA+ ATPases and DNA clamps.  In particular, analyses of protein kinases (In collaboration with Natarajan Kannan at the Univ. of Georgia) have led to multiple follow up experimental studies. More recently, Dr. Neuwald has developed and implemented multidimensional Bayesian sampling programs for the creation, optimization and enhancement of protein domain hierarchies; the National Center for Biotechnology Information (NCBI) has integrated these into their Conserved Domain Database pipeline.  Most recently, he has developed (in collaboration with Stephen Altschul at the NCBI) and applied statistical methods for identifying biologically important protein structural features.  He is now focusing on making the information gleaned through these statistical approaches widely available to biomedical researchers over the World Wide Web.

Research/Clinical Keywords

Protein sequence and structural analysis, Bayesian statistics, Computational Biology

Highlighted Publications

Neuwald, A.F. 2014. A Bayesian sampler for optimization of protein domain hierarchies. Journal of Computational Biology 21(3): 269-286.

Neuwald, A.F. 2016. Gleaning structural and functional information from correlations in protein multiple sequence alignments. Current Opinion in Structural Biology. 38:1-8.

Neuwald, A.F and S. F. Altschul. 2016. Bayesian Top-down Protein Sequence Alignment with Inferred Position-Specific Gap Penalties. Plos Comp. Biol. 12(5): e1004936.

Neuwald, A.F and S. F. Altschul. 2016. Inference of Functionally-Relevant N-Acetyltransferase Residues Based on Statistical Correlations. Plos Comp. Biol.  in revision.

Additional Publication Citations

Awards and Affiliations

Grants and Contracts

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