Flexibility and Conformational Entropy in Protein-Protein Binding

Binding mechanism of adenylate kinase-specific monobodies

Monobodies are synthetic antibody-mimetic proteins that regulate enzyme functions through protein-protein interactions. In this study, we investigated the binding mechanisms of monobodies to adenylate kinase (Adk). Calorimetric and X-ray crystallographic analyses revealed that CL-1, a monobody specific for the CLOSED form of Adk, binds to the CORE domain of Adk in an enthalpy-driven manner, forming several hydrogen bonds and a cation-π interaction at the protein interface, without perturbing the Adk backbone. In contrast, OP-4, an OPEN-form-specific monobody, exhibited entropy-driven binding. 1H-15N 2D nuclear magnetic resonance (NMR), 31P-NMR, and calorimetric studies revealed conformational perturbations to Adk by OP-4, while substrate access remained intact. The different thermodynamic and structural effects between the monobodies highlight the diverse binding mechanisms among monobodies.

Systems Biology-Driven Discovery of Host-Targeted Therapeutics for Oropouche Virus: Integrating Network Pharmacology, Molecular Docking, and Drug Repurposing

Background: Oropouche virus (OROV), part of the Peribunyaviridae family, is an emerging pathogen causing Oropouche fever, a febrile illness endemic in South and Central America. Transmitted primarily through midge bites (Culicoides paraensis), OROV has no specific antiviral treatment or vaccine. This study aims to identify host-targeted therapeutics against OROV using computational approaches, offering a potential strategy for sustainable antiviral drug discovery. Methods: Virus-associated host targets were identified using the OMIM and GeneCards databases. The Enrichr and DSigDB platforms were used for drug prediction, filtering compounds based on Lipinski's rule for drug likeness. A protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape 3.10.3 software. Four key host targets-IL10, FASLG, PTPRC, and FCGR3A-were prioritized based on their roles in immune modulation and OROV pathogenesis. Molecular docking simulations were performed using the PyRx software to evaluate the binding affinities of selected small-molecule inhibitors-Acetohexamide, Deptropine, Methotrexate, Retinoic Acid, and 3-Azido-3-deoxythymidine-against the identified targets. Results: The PPI network analysis highlighted immune-mediated pathways such as Fc-gamma receptor signaling, cytokine control, and T-cell receptor signaling as critical intervention points. Molecular docking revealed strong binding affinities between the selected compounds and the prioritized targets, suggesting their potential efficacy as host-targeting antiviral candidates. Acetohexamide and Deptropine showed strong binding to multiple targets, indicating broad-spectrum antiviral potential. Further in vitro and in vivo validations are needed to confirm these findings and translate them into clinically relevant treatments. Conclusions: This study highlights the potential of using computational approaches to identify host-targeted therapeutics for Oropouche virus (OROV). By targeting key host proteins involved in immune modulation-IL10, FASLG, PTPRC, and FCGR3A-the selected compounds, Acetohexamide and Deptropine, demonstrate strong binding affinities, suggesting their potential as broad-spectrum antiviral candidates. Further experimental validation is needed to confirm their efficacy and potential for clinical application, offering a promising strategy for sustainable antiviral drug discovery.

ATP Binding and Inhibition of Intrinsically Disordered Protein Interactions

Recent studies have shown that ATP at high physiological concentrations (>5 mM) can inhibit liquid-liquid phase separation (LLPS) driven by interactions between intrinsically disordered proteins (IDPs). However, the mechanism underlying such inhibitory effect still remains elusive. Here, we used all-atom molecular dynamics simulation to study the interaction of ATP with two typical IDPs (i.e., FUS PLD and RGG domain of hnRNP G), and its impacts on IDP interactions. ATP exhibits a considerable tendency to bind to both IDPs and effectively inhibits their interactions. For the RGG domain, Arg residues are critical for both ATP binding and IDP interactions. The inhibitory effect of ATP is largely attributed to its competitive binding mode to Arg residues. Similar competitive binding of ATP is also observed in FUS PLD. Both ATP binding and the PLD interaction share the residues including Gln, Ser, and Tyr residues, while the competition is rather modest due to the abundance of these residues in the sequence. Interestingly, ATP undergoes considerable diffusion on the surface of PLD, which is an order of magnitude faster than the evolution of the contact area of PLDs. The temporal separation of these two processes remarkably promotes the inhibitory effect of ATP on PLD interaction. Given the representativeness of these two IDPs, competitive binding may serve as a general mechanism underlying ATP inhibition on IDP interactions at high physiological levels.

