Computational navigation of constrained multidimensional protein fitness landscapes

Protein engineering relies heavily on computational characterization of constrained protein fitness landscapes, in which only a limited fraction of sequence space corresponds to stable and functional biomolecules. Advances in structural biology and machine learning are progressively shifting protein design strategies from empirical optimization toward multidimensional evaluation of sequence-structure-function relationships. This review examines current computational strategies for exploring these landscapes, including sequence-derived evolutionary descriptors, structural fitness assessment, energetic evaluation, and integrated multi-parameter scoring. Recent developments in protein language models, deep-learning-based structure prediction, generative protein design, and consensus scoring approaches support large-scale exploration of biologically accessible sequence space. Negative-design constraints, including aggregation propensity, intrinsic disorder, and developability are important in prioritizing experimentally tractable protein candidates. Finally, the integration of computational prediction with iterative experimental validation is discussed as a central framework for rational protein engineering. By framing structure prediction, sequence representation learning, and generative design as complementary strategies for navigating a single constrained fitness landscape, this review highlights integrated, multidimensional scoring and negative-design filtering as the critical link between computational candidate generation and experimentally tractable protein design.