Nucleosomes play a dual role in regulating transcription dynamics.

Transcription has a mechanical component, as the translocation of the transcription machinery or RNA polymerase (RNAP) on DNA or chromatin is dynamically coupled to the chromatin torsion. This posits chromatin mechanics as a possible regulator of eukaryotic transcription, however, the modes and mechanisms of this regulation are elusive. Here, we first take a statistical mechanics approach to model the torsional response of topology-constrained chromatin. Our model recapitulates the experimentally observed weaker torsional stiffness of chromatin compared to bare DNA and proposes structural transitions of nucleosomes into chirally distinct states as the driver of the contrasting torsional mechanics. Coupling chromatin mechanics with RNAP translocation in stochastic simulations, we reveal a complex interplay of DNA supercoiling and nucleosome dynamics in governing RNAP velocity. Nucleosomes play a dual role in controlling the transcription dynamics. The steric barrier aspect of nucleosomes in the gene body counteracts transcription via hindering RNAP motion, whereas the chiral transitions facilitate RNAP motion via driving a low restoring torque upon twisting the DNA. While nucleosomes with low dissociation rates are typically transcriptionally repressive, highly dynamic nucleosomes offer less of a steric barrier and enhance the transcription elongation dynamics of weakly transcribed genes via buffering DNA twist. We use the model to predict transcription-dependent levels of DNA supercoiling in segments of the budding yeast genome that are in accord with available experimental data. The model unveils a paradigm of DNA supercoiling-mediated interaction between genes and makes testable predictions that will guide experimental 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.

CISD3/MiNT is required for complex I function, mitochondrial integrity, and skeletal muscle maintenance.

Mitochondria play a central role in muscle metabolism and function. A unique family of iron-sulfur proteins, termed CDGSH Iron Sulfur Domain-containing (CISD/NEET) proteins, support mitochondrial function in skeletal muscles. The abundance of these proteins declines during aging leading to muscle degeneration. Although the function of the outer mitochondrial CISD/NEET proteins, CISD1/mitoNEET and CISD2/NAF-1, has been defined in skeletal muscle cells, the role of the inner mitochondrial CISD protein, CISD3/MiNT, is currently unknown. Here, we show that CISD3 deficiency in mice results in muscle atrophy that shares proteomic features with Duchenne muscular dystrophy. We further reveal that CISD3 deficiency impairs the function and structure of skeletal muscles, as well as their mitochondria, and that CISD3 interacts with, and donates its [2Fe-2S] clusters to, complex I respiratory chain subunit NADH Ubiquinone Oxidoreductase Core Subunit V2 (NDUFV2). Using coevolutionary and structural computational tools, we model a CISD3-NDUFV2 complex with proximal coevolving residue interactions conducive of [2Fe-2S] cluster transfer reactions, placing the clusters of the two proteins 10 to 16 Å apart. Taken together, our findings reveal that CISD3/MiNT is important for supporting the biogenesis and function of complex I, essential for muscle maintenance and function. Interventions that target CISD3 could therefore impact different muscle degeneration syndromes, aging, and related conditions.

Germline BARD1 variants predispose to mesothelioma by impairing DNA repair and calcium signaling.

We report that ~1.8% of all mesothelioma patients and 4.9% of those younger than 55, carry rare germline variants of the BRCA1 associated RING domain 1 (BARD1) gene that were predicted to be damaging by computational analyses. We conducted functional assays, essential for accurate interpretation of missense variants, in primary fibroblasts that we established in tissue culture from a patient carrying the heterozygous BARD1V523A mutation. We found that these cells had genomic instability, reduced DNA repair, and impaired apoptosis. Investigating the underlying signaling pathways, we found that BARD1 forms a trimeric protein complex with p53 and SERCA2 that regulates calcium signaling and apoptosis. We validated these findings in BARD1-silenced primary human mesothelial cells exposed to asbestos. Our study elucidated mechanisms of BARD1 activity and revealed that heterozygous germline BARD1 mutations favor the development of mesothelioma and increase the susceptibility to asbestos carcinogenesis. These mesotheliomas are significantly less aggressive compared to mesotheliomas in asbestos workers.

Magnesium ions mitigate metastable states in the regulatory landscape of mRNA elements.

Residing in the 5' untranslated region of the mRNA, the 2'-deoxyguanosine (2'-dG) riboswitch mRNA element adopts an alternative structure upon binding of the 2'-dG molecule, which terminates transcription. RNA conformations are generally strongly affected by positively charged metal ions (especially Mg2+). We have quantitatively explored the combined effect of ligand (2'-dG) and Mg2+ binding on the energy landscape of the aptamer domain of the 2'-dG riboswitch with both explicit solvent all-atom molecular dynamics simulations (99 µsec aggregate sampling for the study) and selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) experiments. We show that both ligand and Mg2+ are required for the stabilization of the aptamer domain; however, the two factors act with different modalities. The addition of Mg2+ remodels the energy landscape and reduces its frustration by the formation of additional contacts. In contrast, the binding of 2'-dG eliminates the metastable states by nucleating a compact core for the aptamer domain. Mg2+ ions and ligand binding are required to stabilize the least stable helix, P1 (which needs to unfold to activate the transcription platform), and the riboswitch core formed by the backbone of the P2 and P3 helices. Mg2+ and ligand also facilitate a more compact structure in the three-way junction region.

