Signatures of the Protein Folding Pathway in Two-Dimensional Ultraviolet Spectroscopy

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors

A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.

A Machine-Learning Protocol for Ultraviolet Protein-Backbone Absorption Spectroscopy under Environmental Fluctuations

Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3-4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.

Near-Ultraviolet Circular Dichroism and Two-Dimensional Spectroscopy of Polypeptides

A fully quantitative theory of the relationship between protein conformation and optical spectroscopy would facilitate deeper insights into biophysical and simulation studies of protein dynamics and folding. In contrast to intense bands in the far-ultraviolet, near-UV bands are much weaker and have been challenging to compute theoretically. We report some advances in the accuracy of calculations in the near-UV, which were realised through the consideration of the vibrational structure of the electronic transitions of aromatic side chains.

A Machine Learning Protocol for Predicting Protein Infrared Spectra

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.

Two-dimensional UV spectroscopy: a new insight into the structure and dynamics of biomolecules

Two-dimensional ultraviolet spectroscopy has the potential to deliver rich structural and dynamical information on biomolecules such as DNA and proteins.

Steady-State Linear and Non-linear Optical Spectroscopy of Organic Chromophores and Bio-macromolecules

Bio-macromolecules as DNA, lipid membranes and (poly)peptides are essential compounds at the core of biological systems. The development of techniques and methodologies for their characterization is therefore necessary and of utmost interest, even though difficulties can be experienced due to their intrinsic complex nature. Among these methods, spectroscopies, relying on optical properties are especially important to determine their macromolecular structures and behaviors, as well as the possible interactions and reactivity with external dyes—often drugs or pollutants—that can (photo)sensitize the bio-macromolecule leading to eventual chemical modifications, thus damages. In this review, we will focus on the theoretical simulation of electronic spectroscopies of bio-macromolecules, considering their secondary structure and including their interaction with different kind of (photo)sensitizers. Namely, absorption, emission and electronic circular dichroism (CD) spectra are calculated and compared with the available experimental data. Non-linear properties will be also taken into account by two-photon absorption, a highly promising technique (i) to enhance absorption in the red and infra-red windows and (ii) to enhance spatial resolution. Methodologically, the implications of using implicit and explicit solvent, coupled to quantum and thermal samplings of the phase space, will be addressed. Especially, hybrid quantum mechanics/molecular mechanics (QM/MM) methods are explored for a comparison with solely QM methods, in order to address the necessity to consider an accurate description of environmental effects on spectroscopic properties of biological systems.

From Linear to Foldamer and Assembly: Hierarchical Transformation of a Coplanar Conjugated Polymer into a Microsphere

Despite the coplanar structure, a conjugated alternating copolymer forms amorphous, well-defined microspheres without π-stacked crystalline domains. Here, we gain insights into the mechanism of how the coplanar conjugated polymer forms amorphous microspheres by means of spectroscopic studies on the assembly/disassembly processes. The difference of the spectral profiles of photoabsorption and photoluminescence with varying solvent/nonsolvent composition clarifies that stepwise assembly takes place through the microsphere formation; [1] intrapolymer linear-to-folding transformation upon diffusion of polar nonsolvent and [2] interpolymer assembly of the foldamers upon further addition of the nonsolvent to form microspheres. As shown in various biopolymers such as proteins and DNA, such stepwise folding and assembly behaviors of conjugated polymers from primary to secondary and tertiary structure open a new way to create transformable functional materials.

On the Simulation of Two-dimensional Electronic Spectroscopy of Indole-containing Peptides

A benchmark study of low-cost multiconfigurational CASSCF/CASPT2 schemes for computing the electronic structure of indole is presented. This facilitates the simulation of near-ultraviolet (UV) pump visible (VIS) probe (i.e. two-color) two-dimensional electronic spectra (2DES) of homo- and hetero-aggregates as well as for processing of multiple snapshots from molecular dynamics simulations. Fingerprint excited-state absorption signatures of indole are identified in a broad spectral window between 10 and 25 k cm^-1 . The 18-24 k cm^-1 spectral window which has no absorption of the monomer and noninteracting aggregates is ideally suited to embed charge-transfer signatures in stacked aggregates. The small peptide Trp-cage, containing a tryptophan and a tyrosine amino acids, having indole and phenol as side chains, respectively, serves to prove the concept. Clear charge-transfer signatures are found in the proposed spectral window for an interchromophore distance of 5 Å making near-UV pump VIS probe 2DES a suitable technique for resolving closely packed aggregates. We demonstrate that 2DES utilizing ultra-short pulses has the potential to resolve the nature of the spectroscopically resolved electronic states and that the line shapes of the excited-state absorption signals can be correlated to the polarity of the relevant states.

Two-Dimensional Linear Dichroism Spectroscopy for Identifying Protein Orientation and Secondary Structure Composition

Quantitative measurements of protein orientation and secondary structure composition are of great importance for protein biotechnology applications and disease treatments, and yet, they are technically challenging for a spectroscopic study. On the basis of quantum mechanics/molecular mechanics simulations, we demonstrate that two-dimensional (2D) linear dichroism spectroscopy is capable of probing the direction of α-helix motifs in proteins. Compared to the conventional linear dichroism (LD) spectra, 2D spectra double the measurable range of orientation of secondary structures. In addition, by calculating the ratio of transverse ππ* signals to longitudinal ππ* signals in 2D spectra, we can achieve quantitative measurement of the fraction of α-helix content in a protein.

High-energy noncollinear optical parametric amplifier producing 4 fs pulses in the visible seeded by a gas-phase filament

We report on the design and characterization of a short-pulse-pumped, single-stage noncollinear optical parametric amplifier (NOPA) that achieves high pulse energies in the few-cycle pulse regime. Optimal pulse-front tilting and temporal compression of the short (35 fs) pump pulse are achieved using a 4f grating compressor, while spatial chirp at the NOPA crystal is eliminated with proper imaging using a pair of reflective telescopes. Gas-phase filamentation in an open-ended argon-filled cell provides a bright, stable seed source with little residual chirp that is suitable for temporal overlap with the short pump pulse without dispersion precompensation. Two seeding geometries are explored, and pulses as short as 3.5 fs are obtained by seeding with the entire filament bandwidth. Fourier-transform-limited 4 fs pulses are obtained by filtering the IR portion of the spectrum.

Two-dimensional near ultraviolet (2DNUV) spectroscopic probe of structural-dependent exciton dynamics in a protein

Understanding the exciton dynamics in biological systems is crucial for the manipulation of their function. We present a combined quantum mechanics (QM) and molecular dynamics (MD) simulation study that demonstrates how coherent two-dimensional near-ultraviolet (2DNUV) spectra can be used to probe the exciton dynamics in a mini-protein, Trp-cage. The 2DNUV signals originate from aromatic transitions that are significantly affected by the couplings between residues, which determine exciton transport and energy relaxation. The temporal evolution of 2DNUV features captures important protein structural information, including geometric details and peptide orientations.