Interprotein Contacts Research Articles

Protein language model-embedded geometric graphs power inter-protein contact prediction

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein–protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein–protein docking can dramatically improve its performance for protein complex structure prediction.

Protein language model-embedded geometric graphs power inter-protein contact prediction.

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

Improved inter-protein contact prediction using dimensional hybrid residual networks and protein language models.

The knowledge of contacting residue pairs between interacting proteins is very useful for the structural characterization of protein-protein interactions (PPIs). However, accurately identifying the tens of contacting ones from hundreds of thousands of inter-protein residue pairs is extremely challenging, and performances of the state-of-the-art inter-protein contact prediction methods are still quite limited. In this study, we developed a deep learning method for inter-protein contact prediction, which is referred to as DRN-1D2D_Inter. Specifically, we employed pretrained protein language models to generate structural information-enriched input features to residual networks formed by dimensional hybrid residual blocks to perform inter-protein contact prediction. Extensively bechmarking DRN-1D2D_Inter on multiple datasets, including both heteromeric PPIs and homomeric PPIs, we show DRN-1D2D_Inter consistently and significantly outperformed two state-of-the-art inter-protein contact prediction methods, including GLINTER and DeepHomo, although both the latter two methods leveraged the native structures of interacting proteins in the prediction, and DRN-1D2D_Inter made the prediction purely from sequences. We further show that applying the predicted contacts as constraints for protein-protein docking can significantly improve its performance for protein complex structure prediction.

Computational modeling of the effect of five mutations on the structure of the ACE2 receptor and their correlation with infectivity and virulence of some emerged variants of SARS-CoV-2 suggests mechanisms of binding affinity dysregulation

Interactions between the human angiotensin-converting enzyme 2 (ACE2) and the RBD region of the SARS-CoV-2 Spike protein are critical for virus entry into the host cell. The objective of this work was to identify some of the most relevant SARS-CoV-2 Spike variants that emerged during the pandemic and evaluate their binding affinity with human variants of ACE2 since some ACE2 variants can enhance or reduce the affinity of the interaction between the ACE2 and S proteins. However, no information has been sought to extrapolate to different variants of SARS-CoV-2. Therefore, to understand the impact on the affinity of the interaction between ACE2 protein variants and SARS-CoV-2 protein S variants, molecular docking was used in this study to predict the effects of five mutations of ACE2 when they interact with Alpha, Beta, Delta, Omicron variants and a hypothetical variant, which present mutations in the RBD region of the SARS-CoV-2 Spike protein. Our results suggest that these variants could alter the interaction of the Spike and the human ACE2 protein, losing or creating new inter-protein contacts, enhancing viral fitness by improving binding affinity, and leading to an increase in infectivity, virulence, and transmission.This investigation highlighted that the S19P mutation of ACE2 decreases the binding affinity between the ACE2 and Spike proteins in the presence of the Beta variant and the wild-type variant of SARS-CoV-2 isolated in Wuhan-2019. The R115Q mutation of ACE2 lowers the binding affinity of these two proteins in the presence of the Beta and Delta variants. Similarly, the K26R mutation lowers the affinity of the interaction between the ACE2 and Spike proteins in the presence of the Alpha variant. This decrease in binding affinity is probably due to the lack of interaction between some of the key residues of the interaction complex between the ACE2 protein and the RBD region of the SARS-CoV-2 Spike protein. Therefore, ACE2 mutations appear in the presence of these variants, they could suggest an intrinsic resistance to COVID-19 disease. On the other hand, our results suggested that the K26R, M332L, and K341R mutations of ACE2 expressively showed the affinity between the ACE2 and Spike proteins in the Alpha, Beta, and Delta variants. Consequently, these ACE2 mutations in the presence of the Alpha, Beta, and delta variants of SARS-CoV-2 could be more infectious and virulent in human cells compared to the SARS-CoV-2 isolated in Wuhan-2019 and it could have a negative prognosis of the disease.Finally, the Omicron variant in interaction with ACE2 WT, S19P, R115Q, M332L, and K341R mutations of ACE2 showed a significant decrease in binding affinity. This could be consistent that the Omicron variant causes less severe symptoms than previous variants. On the other hand, our results suggested Omicron in the complex with K26R, the binding affinity is increased between ACE2/RBD, which could indicate a negative prognosis of the disease in people with these allelic conditions.

CpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein-Protein Interactions.

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.

HDIContact: a novel predictor of residue-residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy.

Proteins maintain the functional order of cell in life by interacting with other proteins. Determination of protein complex structural information gives biological insights for the research of diseases and drugs. Recently, a breakthrough has been made in protein monomer structure prediction. However, due to the limited number of the known protein structure and homologous sequences of complexes, the prediction of residue-residue contacts on hetero-dimer interfaces is still a challenge. In this study, we have developed a deep learning framework for inferring inter-protein residue contacts from sequential information, called HDIContact. We utilized transfer learning strategy to produce Multiple Sequence Alignment (MSA) two-dimensional (2D) embedding based on patterns of concatenated MSA, which could reduce the influence of noise on MSA caused by mismatched sequences or less homology. For MSA 2D embedding, HDIContact took advantage of Bi-directional Long Short-Term Memory (BiLSTM) with two-channel to capture 2D context of residue pairs. Our comprehensive assessment on the Escherichia coli (E. coli) test dataset showed that HDIContact outperformed other state-of-the-art methods, with top precision of 65.96%, the Area Under the Receiver Operating Characteristic curve (AUROC) of 83.08% and the Area Under the Precision Recall curve (AUPR) of 25.02%. In addition, we analyzed the potential of HDIContact for human-virus protein-protein complexes, by achieving top five precision of 80% on O75475-P04584 related to Human Immunodeficiency Virus. All experiments indicated that our method was a valuable technical tool for predicting inter-protein residue contacts, which would be helpful for understanding protein-protein interaction mechanisms.

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural data set, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.

Natural variant frequencies across domains from different sarcomere proteins cross-correlate to identify inter-protein contacts associated with cardiac muscle function and disease

Coordinated sarcomere proteins produce contraction force for muscle shortening. In human ventriculum they include the cardiac myosin motor (βmys), repetitively converting ATP free energy into work, and myosin binding protein C (MYBPC3) that in complex with βmys is regulatory. Single nucleotide variants (SNVs) causing hereditary heart diseases frequently target this protein pair. The βmys/MYBPC3 complex models a regulated motor and is used here to study how the proteins couple. SNVs in βmys or MYBPC3 survey human populations worldwide. Their protein expression modifies domain structure affecting phenotype and pathogenicity outcomes. When the SNV modified domain locates to inter-protein contacts it could affect complex coordination. Domains involved, one in βmys the other in MYBPC3, form coordinated domains (co-domains). Co-domain bilateral structure implies the possibility for a shared impact from SNV modification in either domain suggesting a correlated response to a common perturbation could identify their location. Genetic divergence over human populations is proposed to perturb SNV probability coupling that is detected by cross-correlation in 2D correlation genetics (2D-CG). SNV probability data and 2D-CG identify three critical sites, two in MYBPC3 with links to several domains across the βmys motor, and, one in βmys with links to the MYBPC3 regulatory domain. MYBPC3 sites are hinges sterically enabling regulatory interactions with βmys. The βmys site is the actin binding C-loop (residues 359-377). The C-loop is a trigger for actin-activated myosin ATPase and a contraction velocity modulator. Co-domain identification implies their spatial proximity suggesting a novel approach for in vivo protein complex structure determination.

Deep graph learning of inter-protein contacts.

Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein-protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction. We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments. Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. The software is available at https://github.com/zw2x/glinter. The datasets are available at https://github.com/zw2x/glinter/data. Supplementary data are available at Bioinformatics online.

Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data.

