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Parres-Gold, J.

Publications and source records attributed to Parres-Gold, J..

2 recordsLinked to original sources

Conformational Ensembles Reveal the Origins of Serine Protease Catalysis

Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases reveal atomic-level changes across their reaction states, identify molecular features that provide catalysis, and quantify their energetic contributions to catalysis. These enzymes precisely position their reactants in destabilized conformers, creating a downhill energetic gradient that selectively favors the motions required for reaction while limiting off-pathway conformational states. A local catalytic motif, the "nucleophilic elbow", has repeatedly evolved, generating ground state destabilization in 50 proteases and 52 additional enzymes spanning 32 distinct structural folds. Ensemble-function analyses reveal previously unknown catalytic features, provide quantitative models based on simple physical and chemical principles, and identify motifs recurrent in Nature that may inspire enzyme design. One sentence summary: Ensemble-function analyses provide a quantitative model for serine protease catalysis, reveal previously unknown conformational features that contribute to their catalysis, and identify a structural motif that underlie these features and has evolved in >100 different enzymes from 32 protein folds.

biophysics↗

Principles of Computation by Competitive Protein Dimerization Networks

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough ([≥]8 proteins), can perform nearly all ([~]90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/564854v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1917f39org.highwire.dtl.DTLVardef@13795bcorg.highwire.dtl.DTLVardef@47913eorg.highwire.dtl.DTLVardef@90bd30_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