Biomolecular Function Research Articles

EvoAI enables extreme compression and reconstruction of the protein sequence space.

Designing proteins with improved functions requires a deep understanding of how sequence and function are related, a vast space that is hard to explore. The ability to efficiently compress this space by identifying functionally important features is extremely valuable. Here we establish a method called EvoScan to comprehensively segment and scan the high-fitness sequence space to obtain anchor points that capture its essential features, especially in high dimensions. Our approach is compatible with any biomolecular function that can be coupled to a transcriptional output. We then develop deep learning and large language models to accurately reconstruct the space from these anchors, allowing computational prediction of novel, highly fit sequences without prior homology-derived or structural information. We apply this hybrid experimental-computational method, which we call EvoAI, to a repressor protein and find that only 82 anchors are sufficient to compress the high-fitness sequence space with a compression ratio of 1048. The extreme compressibility of the space informs both applied biomolecular design and understanding of natural evolution.

Structural Plasticity as a Driver of the Maturation of Pro-Interleukin-18.

Dynamics are often critical for biomolecular function. Herein we explore the role of motion in driving the maturation process of pro-IL-18, a potent pro-inflammatory cytokine that is cleaved by caspases-1 and -4 to generate the mature form of the protein. An NMR dynamics study of pro-IL-18, probing time scales over 12 orders of magnitude and focusing on 1H, 13C, and 15N spin probes along the protein backbone and amino-acid side chains, reveals a plastic structure, with millisecond time scale dynamics occurring in a pair of β-strands, β1 and β*, that show large structural variations in a comparison of caspase-free and bound pro-IL-18 states. Fits of the relaxation data to a three-site model of exchange showed that the ground state secondary structure is maintained in the excited conformers, with the side chain of I48 that undergoes a buried-to-exposed conformational change in the caspase-free to -bound transition of pro-IL-18, sampling a more extensive range of torsion angles in one of the excited states characterized, suggesting partial unpacking in this region. Hydrogen exchange measurements establish the occurrence of an additional process, whereby strands β1 and β* locally unfold. Our data are consistent with a hierarchy of dynamic events that likely prime pro-IL-18 for facile caspase binding.

Transforming Aryl-Tetrazines into Bioorthogonal Scissors for Systematic Cleavage of trans-Cyclooctenes.

Bioorthogonal bond-cleavage reactions have emerged as a powerful tool for precise spatiotemporal control of (bio)molecular function in the biological context. Among these chemistries, the tetrazine-triggered elimination of cleavabletrans-cyclooctenes (click-to-release) stands out due to high reaction rates, versatility, and selectivity. Despite an increasing understanding of the underlying mechanisms, application of this reaction remains limited by the cumulative performance trade-offs (i.e., click kinetics, release kinetics, release yield) of existing tools. Efficient release has been restricted to tetrazine scaffolds with comparatively low click reactivity, while highly reactive aryl-tetrazines give only minimal release. By introducing hydroxyl groups onto phenyl- and pyridyl-tetrazine scaffolds, we have developed a new class of 'bioorthogonal scissors' with unique chemical performance. We demonstrate that hydroxyaryl-tetrazines achieve near-quantitative release upon accelerated click reaction with cleavabletrans-cyclooctenes, as exemplified by click-triggered activation of a caged prodrug, intramitochondrial cleavage of a fluorogenic probe (turn-on) in live cells, and rapid intracellular bioorthogonal disassembly (turn-off) of a ligand-dye conjugate.

