Background

The rapid development of protein structure prediction methods, particularly recent advances in protein complex prediction, has generated vast numbers of computationally predicted protein structural models [1–4]. This rapid expansion has created a key challenge: reliable identification of high-quality structures from large candidate model pools [5]. Protein structure model accuracy estimation (EMA), also referred to as model quality assessment (MQA), aims to evaluate the reliability of predicted structures in the absence of experimentally determined reference structures, which is critical for downstream model ranking and selection in structural biology applications [6].

Existing EMA methods can be broadly categorized into three classes: consensus-based methods, quasi-single-model methods, and single-model methods [7–9]. Consensus-based approaches estimate model accuracy by comparing a set of candidate structures and exploiting structural agreement among models [10–13]. These methods often achieve high accuracy when a sufficiently large and diverse model pool is available, but their performance is influenced by the quality of the input models [9]. Quasi-single-model methods use externally derived structural references to augment single-model features, improving robustness through additional structural context [14–16], but their effectiveness may be influenced by the quality of external references and incur additional computational cost [9]. In contrast, single-model methods directly estimate model accuracy from an individual structure [17–23], but a substantial number of these methods rely on computationally expensive feature generation procedures, such as multiple sequence alignment (MSA), template search, or protein language model-based feature extraction.

This protocol describes a step-by-step workflow for applying MViewEMA [20] to global accuracy estimation of protein complex structural models. MViewEMA is a single-model framework based on multi-view representation learning, which enables global accuracy estimation from a single input protein complex structure [20]. Specifically, MViewEMA characterizes residue–residue interaction features from three complementary perspectives, namely micro-environmental (MiE), meso-environmental (MeE), and macro-environmental (MaE) views. These views are processed using graph attention, convolutional, and Transformer architectures and integrated via cross-view integration to generate a global confidence score [20].

This protocol operates directly on a single protein complex structure and is independent of structure prediction pipelines, enabling evaluation across models generated by different methods. By avoiding reliance on model ensembles, external structural references, and computationally intensive feature generation, this protocol facilitates efficient large-scale evaluation [20], being applicable to downstream structural biology tasks. In protein structure prediction, it can support confidence scoring and quality annotation of models, facilitating functional annotation and protein–protein interaction analysis. In high-throughput settings, it enables rapid screening of large-scale structural model collections, supporting automated prioritization workflows. The implementation is specifically designed for global quality assessment of protein complex structural models. Its applicability is currently limited to protein-only complex structural models and does not extend to protein–ligand or protein–nucleic acid complexes. Furthermore, the protocol focuses on global model accuracy estimation rather than interface-level or residue-level local quality assessment.