Biology subjects

Tarafder, S.

Publications and source records attributed to Tarafder, S..

9 recordsLinked to original sources

bioRxiv2026

Tissue-Derived Insoluble Type I and Type II Collagen Inks for 3D Printing of Fibrillar Collagen Scaffolds

Collagen is the primary structural component of the extracellular matrix, yet most printable collagen systems rely on soluble collagen, support baths, secondary polymers, or photochemical crosslinking. Here, we developed tissue derived insoluble type I and type II collagen inks for direct extrusion-based 3D printing without a sacrificial support bath. Type I collagen was isolated from bovine Achilles tendon and type II collagen from bovine articular cartilage. Both inks exhibited shear thinning with a predominantly elastic response that facilitated extrusion and supported shape retention after printing. The formulations were printed into grid scaffolds with infill densities of 25, 35, 40, and 50 percent and into multilayer constructs measuring approximately 20*20*20 mm. The printed constructs were treated with ammonium hydroxide to induce fibrillization and subsequently lyophilized, producing dimensionally stable and readily handleable scaffolds. Scanning electron microscopy revealed comparable porous morphologies in both scaffold types. Live and Dead staining showed abundant viable human bone marrow derived mesenchymal stem cells across the scaffold surfaces and internal regions. Cells appeared predominantly spindle shaped on type I collagen and rounded on type II collagen, suggesting distinct cell responses to the two matrices. To our knowledge, this is the first study to report 3D printable tissue derived insoluble type I and type II collagen inks within a common fabrication platform, including direct fabrication of 3D scaffolds from tissue derived type II collagen. These collagen dominant scaffolds offer promising platforms for musculoskeletal tissue engineering, regenerative grafts, and 3D in vitro models.

bioengineering↗

bioRxiv2026

Hydrophilic Polydopamine (hPDA) Fueled Bioglue Enhances Tissue Adhesion and Promotes Healing of Avascular Meniscus Tears

Avascular meniscus tears exhibit minimal intrinsic healing and often progress to joint degeneration due to restricted biological repair capacity and inadequate restoration of tissue-level structure and function. Here, we report a hydrophilic polydopamine (hPDA) fueled bioglue platform that overcomes the solubility limitations of conventional polydopamine (PDA) and enables functional repair of avascular meniscus injuries. Water-soluble hPDA was synthesized via controlled depolymerization and recrystallization, yielding monomeric and oligomeric species rich in catechol, amine, and hydroxyl functionalities. Incorporation of hPDA into fibrin bioglues markedly enhanced mechanical performance, producing 520-525% increases in lap-shear modulus, 165-190% increases in adhesive strength, and a 160% increase in compressive modulus relative to fibrin controls, while degradation was markedly attenuated over 14 days. hPDA exhibited excellent cytocompatibility in both 2D and 3D cultures. In a bovine avascular meniscus explant model, hPDA fueled bioglues promoted tissue integration and aligned collagen remodeling, restoring interfacial mechanics with a 488% increase in tensile modulus and up to 150% higher pull-out strength after 6 weeks. These findings establish hPDA as a versatile bioadhesive building block with strong potential for repairing avascular meniscus tears and other mechanically demanding connective tissues. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/738365v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@d8c813org.highwire.dtl.DTLVardef@375beeorg.highwire.dtl.DTLVardef@748f66org.highwire.dtl.DTLVardef@72a409_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings

MotivationHigh-fidelity scoring of RNA 3D structures remains a major challenge in RNA structure prediction and conformational sampling. While single-model methods for scoring RNA structures can capture individual structural features, they fail to capture the broader structural consensus within a conformational ensemble, limiting their effectiveness in ranking and model selection. ResultsWe present PARSEbp, a fast and effective multi-model RNA scoring method that integrates pairwise structural agreement across the conformational ensemble with base pairing consistency. By leveraging both alignment-based global structural agreement at the 3D level and base pairing consistency at the 2D level, PARSEbp efficiently constructs a consensus similarity matrix from which per-structure accuracy scores are computed. Tested on 16th Critical Assessment of Structure Prediction (CASP16) RNA targets, PARSEbp significantly outperforms existing single- and multi-model RNA scoring functions, including traditional statistical potentials, state-of-the-art deep learning methods, and consensus-based approaches, as well as a baseline variant of PARSEbp without the emphasis on base pairings, across a wide range of complementary assessment metrics. AvailabilityPARSEbp is freely available at https://github.com/Bhattacharya-Lab/PARSEbp.

