Multidomain assembled states of Hck tyrosine kinase in solution

Perspectives on solution-based small angle X-ray scattering for protein and biological macromolecule structural biology

Small-angle X-ray scattering (SAXS) is used to extract structural information from a wide variety of non-crystalline samples in different fields (e.g., materials science, physics, chemistry, and biology). This review provides an overview of SAXS as applied to structural biology, specifically for proteins and other biomacromolecules in solution with an emphasis on extracting key structural parameters and the interpretation of SAXS data using a diverse array of techniques. These techniques cover aspects of building and assessing models to describe data measured from monodispersed and ideal dilute samples through to more complicated structurally polydisperse systems. Ab initio modelling, rigid body modelling as well as normal-mode analysis, molecular dynamics, mixed component and structural ensemble modelling are discussed. Dealing with polydispersity both physically in terms of component separation as well as approaching the analysis and modelling of data of mixtures and evolving systems are described, including methods for data decomposition such as single value decomposition/principle component analysis and evolving factor analysis. This review aims to highlight that solution SAXS, with the cohort of developments in data analysis and modelling, is well positioned to build upon the traditional 'single particle view' foundation of structural biology to take the field into new areas for interpreting the structures of proteins and biomacromolecules as population-states and dynamic structural systems.

Computational modeling multiple conformational states of proteins with residual dipolar coupling data

Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.

DARVIC: Dihedral angle-reliant variant impact classifier for functional prediction of missense VUS

BACKGROUND

Of the large number of genetic variants identified, the functional impact for most of them remains unknown. Mutations in DNA damage repair genes such as MUTYH, which is involved in repairing A:8-oxoG mismatches caused by reactive oxygen species, can cause a higher risk of cancer. Mutations happening in other key genes such as TP53 also pose huge health threats and risk of cancer. The interpretation of genetic variants' functional impact is a forefront issue that needs to be addressed. Many different in silico methods based on different principles have been developed and applied in interpreting genetic variants. However, a current challenge is that many existing methods tend to overpredict the pathogenicity of benign variants. A new approach is needed to tackle this issue to improve genetic variant interpretation through the use of in silico methods.

METHODS

In this study, we developed another protein structural-based approach called Dihedral angle-reliant variant impact classifier (DARVIC) to predict the deleterious impact of the coding-changing missense variants. DARVIC uses Ramachandran's principle of protein stereochemistry as the theoretical foundation and uses molecular dynamics simulations coupled with a supervised machine learning algorithm XGBoost to determine the functional impact of missense variants on protein structural stability.

RESULTS

We characterized the features of dihedral angles in dynamic protein structures. We also tested the performance of DARVIC in MUTYH and TP53 missense variants and achieved satisfactory results in reflecting the functional impacts of the variants on protein structure. The method achieved a balanced accuracy of 84% in a functionally validated MUTYH dataset containing both benign and pathogenic missense variants, higher than other existing in silico methods. Along with that, DARVIC was able to predict 119 (47%) deleterious variants from a dataset of 254 MUTYH VUS. Further application of DARVIC to a functionally validated TP53 dataset had a balanced accuracy of 94%, topping other methods, demonstrating DARVIC's robustness.

CONCLUSION

DARVIC provides a valuable tool to predict the functional impacts of missense variants based on their effects on protein structural stability and motion. At its current state, DARVIC performed well in predicting the functional impact of the missense variants both in MUTYH and TP53. We expect its high potential to predict functional impact for the missense variants in other genes.

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

From dilute to concentrated solutions of intrinsically disordered proteins: Interpretation and analysis of collected data

Intrinsically disordered proteins (IDPs) have a broad energy landscape and consequently sample many different conformations in solution. The innate flexibility of IDPs is exploited in their biological function, and in many instances allows a single IDP to regulate a range of processes in vivo. Due to their highly flexible nature, characterizing the structural properties of IDPs is not straightforward. Often solution-based methods such as Nuclear Magnetic Resonance (NMR), Förster Resonance Energy Transfer (FRET), and Small-Angle X-ray Scattering (SAXS) are used. SAXS is indeed a powerful technique to study the structural and conformational properties of IDPs in solution, and from the obtained SAXS spectra, information about the average size, shape, and extent of oligomerization can be determined. In this chapter, we will introduce model-free methods that can be used to interpret SAXS data and introduce methods that can be used to interpret SAXS data beyond analytical models, for example, by using atomistic and different levels of coarse-grained models in combination with molecular dynamics (MD) and Monte Carlo simulations.

SKAP2 Modular Organization Differently Recognizes SRC Kinases Depending on Their Activation Status and Localization

Dimerization of SRC kinase adaptor phosphoprotein 2 (SKAP2) induces an increase of binding for most SRC kinases suggesting a fine-tuning with transphosphorylation for kinase activation. This work addresses the molecular basis of SKAP2-mediated SRC kinase regulation through the lens of their interaction capacities. By combining a luciferase complementation assay and extensive site-directed mutagenesis, we demonstrated that SKAP2 interacts with SRC kinases through a modular organization depending both on their phosphorylation-dependent activation and subcellular localization. SKAP2 contains three interacting modules consisting in the dimerization domain, the SRC homology 3 (SH3) domain, and the second interdomain located between the Pleckstrin homology and the SH3 domains. Functionally, the dimerization domain is necessary and sufficient to bind to most activated and myristyl SRC kinases. In contrast, the three modules are necessary to bind SRC kinases at their steady state. The Pleckstrin homology and SH3 domains of SKAP2 as well as tyrosines located in the interdomains modulate these interactions. Analysis of mutants of the SRC kinase family member hematopoietic cell kinase supports this model and shows the role of two residues, Y390 and K7, on its degradation following activation. In this article, we show that a modular architecture of SKAP2 drives its interaction with SRC kinases, with the binding capacity of each module depending on both their localization and phosphorylation state activation. This work opens new perspectives on the molecular mechanisms of SRC kinases activation, which could have significant therapeutic impact.

