Millisecond Prediction of Protein Contact Maps from Amino Acid Sequences

Abstract

Protein structure prediction typically outputs static coordinates, often obscuring the underlying physical principles and conformational flexibility. In this work, we present a coarse-grained generative framework to recover the Circuit Topology (CT) of proteins using Generative Flow Matching. We represent protein architecture using highly compressed Secondary Structure Elements (SSEs), reducing the sequence length to roughly 1/13 of the original amino acid sequence. We show that this minimal representation captures the essential "topological fingerprint" required to determine the global fold. By employing a joint-prediction head, our model simultaneously generates contact probabilities and asymmetric topological features, achieving a mean F1 score of 0.822 at the SSE level. Notably, our results demonstrate a counter-intuitive robustness in capturing long-range interactions, suggesting that global topology acts as a stable constraint compared to local residue packing. Furthermore, we show that these coarse-grained predictions can be mapped back to residue-level contact maps with sub-helical precision, yielding a mean alignment error of 2.69 residues. The probabilistic nature of the flow model effectively separates the stable structural signal of the folding core from flexible regions, providing a physically interpretable view of the proteins conformational ensemble. This pipeline is extremely fast, capable of completing a contact map prediction from amino acid sequence in an average of 110 milliseconds on a single GPU. These ultra-fast and accurate predictions provide a valuable tool for identifying conserved protein folding cores, facilitating the exploration of the protein structural genotype-phenotype (GP) map through large-scale sampling of mutants with highly similar folding cores.