Biology subjects

Ibtehaz, N.

Publications and source records attributed to Ibtehaz, N..

9 recordsLinked to original sources

bioRxiv2026

PathFold: Predicting the Entire Protein Folding Pathway from Protein Sequence Alone

Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native states, remains a significant challenge. Here, we present PathFold, a deep learning framework that predicts protein folding pathways directly from sequence information. PathFold leverages an AlphaFold-based module to extract structural information from the sequence and generates a progressive folding trajectory from an extended conformation using a diffusion model. By modeling the full trajectory, it enables prediction of folding intermediates and transition pathways, analogous to those observed in steered molecular dynamics (SMD) simulations. The predicted pathways reveal well-defined intermediates and sequential folding events, and show agreement with experimental folding data, including measured {Phi}-values.

bioinformatics↗

bioRxiv2026

Prot-LAMBDA: Explicit Distance Learning Enhances Structural Reasoning in Protein Language Models

Protein language models (PLMs) learn evolutionary information from large-scale sequence data, but three-dimensional relationships are encoded only implicitly. Here, we introduce Prot-LAMBDA (Protein LAnguage Model Boosted with Distance Awareness), a PLM that explicitly incorporates spatial relationships by coupling residue embeddings with inter-residue contacts. Prot-LAMBDA improves performance across diverse structure-related tasks, including contact, secondary structure, backbone geometry, solvent accessibility, and protein fold prediction. Notably, it achieves a twofold improvement in long-range contact recall and an 11.7% reduction in {psi}-angle prediction error relative to ESM2-3B. Despite having approximately fivefold fewer parameters, Prot-LAMBDA also improves 3D structure prediction over ESM2-3B by 5-7% in TM-score when coupled to the same structure-prediction module. Building on these representations, we developed LambdaFold, a lightweight distance-guided structure prediction framework that achieves performance comparable to ESMFold on proteins strictly non-redundant to the training data. Finally, retrieval-augmented integration of structural templates increases mean TM-score substantially for targets with high template coverage and rescues several incorrect folds. Together, these results demonstrate that explicit spatial constraints enable efficient and generalizable structural representation learning and protein structure prediction.

bioinformatics↗

bioRxiv2026

Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta

Cryogenic electron microscopy (cryo-EM) has become a widely used technique for determining the three-dimensional structures of biological macromolecules. Despite its advantages, building accurate structural models from cryo-EM data remains challenging, particularly at non-atomic resolutions. Here, we present CryoZeta, a de novo structure modeling program that leverages a diffusion-based generative deep neural network to integrate cryo-EM map density features with a biomolecular structure prediction pipeline similar to Alphafold3. By jointly leveraging sequence information and density-based features, CryoZeta generates highly accurate structural models that are consistent with the experimental map density. Evaluated on benchmark datasets covering protein complexes, protein-nucleic acid assemblies, and nucleic acid-only systems at resolutions up to 10 [A], CryoZeta consistently outperforms existing cryo-EM modeling methods in atomic accuracy. These results highlight the benefits of directly incorporating cryo-EM density into modern structure prediction pipelines and establish the method as a robust tool for automated, high-fidelity modeling from cryo-EM maps.

bioinformatics↗

bioRxiv2025

Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature

The rapid expansion of biomedical literature has made comprehensive manual synthesis increasingly difficult to perform effectively, creating a pressing need for AI systems capable of reasoning across verified evidence rather than merely retrieving it. However, existing retrieval-augmented generation (RAG) methods often fall short when faced with complex biomedical questions that require iterative reasoning and multi-step synthesis. Here, we developed Queryome, a deep research system consisting of specialized large language model (LLM) agents that can adapt their orchestration dynamically to a wide range of queries. Using a hybrid semantic-lexical retrieval engine spanning 28.3 million PubMed abstracts, it performs iterative, evidence-grounded synthesis. On the MIRAGE benchmark, Queryome achieved 88.98 % accuracy, surpassing prior systems by up to 14 points, and improved reasoning accuracy on the biomedical Humans Last Exam (HLE) subset from 15.8% to 19.3%. Moreover, in a task for constructing a review article, it earned the highest composite score in comparison with Deep Research from OpenAI, Google, Perplexity, and Scite.AI, reflecting its strong literature retrieval and synthesis capabilities.