Interdomain Dynamics via Paramagnetic NMR on the Highly Flexible Complex Calmodulin/Munc13-1

Paramagnetic NMR constraints are very useful to study protein interdomain motion, but their interpretation is not always straightforward. On the example of the particularly flexible complex Calmodulin/Munc13-1, we present a new approach to characterize this motion with pseudocontact shifts and residual dipolar couplings. Using molecular mechanics, we sampled the conformational space of the complex and used a genetic algorithm to find ensembles that are in agreement with the data. We used the Bayesian information criterion to determine the ideal ensemble size. This way, we were able to make an accurate, unambiguous, reproducible model of the interdomain motion of Calmodulin/Munc13-1 without prior knowledge about the domain orientation from crystallography.

Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling

Multidomain proteins composed of individual domains connected by flexible linkers pose a challenge for structural studies due to their intrinsic conformational dynamics. Integrated modelling approaches provide a means to characterise protein flexibility by combining experimental measurements with molecular simulations. In this study, we characterise the conformational dynamics of the catalytic RBR domain of the E3 ubiquitin ligase HOIP, which regulates immune and inflammatory signalling pathways. Specifically, we combine small angle X-ray scattering experiments and molecular dynamics simulations to generate weighted conformational ensembles of the HOIP RBR domain using two different approaches based on maximum parsimony and maximum entropy principles. Both methods provide optimised ensembles that are instrumental in rationalising observed differences between SAXS-based solution studies and available crystal structures and highlight the importance of interdomain linker flexibility.

Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding

The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we show that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that is consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein.

The nucleocapsid (N) protein of the SARS-CoV-2 virus plays an essential role in virus particle assembly as it specifically binds to and wraps the virus genomic RNA into a well-organized structure known as the ribonucleoprotein. Understanding how the N protein wraps around the virus RNA is critical for the development of strategies to inhibit virus assembly within host cells. One of the limitations regarding the molecular structure of the ribonucleoprotein, however, is that the N protein has several unstructured and mobile regions that preclude the resolution of its full atomic structure. Moreover, the N protein can form higher-order oligomers, both in the presence and absence of RNA. Here we employed computational methods, supported by experimental data, to simulate the N protein structural dynamics in the absence and presence of RNA. Our data suggest that the N protein forms structurally dynamic dimers in the absence of RNA, with its structured N- and C-terminal domains oriented in extended conformations. In the presence of RNA, however, the N protein assumes a more compact conformation. Our model for the oligomeric structure of the N protein bound to RNA helps to understand how N protein dimers interact to each other to form the ribonucleoprotein.

Protein Dynamics to Define and Refine Disordered Protein Ensembles

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R₂ relaxation rates and Lipari-Szabo order parameters (S² values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins

Intrinsically disordered proteins (IDPs) and multidomain proteins with flexible linkers show a high level of structural heterogeneity and are best described by ensembles consisting of multiple conformations with associated thermodynamic weights. Determining conformational ensembles usually involves the integration of biophysical experiments and computational models. In this review, we discuss current approaches to determine conformational ensembles of IDPs and multidomain proteins, including the choice of biophysical experiments, computational models used to sample protein conformations, models to calculate experimental observables from protein structure, and methods to refine ensembles against experimental data. We also provide examples of recent applications of integrative conformational ensemble determination to study IDPs and multidomain proteins and suggest future directions for research in the field.

K-edge anomalous SAXS for protein solution structure modeling

K-edge anomalous SAXS intensity was measured from a small, dimeric, partly unstructured protein segment of myosin X by using cupric ions bound to its C-terminal polyhistidine tags. Energy-dependent anomalous SAXS can provide key location-specific information about metal-labeled protein structures in solution that cannot be obtained from routine SAXS analysis. However, anomalous SAXS is seldom used for protein research due to practical difficulties, such as a lack of generic multivalent metal-binding tags and the challenges of measuring weak anomalous signal at the metal absorption edge. This pilot feasibility study suggests that weak K-edge anomalous SAXS signal can be obtained from transition metals bound to terminally located histidine tags of small proteins. The measured anomalous signal can provide information about the distribution of all metal–protein distances in the complex. Such an anomalous SAXS signal can assist in the modeling and validation of structured or unstructured proteins in solution and may potentially become a new addition to the repertoire of techniques in integrative structural biology.

Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data

Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.

Studying the Structures of Relaxed and Fuzzy Interactions: The Diverse World of S100 Complexes

S100 proteins are small, dimeric, Ca²⁺-binding proteins of considerable interest due to their associations with cancer and rheumatic and neurodegenerative diseases. They control the functions of numerous proteins by forming protein–protein complexes with them. Several of these complexes were found to display “fuzzy” properties. Examining these highly flexible interactions, however, is a difficult task, especially from a structural biology point of view. Here, we summarize the available in vitro techniques that can be deployed to obtain structural information about these dynamic complexes. We also review the current state of knowledge about the structures of S100 complexes, focusing on their often-asymmetric nature.

Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples

Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression datasets of osteoporosis samples were acquired from the Gene Expression Omnibus database with pre-set criteria. Differentially expressed genes (DEGs) between normal and diseased osteoporosis samples were screened using Limma package in R language. Protein–protein interaction (PPI) network was established based on interaction data of the DEGs from the Human Protein Reference Database. Classification accuracy of the classifier was assessed with sensitivity, specificity and area under curve (AUC) using the pROC package in the R. Pathway enrichment analysis was performed on feature genes with clusterProfiler. A total of 310 differentially expressed genes between two samples were associated with positive regulation of protein secretion and cytokine secretion, neutrophil-mediated immunity, and neutrophil activation. PPI network of DEGs consisted of 12 genes. A SVM classifier based on five feature genes was developed to classify osteoporosis samples, showing a higher prediction accuracy and AUC for GSE35959, GSE62402, GSE13850, GSE56814, GSE56815 and GSE7429 datasets. A SVM classifier with a high accuracy was developed for predicting osteoporosis. The genes included may be the potential feature genes in osteoporosis development.AbbreviationsDEGs: Differentially expressed genes; PPI: protein–protein interaction; WHO: World Health Organization; SVM: Support vector machine; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; BP: Biological Process; CC: Cellular Component; MF: Molecular Function; SVM: Support vector machines

TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES

Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.

Automatic Bayesian Weighting for SAXS Data

Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble. To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state.

Comment on the Optimal Parameters to Derive Intrinsically Disordered Protein Conformational Ensembles from Small-Angle X-ray Scattering Data Using the Ensemble Optimization Method

The Ensemble Optimization Method (EOM) is a popular approach to describe small-angle X-ray scattering (SAXS) data from highly disordered proteins. The EOM algorithm selects subensembles of coexisting states from large pools of randomized conformations to fit the SAXS data. Based on the unphysical bimodal radius of gyration (R_g) distribution of conformations resulting from the EOM analysis, a recent article (Fagerberg et al. J. Chem. Theory Comput. 2019, 15 (12), 6968-6983) concluded that this approach inadequately described the SAXS data measured for human Histatin 5 (Hst5), a peptide with antifungal properties. Using extensive experimental and synthetic data, we explored the origin of this observation. We found that the one-bead-per-residue coarse-grained representation with averaged scattering form factors (provided in the EOM as an add-on to represent disordered missing loops or domains) may not be appropriate for EOM analyses of scattering data from short (below 50 residues) proteins/peptides. The method of choice for these proteins is to employ atomistic models (e.g., from molecular dynamics simulations) to sample the protein conformational landscape. As a convenient alternative, we have also improved the coarse-grained approach by introducing amino acid specific form factors in the calculations. We also found that, for small proteins, the search for relatively large subensembles of 20-50 conformers (as implemented in the original EOM version) more adequately describes the conformational space sampled in solution than the procedures optimizing the ensemble size. Our observations have been added as recommendations into the information for EOM users to promote the proper utilization of the program for ensemble-based modeling of SAXS data for all types of disordered systems.

Integrative structural modeling of a multidomain polo-like kinase

Polo-like kinase 1 (PLK1) is a key regulator and coordinator for mitotic signaling that contains two major functional units of a kinase domain (KD) and a polo-box domain (PBD). While individual domain structures of the KD and the PBD are known, how they interact and assemble into a functional complex remains an open question. The structural model from the KD-PBD-Map205PBM heterotrimeric crystal structure of zebrafish PLK1 represents a major step in understanding the KD and the PBD interactions. However, how these two domains interact when connected by a linker in the full length PLK1 needs further investigation. By integrating different sources of structural data from small-angle X-ray scattering, hydroxyl radical protein footprinting, and computational sampling, here we report an overall architecture for PLK1 multidomain assembly between the KD and the PBD. Our model revealed that the KD uses its C-lobe to interact with the PBD via the site near the phosphopeptide binding site in its auto-inhibitory state in solution. Disruption of this auto-inhibition via site-directed mutagenesis at the KD-PBD interface increases its kinase activity, supporting the functional role of KD-PBD interactions predicted for regulating the PLK1 kinase function. Our results indicate that the full length human PLK1 takes dynamic structures with a variety of domain-domain interfaces in solution.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

Hydration-Induced Structural Changes in the Solid State of Protein: A SAXS/WAXS Study on Lysozyme

The stability of biologically produced pharmaceuticals is the limiting factor to various applications, which can be improved by formulation in solid-state forms, mostly via lyophilization. Knowledge about the protein structure at the molecular level in the solid state and its transition upon rehydration is however scarce, and yet it most likely affects the physical and chemical stability of the biological drug. In this work, synchrotron small- and wide-angle X-ray scattering (SWAXS) are used to characterize the structure of a model protein, lysozyme, in the solid state and its structural transition upon rehydration to the liquid state. The results show that the protein undergoes distortion upon drying to adopt structures that can continuously fill the space to remove the protein–air interface that may be formed upon dehydration. Above a hydration threshold of 35 wt %, the native structure of the protein is recovered. The evolution of SWAXS peaks as a function of water content in a broad range of concentrations is discussed in relation to the structural changes in the protein. The findings presented here can be used for the design and optimization of solid-state formulations of proteins with improved stability.

