The goal of my research is to understand protein mechanisms at the atomic level. The standard approach toward achieving this goal is to determine high quality crystal structures of functionally important conformational states of a particular protein in order to identify the dynamic changes associated with underlying mechanisms. My research focuses on an alternative and complementary approach that uses Bayesian statistical inference to predict aspects of protein mechanisms based on limited structural data augmented by vast numbers of protein sequences. In doing this, we are essentially following the example of Mendel and the geneticists that followed him: Just as Mendel obtained insight into unobserved genetic mechanisms through statistical inferences based on observed patterns of inherited traits, we seek to obtain insight into protein mechanisms through statistical inferences based on patterns of conserved residues in protein sequences - the cell's own language for encoding those mechanisms. Sequence patterns that have been conserved for a billion years or more reflect strong selective pressures maintaining mechanistic similarities. Divergent patterns that are conserved in descendent proteins maintaining a particular divergent function likewise reflect mechanistic differences. Thus, non-random patterns of sequence conservation and divergence correspond to conservation and divergence of underlying mechanisms, which we define very broadly to include all atomic properties required for a protein's function. As a result, Bayesian inference of the evolutionary constraints imposed on functionally divergent proteins can reveal key components of the molecular machinery and thereby suggest likely mechanisms to test experimentally. We are currently applying this approach to P loop ATPases and GTPases, to protein kinases and to other, functionally-associated proteins. Our efforts have been greatly enhanced by the abundant sequence data provided by the genome projects, which, in this way, are opening up entirely new approaches to understanding biological mechanisms. We seek to apply the functional and mechanistic information gleaned from our research into other genome analysis efforts.
Neuwald, A.F. 2007. Galpha Gbetagamma dissociation may be due to retraction of a buried lysine and disruption of an aromatic cluster by a GTP-sensing Arg-Trp pair. Protein Science 16(11): 2570-2577.
Neuwald, A.F. 2007. The CHAIN program: forging evolutionary links to underlying mechanisms. Trends in Biochemical Sciences 32: 487-493. Review article announcing the availability of the CHAIN program and illustrating how it works.
Kannan, N., N. Haste, S. S. Taylor and A.F. Neuwald. 2007. The hallmark of AGC kinase functional divergence is its C-terminal tail, a cis-acting regulatory module. Proc. Natl. Acad. Sci., USA 104(4):1272-1277.
Neuwald, A.F. 2006. Hypothesis: bacterial clamp loader AAA+ ATPase activation through DNA-dependent repositioning of the catalytic base and of a trans-acting catalytic threonine. Nucleic Acids Research 34(18): 5280-5290.
Neuwald, A.F. 2006. Bayesian shadows of molecular mechanisms cast in the light of evolution. Trends in Biochemical Sciences 31(7): 374-382. (Reviews the statistical and scientific basis for CHAIN analysis using as an example eukaryotic DNA clamp loader ATPases.)
Kannan, N. and A.F. Neuwald. 2005. Did protein kinase regulatory mechanisms evolve through elaboration of a simple structural component? Journal of Molecular Biology 351: 956-972.
Neuwald, A.F. and J.S. Liu. 2004. Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model. BMC Bioinformatics 5: 157 (16 pages).