Using Resonance Raman Cross-section Data to Estimate the Spin State Populations of Cytochromes P450

The cytochromes P450 (CYPs) are heme proteins responsible for the oxidation of xenobiotics and pharmaceuticals and the biosynthesis of essential steroid products. In all cases, substrate binding initiates the enzymatic cycle, converting ferric low-spin to high-spin state, with the efficiency of the conversion varying widely for different substrates, so documentation of this conversion for a given substrate is an important objective. Resonance Raman (rR) spectroscopy can effectively yield distinctive frequencies for the ν3 ‘spin state marker’ bands. Here, employing a reference cytochrome P450 (CYP101), the intensities of the ν3 modes (ILS) and (IHS) relative to an internal standard (sodium sulfate) yield relative populations for the two spin states; i.e., a value of 1.24 was determined for the ratio of the relative cross sections for the ν3 modes. The use of this value was then shown to permit a reliable calculation of relative populations of the two spin states from rR spectra of several other CYPs P450. The importance of this work is that, using this information, it is now possible to conveniently document by rR the spin state population without conducting separate experiments requiring different analytical methods, instrumentation, and additional sample

Active Site Structures of CYP11A1 in the Presence of Its Physiological Substrates and Alterations upon Binding of Adrenodoxin

The rate-limiting step in the steroid synthesis pathway is catalyzed by CYP11A1 through three sequential reactions. The first two steps involve hydroxylations at positions 22 and 20, generating 20(R),22(R)-dihydroxycholesterol (20R,22R-DiOHCH), with the third stage leading to a C20–C22 bond cleavage, forming pregnenolone. This work provides detailed information about the active site structure of CYP11A1 in the resting state and substrate-bound ferric forms as well as the CO-ligated adducts. In addition, high-quality resonance Raman spectra are reported for the dioxygen complexes, providing new insight into the status of Fe–O–O fragments encountered during the enzymatic cycle. Results show that the three natural substrates of CYP11A1 have quite different effects on the active site structure, including variations of spin state populations, reorientations of heme peripheral groups, and, most importantly, substrate-mediated distortions of Fe–CO and Fe–O2 fragments, as revealed by telltale shifts of the observed vibrational modes. Specifically, the vibrational mode patterns observed for the Fe–O–O fragments with the first and third substrates are consistent with H-bonding interactions with the terminal oxygen, a structural feature that tends to promote O–O bond cleavage to form the Compound I intermediate. Furthermore, such spectral data are acquired for complexes with the natural redox partner, adrenodoxin (Adx), revealing protein–protein-induced active site structural perturbations. While this work shows that Adx has an only weak effect on ferric and ferrous CO states, it has a relatively stronger impact on the Fe–O–O fragments of the functionally relevant oxy complexes

Household Investment in 529 College Savings Plans and Information Processing Frictions

We investigate how information processing frictions contribute to household suboptimal saving and investment behavior. We find that 60% of open accounts in college 529 savings plans are invested suboptimally due to high expenses and tax inefficiency. Such investments yield an expected loss of 9% over the accounts’ projected lifetimes. Consistent with information processing frictions contributing to inefficient investment, the extent of investment in suboptimal home-state accounts decreases with household financial literacy and increases with plan document disclosure complexity. Overall, our results suggest that information processing frictions shape households’ suboptimal investment in college savings plans and reduce their financial well-being

Enhancing Traffic Prediction with Learnable Filter Module

Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-term peaks or drops in traffic observation, is typically caused by traffic accidents or inherent sensor vibration. In practice, such noise can be challenging to model due to its stochastic nature and can lead to overfitting risks if a neural network is designed to learn this behavior. To address this issue, we propose a learnable filter module to filter out noise in traffic data adaptively. This module leverages the Fourier transform to convert the data to the frequency domain, where noise is filtered based on its pattern. The denoised data is then recovered to the time domain using the inverse Fourier transform. Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field. We demonstrate that the proposed module is lightweight, easy to integrate with existing models, and can significantly improve traffic prediction performance. Furthermore, we validate our approach with extensive experimental results on real-world datasets, showing that it effectively mitigates noise and enhances prediction accuracy