Glycine substitution in SH3-SH2 connector of Hck tyrosine kinase causes population shift from assembled to disassembled state

A combination of small angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations based on a coarse grained model is used to examine the effect of glycine substitutions in the short connector between the SH3 and SH2 domains of Hck, a member of the Src-family kinases. It has been shown previously that the activity of cSrc kinase is upregulated by substitution of 3 residues by glycine in the SH3-SH2 connector. Here, analysis of SAXS data indicates that the population of Hck in the disassembled state increases from 25% in the wild type kinase to 76% in the glycine mutant. This is consistent with the results of free energy perturbation calculations showing that the mutation in the connector shifts the equilibrium from the assembled to the disassembled state. This study supports the notion that the SH3-SH2 connector helps to regulate the activity of tyrosine kinases by shifting the population of the active state of the multidomain protein independent of C-terminal phosphorylation.

Combined SAXS and computational approaches for structure determination and binding characteristics of Chimera (CtGH1-L1-CtGH5-F194A) generated by assembling β-glucosidase (CtGH1) and a mutant endoglucanase (CtGH5-F194A) from Clostridium thermocellum

Chimera (CtGH1-L1-CtGH5-F194A) developed by fusing β-glucosidase (CtGH1) at N-terminal and endoglucanase (CtGH5-F194A) at C-terminal was structurally characterized. Its secondary structure analysis by CD showed 38% α-helix, 9.3% β-sheets and 52.7% random coils corroborating with prediction. In-silico modeled structure of Chimera comprised two modules, CtGH1 and CtGH5-F194A displaying (α/β)8 fold. Ramachandran plot of Chimera showed 99.9% residues in allowed region. Binding interaction of Chimera with cello-oligosaccharides suggested active forms of CtGH1 and CtGH5-F194A and their involvement in catalysis. MD simulation of cellohexaose bound endoglucanase module of Chimera showed favourable flexibility in loops, LA with H-bond formation with Asn510 and in loop LC relocation of Tyr687 away from active site efficiently releasing the product after catalysis. Higher short range interaction energy of Chimera, -383 kJ/mol than the individual endoglucanase, 254 kJ/mol against cellohexaose suggested higher efficient catalysis by Chimera. β-Glucosidase module of Chimera showed fluctuations in outer loops suggesting conformational changes that might be contributing to improved hydrolysis. SAXS analysis of Chimera displayed monodispersed state. Guinier analysis of Chimera showed globular shape (Rg= 3.15 ± 0.10 nm). Kratky plot confirmed fully folded and flexible behaviour in solution. Gasbor modeled structure of Chimera displayed an elongated structure with two modules having shape similar to bean-bag contour.

Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Many proteins contain multiple folded domains separated by flexible linkers, and the ability to describe the structure and conformational heterogeneity of such flexible systems pushes the limits of structural biology. Using the three-domain protein TIA-1 as an example, we here combine coarse-grained molecular dynamics simulations with previously measured small-angle scattering data to study the conformation of TIA-1 in solution. We show that while the coarse-grained potential (Martini) in itself leads to too compact conformations, increasing the strength of protein-water interactions results in ensembles that are in very good agreement with experiments. We show how these ensembles can be refined further using a Bayesian/Maximum Entropy approach, and examine the robustness to errors in the energy function. In particular we find that as long as the initial simulation is relatively good, reweighting against experiments is very robust. We also study the relative information in X-ray and neutron scattering experiments and find that refining against the SAXS experiments leads to improvement in the SANS data. Our results suggest a general strategy for studying the conformation of multi-domain proteins in solution that combines coarse-grained simulations with small-angle X-ray scattering data that are generally most easy to obtain. These results may in turn be used to design further small-angle neutron scattering experiments that exploit contrast variation through 1H/2H isotope substitutions.

Structural Modeling Using Solution Small-Angle X-ray Scattering (SAXS)

Small-angle X-ray scattering (SAXS) offers a way to examine the overall shape and oligomerization state of biological macromolecules under quasi native conditions in solution. In the past decades, SAXS has become a standard tool for structure biologists due to the availability of high brilliance X-ray sources and the development of data analysis/interpretation methods. Sample handling robots and software pipelines have significantly reduced the time necessary to conduct SAXS experiments. Presently, most synchrotrons feature beamlines dedicated to biological SAXS, and the SAXS-derived models are deposited into dedicated and accessible databases. The size of macromolecules that may be analyzed ranges from small peptides or snippets of nucleic acids to gigadalton large complexes or even entire viruses. Compared to other structural methods, sample preparation is straightforward, and the risk of inducing preparation artefacts is minimal. Very importantly, SAXS is a method of choice to study flexible systems like unfolded or disordered proteins, providing the structural ensembles compatible with the data. Although it may be utilized stand-alone, SAXS profits a lot from available experimental or predicted high-resolution data and information from complementary biophysical methods. Here, we show the basic principles of SAXS and review latest developments in the fields of hybrid modeling and flexible systems.

