Trapped-ion quantum simulation of electron transfer models with tunable dissipation.

Electron transfer is at the heart of many fundamental physical, chemical, and biochemical processes essential for life. The exact simulation of these reactions is often hindered by the large number of degrees of freedom and by the essential role of quantum effects. Here, we experimentally simulate a paradigmatic model of molecular electron transfer using a multispecies trapped-ion crystal, where the donor-acceptor gap, the electronic and vibronic couplings, and the bath relaxation dynamics can all be controlled independently.

The sensory shark: high-quality morphological, genomic and transcriptomic data for the small-spotted catshark Scyliorhinus canicula reveal the molecular bases of sensory organ evolution in jawed vertebrates.

Cartilaginous fishes (chondrichthyans: chimaeras and elasmobranchs -sharks, skates and rays) hold a key phylogenetic position to explore the origin and diversifications of jawed vertebrates. Here, we report and integrate reference genomic, transcriptomic and morphological data in the small-spotted catshark Scyliorhinus canicula to shed light on the evolution of sensory organs. We first characterise general aspects of the catshark genome, confirming the high conservation of genome organisation across cartilaginous fishes, and investigate population genomic signatures.

Antimicrobial Peptides as Broad-Spectrum Therapeutics: Computational Analysis to Identify Universal Physical-Chemical Features Responsible for Multitarget Activity.

Antimicrobial peptides (AMPs) hold significant potential as broad-spectrum therapeutics due to their ability to target a variety of different pathogens, including bacteria, fungi, and viruses. However, the rational design of these peptides requires the molecular understanding of properties that enable such broad-spectrum activity. In this study, we present a computational analysis that utilizes machine-learning methods to distinguish peptides with single-target activity from those with activity against multiple pathogens.

Chromatin folding through nonuniform motorization by responsive motor proteins.

Chromatin is partially structured through the effects of biological motors. "Swimming motors" such as RNA polymerases and chromatin remodelers are thought to act differentially on the active parts of the genome and the stored inactive part. By systematically expanding the many-body master equation for chromosomes driven by swimming motors, we show that this nonuniform aspect of motorization leads to heterogeneously folded conformations, thereby contributing to chromosome compartmentalization.

How dynamic surface restructuring impacts intra-particle catalytic cooperativity.

Recent experiments indicated that nanoparticles (NPs) might efficiently catalyze multiple chemical reactions, frequently exhibiting new phenomena. One of those surprising observations is intra-particle catalytic cooperativity, when the reactions at one active site can stimulate the reactions at spatially distant sites. Theoretical explanations of these phenomena have been presented, pointing out the important role of charged hole dynamics.

Designing proteins: Mimicking natural protein sequence heterogeneity.

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins.

Integration of kinetic data into affinity-based models for improved T cell specificity prediction.

T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) interactions that result in T cell activation are complex and have been distinguished by their equilibrium affinity and kinetic profiles. While prior affinity-based models can successfully predict meaningful TCR-pMHC interactions in many cases, they occasionally fail at identifying TCR-pMHC interactions with low binding affinity. This study analyzes TCR-pMHC systems for which empirical kinetic and affinity data exist and prior affinity-based predictions have failed.

Understanding the interplay between extracellular matrix topology and tumor-immune interactions: Challenges and opportunities.

Modern cancer management comprises a variety of treatment strategies. Immunotherapy, while successful at treating many cancer subtypes, is often hindered by tumor immune evasion and T cell exhaustion as a result of an immunosuppressive tumor microenvironment (TME). In solid malignancies, the extracellular matrix (ECM) embedded within the TME plays a central role in T cell recognition and cancer growth by providing structural support and regulating cell behavior.

Multistability and predominant hybrid phenotypes in a four node mutually repressive network of Th1/Th2/Th17/Treg differentiation.

Elucidating the emergent dynamics of cellular differentiation networks is crucial to understanding cell-fate decisions. Toggle switch - a network of mutually repressive lineage-specific transcription factors A and B - enables two phenotypes from a common progenitor: (high A, low B) and (low A, high B). However, the dynamics of networks enabling differentiation of more than two phenotypes from a progenitor cell has not been well-studied.