Direct coupling analysis (DCA) is a global statistical approach that uses information encoded in protein sequence data to predict spatial contacts in a three-dimensional structure of a folded protein. DCA has been widely used to predict the monomeric fold at amino acid resolution and to identify biologically relevant interaction sites within a folded protein. Going beyond single proteins, DCA has also been used to identify spatial contacts that stabilize the interaction in protein complex formation. However, extracting this higher order information necessary to predict dimer contacts presents a significant challenge. A DCA evolutionary signal is much stronger at the single protein level (intraprotein contacts) than at the protein-protein interface (interprotein contacts). Therefore, if DCA-derived information is to be used to predict the structure of these complexes, there is a need to identify statistically significant DCA predictions. We propose a simple Z-score measure that can filter good predictions despite noisy, limited data. This new methodology not only improves our prediction ability but also provides a quantitative measure for the validity of the prediction.

D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions

We combine advances in neural language modeling and structurally motivated design to develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts interaction between two proteins using only their sequence and maintains high accuracy with limited training data and across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared with the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. We apply D-SCRIPT to screen for PPIs in cow (Bos taurus) at a genome-wide scale and focusing on rumen physiology, identify functional gene modules related to metabolism and immune response. The predicted interactions can then be leveraged for function prediction at scale, addressing the genome-to-phenome challenge, especially in species where little data are available.

Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data.

The evolution of the SARS‐CoV‐2 new variants reported to be 70% more contagious than the earlier one is now spreading fast worldwide. There is an instant need to discover how the new variants interact with the host receptor (ACE2). Among the reported mutations in the Spike glycoprotein of the new variants, three are specific to the receptor‐binding domain (RBD) and required insightful scrutiny for new therapeutic options. These structural evolutions in the RBD domain may impart a critical role to the unique pathogenicity of the SARS‐CoV‐2 new variants. Herein, using structural and biophysical approaches, we explored that the specific mutations in the UK (N501Y), South African (K417N‐E484K‐N501Y), Brazilian (K417T‐E484K‐N501Y), and hypothetical (N501Y‐E484K) variants alter the binding affinity, create new inter‐protein contacts and changes the internal structural dynamics thereby increases the binding and eventually the infectivity. Our investigation highlighted that the South African (K417N‐E484K‐N501Y), Brazilian (K417T‐E484K‐N501Y) variants are more lethal than the UK variant (N501Y). The behavior of the wild type and N501Y is comparable. Free energy calculations further confirmed that increased binding of the spike RBD to the ACE2 is mainly due to the electrostatic contribution. Further, we find that the unusual virulence of this virus is potentially the consequence of Darwinian selection‐driven epistasis in protein evolution. The triple mutants (South African and Brazilian) may pose a serious threat to the efficacy of the already developed vaccine. Our analysis would help to understand the binding and structural dynamics of the new mutations in the RBD domain of the Spike protein and demand further investigation in in vitro and in vivo models to design potential therapeutics against the new variants.

Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.

Protein–protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein–protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein–protein interactions. Recently, deep learning has led to a breakthrough in intra-protein contact prediction, achieving an unusual high accuracy in recent Critical Assessment of protein Structure Prediction (CASP) structure prediction challenges. However, due to the limited number of known homologous protein–protein interactions and the challenge to generate joint multiple sequence alignments of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue–residue contacts across homo-oligomeric protein interfaces, named as DeepHomo. Unlike previous deep learning approaches, we integrated intra-protein distance map and inter-protein docking pattern, in addition to evolutionary coupling, sequence conservation, and physico-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-Critical Assessment of Predicted Interaction (CAPRI) targets. It was shown that DeepHomo achieved a high precision of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis and machine learning-based approaches. Integrating predicted inter-chain contacts into protein–protein docking significantly improved the docking accuracy on the benchmark dataset of realistic homo-dimeric targets from CASP-CAPRI experiments. DeepHomo is available at http://huanglab.phys.hust.edu.cn/DeepHomo/

Chasing coevolutionary signals in intrinsically disordered proteins complexes

Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution.

