APIPred Web 1.0: A Web Platform to Predict Potential Aptamer Sequences for Protein targets

Abstract Aptamers are short single-stranded nucleic acids that bind protein targets with high specificity and are increasingly used in diagnostics and therapeutics, yet experimental discovery remains slow and variable in success. This creates a demand for computational systems that not only score candidate binders but also generate experimentally usable libraries under biologically meaningful constraints. Here, we present APIPred Web 1.0, a unified web platform that integrates constraint-aware aptamer library generation, machine learning- based aptamer- protein interaction prediction, and DNA secondary-structure analysis within a user-facing workflow. Users submit a target protein aminoacid sequence and define an aptamer template in a PREFIX - [VARIABLE] - SUFFIX format with real-time validation of key biological constraints (GC content and homopolymer limits). On the backend, sequences are converted into model- compatible features via optimized k-mer encodings (aptamer) and pseudo amino acid composition (PAAC) descriptors (protein), followed by inference with a trained XGBoost predictor. APIPred Web 1.0 improves the computational efficiency by applying precomputed protein features, vectorized batch processing (hundreds of sequences per batch), optimized XGBoost DMatrix inference, and a bounded heap that retains only the top 25 candidates during generation. The platform then computes minimum free energy (MFE) structures using ViennaRNA with parallel folding and returns ranked list of the top candidates with log-transformed interaction scores, complete sequences (variable region highlighted), dot-bracket structures, MFE values, and interactive 2D visualizations via persistent result links. In a demonstration study targeting CD64 protein, the platform produced 25 putative binders from a custom 40- nucleotide library and enabled selection of structurally diverse candidates for experimental testing. Flow cytometry showed specific binding to CD64-expressing THP-1 cells with minimal signal in Ramos control cells. Collectively, APIPred Web 1.0 offers a reproducible, structure-informed, and computationally efficient pipeline for rapid generation of aptamer candidates against target proteins for downstream experimental validation. Graphical Abstract