MMAllo: a multimodal deep learning framework and GaMD simulations for predicting protein allosteric sites and allosteric mutations

Allosteric regulation, whereby small molecules or mutations modulate protein function from sites distal to the orthosteric active site, is a pervasive mechanism of biological control and an important opportunity for drug discovery. Yet the prospective identification of allosteric binding sites and functionally relevant mutations remains difficult because allosteric effects arise from subtle long-range communication and are not readily captured from heterogeneous sequence, structural, and ligand information. Although recent computational methods have improved allosteric-site prediction, most remain limited to pocket-level classification, are built on a single modality, or do not address the functional consequences of mutations. Here, we present MMAllo, a multimodal deep learning framework that integrates protein language model embeddings, ligand Morgan fingerprints, and graph-based structural representations to enable unified prediction of allosteric binding residues and allosteric mutation effects. MMAllo comprises two modules: MMAlloBind, for ligand-specific allosteric binding-residue prediction, and MMAlloMut, for assessing the functional impact of candidate allosteric mutations. On benchmark datasets, MMAlloBind achieves competitive residue-level performance on AlphaFold3-predicted structures, while MMAlloMut effectively prioritizes curated allosteric mutations over putative neutral mutations. In case studies of PTPN2–FRJ (9C56) and CBLB–compound 9 (8QTG), MMAlloBind recovers experimentally characterized allosteric pockets and identifies representative high-probability candidate communication paths connecting ligand-contact residues to distal functional regions. For the disease-related THRβ V264D mutation (P10828; 6KKB/6KNU), MMAlloMut predicts an above-threshold allosteric effect using only the wild-type AlphaFold3 model; subsequent Gaussian accelerated molecular dynamics, NRIMD network analysis and MM/PBSA calculations provide supporting evidence that V264D perturbs local packing, long-range communication patterns and protein–ligand interaction energetics. Together, these results establish MMAllo as a general framework for linking residue-level allosteric site discovery with mutation-impact prediction and for supporting allosteric drug discovery, particularly in settings where high-resolution experimental structures are unavailable. Scientific contribution The key advance of this work is the development of a unified multimodal framework that moves beyond the current scope of computational allostery prediction. Whereas prior methods have largely focused on identifying allosteric pockets, often from a single source of information, MMAllo performs ligand-specific prediction at residue resolution and explicitly evaluates the functional impact of mutations within the same framework. By integrating protein language model embeddings, graph-based structural representations, and ligand fingerprints, MMAllo captures complementary features of allosteric regulation that are not jointly modeled in existing approaches. Importantly, the framework does not only improve predictive capability, but also provides interpretable computational outputs, including candidate communication paths and mutation-associated changes in inferred long-range coupling patterns. Its ability to operate on AlphaFold3-predicted wild-type structures further extends allostery analysis to proteins lacking experimentally resolved mutant or complex structures. In this way, MMAllo differentiates itself from prior work by unifying site discovery and mutation-effect prediction, while offering a practically deployable strategy for mechanistic analysis, functional variant prioritization, and allosteric drug design.