Biology subjects

Pulugurta, R.

Publications and source records attributed to Pulugurta, R..

3 recordsLinked to original sources

bioRxiv2025

DPAC: Prediction and Design of Protein-DNA Interactions via Sequence-Based Contrastive Learning

Interactions between DNA and proteins are pivotal in natural biological processes, and designing proteins that can bind to DNA with high specificity is crucial for advancing genomic technologies. Existing state-of-the-art models for both modeling and designing protein-DNA interactions primarily rely on structural information, facing limitations in scalability and efficiency for large-scale applications. Notable methods like AlphaFold 3 and RosettaTTAFold All-Atom exist, but they are inefficient and inherently struggle at modeling conformationally unstable proteins, such as transcription factors, which arguably represent the most important class of DNA-binding proteins. Here, we present DPAC1 (DNA-Protein binding Alignment via Contrastive learning), which leverages pre-trained protein and DNA language models via a contrastive loss to align the two modalities in a high-dimensional shared latent space. DPAC not only significantly accelerates the design process compared to current structure-based methods but also demonstrates a strong ability to differentiate real binders from non-binders. Our model achieves an AUC score of 0.591 on a low identity set, outperforming state-of-the-art structure-based methods. Additionally, DPAC integrates simulated annealing for the design of new protein sequences with optimized DNA binding affinity, successfully recovering binding affinity in engineered sequences by up to 20% in in silico tests. Our results highlight DPACs potential for facilitating the design and discovery of sequence-specific DNA-binding proteins, paving the way for advancements in genomic research and biotechnology applications.

bioengineering↗

bioRxiv2024

FusOn-pLM: A Fusion Oncoprotein-Specific Language Model via Focused Probabilistic Masking

Fusion oncoproteins, a class of chimeric proteins arising from chromosomal translocations, drive and sustain various cancers, particularly those impacting children. Unfortunately, due to their intrinsically disordered nature, large size, and lack of well-defined, druggable pockets, they have been historically challenging to target therapeutically: neither small molecule-based methods nor structure-based approaches for binder design are strong options for this class of molecules. Recently, protein language models (pLMs) have demonstrated success at representing protein sequences with information-rich embeddings, enabling downstream design applications from sequence alone. However, no current pLM has been trained on fusion oncoprotein sequences and thus may not produce optimal representations for these proteins. In this work, we introduce FusOn-pLM, a novel pLM that fine-tunes the state-of-the-art ESM-2 model on fusion oncoprotein sequences. We specifically introduce a novel masked language modeling (MLM) strategy, employing a binding-site probability predictor to focus masking on key amino acid residues, thereby generating more optimal fusion oncoprotein-aware embeddings. Our model improves performance on both fusion oncoprotein-specific benchmarks and disorder prediction tasks in comparison to baseline ESM-2 representations, as well as manually-constructed biophysical embeddings, motivating downstream usage of FusOn-pLM embeddings for therapeutic design tasks targeting these fusions. We have made our model publicly available to the community at https://huggingface.co/ChatterjeeLab/FusOn-pLM.

bioinformatics↗

bioRxiv2024

AMP-Diffusion: Integrating Latent Diffusion with Protein Language Models for Antimicrobial Peptide Generation

Denoising Diffusion Probabilistic Models (DDPMs) have emerged as a potent class of generative models, demonstrating exemplary performance across diverse AI domains such as computer vision and natural language processing. In the realm of protein design, while there have been advances in structure-based, graph-based, and discrete sequence-based diffusion, the exploration of continuous latent space diffusion within protein language models (pLMs) remains nascent. In this work, we introduce AMP-Diffusion, a latent space diffusion model tailored for antimicrobial peptide (AMP) design, harnessing the capabilities of the state-of-the-art pLM, ESM-2, to de novo generate functional AMPs for downstream experimental application. Our evaluations reveal that peptides generated by AMP-Diffusion align closely in both pseudo-perplexity and amino acid diversity when benchmarked against experimentally-validated AMPs, and further exhibit relevant physicochemical properties similar to these naturally-occurring sequences. Overall, these findings underscore the biological plausibility of our generated sequences and pave the way for their empirical validation. In total, our framework motivates future exploration of pLM-based diffusion models for peptide and protein design.

bioinformatics↗