bioinformatics↗

bioRxiv2024

Predicting transcriptional activation domain function using Graph Neural Networks

Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.

bioinformatics↗

bioRxiv2023

GO2Sum: Generating Human Readable Functional Summary of Proteins from GO Terms

Understanding the biological functions of proteins is of fundamental importance in modern biology. To represent function of proteins, Gene Ontology (GO), a controlled vocabulary, is frequently used, because it is easy to handle by computer programs avoiding open-ended text interpretation. Particularly, the majority of current protein function prediction methods rely on GO terms. However, the extensive list of GO terms that describe a protein function can pose challenges for biologists when it comes to interpretation. In response to this issue, we developed GO2Sum (Gene Ontology terms Summarizer), a model that takes a set of GO terms as input and generates a human-readable summary using the T5 large language model. GO2Sum was developed by fine-tuning T5 on GO term assignments and free-text function descriptions for UniProt entries, enabling it to recreate function descriptions by concatenating GO term descriptions. Our results demonstrated that GO2Sum significantly outperforms the original T5 model that was trained on the entire web corpus in generating Function, Subunit Structure, and Pathway paragraphs for UniProt entries.

bioinformatics↗

bioRxiv2023

NuFold: A Novel Tertiary RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation

RNA is not only playing a core role in the central dogma as mRNA between DNA and protein, but also many non-coding RNAs have been discovered to have unique and diverse biological functions. As genome sequences become increasingly available and our knowledge of RNA sequences grows, the study of RNAs structure and function has become more demanding. However, experimental determination of three-dimensional RNA structures is both costly and time-consuming, resulting in a substantial disparity between RNA sequence data and structural insights. In response to this challenge, we propose a novel computational approach that harnesses state-of-the-art deep learning architecture NuFold to accurately predict RNA tertiary structures. This approach aims to offer a cost-effective and efficient means of bridging the gap between RNA sequence information and structural comprehension. NuFold implements a nucleobase center representation, which allows it to reproduce all possible nucleotide conformations accurately.

bioinformatics↗

bioRxiv2023

Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations

Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, significantly outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.

bioinformatics↗

bioRxiv2019

NORTH: a highly accurate and scalable Naive Bayes based ORTHologous gene clustering algorithm

BackgroundThe principal objective of comparative genomics is inferring attributes of an unknown gene by comparing it with well-studied genes. In this regard, identifying orthologous genes plays a pivotal role as the orthologous genes remain less diverged in the course of evolution. However, identifying orthologous genes is often difficult, slow, and idiosyncratic, especially in the presence of multiplicity of domains in proteins, evolutionary dynamics (gene duplication, transfer, loss, introgression etc.), multiple paralogous genes, incomplete genome data, and for distantly related species where similarity is hard to recognize. MotivationAdvances in identifying orthologs have mostly been constrained to developing databases of genes or methods which involve computationally expensive BLAST search or constructing phylogenetic trees to infer orthologous relationships. These methods do not generally scale well and cannot analyze large amount of data from diverse organisms with high accuracy. Moreover, most of these methods involve manual parameter tuning, and hence are neither fully automated nor free from human bias. ResultsWe present NORTH, a novel, automated, highly accurate and scalable machine learning based orhtologous gene clustering method. We have utilized the biological basis and intuition of orthologous genes and made an effort to incorporate appropriate ideas from machine learning (ML) and natural language processing (NLP). We have discovered that the BLAST search based protocols deeply resemble a "text classification" problem. Thus, we employ the robust bag-of-words model accompanied by a Naive Bayes classifier to cluster the orthologous genes. We studied 1,255,877 genes in the largest 250 ortholog clusters from the KEGG database, across 3,880 organisms comprising the six major groups of life, namely, Archaea, Bacteria, Animals, Fungi, Plants and Protists. Despite having more than a million of genes on distantly related species with acute data imbalance, NORTH is able to cluster them with 98.48% Precision, 98.43% Recall and 98.44% F1 score, showing that automatic orthologous gene clustering can be both highly accurate and scalable. NORTH is available as a web interface with a server side application, along with cross-platform native applications (available at https://nibtehaz.github.io/NORTH/) - allowing queries based on individual genes.

bioinformatics↗