Stimuli‐Responsive Molecular Duplexes Displaying Duplex‐to‐Duplex Switching

AbstractSynthetic duplexes with high stabilities have promising potential for mimicking biomolecular functions and developing supramolecular smart materials. Herein, we describe the synthesis and stimuli‐responsive properties of molecular duplexes derived from indolocarbazole–pyridine (I−P) oligomers. These duplexes adopt nonclassical helical structures, stabilized by I−P hydrogen‐bonding pairs in anhydrous chlorinated solvents. Notably, the longest duplex 62 (11‐mer)2 displays remarkable stability, forming twenty hydrogen bonds; its exchange energy barrier was determined to be ΔG≠=22.0 kcal ⋅ mol−1 at 75 °C in anhydrous (CDCl2)2. Upon the addition of water, a hydrated duplex 62 (11‐mer)2⊃10H2O was formed, with one water molecule inserted between each I−P hydrogen‐bonding pair. The Hill coefficient (n) for this process is 6.1, demonstrating extremely positive cooperativity. Conversely, the hydrated duplex 62 (11‐mer)2⊃10H2O was completely converted into the original anhydrous duplex 62 (11‐mer)2 when the temperature was increased. Interconversion between these two distinct duplexes can be repeatedly carried out by varying the temperature. Furthermore, reversible switching between hetero‐duplexes and homo‐duplexes was also demonstrated by controlling the temperature, with concomitant changes in the characteristic emission signals.

Stimuli-Responsive Molecular Duplexes Displaying Duplex-to-Duplex Switching.

Synthetic duplexes with high stabilities have promising potential for mimicking biomolecular functions and developing supramolecular smart materials. Herein, we describe the synthesis and stimuli-responsive properties of molecular duplexes derived from indolocarbazole-pyridine (I-P) oligomers. These duplexes adopt nonclassical helical structures, stabilized by I-P hydrogen-bonding pairs in anhydrous chlorinated solvents. Notably, the longest duplex 62 (11-mer)2 displays remarkable stability, forming twenty hydrogen bonds; its exchange energy barrier was determined to be ΔG≠=22.0 kcal ⋅ mol-1 at 75 °C in anhydrous (CDCl2)2. Upon the addition of water, a hydrated duplex 62 (11-mer)2⊃10H2O was formed, with one water molecule inserted between each I-P hydrogen-bonding pair. The Hill coefficient (n) for this process is 6.1, demonstrating extremely positive cooperativity. Conversely, the hydrated duplex 62 (11-mer)2⊃10H2O was completely converted into the original anhydrous duplex 62 (11-mer)2 when the temperature was increased. Interconversion between these two distinct duplexes can be repeatedly carried out by varying the temperature. Furthermore, reversible switching between hetero-duplexes and homo-duplexes was also demonstrated by controlling the temperature, with concomitant changes in the characteristic emission signals.

GalaxyDock-DL: Protein-Ligand Docking by Global Optimization and Neural Network Energy.

With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein-ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein-ligand docking method. The advantage of this method, called GalaxyDock-DL, lies in its minimal overfitting to the training data compared to several existing deep learning-based protein-ligand docking methods. Unlike some recent deep learning methods, it does not use information about known binding pocket center positions. Instead, the results of this docking method show a systematic dependence on the physical properties of the target protein-ligand complexes such as atomic thermal fluctuations and binding affinity. GalaxyDock-DL utilizes the global optimization technique of the conventional protein-ligand docking method, GalaxyDock, and a neural network energy function trained to stabilize the native state compared to non-native states, just as physical free energy does. This physical principle-based approach suggests directions not only for future structure prediction involving structurally flexible biomolecular complexes but also for predicting binding affinity, thereby providing guidance for the effective design of biofunctional ligands.

A simulation study on the effect of penetration of gold nanoparticles in the cytoplasm of healthy eye organs on dose enhancement of brachytherapy

Purpose Special properties and recent advances in the synthesis and biomolecular functionalization of gold nanoparticles (GNPs) have led to the evolution of their use in biomedical applications such as photon radiotherapy. Simulation-based studies on the effect of various parameters that govern the dose enhancement due to utilizing GNPs have facilitated the progress of knowledge in this field. Due to their flexibility and easier accessibility compared with experimental works, simulations have the potential to be considered for pre-clinical tests and, therefore, should be close to the realistic conditions as much as possible. Materials and methods To this aim, the present work investigates the effect of the presence of GNPs that are accumulated in the cytoplasm of the constituent cells in healthy tissues of a human eye phantom, inspired by the published experimental results which report that non-target tissues also receive the drugs containing GNPs. The GNPs’ concentrations are assumed to decrease by moving from the tumor toward the depth of the phantom through a suggested pattern. The MCNPX Monte Carlo code is used for the simulations. Results The results show that for four concentrations tested, the dose enhancement factor in the shallower layer reaches 6, and decreases to 1.2 in the last layer. The dose enhancements are also examined for critical structures of the iris, cornea, sclera, and lens, showing maximum deviations of about 3 to 200% compared with the absence of GNPs in the healthy tissue. Considering the reported doses to the lens by clinical institutions, the effect of penetration of GNPs to deep layers on treatment time is also investigated. Conclusions The results show that the penetration of GNPs from the tumor toward healthy tissues strongly controls the dose enhancement over the various eye structures and emphasizes the importance of modeling the GNPs’ distribution in the medium on the overall dose enhancement. Considering the current challenges in the clinical use of GNPs, more effort needs to be made to reach an effective endpoint in treatment.

