Biology subjects

Wasim, A.

Publications and source records attributed to Wasim, A..

7 recordsLinked to original sources

bioRxiv2024

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories

With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation datasets has emerged. By leveraging AutoEncoders, a type of artificial neural network, we developed a method to compress MD trajectories into significantly smaller latent spaces. Our method can save up to 98% in disk space compared to xtc, a highly compressed trajectory format from the widely used MD program package GROMACS, thus facilitating storage and sharing of simulation trajectories. Atom coordinates are very accurately reconstructed from compressed data. The method was tested across a diverse sets of biomolecular systems, including folded proteins, intrinsically disordered proteins, phospholipid bilayers, proteinligand complexes, large protein complexes and membrane-bound protein systems. The reconstructed trajectories demonstrated consistent accuracy in recovering key biophysically relevant properties for proteins, lipids and composite systems. The compression efficiency was particularly beneficial for larger systems. This approach enables the scientific community to efficiently store and share large-scale biomolecular simulation data, potentially enhancing collaborative research efforts. The workflow, termed "compresstraj", is implemented in PyTorch and is publicly available at https://github.com/SerpentByte/compresstraj, offering a practical solution for handling the increasing volumes of data generated in biomolecular simulation studies.

biophysics↗

bioRxiv2024

A Transformer Based Machine Learning of Molecular Grammar Inherent in Proteins Prone to Liquid Liquid Phase Separation

Understanding the molecular grammar that governs protein phase separation is essential for advancements in bioinformatics and protein engineering. This study leverages Generative Pre-trained Transformer (GPT)-based Protein Language Models (PLMs) to decode the complex grammar of proteins prone to liquid-liquid phase separation (LLPS). We trained three distinct GPT models on datasets comprising amino acid sequences with varying LLPS propensities: highly predisposed (LLPS+ GPT), moderate (LLPS-GPT), and resistant (PDB* GPT). As training progressed, the LLPS-prone model began to learn embeddings that were distinct from those in LLPS-resistant sequences. These models generated 18,000 protein sequences ranging from 20 to 200 amino acids, which exhibited low similarity to known sequences in the SwissProt database. Statistical analysis revealed subtle but significant differences in amino acid occurrence probabilities between sequences from LLPS-prone and LLPS-resistant models, suggesting distinct molecular grammar underlying their phase separation abilities. Notably, sequences from LLPS+ GPT showed fewer aromatic residues and a higher fraction of charge decoration. Short peptides (20-25 amino acids) generated from LLPS+ GPT underwent computational and wet-lab validation, demonstrating their ability to form phase-separated states in vitro. The generated sequences enriched the existing database and enabled the development of a robust classifier that accurately distinguishes LLPS-prone from non-LLPS sequences. This research marks a significant advancement in using computational models to explore and engineer the vast protein sequence space associated with LLPS-prone proteins.

biophysics↗

bioRxiv2023

Modulation of Liquid-liquid Phase separation of alpha-Synuclein by Saline and Crowded Environment

Intrinsically disordered protein -Synuclein (S) is implicated in Parkinsons disease due to its aberrant aggregation propensity. In a bid to identify the traits of its aggregation, here we computationally simulate the multi-chain association process of S in aqueous as well as under diverse environmental perturbations. In particular, the aggregation of S in aqueous and varied environmental condition led to marked concentration differences within protein aggregates, resembling liquid-liquid phase separation (LLPS). Both saline and crowded settings enhanced the LLPS propensity. However, the surface tension of S droplet responds differently to crowders (entropy-driven) and salt (enthalpy-driven). Conformational analysis reveals that the IDP chains would adopt extended conformations within aggregates and would maintain mutually perpendicular orientations to minimize inter-chain electrostatic repulsions. The droplet stability is found to stem from a diminished intra-chain interactions in the C-terminal regions of S, fostering inter-chain residue-residue interactions. Intriguingly, a graph theory analysis identifies small-world-like networks within droplets across environmental conditions, suggesting the prevalence of a consensus interaction patterns among the chains. Together these findings suggest a delicate balance between molecular grammar and environment-dependent nuanced aggregation behaviour of S.

biophysics↗

bioRxiv2023

Development of a Data-driven Integrative Model of Bacterial Chromosome

The chromosome of archetypal bacteria E. coli is known for a complex topology with 4.6 x 106 base pairs (bp) long sequence of nucleotide packed within a micrometer-sized celllular confinement. The inherent organization underlying this chromosome eludes general consensus due to the lack of a high-resolution picture of its conformation. Here we present our development of an integrative model of E. coli at a 500 bp resolution (https://github.com/JMLab-tifrh/ecoli_finer), which optimally combines a set of multi-resolution genome-wide experimentally measured data within a framework of polymer based architecture. In particular the model is informed with intra-genome contact probability map at 5000 bp resolution derived via Hi-C experiment and RNA-sequencing data at 500 bp resolution. Via dynamical simulations, this data-driven polymer based model generates appropriate conformational ensemble commensurate with chromosome architectures that E. coli adopts. As a key hallmark, the model chromosome spontaneously self-organizes into a set of non-overlapping macrodomains and suitably locates plectonemic loops near the cell membrane. As novel extensions, it predicts a contact probability map simulated at a higher resolution than precedent experiments and can demonstrate segregation of chromosomes in a partially replicating cell. Finally, the modular nature of the model helps us to devise control simulations to quantify the individual role of key features in hierarchical organization of the bacterial chromosome.