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions.

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs.

Accurate nuclear quantum statistics on machine-learned classical effective potentials.

The contribution of nuclear quantum effects (NQEs) to the properties of various hydrogen-bound systems, including biomolecules, is increasingly recognized. Despite the development of many acceleration techniques, the computational overhead of incorporating NQEs in complex systems is sizable, particularly at low temperatures. In this work, we leverage deep learning and multiscale coarse-graining techniques to mitigate the computational burden of path integral molecular dynamics (PIMD).

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC.

Three-dimensional nuclear DNA architecture comprises well-studied intra-chromosomal () folding and less characterized inter-chromosomal () interfaces. Current predictive models of 3D genome folding can effectively infer pairwise -chromatin interactions from the primary DNA sequence but generally ignore contacts. There is an unmet need for robust models of -genome organization that provide insights into their underlying principles and functional relevance.

Origin of yield stress and mechanical plasticity in biological tissues.

During development and under normal physiological conditions, biological tissues are continuously subjected to substantial mechanical stresses. In response to large deformations cells in a tissue must undergo multicellular rearrangements in order to maintain integrity and robustness. However, how these events are connected in time and space remains unknown.

Linear-Decoupling Enables Accurate Speed and Accuracy Predictions for Copolymerization Processes.

Biological processes exhibit remarkable accuracy and speed and can be theoretically explored through various approaches. The Markov-chain copolymerization theory, describing polymer growth kinetics as a Markov chain, provides an exact set of equations to solve for error and speed. Still, due to nonlinearity, these equations are hard to solve.

Theta-curves in proteins.

We study and characterize the topology of connectivity circuits observed in natively folded protein structures whose coordinates are deposited in the Protein Data Bank (PDB). Polypeptide chains of some proteins naturally fold into unique knotted configurations. Another kind of nontrivial topology of polypeptide chains is observed when, in addition to covalent bonds connecting consecutive amino acids in polypeptide chains, one also considers disulfide and ionic bonds between non-consecutive amino acids.

How Transcription Factors Binding Stimulates Transcriptional Bursting.

Transcription is a fundamental biological process of transferring genetic information which often occurs in stochastic bursts when periods of intense activity alternate with quiescent phases. Recent experiments identified strong correlations between the association of transcription factors (TFs) to gene promoters on DNA and transcriptional activity. However, the underlying molecular mechanisms of this phenomenon remain not well understood.

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited.

Nonlinear Poisson effect in affine semiflexible polymer networks.

Stretching an elastic material along one axis typically induces contraction along the transverse axes, a phenomenon known as the Poisson effect. From these strains, one can compute the specific volume, which generally either increases or, in the incompressible limit, remains constant as the material is stretched. However, in networks of semiflexible or stiff polymers, which are typically highly compressible yet stiffen significantly when stretched, one instead sees a significant reduction in specific volume under finite strains.

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.

Breakdown of Boltzmann-type models for the alignment of self-propelled rods.

Studies in the collective motility of organisms use a range of analytical approaches to formulate continuous kinetic models of collective dynamics from rules or equations describing agent interactions. However, the derivation of these kinetic models often relies on Boltzmann's "molecular chaos" hypothesis, which assumes that correlations between individuals are short-lived. While this assumption is often the simplest way to derive tractable models, it is often not valid in practice due to the high levels of cooperation and self-organization present in biological systems.

Reorganizing chromatin by cellular deformation.

Biologists have the capability to edit a genome at the nanometer scale and then observe whether or not the edit affects the structure of a developing organ or organism at the centimeter scale. Our understanding of the underlying mechanisms driving this emergent phenomenon from a multiscale perspective remains incomplete. This review focuses predominantly on recent experimental developments in uncovering the mechanical interplay between the chromatin and cell scale since mechanics plays a major role in determining nuclear, cellular, and tissue structure.