Structural Analyses of Intrinsically Disordered Proteins by Small-Angle X-Ray Scattering

Small-angle X-ray scattering (SAXS) is a low-resolution method for the structural characterization of biological macromolecules in solution. Information about the overall structural features provided by SAXS is highly complementary to X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and cryo-electron microscopy, which are high-resolution methods. SAXS not only provides the shape, oligomeric state, and quaternary structure of folded proteins and protein complexes but also allows for quantitative analysis of flexible biomolecules. In this chapter, the most relevant SAXS procedures for structural characterization of flexible macromolecules, including intrinsically disordered proteins (IDPs), are presented. The sample requirements for SAXS experiments on protein solutions and the sequence of steps in data collection and processing are described. The use of the advanced data analysis tools to quantitatively characterize flexible proteins is presented in detail. Typical experimental issues and potential problems encountered during SAXS data measurements and analyses are discussed.

Membrane Anchoring of Hck Kinase via the Intrinsically Disordered SH4-U and Length Scale Associated with Subcellular Localization

Src family kinases (SFKs) are a group of nonreceptor tyrosine kinases that are characterized by their involvement in critical signal transduction pathways. SFKs are often found attached to membranes, but little is known about the conformation of the protein in this environment. Here, solution nuclear magnetic resonance (NMR), neutron reflectometry (NR), and molecular dynamics (MD) simulations were employed to study the membrane interactions of the intrinsically disordered SH4 and Unique domains of the Src family kinase Hck. Through development of a procedure to combine the information from the different techniques, we were able produce a first-of-its-kind atomically detailed structural ensemble of a membrane-bound intrinsically disordered protein. Evaluation of the model demonstrated its consistency with previous work and provided insight into how SFK Unique domains act to differentiate the family members from one another. Fortuitously, the position of the ensemble on the membrane allowed the model to be combined with configurations of the multidomain Hck kinase previously determined from small-angle solution X-ray scattering to produce full-length models of membrane-anchored Hck. The resulting models allowed us to estimate that the kinase active site is positioned about 65 ± 35 Å away from the membrane surface, offering the first estimations of the length scale associated with the concept of SFK subcellular localization.

Combination of coarse-grained molecular dynamics simulations and small-angle X-ray scattering experiments

The combination of molecular dynamics (MD) simulations and small-angle X-ray scattering (SAXS), called the MD-SAXS method, is efficient for investigating protein dynamics. To overcome the time-scale limitation of all-atom MD simulations, coarse-grained (CG) representations are often utilized for biomolecular simulations. In this study, we propose a method to combine CG MD simulations with SAXS, termed the CG-MD-SAXS method. In the CG-MD-SAXS method, the scattering factors of CG particles for proteins and nucleic acids are evaluated using high-resolution structural data in the Protein Data Bank, and the excluded volume and the hydration shell are modeled using two adjustable parameters to incorporate solvent effects. To avoid overfitting, only the two parameters are adjusted for an entire structure ensemble. To verify the developed method, theoretical SAXS profiles for various proteins, DNA/RNA, and a protein-RNA complex are compared with both experimental profiles and theoretical profiles obtained by the all-atom representation. In the present study, we applied the CG-MD-SAXS method to the Swi5-Sfr1 complex and three types of nucleosomes to obtain reliable ensemble models consistent with the experimental SAXS data.

Discovery of Non-peptide Small Molecule Allosteric Modulators of the Src-family Kinase, Hck

The eight mammalian Src-family tyrosine kinases are dynamic, multi-domain structures, which adopt distinct “open” and “closed” conformations. In the closed conformation, the regulatory SH3 and SH2 domains pack against the back of the kinase domain, providing allosteric control of kinase activity. Small molecule ligands that engage the regulatory SH3-SH2 region have the potential to modulate Src-family kinase activity for therapeutic advantage. Here we describe an HTS-compatible fluorescence polarization assay to identify small molecules that interact with the unique-SH3-SH2-linker (U32L) region of Hck, a Src-family member expressed exclusively in cells of myeloid lineage. Hck has significant potential as a drug target in acute myeloid leukemia, an aggressive form of cancer with substantial unmet clinical need. The assay combines recombinant Hck U32L protein with a fluorescent probe peptide that binds to the SH3 domain in U32L, resulting in an increased FP signal. Library compounds that interact with the U32L protein and interfere with probe binding reduce the FP signal, scoring as hits. Automated 384-well high-throughput screening of 60,000 compounds yielded Z'-factor coefficients > 0.7 across nearly 200 assay plates, and identified a series of hit compounds with a shared pyrimidine diamine substructure. Surface plasmon resonance assays confirmed direct binding of hit compounds to the Hck U32L target protein as well as near-full-length Hck. Binding was not observed with the individual SH3 and SH2 domains, demonstrating that these compounds recognize a specific three-dimensional conformation of the regulatory regions. This conclusion is supported by computational docking studies, which predict ligand contacts with a pocket formed by the juxtaposition of the SH3 domain, the SH3-SH2 domain connector, and the SH2-kinase linker. Each of the four validated hits stimulated recombinant, near-full-length Hck activity in vitro, providing evidence for allosteric effects on the kinase domain. These results provide a path to discovery and development of chemical scaffolds to target the regulatory regions of Hck and other Src family kinases as a new approach to pharmacological kinase control.

