Biology subjects

Sang, M.

Publications and source records attributed to Sang, M..

8 recordsLinked to original sources

bioRxiv2026

Defining reversible binding rates in 1D systems dependent on diffusion, density, and excluded volume

Binding reactions in effectively one-dimensional systems, such as proteins diffusing along DNA or other filaments, pose a fundamental coarse-graining challenge because stochastic trajectories are recurrent in one dimension and therefore do not admit a unique, separation-independent macroscopic association rate. As a result, continuum rate equations are not exact in 1D even for initially homogeneous systems. Here we develop a practical framework for mapping stochastic 1D reaction-diffusion dynamics onto effective kinetic models. Using mean-first-passage arguments and particle-based simulations, we define a density-dependent association rate and a corresponding single-rate approximation, and quantify when each provides an accurate description of the underlying stochastic dynamics. We implement 1D reaction-diffusion with excluded volume in the NERDSS software using a free-propagator reweighting algorithm and validate it against known pairwise and many-body limits. Our results show that ordinary rate equations with a single effective rate can accurately reproduce 1D reaction kinetics when the dimensionless parameter governing the ratio of intrinsic to diffusion-limited reactivity is small, with excellent agreement in the strongly rate-limited regime and increasing deviations as diffusion control strengthens. We further show that excluded volume in 1D can appreciably alter both kinetics and equilibrium populations, even at modest particle densities, by reducing accessible length and introducing blockade effects. Together, these results provide quantitative guidance for selecting between spatial simulations, density-dependent rate models, and single-rate continuum descriptions of reversible 1D binding reactions.

biophysics↗

bioRxiv2026

Transforming macromolecular structures into simulations of self-assembly

Macromolecular self-assembly is a fundamental process in living and engineered systems, producing molecular machines like the ribosome or highly symmetric viral capsids. Thanks to sources like the Protein Data Bank (PDB) and AlphaFold3, the final target complexes are often known, but these static structures do not provide information on the self-assembly process directly. Computational models provide critical tools to study these essential pathways of self-assembly, but substantial coarse-graining of assembly subunits is necessary to achieve computational tractability of these relatively slow processes while retaining multi-valency. While rule-based or local interactions overcome the often-prohibitive enumeration of all possible assembly intermediates, they must ensure global structural constraints are met. We here demonstrate ioNERDSS, a user-friendly Python package that transforms 3D atomic structures into coarse-grained models for immediate simulation with the stochastic reaction-diffusion NERDSS software, converting static structures into time-resolved assembly trajectories. NERDSS uses rule-based interactions to simulate multi-component self-assembly at the minutes timescales and without limits to complex size or growth pathways. With ioNERDSS, each protein chain is defined by a rigid subunit with discrete interfaces and explicit orientational constraints that enforce a structured assembly. Repeated subunits (such as in viral capsids) are regularized to preserve the target topology across distinct stochastic assembly pathways, supporting assembly of structures with thousands of subunits. We initialize pairwise binding affinities using open-source machine-learned prediction tools, and our default coarse-grained (CG) models are all constrained by thermodynamic reversibility to reach an equilibrium steady-state. The binding rates and subunit abundances necessary to perform simulations are initialized at default values but represent the key variables (along with affinities) that cells and thus users would tune to control productive assembly. Benchmarking on over 40,000 PDB structures shows that the majority of CG models stochastically assemble into target structures. The ioNERDSS Python library links directly to open-source tools for visualization and analysis to facilitate fast and user-friendly structure validation and analysis of output for thermodynamic, kinetic, and nonequilibrium drivers of macromolecular self-assembly.

biophysics↗

bioRxiv2025

Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization

Successful DNA transcription demands coordination between proteins that bind DNA while simultaneously binding to one another to form dimers or higher-order complexes. For proteins with numerous DNA targets throughout the genome, measurements that report on their dwell time or occupancy thus represent a convolution over a population interacting with specific DNA, nonspecific DNA, or protein partners on DNA. Dimerization is known to add contacts that can help a single protein to stably bind DNA. However, we show here that dimerization can also impair measured dwell times and occupancy on target sequences because the population redistributes across DNA. We combine mass-action kinetic models of pairwise reversible reactions between proteins and DNA with theory and spatial stochastic simulations to isolate the role of dimerization on observed DNA dwell times, occupancy, and spatial distribution of proteins on DNA. Three key themes emerge: (i) Protein-protein interactions, in addition to protein-DNA interactions, can localize a protein to DNA, and relative binding rates can thus widely tune dwell times. (ii) Dimensional reduction achieved through nonspecific binding and subsequent 1D diffusion controls the order-of-magnitude of enhancements despite nucleosome barriers. (iii) Dimerization enhances selectivity for locally clustered targets and often impairs binding to widely-spaced targets by sequestration. Compared with ChIP-seq data, our model explains how the distribution of the essential GAF protein throughout the genome is highly selective for clustered targets due to protein interactions. This model framework predicts when even weak dimerization can redistribute and stabilize proteins on DNA as a necessary part of transcription. SignificanceTranscription of DNA into RNA relies on a globally orchestrated but microscopically stochastic recruitment of multiple proteins to targeted DNA sequences. DNA binding is only a first step. Here we show how proteins with many targets throughout the genome can use protein-protein dimerization to not only help proteins bind DNA, but also to redistribute the population genome-wide, significantly enhancing or impairing dwell times and target occupancies dependent on target spacing. Using theory and simulations along with experimental comparison, outcomes depend on protein-to-target ratios, dimensional gains of 1D searchers, and relative binding rates. Our relatively simple formulas predict how these factors yield negligible or dramatic changes in target selection, with direct mapping to experimental measurements on essential transcription factors.

