Biology subjects

Kondaparthi, V.

Publications and source records attributed to Kondaparthi, V..

8 recordsLinked to original sources

bioRxiv2026

InversePep: Diffusion-Driven Structure-Based Inverse Folding for Functional Peptides

Designing functional peptides with specific structural and biochemical properties is critical for applications in protein engineering and therapeutic discovery. However, most peptide design approaches rely on evolutionary or local sequence optimization methods, which are limited when adapting to peptides shorter length, high conformational flexibility, and unique physicochemical constraints. While recent structure-based inverse folding models have shown success for proteins, these models often underperform on peptides because sequence recovery alone is not a reliable indicator of stability or foldability in short, flexible backbones. To address this challenge, we introduce InversePep, a generative diffusion model for structure-based peptide inverse folding. InversePep learns the conditional distribution of sequences that can adopt a given backbone conformation, enabling direct generation of peptides tailored to target structural geometries. The framework integrates a geometric graph neural network to encode 3D backbone features with a Transformer-based sequence refinement module that iteratively denoises candidate sequences during diffusion. Trained on a diverse set of peptide backbones sourced from Propedia and SATPdb, InversePep effectively captures structural and biochemical diversity across peptide families. In systematic evaluations on held-out peptide structures and the PepBDB benchmark, InversePep achieves a mean TM score of 0.38 and a median of 0.28, outperforming ProteinMPNN and ESM-IF1 in generating geometry-consistent peptide sequences. In-silico folding analyses confirm that sampled peptides reliably adopt the target conformations. These results highlight InversePeps capability for designing structurally stable and sequence-diverse peptides, demonstrating its potential in antimicrobial peptide discovery, peptide therapeutics, and molecular probe development.

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bioRxiv2026

OligoGraph: A novel geometric graph-based approach for siRNA efficacy prediction

RNA interference (RNAi) is a biological process in which a small interfering RNA (siRNA) prevents the translation of a messenger RNA (mRNA) into a protein by cleaving the mRNA before translation. We exploit this process to prevent the formation of harmful proteins by using an effective siRNA on the target mRNA. The current rapidly emerging RNAi-based drugs show immense potential for therapeutic applications. Traditionally, designing a potent siRNA for an mRNA requires extensive lab experimentation and trials; therefore, there is a need to develop a model that reliably predicts a siRNAs efficacy against mRNA. This saves both cost and time. But designing such models is challenging, as the data available is either scarce or biased. The current models available exhibit limited generalization and are restricted to a fixed siRNA lengths of either 19 or 21 nucleotides, limiting flexible use. To address these challenges, we introduce OligoGraph, a graph-based deep learning architecture that operates on the siRNA-mRNA duplex. It leverages RiNALMo embeddings, multiple GATconv and Transformerconv layers, and self-supervised pretraining, and outperforms all other existing models in our testing on seen and unseen data. We implemented specialized OligoGraph variants for 19- and 21-nucleotide siRNAs, both of which outperformed the current state-of-the-art models on unseen data. The 19-nucleotide model yielded AUC-ROC and PCC increases of 1.1% and 4.6% on the Mixset; 19.07% and 127.3% on the Takayuki dataset, respectively. Furthermore, the 21-nucleotide model improved predictive performance on the Simone dataset by 2.62% (AUC-ROC) and 6.65% (PCC).

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bioRxiv2026

Pro-GAT: Reconnecting Fragmented PROTACs Using Graph Attention Transformer

PROTACs work by bringing together a protein-of-interest ligand and an E3 ligase recruiter to trigger targeted degradation. However, Diffusion-based generative models frequently produce chemically invalid or disconnected linker structures that satisfy global geometric constraints but violate local bonding requirements. These models operate in continuous coordinate space and therefore lack explicit mechanisms for enforcing discrete chemical connectivity under fixed-anchor constraints. Invalid, disconnected outputs recur rather than being a rare exception, such that naive resampling is not an effective method to obtain valid chimeras. Pro-GAT is a graph attention-based framework for geometry-preserving molecular graph repair, capable of functioning on chemically disconnected diffusion-generated PROTAC candidates by predicting bounded coordinate corrections and constrained atom-type modifications using geometry-aware graph attention network (GAT) layers. The proposed model is trained on PROTAC datasets with added disconnections to overcome systematic connectivity failures in diffusion-based PROTAC generation with fixed anchors. When combined with DiffPROTACs and DiffLinker, Pro-GAT improves the percentage of chemically valid candidates in the aggregated output from 76.70% to 83.92% and 63.16% to 68.73% while maintaining 80.18% and 63.80% uniqueness levels of valid candidates respectively, thus facilitating the generation of usable PROTAC candidates from invalid diffusion samples. Pro-GAT was used in a case study of the 7Z76 ternary complex to repair DiffPROTACs and DiffLinker generated samples, which gave rise to connected chimeras whose docking scores were comparable to the original 7Z76 structure.

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bioRxiv2026

RNAiSpline: A Deep learning model for siRNA efficacy prediction

RNA interference (RNAi) is a crucial biological post-transcriptional gene silencing mechanism where small interfering RNA (siRNA) guides RNA-induced silencing complex (RISC) to bind with messenger RNA (mRNA) thereby silencing it and stopping protein formation. We exploit this process to prevent the formation of harmful proteins by silencing mRNA before it is translated into protein through an effective siRNA. There exists a need to develop a computational model that predicts the effectiveness of siRNA on a given mRNA. Designing a model is challenging, as the data availability is either scarce or biased, and existing models lack generalization ability, even though the parameters to training samples ratio is very high. To overcome these challenges, we introduce RNAiSpline, which incorporates self-supervised pretraining and fine-tuning with Kalmogorov-Arnold Network (KAN), Convolutional Neural Network (CNN), and Transformer Encoder. Evaluation on the independent test dataset yields an ROC-AUC of 0.8175, an F1 score of 0.7717, and Pearson correlation of 0.6032, making RNAiSpline a robust model for siRNA efficacy prediction.

