Three-dimensional Structures Of Biological Macromolecules

National Heart, Lung, And Blood Institute

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Abstract

Several projects have been pursued in the reporting period: An analysis of the structural changes of the oxygen evolving complex of Photosystem II in the S1 and S3 states revealed by serial femtosecond crystallography Photosystem II (PSII) is a unique natural catalyst that converts solar energy into chemical energy using earth abundant elements in water at physiological pH. Understanding the reaction mechanism will aid the design of biomimetic artificial catalysts for efficient solar energy conversion. The Mn4O5Ca cluster cycles through five increasingly oxidized intermediates before oxidizing two water molecules into O2 and releasing protons to the lumen and electrons to drive PSII reactions. The Mn coordination and OEC electronic structure changes through these intermediates. Thus, obtaining a high-resolution structure of each catalytic intermediate would help reveal the reaction mechanism. While valuable structural information was obtained from conventional X-ray crystallography, time-resolution of conventional X-ray crystallography limits the analysis of shorted-lived reaction intermediates. Serial Femtosecond X-ray crystallography (SFX), which overcomes the radiation damage by using ultra short laser pulse for imaging, has been used extensively to study the water splitting intermediates in PSII. Here, we review the state of the art and our understanding of the water splitting reaction before and after the advent of SFX. Furthermore, we analyze the likely Mn coordination in multiple XFEL structures prepared in the dark-adapted S1 state and those following two-flashes which are poised in the penultimate S3 oxidation state based on Mn coordination chemistry. Finally, we summarize the major contributions of the SFX to our understanding of the structures of the S1 and S3 states. Laser Induced Alignment of Proteins for Single Particle Imaging Laser-induced alignment of particles and molecules was long envisioned to support three-dimensional structure determination using “single-molecule diffraction” with X-ray free-electron lasers [PRL 92, 198102 (2004)]. However, the alignment of isolated macromolecules has not yet been demonstrated also because quantitative modeling is very expensive. We computationally demonstrated that the alignment of nanorods and proteins is possible with a standard laser technology. We performed a comprehensive analysis on the dependence of the degree of alignment on molecular properties and experimental details, e.g., particle temperature and laser-pulse energy. Considering the polarizability anisotropy of about 150,000 proteins, our analysis revealed that most of these proteins can be aligned using realistic experimental parameters. Machine Learning models for predicting oxidation states of Fe-S cluster in proteins Iron–sulfur (Fe–S) clusters are critical cofactors in metalloproteins, essential for cellular processes such as energy production, DNA repair, enzymatic catalysis, and metabolic regulation. While Fe–S cluster functions are intimately linked to their redox properties, their precise roles in many proteins remain unclear. In this study, we present a regression model based on experimental redox potential (Em) data, utilizing only two features: the Fe–S cluster’s total charge and the Fe atoms’ average valence. This model achieves a high correlation with experimental data (R2 = 0.82) and an average prediction error of 0.12 V. Applying this model across the Protein Data Bank, we predict Em values for all cataloged Fe–S clusters, uncovering redox potential trends across diverse cluster classes. The computed redox potentials showed strong agreement with experimental values, achieving an overall accuracy of 88%. This streamlined, computationally accessible approach enhances the annotation and mechanistic understanding of Fe–S proteins, offering new insights into the redox variability of electron transport proteins. Our model holds promise for advancing studies of metalloprotein function and facilitating the design of bioinspired redox systems. Protein structure prediction via deep learning of protein folding Protein structure prediction (PSP) has long been a central problem in biochemistry, driven by the dogma that sequence determines structure and structure determines function. Modern PSP systems generally comprise four components: (i) an input module (Section Inputs) that takes a single protein sequence to generate additional input features, almost always including a multiple sequence alignment (MSA) of homologous proteins, (ii) a ‘trunk’ (Section Trunks), typically a neural network capable of sophisticated pattern recognition, which transforms features from the input module to spatial information that partially encode the 3D structure, (iii) an output module (Section Outputs) that converts this spatial information into an initial 3D structure, sometimes without explicit side-chain atoms, and (iv) a refinement module (Section Refinement) that improves the initial structure and produces all atomic coordinates. Traditionally, these modules relied on a mixture of physics-based energy functions, knowledge-based statistical reasoning, and heuristic algorithms. The last few years however have witnessed an infusion of machine learning, particularly neural networks, into every aspect of PSP. What started as a trickle of progress accelerated over the subsequent decade and, last year, reached a crescendo with DeepMind's AlphaFold2 [14], a system that arguably solves single apo domain PSP. Currently ML for PSP use binary contact map (BCM) or discretized inter-residue distances as output. All information comes from existing structures. To improve ML for PSP, increase information source will be beneficial. We believe protein folding pathway will provide abundant information for PSP. Therefore, we employ ML to recognize the folding movement at every stage of folding pathway to produce the movement of proteins. We utilize the Nudged elastic band simulation to produce pathway from the extended state to the folded state. The movement of protein at each conformation are studied with ML. This work is still in progress and hopefully lead to more accurate PSP, as well as folding pathway identification.