A difference contact network analysis (dCNA) method is developed for delineating allosteric mechanisms in proteins. The new method addresses limitations of conventional network analysis methods and is particularly suitable for allosteric systems undergoing large-amplitude conformational changes during function. Tests show that dCNA works well for proteins of varying sizes and functions. The design of dCNA is general enough to facilitate analyses of diverse dynamic data generated by molecular dynamics, crystallography, or nuclear magnetic resonance.

The evolution of protein conformational dynamics contains important information about protein function and regulation. Here, we describe an approach to dynamical-evolution analysis based on multiple microsecond molecular dynamics simulations and residue− residue contact analysis. We illustrate our approach by comparing three human cyclophilin isoforms, cyclophilin A, D, and E, which belong to a family of enzymes catalyzing peptidyl-prolyl cis−trans isomerization. Our results reveal that despite distinct overall equilibrium conformations between cyclophilins under substrate-f ree conditions, functional dynamical changes resembling substrate-binding and catalytic processes tend to be conserved. Key residues displaying either concerted or specific dynamical changes among isoforms during the reactions are identified, which delineate two distinct allosteric pathways for cyclophilin function consistent with recent nuclear magnetic resonance experiments. A sequence-based coevolution analysis is also employed for further understanding dynamical consequences. Our results collectively provide a framework where both common and specific functional mechanisms of a protein family can be elucidated.

Pin1 is a unique phosphorylation-dependent peptidyl-prolyl isomerase that regulates diverse subcellular processes and an important potential therapeutic target. Functional mechanisms of Pin1 are complicated because of the two-domain structural organization: the catalytic domain both binds the specific pSer/Thr-Pro motif and catalyzes the cis/trans isomerization, whereas the WW domain can only bind the trans configuration and is speculated to be responsible for substrate-binding specificity. Numerous studies of Pin1 have led to two divergent conclusions on the functional role of the WW domain. One opinion states that the WW domain is an allosteric effector, and substrate binding to this domain modulates the binding and catalysis in the distal catalytic domain. The other opinion, however, argues that the WW domain does not have any allosteric role. Here, using molecular dynamics and binding free-energy calculations, we examine catalysis and allosteric mechanisms in Pin1 under various substrate- and WW-binding conditions. Our results reveal a strong substrate sequence dependency of catalysis, domain-binding preferences, and allosteric outputs in Pin1. Importantly, we show that the different opinions about the WW domain can be unified in one framework, in which substrate sequences determine whether a positive, negative, or neural allosteric effect will be elicited. Our work further elucidates detailed mechanisms underlying the sequence-dependent allostery of Pin1 and finds that interdomain contacts are key mediators of intraprotein allosteric communications. Our findings collectively provide new insights into the function of Pin1, which may facilitate the development of novel therapeutic drugs targeting Pin1 in the future.

Detailed understanding of interactions between amino acid residues is critical in using promising difference network analysis approaches to map allosteric communication pathways. Using experimental data as benchmarks, we scan values of two essential residue–residue contact parameters: the distance cutoff (d c) and the cutoff of residue separation in sequence (n c). The optimal d c = 4.5 Å is revealed, which defines the upper bound of the first shell of residue–residue packing in proteins, whereas n c is found to have little effects on performance. We also develop a new energy-based contact method for network analyses and find an equivalency between the energy network using the optimal energy cutoff e c = 1.0 k B T and the structure network using d c = 4.5 Å. The simple 4.5–Å contact method is further shown to have comparable prediction accuracy to a contact method using amino acid type-specific distance cutoffs and chemical shift prediction-based methods. This study provides necessary tools in mapping dynamics to functions.

Proteins are inherently dynamic, and proper enzyme function relies on conformational flexibility. In this study, we demonstrated how an active site residue changes an enzyme’s reactivity by modulating fluctuations between conformational states. Replacement of tyrosine 249 (Y249) with phenylalanine in the active site of the flavin-dependent d-arginine dehydrogenase yielded an enzyme with both an active yellow FAD (Y249F-y) and an inactive chemically modified green FAD, identified as 6-OH-FAD (Y249F-g) through various spectroscopic techniques. Structural investigation of Y249F-g and Y249F-y variants by comparison to the wild-type enzyme showed no differences in the overall protein structure and fold. A closer observation of the active site of the Y249F-y enzyme revealed an alternative conformation for some active site residues and the flavin cofactor. Molecular dynamics simulations probed the alternate conformations observed in the Y249F-y enzyme structure and showed that the enzyme variant with FAD samples a metastable conformational state, not available to the wild-type enzyme. Hybrid quantum/molecular mechanical calculations identified differences in flavin electronics between the wild type and the alternate conformation of the Y249F-y enzyme. The computational studies further indicated that the alternate conformation in the Y249F-y enzyme is responsible for the higher spin density at the C6 atom of flavin, which is consistent with the formation of 6-OH-FAD in the variant enzyme. The observations in this study are consistent with an alternate conformational space that results in fine-tuning the microenvironment around a versatile cofactor playing a critical role in enzyme function.