Evolving SAXS versatility: solution X-ray scattering for macromolecular architecture, functional landscapes, and integrative structural biology

Small-angle X-ray scattering (SAXS) has emerged as an enabling integrative technique for comprehensive analyses of macromolecular structures and interactions in solution. Over the past two decades, SAXS has become a mainstay of the structural biologist's toolbox, supplying multiplexed measurements of molecular shape and dynamics that unveil biological function. Here, we discuss evolving SAXS theory, methods, and applications that extend the field of small-angle scattering beyond simple shape characterization. SAXS, coupled with size-exclusion chromatography (SEC-SAXS) and time-resolved (TR-SAXS) methods, is now providing high-resolution insight into macromolecular flexibility and ensembles, delineating biophysical landscapes, and facilitating high-throughput library screening to assess macromolecular properties and to create opportunities for drug discovery. Looking forward, we consider SAXS in the integrative era of hybrid structural biology methods, its potential for illuminating cellular supramolecular and mesoscale structures, and its capacity to complement high-throughput bioinformatics sequencing data. As advances in the field continue, we look forward to proliferating uses of SAXS based upon its abilities to robustly produce mechanistic insights for biology and medicine.

Generation of the configurational ensemble of an intrinsically disordered protein from unbiased molecular dynamics simulation

Intrinsically disordered proteins (IDPs) are abundant in eukaryotic proteomes, play a major role in cell signaling, and are associated with human diseases. To understand IDP function it is critical to determine their configurational ensemble, i.e., the collection of 3-dimensional structures they adopt, and this remains an immense challenge in structural biology. Attempts to determine this ensemble computationally have been hitherto hampered by the necessity of reweighting molecular dynamics (MD) results or biasing simulation in order to match ensemble-averaged experimental observables, operations that reduce the precision of the generated model because different structural ensembles may yield the same experimental observable. Here, by employing enhanced sampling MD we reproduce the experimental small-angle neutron and X-ray scattering profiles and the NMR chemical shifts of the disordered N terminal (SH4UD) of c-Src kinase without reweighting or constraining the simulations. The unbiased simulation results reveal a weakly funneled and rugged free energy landscape of SH4UD, which gives rise to a heterogeneous ensemble of structures that cannot be described by simple polymer theory. SH4UD adopts transient helices, which are found away from known phosphorylation sites and could play a key role in the stabilization of structural regions necessary for phosphorylation. Our findings indicate that adequately sampled molecular simulations can be performed to provide accurate physical models of flexible biosystems, thus rationalizing their biological function.

Using Small-Angle Scattering Data and Parametric Machine Learning to Optimize Force Field Parameters for Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) play important roles in many aspects of normal cell physiology, such as signal transduction and transcription, as well as pathological states, including Alzheimer's, Parkinson's, and Huntington's disease. Unlike their globular counterparts that are defined by a few structures and free energy minima, IDP/IDR comprise a large ensemble of rapidly interconverting structures and a corresponding free energy landscape characterized by multiple minima. This aspect has precluded the use of structural biological techniques, such as X-ray crystallography and nuclear magnetic resonance (NMR) for resolving their structures. Instead, low-resolution techniques, such as small-angle X-ray or neutron scattering (SAXS/SANS), have become a mainstay in characterizing coarse features of the ensemble of structures. These are typically complemented with NMR data if possible or computational techniques, such as atomistic molecular dynamics, to further resolve the underlying ensemble of structures. However, over the past 10–15 years, it has become evident that the classical, pairwise-additive force fields that have enjoyed a high degree of success for globular proteins have been somewhat limited in modeling IDP/IDR structures that agree with experiment. There has thus been a significant effort to rehabilitate these models to obtain better agreement with experiment, typically done by optimizing parameters in a piecewise fashion. In this work, we take a different approach by optimizing a set of force field parameters simultaneously, using machine learning to adapt force field parameters to experimental SAXS scattering profiles. We demonstrate our approach in modeling three biologically IDP ensembles based on experimental SAXS profiles and show that our optimization approach significantly improve force field parameters that generate ensembles in better agreement with experiment.

Dynamic regulatory features of the protein tyrosine kinases

The SRC, Abelson murine leukemia viral oncogene homolog 1, TEC and C-terminal SRC Kinase families of non-receptor tyrosine kinases (collectively the Src module kinases) mediate an array of cellular signaling processes and are therapeutic targets in many disease states. Crystal structures of Src modules kinases provide valuable insights into the regulatory mechanisms that control activation and generate a framework from which drug discovery can advance. The conformational ensembles visited by these multidomain kinases in solution are also key features of the regulatory machinery controlling catalytic activity. Measurement of dynamic motions within kinases substantially augments information derived from crystal structures. In this review, we focus on a body of work that has transformed our understanding of non-receptor tyrosine kinase regulation from a static view to one that incorporates how fluctuations in conformational ensembles and dynamic motions influence activation status. Regulatory dynamic networks are often shared across and between kinase families while specific dynamic behavior distinguishes unique regulatory mechanisms for select kinases. Moreover, intrinsically dynamic regions of kinases likely play important regulatory roles that have only been partially explored. Since there is clear precedence that kinase inhibitors can exploit specific dynamic features, continued efforts to define conformational ensembles and dynamic allostery will be key to combating drug resistance and devising alternate treatments for kinase-associated diseases.