Predicting three-dimensional protein structure and assembling protein complexes using sequence information belongs to the most prominent tasks in computational biology. Recently substantial progress has been obtained in the case of single proteins using a combination of unsupervised coevolutionary sequence analysis with structurally supervised deep learning. While reaching impressive accuracies in predicting residue-residue contacts, deep learning has a number of disadvantages. The need for large structural training sets limits the applicability to multi-protein complexes; and their deep architecture makes the interpretability of the convolutional neural networks intrinsically hard. Here we introduce FilterDCA, a simpler supervised predictor for inter-domain and inter-protein contacts. It is based on the fact that contact maps of proteins show typical contact patterns, which results from secondary structure and are reflected by patterns in coevolutionary analysis. We explicitly integrate averaged contacts patterns with coevolutionary scores derived by Direct Coupling Analysis, improving performance over standard coevolutionary analysis, while remaining fully transparent and interpretable. The FilterDCA code is available at http://gitlab.lcqb.upmc.fr/muscat/FilterDCA.

Vibrational Spectroscopic Map, Vibrational Spectroscopy, and Intermolecular Interaction.

Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning.

Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield accurate prediction. Inspired by the success of our deep-learning method for intraprotein contact prediction, we have developed RaptorX-ComplexContact, a web server for interprotein residue-residue contact prediction. Given a pair of interacting protein sequences, RaptorX-ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA) based on genomic distance and phylogeny information, respectively. Then, RaptorX-ComplexContact uses two deep convolutional residual neural networks (ResNet) to predict interprotein contacts from sequential features and coevolution information of paired MSAs. RaptorX-ComplexContact shall be useful for protein docking, protein-protein interaction prediction, and protein interaction network construction.

Plasticity in binding confers selectivity in ligand-induced protein degradation.

SUMMARYHeterobifunctional small molecule degraders that induce protein degradation through ligase-mediated ubiquitination have shown considerable promise as a new pharmacological modality. However, we currently lack a detailed understanding of the molecular basis for target recruitment and selectivity, which is critically required to enable rational design of degraders. Here we utilize comprehensive characterization of the ligand dependent CRBN/BRD4 interaction to demonstrate that binding between proteins that have not evolved to interact is plastic. Multiple X-ray crystal structures show that plasticity results in several distinct low energy binding conformations, which are selectively bound by ligands. We demonstrate that computational protein-protein docking can reveal the underlying inter-protein contacts and inform the design of a BRD4 selective degrader that can discriminate between highly homologous BET bromodomains. Our findings that plastic inter-protein contacts confer selectivity for ligand-induced protein dimerization provide a conceptual framework for the development of heterobifunctional ligands.

ComplexContact: a web server for inter-protein contact prediction using deep learning.

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

Recognition and stabilization of geranylgeranylated human Rab5 by the GDP Dissociation Inhibitor (GDI)

ABSTRACTThe small GTPase Rab5 is the key regulator of early endosomal fusion. It is post-translationally modified by covalent attachment of two geranylgeranyl (GG) chains to adjacent cysteine residues of the C-terminal hypervariable region (HVR). The GDP dissociation inhibitor (GDI) recognizes membrane-associated Rab5(GDP) and serves to release it into the cytoplasm where it is kept in a soluble state. A detailed new structural and dynamic model for human Rab5(GDP) recognition and binding with human GDI at the early endosome membrane and in its dissociated state is presented. In the cytoplasm, the GDI protein accommodates the GG chains in a transient hydrophobic binding pocket. In solution, two different binding modes of the isoprenoid chains inserted into the hydrophobic pocket of the Rab5(GDP):GDI complex can be identified. This equilibrium between the two states helps to stabilize the protein-protein complex in solution. Interprotein contacts between the Rab5 switch regions and characteristic patches of GDI residues from the Rab binding platform (RBP) and the C-terminus coordinating region (CCR) reveal insight on the formation of such a stable complex. GDI binding to membrane-anchored Rab5(GDP) is initially mediated by the solvent accessible switch regions of the Rab-specific RBP. Formation of the membrane-associated Rab5(GDP):GDI complex induces a GDI reorientation to establish additional interactions with the Rab5 HVR. These results allow to devise a detailed structural model for the process of extraction of GG-Rab5(GDP) by GDI from the membrane and the dissociation from targeting factors and effector proteins prior to GDI binding.