Gabriel Žoldák

Quantum biology is the study of how quantum-mechanical effects influence living systems, and hence, it is an advanced interdisciplinary field that integrates biology, physics, chemistry, and mathematics. While the belief that quantum phenomena such as entanglement, tunneling, and coherence exist only in the domain of physical sciences persisted for a long time, recent evidence suggests that their presence in biology should not be overlooked. In the past decade, there have been important advancements in ultrafast structural techniques, such as x-ray free-electron lasers and ultrafast electron diffraction (scattering), which complement optical spectroscopy methods. Historically, ultrafast optical spectroscopy has played a key role in unraveling the mysteries of quantum biology. In this context, integrative ultrafast methods should be utilized to demonstrate and validate quantum events in biology. Furthermore, we emphasize that recent advancements in protein structure prediction and engineering using machine learning can identify and test quantum effects in biology.

Neoplastic transformation of B cells of the post-germinative center can lead to oncohematological dyscrasias, which often results in an abnormal production of monoclonal immunoglobulin light chains. The non-physiological production of large amounts of IgG light chains leads to the formation of extracellular deposits called 'aggregomas' and rare conditions such as light chain crystal deposition disease. Kidney manifestations and heavy-chain deposition disease can also occur in plasma cell dyscrasias, emphasizing the role of IgG misfolding and aggregation. This minireview describes molecular mechanisms of IgG light-chain aggregation, as well as the consequences and therapeutic implications of IgG light chain misfolding in these disorders. By elucidating the mechanisms of IgG light chain misfolding and aggregation, researchers can identify specific molecular and cellular pathways. This knowledge opens the door to novel therapeutic targets, offering the potential for interventions that can either prevent the initial misfolding events, promote the proper folding and processing of immunoglobulins, or enhance the clearance of misfolded proteins and aggregates. These protein folding-related issues persist even after the successful elimination of the malignant B cells. Such targeted protein-folding therapies could significantly improve patients' quality of life and contribute to their recovery. Thus, a deep understanding of IgG light chain misfolding and its consequences not only sheds light on the complex biology of oncohematological dyscrasias but also opens the way for innovative treatment strategies that could transform patient care in these conditions, instilling hope and motivation in the healthcare professionals and researchers in this field.

Flavocytochrome c sulfide dehydrogenase (FCC) is an important enzyme of sulfur metabolism in sulfur-oxidizing bacteria, and its catalytic properties have been extensively studied. However, the ultrafast dynamics of FCC is not well understood. We present ultrafast transient absorption and fluorescence spectroscopy measurements to unravel the early events upon excitation of the heme and flavin chromophores embedded in the flavocytochrome c (FccAB) from the bacterium Thiocapsa roseopersicina. The fluorescence kinetics of FccAB suggests that the majority of the photoexcited species decay nonradiatively within the first few picoseconds. Transient absorption spectroscopy supports these findings by suggesting two major dynamic processes in FccAB, internal conversion occurring in about 400 fs and the vibrational cooling occurring in about 4 ps, mostly affecting the heme moiety.

Laser cleaning is transforming surface treatment across industries by providing a precise, efficient, and eco-friendly alternative to traditional methods. Using focused laser beams, it removes contaminants, coatings, and corrosion from materials such as metals, plastics, and ceramics through ablation, evaporation, and thermal shock. Key parameters like wavelength, power, and scanning speed must be optimized to ensure safety and effectiveness. The technology's versatility spans applications from aerospace manufacturing to artifact restoration, offering sustainable, waste-minimizing solutions. Advances in handheld systems and AI-driven optimization are expanding its use in shipbuilding, automotive maintenance, and beyond. With ongoing research enhancing performance and adaptability, laser cleaning is set to become a cornerstone of sustainable industrial and conservation practices.

Hemoglobin is an oxygen-transport protein in red blood cells that interacts with multiple ligands, e.g., oxygen, carbon dioxide, carbon monoxide, and nitric oxide. Genetic variations in hemoglobin chains, such as those underlying sickle cell disease and thalassemias, present substantial clinical challenges. Here, we review the progress in research, including the use of allosteric modulators, pharmacological chaperones, and antioxidant treatments, which has begun to improve hemoglobin stability and oxygen affinity. According to UniProt (as of 7 August 2024), 819 variants of the α-hemoglobin subunit and 771 variants of the β-hemoglobin subunit have been documented, with over 116 classified as unstable. These data demonstrate the urgent need to develop variant-specific stabilizing options. Beyond small-molecule drugs/binders, novel protein-based strategies—such as engineered hemoglobin-binding proteins (including falcilysin, llama-derived nanobodies, and α-hemoglobin-stabilizing proteins)—offer promising new options. As our understanding of hemoglobin’s structural and functional diversity grows, so does the potential for genotype-driven approaches. Continued research into hemoglobin stabilization and ligand-binding modification may yield more precise, effective treatments and pave the way toward effective strategies for hemoglobinopathies.

Hemoglobin is a vital protein in red blood cells primarily responsible for transporting oxygen from the lungs to tissues. It binds to various ligands, including oxygen, carbon dioxide, carbon monoxide, and nitric oxide, influencing its function and regulation. Genetic mutations of Hb can lead to variants causing disorders like sickle cell disease and thalassemias, which present significant clinical challenges. Recent advances in therapies, such as allosteric modulators, pharmacological chaperones, and antioxidant treatments, have improved the management of hemoglobinopathies by enhancing hemoglobin stability and increasing oxygen affinity. According to the UniProt database (as of 7th August 2024), there are currently 819 variants of the -hemoglobin subunit and 771 variants of the -hemoglobin subunit. Over 116 hemoglobin subunit variants are classified as unstable, highlighting the need to further develop variant-specific hemoglobin-stabilizing molecules. In addition to several small-molecule binders, a potential in the engineering of proteins with hemoglobin-binding capabilities, e.g. falcilysin, lama-derived nanobodies, -hemoglobin-stabilizing proteins and others, offer promising options for further development. Continued research is essential for developing personalized treatments that consider the genetic diversity of hemoglobin variants, offering hope for more targeted therapies, improved management, and potential cures for hemoglobin-related disorders.

This chapter explores the innovative application of machine learning techniques to understand and predict the stability of protein substructures. Accurately identifying stable substructures within proteins necessitates incorporating the local context, crucial for elucidating the roles of supersecondary structures. This approach emphasizes the importance of contextual information in understanding the stability and functionality of protein regions, thereby providing a more comprehensive view of protein mechanics and interactions. The chapter focuses on our findings regarding the DnaK Hsp70 chaperone protein, utilizing it as a case study. This research highlights how context-dependent physico-chemical features derived from protein sequences can accurately classify residues into stable and unstable substructures by leveraging logistic regression, random forest, and support vector machine methods. The findings represent a pivotal step towards the rational design of proteins with tailored properties, offering new insights into protein engineering and the fundamental principles underpinning protein supersecondary structures.