Activation mechanisms and structural dynamics of STIM proteins

The family of stromal interaction molecules (STIM) includes two widely expressed single-pass endoplasmic reticulum (ER) transmembrane proteins and additional splice variants that act as precise ER-luminal Ca2+ sensors. STIM proteins mainly function as one of the two essential components of the so-called Ca2+ release-activated Ca2+ (CRAC) channel. The second CRAC channel component is constituted by pore-forming Orai proteins in the plasma membrane. STIM and Orai physically interact with each other to enable CRAC channel opening, which is a critical prerequisite for various downstream signalling pathways such as gene transcription or proliferation. Their activation commonly requires the emptying of the intracellular ER Ca2+ store. Using their Ca2+ sensing capabilities, STIM proteins confer this Ca2+ content-dependent signal to Orai, thereby linking Ca2+ store depletion to CRAC channel opening. Here we review the conformational dynamics occurring along the entire STIM protein upon store depletion, involving the transition from the quiescent, compactly folded structure into an active, extended state, modulation by a variety of accessory components in the cell as well as the impairment of individual steps of the STIM activation cascade associated with disease.

Interaction of Asn297-Linked Glycan Ligands with the Fc Fragment of the Immunoglobulin Class G1: A Molecular Dynamics Simulation Study

The allosteric modulation of the homodimeric H10-03-6 protein to glycan ligands L1 and L2, and the STAB19 protein to glycan ligands L3 and L4, respectively, has been studied by molecular dynamics simulations and free energy calculations. The results revealed that the STAB19 protein has a significantly higher affinity for L3 (-11.38 ± 2.32 kcal/mol) than that for L4 (-5.51 ± 1.92 kcal/mol). However, the combination of the H10-03-6 protein with glycan L2 (1.23 ± 6.19 kcal/mol) is energetically unfavorable compared with that of L1 (-13.96 ± 0.35 kcal/mol). Further, the binding of glycan ligands L3 and L4 to STAB19 would result in the significant closure of the two CH2 domains of the STAB19 conformation with the decrease of the centroid distances between the two CH2 domains compared with the H10-03-6/L1/L2 complex. The CH2 domain closure of STAB19 relates directly to the formation of new hydrogen bonds and hydrophobic interactions between the residues Ser239, Val240, Asp265, Glu293, Asn297, Thr299, Ser337, Asp376, Thr393, Pro395, and Pro396 in STAB19 and glycan ligands L3 and L4, which suggests that these key residues would contribute to the specific regulation of STAB19 to L3 and L4. In addition, the distance analysis revealed that the EF loop in the H10-03-6/L1/L2 model presents a high flexibility and partial disorder compared with the stabilized STAB19/L3/L4 complex. These results will be helpful in understanding the specific regulation through the asymmetric structural characteristics in the CH2 and CH3 domains of the H10-03-6 and STAB19 proteins.

Insights into Modeling Approaches in Chemistry: Assessing Ligand-Protein Binding Thermodynamics Based on Rigid-Flexible Model Molecules

In the field of chemistry, model compounds find extensive use for investigating complex objects. One prime example of such object is the protein-ligand supramolecular interaction. Prediction the enthalpic and entropic contribution to the free energy associated with this process, as well as the structural and dynamic characteristics of protein-ligand complexes poses considerable challenges. This review exemplifies modeling approaches used to study protein-ligand binding (PLB) thermodynamics by employing pairs of conformationally constrained/flexible model molecules. Strategically designing the model molecules can reduce the number of variables that influence thermodynamic parameters. This enables scientists to gain deeper insights into the enthalpy and entropy of PLB, which is relevant for medicinal chemistry and drug design. The model studies reviewed here demonstrate that rigidifying ligands may induce compensating changes in the enthalpy and entropy of binding. Some "rules of thumb" have started to emerge on how to minimize entropy-enthalpy compensation and design efficient rigidified or flexible ligands.

