PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang, Bozitao Zhong, Zuobai Zhang, Narendra Chaudhary, Sanchit Misra, Jian Tang

Keywords: protein design, protein inverse folding, benchmark, protein science, generative model

TL;DR: We introduce the PDB-Struct benchmark with two novel metrics for a comprehensive evaluation of structure-based protein design methods, highlighting standout performers and areas for improvement

Abstract:

Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years.
However, a universally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the $\textit{in silico}$ validation with recovery and perplexity metrics is efficient but may not precisely reflect true foldability.
To address this gap, we introduce two novel metrics: refoldability-based metric, which leverages high-accuracy protein structure prediction models as a proxy for wet lab experiments, and stability-based metric, which assesses whether models can assign high likelihoods to experimentally stable proteins.
We curate datasets from high-quality CATH protein data, high-throughput $\textit{de novo}$ designed proteins, and mega-scale experimental mutagenesis experiments,
and in doing so, present the $\textbf{PDB-Struct}$ benchmark that evaluates both recent and previously uncompared protein design methods.
Experimental results indicate that ByProt, ProteinMPNN, and ESM-IF perform exceptionally well on our benchmark, while ESM-Design and AF-Design fall short on the refoldability metric.
We also show that while some methods exhibit high sequence recovery, they do not perform as well on our new benchmark.
Our proposed benchmark paves the way for a fair and comprehensive evaluation of protein design methods in the future. Code is available at https://github.com/WANG-CR/PDB-Struct.

Submission Number: 33