Residue coevolution and mutational landscape for OmpR and NarL response regulator subfamilies.

DNA-binding response regulators (DBRRs) are a broad class of proteins that operate in tandem with their partner kinase proteins to form two-component signal transduction systems in bacteria. Typical DBRRs are composed of two domains where the conserved N-terminal domain accepts transduced signals and the evolutionarily diverse C-terminal domain binds to DNA. These domains are assumed to be functionally independent, and hence recombination of the two domains should yield novel DBRRs of arbitrary input/output response, which can be used as biosensors. This idea has been proved to be successful in some cases; yet, the error rate is not trivial. Improvement of the success rate of this technique requires a deeper understanding of the linker-domain and inter-domain residue interactions, which have not yet been thoroughly examined. Here, we studied residue coevolution of DBRRs of the two main subfamilies (OmpR and NarL) using large collections of bacterial amino acid sequences to extensively investigate the evolutionary signatures of linker-domain and inter-domain residue interactions. Coevolutionary analysis uncovered evolutionarily selected linker-domain and inter-domain residue interactions of known experimental structures, as well as previously unknown inter-domain residue interactions. We examined the possibility of these inter-domain residue interactions as contacts that stabilize an inactive conformation of the DBRR where DNA binding is inhibited for both subfamilies. The newly gained insights on linker-domain/inter-domain residue interactions and shared inactivation mechanisms improve the understanding of the functional mechanism of DBRRs, providing clues to efficiently create functional DBRR-based biosensors. Additionally, we show the feasibility of applying coevolutionary landscape models to predict the functionality of domain-swapped DBRR proteins. The presented result demonstrates that sequence information can be used to filter out bioengineered DBRR proteins that are predicted to be nonfunctional due to a high negative predictive value.

Spatial-temporal order-disorder transition in angiogenic NOTCH signaling controls cell fate specification.

Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell-cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip-Stalk cell patterning can undergo an order-disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.

Blood vessels are vital for transporting blood containing oxygen, nutrients and waste around the body. To maintain this function, new blood vessels are continually formed through a process called angiogenesis. Often triggered in areas requiring oxygen, new blood vessels form from existing vessels as 'sprouts' in response to elevated levels of a signaling molecule called vascular endothelial growth factor (or VEGF for short). For 'sprouting' to occur, endothelial cells lining the parental blood vessel must become either 'Tip' or 'Stalk' cells. Tip cells lead the extension of the blood vessel sprouts, while Stalk cells proliferate rapidly, ensuring the growth of the sprout. Correct spatial arrangement of these different cell types is crucial for the development of functional blood vessels. Previous work has shown that VEGF promotes differentiation of endothelial cells lining blood vessels into different cell types. In neighboring cells, a signaling pathway known as NOTCH is activated due to interactions between adjacent cells, promoting differentiation of Tip cells and Stalk cells. Ideally, Tip cells are spaced out by intervals of Stalk cells to allow separate sprouts to form. Throughout this process, a single cell can receive contradictory signals, with VEGF promoting Tip cell formation and NOTCH signaling promoting Stalk cell differentiation. It remained unclear how the right cells are formed in the right places when surrounded by these conflicting inputs. To better understand these dynamics Kang, Bocci et al. combined a laboratory model of angiogenesis with mathematical modelling. Experiments using these approaches showed that the overall pattern of cell type specification induced by VEGF and NOTCH signaling is consistent with so-called order-disorder transition, commonly observed in crystals in other ordered structures. For blood vessel cells, this transition means that they can still robustly take on either the Tip or Stalk cell identities, but this fate selection is not stable in time. Additionally, the overall pattern is much more sensitive to additional cues and self-organization mechanisms. Further analysis revealed that one such cue can be local fluctuations the density of fibronectin, a key pro-angiogenic extracellular component, leading to formation of sprouts that tend to distance themselves as much as possible from other fully formed sprouts. These findings provide a framework for understanding NOTCH-mediated patterning processes in the context of responding to a variety of environmental cues. This sensitivity in cell type specification is important for determining the dynamic nature of the initial steps of angiogenesis and may be crucial for understanding growth of new blood vessels in damaged organs, cancer and other diseases.

RACER-m leverages structural features for sparse T cell specificity prediction.

Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.

Structure and inhibition of SARS-CoV-2 spike refolding in membranes.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein binds the receptor angiotensin converting enzyme 2 (ACE2) and drives virus-host membrane fusion through refolding of its S2 domain. Whereas the S1 domain contains high sequence variability, the S2 domain is conserved and is a promising pan-betacoronavirus vaccine target. We applied cryo-electron tomography to capture intermediates of S2 refolding and understand inhibition by antibodies to the S2 stem-helix. Subtomogram averaging revealed ACE2 dimers cross-linking spikes before transitioning into S2 intermediates, which were captured at various stages of refolding. Pan-betacoronavirus neutralizing antibodies targeting the S2 stem-helix bound to and inhibited refolding of spike prehairpin intermediates. Combined with molecular dynamics simulations, these structures elucidate the process of SARS-CoV-2 entry and reveal how pan-betacoronavirus S2-targeting antibodies neutralize infectivity by arresting prehairpin intermediates.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma.

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.