# MAKA-Map: Real-Valued Distance Prediction for Protein Folding Mechanisms via a Hybrid Neural Framework Integrating the Mamba and Kolmogorov–Arnold Networks

## TL;DR

This paper introduces MAKA-Map, a new method for predicting protein distances that improves accuracy and helps with structural and functional studies.

## Contribution

The novel hybrid framework combines Mamba and Kolmogorov–Arnold Networks for improved real-valued distance prediction in proteins.

## Key findings

- MAKA-Map achieved 86.53% accuracy on CASP13 benchmark dataset.
- The method outperformed existing state-of-the-art approaches in distance prediction.
- Results show improved fidelity for capturing long-range dependencies in protein structures.

## Abstract

Real-valued inter-residue distance maps provide essential spatial information for understanding protein folding mechanisms and guiding downstream applications such as function annotation, drug discovery, and structural modeling. However, existing prediction methods often struggle to capture long-range dependencies and to maintain topological consistency across different structural scales. To address these challenges, we propose a novel prediction framework that integrates a Mamba architecture, based on a selective state space model, to effectively model global interactions, and incorporates the Kolmogorov–Arnold Network (KAN) to enhance nonlinear structural representation. Extensive experiments on standard benchmark datasets, including CASP13, CASP14, and CASP15, demonstrate prediction accuracies of 86.53%, 85.44%, and 82.77%, respectively, outperforming state-of-the-art approaches. These results indicate that the proposed framework substantially improves the fidelity of real-valued distance prediction and offers a promising tool for downstream structural and functional studies.