BEES: Bayesian Ensemble Estimation from SAS

Many biomolecular complexes exist in a flexible ensemble of states in solution that is necessary to perform their biological function. Small-angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules because of their relative ease of use and their ability to simultaneously probe the full ensemble of states. However, SAS data is typically low dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries a significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the Bayesian Ensemble Estimation from SAS (BEES) program. Two forks of BEES are available, the primary one existing as a module for the SASSIE web server and a developmental version that is a stand-alone Python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model's performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES' capabilities.

Neutron scattering in the biological sciences: progress and prospects

The scattering of neutrons can be used to provide information on the structure and dynamics of biological systems on multiple length and time scales. Pursuant to a National Science Foundation-funded workshop in February 2018, recent developments in this field are reviewed here, as well as future prospects that can be expected given recent advances in sources, instrumentation and computational power and methods. Crystallography, solution scattering, dynamics, membranes, labeling and imaging are examined. For the extraction of maximum information, the incorporation of judicious specific deuterium labeling, the integration of several types of experiment, and interpretation using high-performance computer simulation models are often found to be particularly powerful.

A Metastable Contact and Structural Disorder in the Estrogen Receptor Transactivation Domain

The N-terminal transactivation domain (NTD) of estrogen receptor alpha, a well-known member of the family of intrinsically disordered proteins, mediates the receptor's transactivation function. However, an accurate molecular dissection of NTD's structure-function relationships remains elusive. Here, we show that the NTD adopts a mostly disordered, unexpectedly compact conformation that undergoes structural expansion on chemical denaturation. By combining small-angle X-ray scattering, hydroxyl radical protein footprinting, and computational modeling, we derive the ensemble-structures of the NTD and determine its ensemble-contact map revealing metastable long-range contacts, e.g., between residues I33 and S118. We show that mutation at S118, a known phosphorylation site, promotes conformational changes and increases coactivator binding. We further demonstrate via fluorine-19 (¹⁹F) nuclear magnetic resonance that mutations near I33 alter ¹⁹F chemical shifts at S118, confirming the proposed I33-S118 contact in the ensemble of structural disorder. These findings extend our understanding of how specific contact metastability mediates critical functions of disordered proteins.

SAXS-guided Enhanced Unbiased Sampling for Structure Determination of Proteins and Complexes

Molecular simulations can be utilized to predict protein structure ensembles and dynamics, though sufficient sampling of molecular ensembles and identification of key biologically relevant conformations remains challenging. Low-resolution experimental techniques provide valuable structural information on biomolecule at near-native conditions, which are often combined with molecular simulations to determine and refine protein structural ensembles. In this study, we demonstrate how small angle x-ray scattering (SAXS) information can be incorporated in Markov state model-based adaptive sampling strategy to enhance time efficiency of unbiased MD simulations and identify functionally relevant conformations of proteins and complexes. Our results show that using SAXS data combined with additional information, such as thermodynamics and distance restraints, we are able to distinguish otherwise degenerate structures due to the inherent ambiguity of SAXS pattern. We further demonstrate that adaptive sampling guided by SAXS and hybrid information can significantly reduce the computation time required to discover target structures. Overall, our findings demonstrate the potential of this hybrid approach in predicting near-native structures of proteins and complexes. Other low-resolution experimental information can be incorporated in a similar manner to collectively enhance unbiased sampling and improve the accuracy of structure prediction from simulation.

Effect of Weakly Interacting Cosolutes on Lysozyme Conformations

Exposure of a protein to cosolutes, like denaturants, changes its folding equilibrium. To determine the ensemble of protein conformations at equilibrium, in the presence of weakly interacting cosolutes, we present a two-stage analysis of solution X-ray scattering data. In the first stage, Guinier analysis and Kratky plot revealed information about the compactness and flexibility of the protein. In the second stage, elastic network contact model and coarse-grained normal mode analysis were used to generate an ensemble of conformations. The scattering curves of the conformations were computed and fitted to the measured scattering curves to get insights into the dominating folding states at equilibrium. Urea and guanidine hydrochloride (GuHCl) behaved as preferentially included weakly interacting cosolutes and induced denaturation of hen egg-white lysozyme, which served as our test case. The computed models adequately fit the data and gave ensembles of conformations that were consistent with our measurements. The analysis suggests that in the presence of urea, lysozyme retained its compactness and assumed molten globule characteristics, whereas in the presence of GuHCl lysozyme adopted random coiled conformations. Interestingly, no equilibrium intermediate states were observed in both urea and GuHCl.

Challenges in the Structural-Functional Characterization of Multidomain, Partially Disordered Proteins CBP and p300: Preparing Native Proteins and Developing Nanobody Tools

The structural and functional characterization of large multidomain signaling proteins containing long disordered linker regions represents special methodological and conceptual challenges. These proteins show extreme structural heterogeneity and have complex posttranslational modification patterns, due to which traditional structural biology techniques provide results that are often difficult to interpret. As demonstrated through the example of two such multidomain proteins, CREB-binding protein (CBP) and its paralogue, p300, even the expression and purification of such proteins are compromised by their extreme proteolytic sensitivity and structural heterogeneity. In this chapter, we describe the effective expression of CBP and p300 in a eukaryotic host, Sf9 insect cells, followed by their tandem affinity purification based on two terminal tags to ensure their structural integrity. The major focus of this chapter is on the development of novel accessory tools, single-domain camelid antibodies (nanobodies), for structural-functional characterization. Specific nanobodies against full-length CBP and p300 can specifically target their different regions and can be used for their marking, labeling, and structural stabilization in a broad range of in vitro and in vivo studies. Here, we describe four high-affinity nanobodies binding to the KIX and the HAT domains, either mimicking known interacting partners or revealing new functionally relevant conformations. As immunization of llamas results in nanobody libraries with a great sequence variation, deep sequencing and interaction analysis with different regions of the proteins provide a novel approach toward developing a panel of specific nanobodies.