microbiology↗

bioRxiv2022

A mechanistic understanding of biofilm morphogenesis: Coexistence of mobile and sessile aggregates and phase-separated patterns

Most bacteria in the natural environment self-organize into collective phases such as cell clusters, swarms, patterned colonies, or biofilms. The occurrence of different phases and their coexistence is governed by several intrinsic and extrinsic factors such as the growth, motion, and physicochemical interactions. Hence, it is crucial to predict the conditions under which a collective phase emerges due to the individual-level interactions. Here we develop a particle-based biophyiscal model of bacterial cells and self-secreted extracellular polymeric substances (EPS) to decipher the interplay of growth, motility-mediated dispersal, and mechanical interactions during microcolony morphogenesis. We show that depending upon the heterogeneous production and physicochemical properties of EPS, whether sticky or nonadsorbing in nature, the microcolony dynamics and architecture significantly varies. In particular, in sticky EPS, microcolony shows the coexistence of both motile and sessile aggregates rendering a transition towards biofilm formation. Wherein for the nonad-sorbing EPS, which behaves as depletant in the media, a variety of phase-segregated patterned colonies either localizing the matrix component or cells at the colony periphery may emerge. We identified that the interplay of differential dispersion and the mechanical interactions among the components of the colony determines the fate of the colony morphology. Our results provide a significant understanding of the mechano-self-regulation during biofilm morphogenesis and open up possibilities of designing experiments to test the predictions.

biophysics↗

bioRxiv2021

Hi-C Contacts Encode Heterogeneity in Sub-diffusive Motion of E. coli Chromosomal Loci

Underneath its apparently simple architecture, the circular chromosome of E. coli is known for displaying complex dynamics in its cytoplasm. Recent experiments have hinted at an inherently heterogeneous dynamics of chromosomal loci, the origin of which has largely been elusive. In this regard, here we investigate the loci dynamics of E. coli chromosome in a minimally growing condition at 30{degrees}C by integrating the experimentally derived Hi-C interaction matrix within a computer model. Our quantitative analysis demonstrates that, while the dynamics of the chromosome is sub-diffusive in a viscoelastic media in general, the diffusion constants and the diffusive exponents are strongly dependent on the spatial coordinates of chromosomal loci. In particular, the loci in Ter Macro-domain display slower mobility compared to the others. The result is found to be robust even in the presence of active noise. Interestingly, a series of control investigations reveal that the absence of Hi-C interactions in the model would have abolished the heterogeneity in loci diffusion, indicating that the observed coordinate-dependent chromosome dynamics is heavily dictated via Hi-C-guided longrange inter-loci communications. Overall, the study underscores the key role of Hi-C interactions in guiding the inter-loci encounter and in modulating the underlying heterogeneity of the loci diffusion.

biophysics↗

bioRxiv2020

Computational Elucidation of self-organization of E. coli chromosome underlying HI-C data

The chromosome of Escherichia Coli (E. coli) is riddled with multi-faceted complexity and its nature of organization is slowly getting recognised. The emergence of chromosome conformation capture techniques and super-resolution microscopy are providing newer ways to explore chromosome organization, and dynamics and its effect on gene expression. Here we combine a beads-on-a-spring polymer-based framework with recently reported high-resolution Hi-C data of E. coli chromosome to develop a comprehensive model of E. coli chromosome at 5 kilo base-pair resolution. The model captures a self-organised chromosome composed of linearly organised genetic loci, and segregated macrodomains within a ring-like helicoid architecture, with no net chirality. Additionally, a genome-wide map identifies multiple chromosomal interaction domains (CIDs) and corroborates well with a transcription-centric model of the E. coli chromosome. The investigation further demonstrates that while only a small fraction of the Hi-C contacts is dictating the underlying chromosomal organization, a random-walk polymer chain devoid of Hi-C encoded contact information would fail to map the key genomic interactions unique to E. coli. Collectively, the present work, integrated with Hi-C interaction, elucidates the organization of bacterial chromosome at multiple scales, ranging from identifying a helical, macro-domain-segregated morphology at coarse-grained scale to a manifestation of CIDs at a fine-grained scale.

biophysics↗