In Silico View of Crowding: Biomolecular Processes to Nanomaterial Design.

It is widely accepted that deciphering biomolecular structure and function requires going beyond the single-molecule or single-complex paradigm. The densely packed macromolecules, cosolutes, and metabolites in the living cell impose crowding effects on the biomolecular structure and dynamics that need to be accounted for. Molecular simulations have proven to be a powerful tool to advance the current molecular-level understanding of such a highly concentrated, complex milieu. This Mini-Review focuses on summarizing the understanding achieved so far for the effects of crowding on biomolecular processes using computational methods, along with highlighting a new direction in employing crowding as a tool for tunable nanomaterial design. The two schools of thought that form the pillars of the current understanding of crowding effects are discussed. The investigation of crowded solutions using physics-based models that encompass different time and length scales to mimic the intracellular environment are described. The limitations and challenges faced by the current models and simulation methods are addressed, highlighting the gaps to be filled for better agreement with experiments. Crowding can also act as an effective tool to modulate the structure-property-function relationships of nanomaterials, leading to the development of novel functional materials. A few recent studies, mostly experimental, have been summarized in this direction. The Mini-Review concludes with an outlook for future developments in this field in order to enable accurate mimicking of the intracellular environment using simulations and to bridge the gap between biological processes and nanomaterial design.

Machine learning in biological physics: From biomolecular prediction to design

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is “learnable” and propose its future use in the generation of unique sequences that can fold into a target structure.

Molecular storytelling: a conceptual framework for teaching and learning with molecular case studies

Molecular case studies (MCSs) provide educational opportunities to explore biomolecular structure and function using data from public bioinformatics resources. The conceptual basis for the design of MCSs has yet to be fully discussed in the literature, so we present molecular storytelling as a conceptual framework for teaching with case studies. Whether the case study aims to understand the biology of a specific disease and design its treatments or track the evolution of a biosynthetic pathway, vast amounts of structural and functional data, freely available in public bioinformatics resources, can facilitate rich explorations in atomic detail. To help biology and chemistry educators use these resources for instruction, a community of scholars collaborated to create the Molecular CaseNet. This community uses storytelling to explore biomolecular structure and function while teaching biology and chemistry. In this article, we define the structure of an MCS and present an example. Then, we articulate the evolution of a conceptual framework for developing and using MCSs. Finally, we related our framework to the development of technological, pedagogical, and content knowledge (TPCK) for educators in the Molecular CaseNet. The report conceptualizes an interdisciplinary framework for teaching about the molecular world and informs lesson design and education research.

Aspartame carcinogenic potential revealed through network toxicology and molecular docking insights

The research employed network toxicology and molecular docking techniques to systematically examine the potential carcinogenic effects and mechanisms of aspartame (l-α-aspartyl-l-phenylalanine methyl ester). Aspartame, a commonly used synthetic sweetener, is widely applied in foods and beverages globally. In recent years, its safety issues, particularly the potential carcinogenic risk, have garnered widespread attention. The study first constructed an interaction network map of aspartame with gastric cancer targets using network toxicology methods and identified key targets and pathways. Preliminary validation was conducted through microarray data analysis and survival analysis, and molecular docking techniques were employed to further examine the binding affinity and modes of action of aspartame with key proteins. The findings suggest that aspartame has the potential to impact various cancer-related proteins, potentially raising the likelihood of cellular carcinogenesis by interfering with biomolecular function. Furthermore, the study found that the action patterns and pathways of aspartame-related targets are like the mechanisms of known carcinogenic pathways, further supporting the scientific hypothesis of its potential carcinogenicity. However, given the complexity of the in vivo environment, we also emphasize the necessity of validating these molecular-level findings in actual biological systems. The study introduces a fresh scientific method for evaluating the safety of food enhancers and provides a theoretical foundation for shaping public health regulations.