biophysics↗

bioRxiv2024

Deciphering the thiolactonization mechanism in thiolactomycin biosynthesis

Thiolactomycin (1), which features a unique{gamma} -thiolactone ring, is a promising antibiotic candidate that specifically targets bacterial type II fatty acid synthase. Despite extensive studies on its pharmacological activities, modes of action, and chemical synthesis, the enzymatic processes responsible for forming the activity-determining{gamma} -thiolactone ring have remained largely unknown. Here, we resolve this problem by revealing that the condensation and heterocyclization (Cy) domain of the nonribosomal peptide synthetase (NRPS) TlnC (TlnCCy), along with the cytochrome P450 enzyme TlnA, cooperatively enable the{gamma} -thiolactone assembly. TlnCCy mediates an unusual sulfurtransfer reaction to sulfurate the polyketide intermediate, generating a thiocarboxylate intermediate. Subsequently, TlnA acts as a{gamma} -thiolactone synthase, converting the linear thiocarboxylate intermediate into 1 via a distal radical-based cyclization mechanism. These findings not only expand the functional and catalytic repertoires of NRPS Cy domains and P450 enzymes, but also highlight a special enzymatic strategy for{gamma} -thiolactone biosynthesis in nature. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/629141v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@7e0ad2org.highwire.dtl.DTLVardef@f86b80org.highwire.dtl.DTLVardef@159d136org.highwire.dtl.DTLVardef@1b633ff_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Parallelization of particle-based reaction-diffusion simulations using MPI

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.

biophysics↗

bioRxiv2024

Glutamate (E299) is a key residue in the evolutionarily divergence of the SAM-dependent methyltransferases DnrK and RdmB in anthracycline biosynthesis

A novel sub-class of S-adenosyl-L-methionine (SAM)-dependent methyltransferases catalyze atypical chemical transformations in the biosynthesis of anthracyclines, which include antineoplastic compounds. Intriguingly, the closely related methyltransferases DnrK and RdmB have markedly divergent functions. We investigated their catalytic activities and discovered a previously unknown 10-hydroxylation activity for DnrK and 4-O-methylation activity for RdmB. Isotope-labeling demonstrated that the 10-hydroxy group introduced by DnrK is derived from water molecules while RdmB utilizes O2. A single residue, E299, was the key modulator in the differing catalytic functions of DnrK and RdmB, especially the decarboxylative oxidation activity. Furthermore, the multifunctionality of DnrK was demonstrated to be SAM-tunable and concerted, whereas RdmB activity was cofactor-dependent and stepwise. Our findings expand the versatility and importance of methyltransferases and should aid studies to enrich the structural diversity and bioactivities of anthracyclines.

biochemistry↗

bioRxiv2020

De novo assembly of trachidermus fasciatus genome by nanopore sequencing

Trachidermus fasciatus is a roughskin sculpin fish widely located at the coastal areas of East Asia. Due to the environmental destruction and overfishing, the populations of this species have been under threat. It is important to have a reference genome to study the population genetics, domestic farming, and genetic resource protection. However, currently, there is no reference genome for Trachidermus fasciatus, which has greatly hurdled the studies on this species. In this study, we proposed to integrate nanopore long reads sequencing, Illumina short reads sequencing and Hi-C methods to thoroughly de novo assemble the genome of Trachidermus fasciatus. Our results provided a chromosome-level high quality genome assembly with a total length of about 543 Mb, and with N50 of 23 Mb. Based on de novo gene prediction and RNA sequencing information, a total of 38728 genes were found, including 23191 protein coding genes, 2149 small RNAs, 5572 rRNAs, and 7816 tRNAs. Besides, about 23% of the genome area is covered by the repetitive elements. Furthermore, The BUSCO evaluation of the completeness of the assembled genome is more than 96%, and the single base accuracy is 99.997%. Our study provided the first whole genome reference for the species of Trachidermus fasciatus, which might greatly facilitate the future studies on this species.

genomics↗

bioRxiv2020

The landscape of mouse epididymal cells defined by the single-cell RNA-Seq

Spermatozoa acquire their fertilizing ability and forward motility properties during epididymal transit. Although lots of attempts elucidating the functions of different cell types in epididymis, the composition of epididymal tubal and cell types are still largely unknown. Using single-cell RNA sequence, we analyzed the cell constitutions and their gene expression profiles of adult epididymis derived from caput, corpus and cauda epididymis with a total of 12,597 cells. This allowed us to elucidate the full range of gene expression changes during epididymis and derive region-specific gene expression signatures along the epididymis. A total of 7 cell populations were identified with all known constituent cells of mouse epididymis, as well as two novel cell types. Our analyses revealed a segment to segment variation of the same cell type in the three different part of epididymis and generated a reference dataset of epididymal cell gene expression. Focused analyses uncovered nine subtypes of principal cell. Two subtypes of principal cell, c0.3 and c.6 respectively, in our results supported with previous finding that they mainly located in the caput of mouse epididymis and play important roles during sperm maturation. We also showed unique gene expression signatures of each cell population and key pathways that may concert epididymal epithelial cell-sperm interactions. Overall, our single-cell RNA seq datasets of epididymis provide a comprehensive potential cell types and information-rich resource for the studies of epididymal composition, epididymal microenvironment regulation by the specific cell type, or contraceptive development, as well as a gene expression roadmap to be emulated in efforts to achieve sperm maturation regulation in the epididymis.

cell biology↗