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bioRxiv2026

Macro-Equi-Diff (MED): Scaffold-based Macrocycles Generation Using Equivariant Diffusion

Macrocyclic compounds are essential in drug discovery as they can modulate protein-protein interactions and enhance selectivity. Their structural complexity enables access to molecular diversity beyond traditional small molecules; however, designing feasible macrocycles remains a challenging task. Current computational methods often fail to generate macrocycles with proper drug-like properties. Here, we present Macro-Equi-Diff (MED), a deep learning framework that combines transformer-based site identification with an E(3)-equivariant Diffusion Model (EDM) for linker creation, and a fragment-linker attachment module. MED transforms acyclic molecules into structurally consistent macrocycles. MED was tested on the ZINC dataset, achieving high validity (93.92%), uniqueness (99.94%), macrocyclization (99.92%), and linker novelty (82.81%). MED improves upon previous methods that lack a macrocyclic geometry context. As a case study, MED was used to macrocyclize four acyclic drugs targeting the JAK2 protein. The generated macrocycles exhibited favourable molecular descriptors and strong binding affinities, establishing MED as a reliable method for expanding the macrocyclic chemical space.

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bioRxiv2026

ABFormer: A Transformer-based Model to Enhance Antibody-Drug Conjugates Activity Prediction through Contextualized Antibody-Antigen Embedding

Computational screening is increasingly becoming a crucial aspect of Antibody-Drug Conjugate (ADC) research, allowing the elimination of dead ends at earlier stages and concentrating on potential candidates, which can significantly reduce the cost of development. The current state-of-the-art deep learning model, ADCNet, usually considers antibodies, antigens, linkers, and payloads as distinct features. However, this overlooks the complex context of antibody-antigen binding, which is primarily responsible for the targeting and uptake of ADCs. To address this limitation, we present ABFormer, a transformer-based framework tailored for ADC activity prediction and in-silico triage. ABFormer integrates high-resolution antibody-antigen interface information through a pretrained interaction encoder and combines it with chemically enriched linker and payload representations obtained from a fine-tuned molecular encoder. This multi-modal design replaces naive feature concatenation with biologically informed contextual embeddings that more accurately reflect molecular recognition. ABFormer outperforms in leave-pair-out evaluation and achieves 100% accuracy on a separate test set of 22 novel ADCs, while the baselines are severely mis-calibrated. Ablation study confirms that the predictive capability is predominantly driven by interaction-aware antibody-antigen representations, while small-molecule encoders enhance specificity by reducing false positives. In conclusion, ABFormer provides a reliable and efficient platform for early filtering of ADC activity and selection of candidates.

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bioRxiv2025

SiaRNA: A Siamese Neural Network with Bidirectional Cross-Attention for Pairwise siRNA-mRNA Efficacy Prediction

Small interfering RNA (siRNA) therapeutics have extraordinary potential for targeted gene silencing. They mediate post-transcriptional gene regulation by binding to complementary messenger RNA (mRNA) sequences and degrading them, thereby preventing the production of unwanted proteins. Recent machine learning and deep learning frameworks for predicting siRNA efficacy have only achieved moderate success as these models solely rely either on handcrafted features or on sequential relations and therefore cannot capture the full complexity of siRNA-mRNA interactions. In this context, we propose SiaRNA, which uses a Siamese Neural Network for feature-derived representations and a bidirectional cross-attention mechanism for sequence-level relationships. It uniquely identifies mRNAs and their corresponding siRNAs as paired entities, allowing unified and context-aware modeling. Unlike previous models, which discard 2-nucleotide (2-nt) overhangs at the 3 end while using 21-nt efficacy labels, SiaRNA both trains and tests on 21-nt sequences to ensure biologically consistent predictions. Our model sets a new performance benchmark, outperforming previous state-of-the-art models. SiaRNA is trained on the HUVK dataset achieving an accuracy of 0.881, while its generalization has been confirmed by testing on the independent Simone dataset. These results prove SiaRNAs potential as a reliable and biologically accurate framework to guide siRNA design and improve therapeutic outcomes.

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bioRxiv2025

CrossPPI: A Cross - Fusion Based Model for Protein - Protein Binding Affinity Prediction.

Many biological processes depend on protein-protein interactions (PPIs), which are particularly important across biology, medicine, and biotechnology. It is essential to accurately predict the binding affinity between protein pairs to prioritize candidate interactions in large-scale studies and expedite drug discovery. The application of cross-attention mechanisms between ligand and receptor protein sequences is often neglected in current computational models, limiting their capacity to accurately represent inter-protein dependencies. In this study, we introduce CrossPPI, a novel deep learning framework that integrates structural and sequential features of interacting proteins to improve binding affinity prediction. To model intricate interactions between protein pairs, CrossPPI uses a transformer-based cross-fusion module and a dual-view feature-extraction approach that combines Graph Attention Networks (GATs) and Convolutional Neural Networks (CNNs). On the test dataset of 300 protein-protein pairs, CrossPPI achieved a Pearson correlation coefficient (PCC) of 0.7616, a Spearman correlation coefficient (SCC) of 0.7644, a mean absolute error (MAE) of 1.2869, and a root mean square error (RMSE) of 1.6824, indicating its ability to predict the binding affinity of two proteins. The results highlight CrossPPIs capability to predict inter-protein binding affinities by leveraging an attention-based integration of sequence and structural features.

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