Mutations in Glycosyltransferases and Glycosidases: Implications for Associated Diseases.

Glycosylation, a crucial and the most common post-translational modification, coordinates a multitude of biological functions through the attachment of glycans to proteins and lipids. This process, predominantly governed by glycosyltransferases (GTs) and glycoside hydrolases (GHs), decides not only biomolecular functionality but also protein stability and solubility. Mutations in these enzymes have been implicated in a spectrum of diseases, prompting critical research into the structural and functional consequences of such genetic variations. This study compiles an extensive dataset from ClinVar and UniProt, providing a nuanced analysis of 2603 variants within 343 GT and GH genes. We conduct thorough MTR score analyses for the proteins with the most documented variants using MTR3D-AF2 via AlphaFold2 (AlphaFold v2.2.4) predicted protein structure, with the analyses indicating that pathogenic mutations frequently correlate with Beta Bridge secondary structures. Further, the calculation of the solvent accessibility score and variant visualisation show that pathogenic mutations exhibit reduced solvent accessibility, suggesting the mutated residues are likely buried and their localisation is within protein cores. We also find that pathogenic variants are often found proximal to active and binding sites, which may interfere with substrate interactions. We also incorporate computational predictions to assess the impact of these mutations on protein function, utilising tools such as mCSM to predict the destabilisation effect of variants. By identifying these critical regions that are prone to disease-associated mutations, our study opens avenues for designing small molecules or biologics that can modulate enzyme function or compensate for the loss of stability due to these mutations.

Sensitive detection of SARS-CoV-2 main protease 3CLpro with an engineered ribonuclease zymogen.

Alongside vaccines and antiviral therapeutics, diagnostic tools are a crucial aid in combating the COVID-19 pandemic caused by the etiological agent SARS-CoV-2. All common assays for infection rely on the detection of viral sub-components, including structural proteins of the virion or fragments of the viral genome. Selective pressure imposed by human intervention of COVID-19 can, however, induce viral mutations that decrease the sensitivity of diagnostic assays based on biomolecular structure, leading to an increase in false-negative results. In comparison, mutations are unlikely to alter the function of viral proteins, and viral machinery is under less selective pressure from vaccines and therapeutics. Accordingly, diagnostic assays that rely on biomolecular function can be more robust than ones that rely on biopolymer structure. Toward this end, we used a split intein to create a circular ribonuclease zymogen that is activated by the SARS-CoV-2 main protease, 3CLpro . Zymogen activation by 3CLpro leads to a >300-fold increase in ribonucleolytic activity, which can be detected with a highly sensitive fluorogenic substrate. This coupled assay can detect low nanomolar concentrations of 3CLpro within a timeframe comparable to that of common antigen-detection protocols. More generally, the concept of detecting a protease by activating a ribonuclease could be the basis of diagnostic tools for other indications.

Sonogenetics for Monitoring and Modulating Biomolecular Function by Ultrasound

AbstractUltrasound technology, synergistically harnessed with genetic engineering and chemistry concepts, has started to open the gateway to the remarkable realm of sonogenetics—a pioneering paradigm for remotely orchestrating cellular functions at the molecular level. This fusion not only enables precisely targeted imaging and therapeutic interventions, but also advances our comprehension of mechanobiology to unparalleled depths. Sonogenetic tools harness mechanical force within small tissue volumes while preserving the integrity of the surrounding physiological environment, reaching depths of up to tens of centimeters with high spatiotemporal precision. These capabilities circumvent the inherent physical limitations of alternative in vivo control methods such as optogenetics and magnetogenetics. In this review, we first discuss mechanosensitive ion channels, the most commonly utilized sonogenetic mediators, in both mammalian and non‐mammalian systems. Subsequently, we provide a comprehensive overview of state‐of‐the‐art sonogenetic approaches that leverage thermal or mechanical features of ultrasonic waves. Additionally, we explore strategies centered around the design of mechanochemically reactive macromolecular systems. Furthermore, we delve into the realm of ultrasound imaging of biomolecular function, encompassing the utilization of gas vesicles and acoustic reporter genes. Finally, we shed light on limitations and challenges of sonogenetics and present a perspective on the future of this promising technology.

