Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.06.18.135426

Phylogenetic Studies and Inhibitor Design Targeting Protein Interacting Interface of Nucleoid-Associated Protein HU

Dey, D.·Ramakumar, S.
View original source ↗Export metadata ↓

Abstract

The formations of nucleoprotein structures by promiscuous DNA binding proteins like HU are assisted with their protein protein interaction capability with other proteins. In E. coli Gal repressosome assembly, GalR piggybacks HU to the critical position on the DNA (hbs site) through a specific GalR-HU interaction using an interface at the bottom of alpha helical region, which we termed as HUpb interface. Similarly, MtbHU also interact with Topoisomerase I with the same interface to enhance its relaxation activity. In an earlier study, we determined the crystal structure of MtbHU, inhibited it using stilbene derivatives which inhibited the cell growth. It motivated us to understand the evolutionary and structural characteristics of the HUpb interface, which has not been investigated previously for HU or for any other NAPs. Our analyses found residue positions corresponding to MtbHU Thr11 to Gln20 form the interface while Ala23 serves the pocket lining residue. Due to ancestral mutations in the duplication event before the HU and IHF split, physicochemical properties of the interface vary among clades. Thus, this interface could engage different proteins in different HU oligomeric states in Proteobacteria. Protein-protein interfaces are usually a challenging target due to its flatter surface. In case of MtbHUpb interface, we observed that due to the presence of a partially hydrophobic pocket, small molecule scaffolds could fit into it, while the ligand can be further designed to interact with D17, which is the crucial residue for Topoisomerase I interaction. We used a two-step virtual screening protocol with known drug like molecules as starting set to an aim to re-purpose drugs. Our docking results showed compounds like Maltotetraose, Valrubicin, Iodixanol, Enalkiren, indinavir, Carfilzomib, oxytetracycline, quinalizarin, Raltitrexed, Epigallocatechin and their analogues exhibit high scoring binding at MtbHUpb interface. Our present report gives a model example of an evolutionary study of an interface of nucleoid associated protein, which is used to computationally design inhibitors. This strategy could be in general useful for designing inhibitors for various types of protein-protein interfaces using evolutionary guided design.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dey, D., Ramakumar, S.. 2020-06-18. Phylogenetic Studies and Inhibitor Design Targeting Protein Interacting Interface of Nucleoid-Associated Protein HU. https://doi.org/10.1101/2020.06.18.135426

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗
bioRxiv2026

SIFT: Syncmer-Informed Selection of Haplotypes for T2T Imputation

Genome patching improves the completeness of fragmented assemblies by filling unresolved gaps with sequences from reference genomes. Pangenome panels provide diverse candidate haplotypes for this task, but identifying suitable references efficiently becomes increasingly challenging as these panels grow. We present SIFT, a compressed indexing method for selecting reference haplotypes for genome patching with ImpuT2T. SIFT combines closed-syncmer parsing, phrase-level indexing, and pseudo-maximal exact match (pseudo-MEM) queries. Closed syncmers provide a context-independent parsing rule with bounded distances between consecutive selected positions, enabling consistent parsing of reference and query sequences. We demonstrate that two closed syncmers determine the distance at which they can occur consecutively in any string, so that equal substrings of two parses represent identical sequences and no gap sequence need be stored. SIFT indexes the reference parse and its reverse using run-length compressed suffix arrays and adapts the Boyer-Moore-Li search procedure to identify pseudo-MEMs between the reference parse and query parse. It then aggregates matching evidence to identify candidate reference haplotypes for downstream patching. This framework connects compressed sequence indexing with haplotype selection and provides a basis for querying large reference panels at the phrase level. SIFT is publicly available under the GNU license at https://github.com/EddieFerro/SIFT.

bioinformatics↗
bioRxiv2026

Assessing the Capacity of Genomic Language Models to Generate Informative Embeddings of Viral Genome Fragments

Genomic language models (gLMs) trained on large volumes of nucleotide sequences are becoming an alternative to classical methods in metagenomic analysis, particularly for studying genomic "dark matter". In this work, we investigate the ability of four gLMs (DNABERT-2, DNABERT-S, ViraLM, and Evo2-7B) to generate taxonomically informative vector representations of short viral genome fragments. We constructed a balanced reference dataset of viruses based on the ICTV Virus Metadata Resource and compared the representations generated by gLMs with conventional k-mer profiles and MinHash sketches using clustering and neighborhood-based metrics (adjusted Rand index (ARI), precision@k, and CROSS precision@k). On the reference dataset, three of the four gLMs outperformed the 4-mer profile in terms of ARI and precision@5 under the same evaluation protocol. The best performance was generally achieved by intermediate model layers. Conventional k-mer profiles consistently outperformed MinHash sketches. We further evaluated the methods on two viral orders (egoviruses and mriyaviruses) absent from the reference dataset, with model layers and k-mer lengths selected exclusively on the reference dataset. DNABERT-S showed the most consistently strong performance across these two orders, ranking first on mriyaviruses and second on egoviruses, while k-mer profiles with Jensen-Shannon distance remained competitive. We also observed the consistency of representations between forward fragments and their reverse complements. Overall, the best-performing gLMs surpassed conventional baselines, although their advantage depended on the dataset and evaluation metric. The consistently strong performance of DNABERT-S suggests a potential benefit of contrastive fine-tuning and indicates that larger models are not necessarily more effective for generating taxonomically informative representations.

bioinformatics↗