Biology subjects

Ullah, F.

Publications and source records attributed to Ullah, F..

3 recordsLinked to original sources

bioRxiv2025

PLM-ICE: A Protein Language Model-based Approach for Prediction of Ice nucleating and Antifreeze Proteins

Many microbial species have developed adaptations for coping with life in extreme cold and in particular in the cryosphere. Ice-binding proteins (IBPs) play a critical role in enabling organisms to survive in extreme cold environments. IBPs can be divided into two distinct functional classes--antifreeze proteins (AFPs) and ice-nucleation proteins (INPs). These classes have been identified based on their specific modes of interaction with ice. Here, we introduce PLM-ICE, a computational system designed to predict IBPs with high precision and sensitivity. Leveraging ESM-2 embeddings, which incorporate evolutionary and functional sequence signals more effectively than conventional embeddings, our model employs a frozen ESM-2 encoder coupled to a Multi-layer Perceptron (MLP) prediction head. The application of this architecture allows for accurate determination of AFPs and INPs, surpassing existing methods (e.g., VotePLMs-AFP) in metrics such as Matthews correlation coefficient (MCC), area under the precision-recall curve (AUPR), and area under the receiver operating characteristic curve (AUROC). Our findings indicate that PLM-ICE exhibits robust performance across broad datasets encompassing bacterial genomic sequences, highlighting its potential for wide-ranging implementation. Notably, the ability of ESM-2 to capture essential sequence patterns confers PLM-ICE with advantages in both basic research and industrial settings, where prompt and reliable identification of IBPs remains a priority. Further, the models strong performance underscores the broader promise of protein language model-based pipelines for decoding complex biological networks and driving innovations in cryopreservation, food technology, and climate studies. Together, these data demonstrate that PLM-ICE provides novel insight into IBP classification and stands poised to advance biotechnology applications focused on freezing tolerance and specialized temperature adaptations.

bioinformatics↗

bioRxiv2021

Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention

Alternative splicing is a widespread regulatory phenomenon that enables a single gene to produce multiple transcripts. Among the different types of alternative splicing, intron retention is one of the least explored despite its high prevalence in both plants and animals. The recent discovery that the majority of splicing is co-transcriptional has led to the finding that chromatin state affects alternative splicing. Therefore it is plausible that transcription factors can regulate splicing outcomes. We provide evidence for this hypothesis by studying regions of open chromatin in retained and excised introns. Using deep learning models designed to distinguish between regions of open chromatin in retained introns and non-retained introns, we identified motifs enriched in IR events with significant hits to known human transcription factors. Our model predicts that the majority of transcription factors that affect intron retention come from the zinc finger family. We demonstrate the validity of these predictions using ChIP-seq data for multiple zinc finger transcription factors and find strong over-representation for their peaks in intron retention events. AvailabilitySource code available at https://github.com/fahadahaf/chromir

genomics↗

bioRxiv2020

A Self-Attention Model for Inferring Cooperativity between Regulatory Features

Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.

genomics↗