The role of small-angle scattering in structure-based screening applications

In many biomolecular interactions, changes in the assembly states and structural conformations of participants can act as a complementary reporter of binding to functional and thermodynamic assays. This structural information is captured by a number of structural biology and biophysical techniques that are viable either as primary screens in small-scale applications or as secondary screens to complement higher throughput methods. In particular, small-angle X-ray scattering (SAXS) reports the average distance distribution between all atoms after orientational averaging. Such information is important when for example investigating conformational changes involved in inhibitory and regulatory mechanisms where binding events do not necessarily cause functional changes. Thus, we summarise here the current and prospective capabilities of SAXS-based screening in the context of other methods that yield structural information. Broad guidelines are also provided to assist readers in preparing screening protocols that are tailored to available X-ray sources.

Multidomain architecture of estrogen receptor reveals interfacial cross-talk between its DNA-binding and ligand-binding domains

Human estrogen receptor alpha (hERα) is a hormone-responsive nuclear receptor (NR) involved in cell growth and survival that contains both a DNA-binding domain (DBD) and a ligand-binding domain (LBD). Functionally relevant inter-domain interactions between the DBD and LBD have been observed in several other NRs, but for hERα, the detailed structural architecture of the complex is unknown. By utilizing integrated complementary techniques of small-angle X-ray scattering, hydroxyl radical protein footprinting and computational modeling, here we report an asymmetric L-shaped “boot” structure of the multidomain hERα and identify the specific sites on each domain at the domain interface involved in DBD–LBD interactions. We demonstrate the functional role of the proposed DBD–LBD domain interface through site-specific mutagenesis altering the hERα interfacial structure and allosteric signaling. The L-shaped structure of hERα is a distinctive DBD–LBD organization of NR complexes and more importantly, reveals a signaling mechanism mediated by inter-domain crosstalk that regulates this receptor’s allosteric function.

The human estrogen receptor alpha (hERα) is a hormone-responsive transcription factor. Here the authors combine small-angle X-ray scattering, hydroxyl radical protein footprinting and computational modeling and show that multidomain hERα adopts an L-shaped boot-like architecture revealing a cross-talk between its DNA-binding domain and Ligand-binding domain.

What Can We Learn from Wide-Angle Solution Scattering?

Extending collection of x-ray solution scattering data into the wide-angle regime (WAXS) can provide information not readily extracted from small angle (SAXS) data. It is possible to accurately predict WAXS scattering on the basis of atomic coordinate sets and thus use it as a means of testing molecular models constructed on the basis of crystallography, molecular dynamics (MD), cryo-electron microscopy or ab initio modeling. WAXS data may provide insights into the secondary, tertiary and quaternary structural organization of macromolecules. It can provide information on protein folding and unfolding beyond that attainable from SAXS data. It is particularly sensitive to structural fluctuations in macromolecules and can be used to generate information about the conformational make up of ensembles of structures co-existing in solution. Novel approaches to modeling of structural fluctuations can provide information on the spatial extent of large-scale structural fluctuations that are difficult to obtain by other means. Direct comparison with the results of MD simulations are becoming possible. Because it is particularly sensitive to small changes in structure and flexibility it provides unique capabilities for the screening of ligand libraries for detection of functional interactions. WAXS thereby provides an important extension of SAXS that can generate structural and dynamic information complementary to that obtainable by other biophysical techniques.

Dynamic Neutron Scattering by Biological Systems

Dynamic neutron scattering directly probes motions in biological systems on femtosecond to microsecond timescales. When combined with molecular dynamics simulation and normal mode analysis, detailed descriptions of the forms and frequencies of motions can be derived. We examine vibrations in proteins, the temperature dependence of protein motions, and concepts describing the rich variety of motions detectable using neutrons in biological systems at physiological temperatures. New techniques for deriving information on collective motions using coherent scattering are also reviewed.

The SRC-family tyrosine kinase HCK shapes the landscape of SKAP2 interactome

The SRC Kinase Adaptor Phosphoprotein 2 (SKAP2) is a broadly expressed adaptor associated with the control of actin-polymerization, cell migration, and oncogenesis. After activation of different receptors at the cell surface, this dimeric protein serves as a platform for assembling other adaptors such as FYB and some SRC family kinase members, although these mechanisms are still poorly understood. The goal of this study is to map the SKAP2 interactome and characterize which domains or binding motifs are involved in these interactions. This is a prerequisite to finely analyze how these pathways are integrated in the cell machinery and to study their role in cancer and other human diseases when this network of interactions is perturbed. In this work, the domain and the binding motif of fourteen proteins interacting with SKAP2 were precisely defined and a new interactor, FAM102A was discovered. Herein, a fine-tuning between the binding of SRC kinases and their activation was identified. This last process, which depends on SKAP2 dimerization, indirectly affects the binding of FYB protein. Analysis of conformational changes associated with activation/inhibition of SRC family members, presently limited to their effect on kinase activity, is extended to their interactive network, which paves the way for therapeutic development.