Sonogenetics for Monitoring and Modulating Biomolecular Function by Ultrasound.

Ultrasound technology, synergistically harnessed with genetic engineering and chemistry concepts, has started to open the gateway to the remarkable realm of sonogenetics-a pioneering paradigm for remotely orchestrating cellular functions at the molecular level. This fusion not only enables precisely targeted imaging and therapeutic interventions, but also advances our comprehension of mechanobiology to unparalleled depths. Sonogenetic tools harness mechanical force within small tissue volumes while preserving the integrity of the surrounding physiological environment, reaching depths of up to tens of centimeters with high spatiotemporal precision. These capabilities circumvent the inherent physical limitations of alternative in vivo control methods such as optogenetics and magnetogenetics. In this review, we first discuss mechanosensitive ion channels, the most commonly utilized sonogenetic mediators, in both mammalian and non-mammalian systems. Subsequently, we provide a comprehensive overview of state-of-the-art sonogenetic approaches that leverage thermal or mechanical features of ultrasonic waves. Additionally, we explore strategies centered around the design of mechanochemically reactive macromolecular systems. Furthermore, we delve into the realm of ultrasound imaging of biomolecular function, encompassing the utilization of gas vesicles and acoustic reporter genes. Finally, we shed light on limitations and challenges of sonogenetics and present a perspective on the future of this promising technology.

Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data

BackgroundMachine learning (ML) has a rich history in structural bioinformatics, and modern approaches, such as deep learning, are revolutionizing our knowledge of the subtle relationships between biomolecular sequence, structure, function, dynamics and evolution. As with any advance that rests upon statistical learning approaches, the recent progress in biomolecular sciences is enabled by the availability of vast volumes of sufficiently-variable data. To be useful, such data must be well-structured, machine-readable, intelligible and manipulable. These and related requirements pose challenges that become especially acute at the computational scales typical in ML. Furthermore, in structural bioinformatics such data generally relate to protein three-dimensional (3D) structures, which are inherently more complex than sequence-based data. A significant and recurring challenge concerns the creation of large, high-quality, openly-accessible datasets that can be used for specific training and benchmarking tasks in ML pipelines for predictive modeling projects, along with reproducible splits for training and testing.ResultsHere, we report ‘Prop3D’, a platform that allows for the creation, sharing and extensible reuse of libraries of protein domains, featurized with biophysical and evolutionary properties that can range from detailed, atomically-resolved physicochemical quantities (e.g., electrostatics) to coarser, residue-level features (e.g., phylogenetic conservation). As a community resource, we also supply a ‘Prop3D-20sf’ protein dataset, obtained by applying our approach to CATH. We have developed and deployed the Prop3D framework, both in the cloud and on local HPC resources, to systematically and reproducibly create comprehensive datasets via the Highly Scalable Data Service (HSDS). Our datasets are freely accessible via a public HSDS instance, or they can be used with accompanying Python wrappers for popular ML frameworks.ConclusionProp3D and its associated Prop3D-20sf dataset can be of broad utility in at least three ways. Firstly, the Prop3D workflow code can be customized and deployed on various cloud-based compute platforms, with scalability achieved largely by saving the results to distributed HDF5 files via HSDS. Secondly, the linked Prop3D-20sf dataset provides a hand-crafted, already-featurized dataset of protein domains for 20 highly-populated CATH families; importantly, provision of this pre-computed resource can aid the more efficient development (and reproducible deployment) of ML pipelines. Thirdly, Prop3D-20sf’s construction explicitly takes into account (in creating datasets and data-splits) the enigma of ‘data leakage’, stemming from the evolutionary relationships between proteins.