bioinformatics↗

bioRxiv2025

RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation

Despite the groundbreaking advances in deep learning-enabled methods for biomolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging due to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. Here we introduce RNAbpFlow, a novel sequence- and base-pair-conditioned SE(3)-equivariant flow matching model for generating RNA 3D structural ensemble. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. Experimental results show that base pairing conditioning leads to broadly generalizable performance improvements over current approaches for RNA topology sampling and predictive modeling in large-scale benchmarking. RNAbpFlow is freely available at https://github.com/Bhattacharya-Lab/RNAbpFlow.

bioinformatics↗

bioRxiv2024

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models

Ground-breaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input. Using a novel geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single enables the prediction of interatomic protein-RNA interaction maps, which are then transformed into multi-scale geometric restraints for modeling 3D structures of protein-RNA complexes via geometry optimization. Benchmark tests show that ProRNA3D-single convincingly outperforms current state-of-the-art methods including AlphaFold 3, particularly when evolutionary information is limited; and exhibits remarkable robustness and performance resilience by attaining better accuracy with only single-sequence input than what most methods can achieve even with explicit evolutionary information. Freely available at https://github.com/Bhattacharya-Lab/ProRNA3D-single, ProRNA3D-single should be broadly useful for modeling 3D structures of protein-RNA complexes at scale, regardless of the availability of evolutionary information.

bioinformatics↗

bioRxiv2023

lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

bioinformatics↗

bioRxiv2023

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.

bioinformatics↗

bioRxiv2021

Figbird: A probabilistic method for filling gaps in genome assemblies

MotivationAdvances in sequencing technologies have led to sequencing of genomes of a multitude of organisms. However, draft genomes of many of these organisms contain a large number of gaps due to repeats in genomes, low sequencing coverage and limitations in sequencing technologies. Although there exist several tools for filling gaps, many of these do not utilize all information relevant to gap filling. ResultsHere, we present a probabilistic method for filling gaps in draft genome assemblies using second generation reads based on a generative model for sequencing that takes into account information on insert sizes and sequencing errors. Our method is based on the expectation-maximization (EM) algorithm unlike the graph based methods adopted in the literature. Experiments on real biological datasets show that this novel approach can fill up large portions of gaps with small number of errors and misassemblies compared to other state of the art gap filling tools. Availability and ImplementationThe method is implemented using C++ in a software named "Filling Gaps by Iterative Read Distribution (Figbird)", which is available at: https://github.com/SumitTarafder/Figbird. Contactatif@cse.buet.ac.bd Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Oxo-M and 4-PPBP Delivery via Multi-Domain Peptide Hydrogel Toward Tendon Regeneration

We have recently identified novel small molecules, Oxo-M and 4-PPBP, which specifically stimulates endogenous tendon stem/progenitor cells (TSCs) leading to potential regenerative healing of fully-transected tendons. Here we investigated an injectable, multi-domain peptide (MDP) hydrogel providing a controlled delivery of the small molecules for regenerative tendon healing. We investigated the release kinetics of Oxo-M and 4-PPBP from MDP hydrogels and the effect of MDP-released small molecules on tenogenic differentiation of TSCs and in vivo tendon healing. In vitro, MDP showed a sustained release of Oxo-M and 4-PPBP and a slower degradation compared to fibrin. In addition, tenogenic gene expression was significantly increased in TSC with MDP-released Oxo-M and 4-PPBP as compared to the fibrin-released. In vivo, MDP releasing Oxo-M and 4-PPBP significantly improved tendon healing, likely associated with prolonged effects of Oxo-M and 4-PPBP on suppression of M1 macrophages and promotion of M2 macrophages. Comprehensive analyses including histomorphology, digital image processing, and modulus mapping with nanoindentation consistently suggested that Oxo-M and 4-PPBP delivered via MDP further improved tendon healing as compared to fibrin-based delivery. In conclusion, MDP delivered with Oxo-M and 4-PPBP may serve as an efficient regenerative therapeutic for in situ tendon regeneration and healing.

bioengineering↗