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

   https://doi.org/10.1093/nar/gkae039  

   Roche, Rahmatullah; Moussad, Bernard; Shuvo, Md Hossain; Tarafder, Sumit; Bhattacharya, Debswapna (January 2024, Nucleic Acids Research)

   Abstract 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. 

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2. pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

   https://doi.org/10.1186/s12859-023-05164-9  

   Pratyush, Pawel; Pokharel, Suresh; Saigo, Hiroto; KC, Dukka B (December 2023, BMC Bioinformatics)

   Abstract BackgroundProtein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. ResultsUsing an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. ConclusionTogether, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available athttps://github.com/KCLabMTU/pLMSNOSite. 

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3. PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

   https://doi.org/10.1038/s41598-023-47624-5  

   Chandra, Abel; Sharma, Alok; Dehzangi, Iman; Tsunoda, Tatsuhiko; Sattar, Abdul (December 2023, Scientific Reports)

   Abstract Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available athttps://github.com/abelavit/PepCNN.git. 

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4. PIQLE: protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries

   https://doi.org/10.1093/bioadv/vbad070  

   Shuvo, Md Hossain; Karim, Mohimenul; Roche, Rahmatullah; Bhattacharya, Debswapna (January 2023, Bioinformatics Advances)

   Gromiha, Michael (Ed.)

   Abstract Motivation Accurate modeling of protein–protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Results Here, we present PIQLE, a deep graph learning method for protein–protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. Availability and implementation An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary information Supplementary data are available at Bioinformatics Advances online. 

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5. Multimodal diffusion for joint design of protein sequence and structure

   https://doi.org/10.1002/pro.70340  

   Zhu, Shaowen; Gulati, Siddhant; Liu, Yuxuan; Kotnis, Siddhi; Sun, Qing; Shen, Yang (December 2025, Protein Science)

   Computational design of functional proteins is of both fundamental and applied interest. This study introduces a generative framework for co‐designing protein sequence and structure in a unified process by modeling their joint distribution, with the goal of enabling cross‐modality interactions toward coherent and functional designs. Each residue is represented by three distinct modalities (type, position, and orientation) and modeled using dedicated diffusion processes: multinomial for types, Cartesian for positions, and special orthogonal group SO(3) for orientations. To couple these modalities, we propose a unified architecture, ReverseNet, which employs a shared graph attention encoder to integrate multimodal information and separate projectors to predict each modality. We benchmark our models, JointDiff and JointDiff‐x, on unconditional monomer design and conditional motif scaffolding tasks. Compared to two‐stage design models that generate sequence and structure separately, our models produce monomer structures with comparable or better designability, while currently lagging in sequence quality and motif scaffolding performance based on computational metrics. However, they are 1–2 orders of magnitude faster and support rapid iterative improvements through classifier‐guided sampling. To complement computational evaluations, we experimentally validate our approach through a case study on green fluorescent protein (GFP) design. Several novel, evolutionarily distant variants generated by our models exhibit measurable fluorescence, confirming functional activity. These results demonstrate the feasibility of joint sequence–structure generation and establish a foundation to accelerate functional protein design in future applications. Codes, data, and trained models are accessible athttps://github.com/Shen-Lab/JointDiff. 

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