Automated Deep Learning-Based Pipelines for Multi-Objective De Novo Protein Design.

Abstract

Computational protein design has been transformed by deep learning models that can accurately predict protein structure and generate sequences compatible with desired folds. Here we present a detailed protocol for EvoPro, an automated platform that uses a genetic algorithm along with iterative structure prediction (AlphaFold2/AlphaFold3) and sequence design (ProteinMPNN/LigandMPNN) to engineer protein-protein interactions with customizable properties. The protocol describes how to implement multistate design objectives to simultaneously optimize positive and negative design goals. We provide step-by-step instructions for setting up the genetic algorithm, configuring scoring functions for different design challenges, and analyzing results. The method builds on our previously validated approach, which successfully generated high-affinity binding domains without requiring experimental optimization. We describe key considerations for adapting the protocol to diverse protein engineering objectives, including binding site targeting, conformational specificity, and symmetric assembly. The complete computational protocol can be installed and executed in a week by a new user and provides a framework for leveraging deep learning models to address challenging protein design problems. © 2025 Wiley Periodicals LLC. Basic Protocol 1: Designing protein binders Basic Protocol 2: Engineering conformational switches Basic Protocol 3: Designing de novo homo-oligomers Support Protocol 1: Setting up the EvoPro code and environment Support Protocol 2: Input preparation for different design scenarios Support Protocol 3: Optimizing the scoring function and other parameters.