Alternate conformations found in protein structures implies biological functions: A case study using cyclophilin A

Protein dynamics linked to numerous biomolecular functions, such as ligand binding, allosteric regulation, and catalysis, must be better understood at the atomic level. Reactive atoms of key residues drive a repertoire of biomolecular functions by flipping between alternate conformations or conformational substates, seldom found in protein structures. Probing such sparsely sampled alternate conformations would provide mechanistic insight into many biological functions. We are therefore interested in evaluating the instance of amino acids adopted alternate conformations, either in backbone or side-chain atoms or in both. Accordingly, over 70000 protein structures appear to contain alternate conformations only 'A' and 'B' for any atom, particularly the instance of amino acids that adopted alternate conformations are more for Arg, Cys, Met, and Ser than others. The resulting protein structure analysis depicts that amino acids with alternate conformations are mainly found in the helical and β-regions and are often seen in high-resolution X-ray crystal structures. Furthermore, a case study on human cyclophilin A (CypA) was performed to explain the pre-existing intrinsic dynamics of catalytically critical residues from the CypA and how such intrinsic dynamics perturbed upon Ser99Thr mutation using molecular dynamics simulations on the ns-μs timescale. Simulation results demonstrated that the Ser99Thr mutation had impaired the alternate conformations or the catalytically productive micro-environment of Phe113, mimicking the experimentally observed perturbation captured by X-ray crystallography. In brief, a deeper comprehension of alternate conformations adopted by the amino acids may shed light on the interplay between protein structure, dynamics, and function.

Liquid-liquid phase separation-inspired design of biomaterials.

Liquid-liquid phase separation (LLPS) is a crucial biological process that governs biomolecular condensation, assembly, and functionality within phase-separated aqueous environments. This phenomenon serves as a source of inspiration for the creation of artificial designs in both structured and functional biomaterials, presenting novel strategies for manipulating the structures of functional protein aggregates in a wide range of biomedical applications. This mini review summarizes my past research endeavors, offering a panoramic overview of LLPS-inspired biomaterials utilized in the design of structured materials, the development of cell mimetics, and the advancement of intelligent biomaterials.

Photo-activatable Reagents for Bioorthogonal Ligation Reactions.

Light-induced bioorthogonal reactions offer spatiotemporal control over selective biomolecular labeling. This review covers the recent advances in the design of photo-activatable reagents for bioorthogonal conjugation reactions in living systems. These reagents are stable in the absence of light, but transformed into reactive species upon light illumination, which then undergo rapid ligation reactions. The light wavelength has been tuned from ultraviolet to near infrared to enable efficient photo-activation in reactions in deep tissues. The most prominent photo-activatable reagents are presented, including tetrazoles, tetrazines, 9,10-phenanthrenequinone, diarylsydnones, and others. A particular focus is on the strategies for improving reaction kinetics and biocompatibility accomplished through careful molecular engineering. The utilities of these photo-activatable reagents are illustrated through a broad range of biological applications, including invivo protein labeling, positron emission tomography (PET) imaging, responsive hydrogels, and fluorescence microscopy. The further development and optimization of these biocompatible photo-activatable reagents should lead to new chemical biology strategies for studying biomolecular structure and function in living systems.

Photochromic Nucleosides and Oligonucleotides.

Photochromism is a reversible phenomenon wherein a material undergoes a change in color upon exposure to light. In organic photochromes, this effect often results from light-induced isomerization reactions, leading to alterations in either the spatial orientation or electronic properties of the photochrome. The incorporation of photochromic moieties into biomolecules, such as proteins or nucleic acids, has become a prevalent approach to render these biomolecules responsive to light stimuli. Utilizing light as a trigger for the manipulation of biomolecular structure and function offers numerous advantages compared to other stimuli, such as chemical or electrical treatments, due to its non-invasive nature. Consequently, light proves particularly advantageous in cellular and tissue applications. In this review, we emphasize recent advancements in the field of photochromic nucleosides and oligonucleotides. We provide an overview of the design principles of different classes of photochromes, synthetic strategies, critical analytical challenges, as well as structure-property relationships. The applications of photochromic nucleic acid derivatives encompass diverse domains, ranging from the precise photoregulation of gene expression to the controlled modulation of the three-dimensional structures of oligonucleotides and the development of DNA-based fluorescence modulators. Moreover, we present a future perspective on potential modifications and applications.