Juno and CD9 protein network organization in oolemma of mouse oocyte

Juno and CD9 protein, expressed in oolemma, are known to be essential for sperm-oocyte binding and fusion. Although evidence exists that these two proteins cooperate, their interaction has not yet been demonstrated. Here in, we present Juno and CD9 mutual localization over the surface of mouse metaphase II oocytes captured using the 3D STED super-resolution technique. The precise localization of examined proteins was identified in different compartments of oolemma such as the microvillar membrane, planar membrane between individual microvilli, and the membrane of microvilli-free region. Observed variance in localization of Juno and CD9 was confirmed by analysis of transmission and scanning electron microscopy images, which showed a significant difference in the presence of proteins between selected membrane compartments. Colocalization analysis of super-resolution images based on Pearson's correlation coefficient supported evidence of Juno and CD9 mutual position in the oolemma, which was identified by proximity ligation assay. Importantly, the interaction between Juno and CD9 was detected by co-immunoprecipitation and mass spectrometry in HEK293T/17 transfected cell line. For better understanding of experimental data, mouse Juno and CD9 3D structure were prepared by comparative homology modelling and several protein-protein flexible sidechain dockings were performed using the ClusPro server. The dynamic state of the proteins was studied in real-time at atomic level by molecular dynamics (MD) simulation. Docking and MD simulation predicted Juno-CD9 interactions and stability also suggesting an interactive mechanism. Using the multiscale approach, we detected close proximity of Juno and CD9 within microvillar oolemma however, not in the planar membrane or microvilli-free region. Our findings show yet unidentified Juno and CD9 interaction within the mouse oolemma protein network prior to sperm attachment. These results suggest that a Juno and CD9 interactive network could assist in primary Juno binding to sperm Izumo1 as a prerequisite to subsequent gamete membrane fusion.

Interactive deciphering electron-shuttling characteristics of Coffea arabica leaves and potential bioenergy-steered anti-SARS-CoV-2 RdRp inhibitor via microbial fuel cells

Due to the pandemics of COVID-19, herbal medicine has recently been explored for possible antiviral treatment and prevention via novel platform of microbial fuel cells. It was revealed that Coffea arabica leaves was very appropriate for anti-COVID-19 drug development. Antioxidant and anti-inflammatory tests exhibited the most promising activities for C. arabica ethanol extracts and drying approaches were implemented on the leaf samples prior to ethanol extraction. Ethanol extracts of C. arabica leaves were applied to bioenergy evaluation via DC-MFCs, clearly revealing that air-dried leaves (CA-A-EtOH) exhibited the highest bioenergy-stimulating capabilities (ca. 2.72 fold of power amplification to the blank). Furthermore, molecular docking analysis was implemented to decipher the potential of C. arabica leaves metabolites. Chlorogenic acid (-6.5 kcal/mol) owned the highest binding affinity with RdRp of SARS-CoV-2, showing a much lower average RMSF value than an apoprotein. This study suggested C. arabica leaves as an encouraging medicinal herb against SARS-CoV-2.

Exploiting the Innate Plasticity of the Programmed Cell Death-1 (PD1) Receptor to Design Pembrolizumab H3 Loop Mimics

Checkpoint blockade of the immunoreceptor programmed cell death-1 (PD1) with its ligand-1 (PDL1) by monoclonal antibodies such as pembrolizumab provided compelling clinical results in various cancer types, yet the molecular mechanism by which this drug blocks the PD1/PDL1 interface remains unclear. To address this question, we examined the conformational motion of PD1 associated with the binding of pembrolizumab. Our results revealed that the innate plasticity of both C'D and FG loops is crucial to form a deep binding groove (371 Å3 ) across several distant epitopes of PD1. This analysis ultimately provided a rational-design to create pembrolizumab H3 loop mimics [RDYRFDMGFD] into β-hairpin scaffolds. As a result, a 20-residue long β-hairpin peptide 1 e was identified as a first-in-class potent PD1-inhibitor (EC50 of 0.29 μM; Ki of 41 nM).

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Kinematic Vibrational Entropy Assessment and Analysis of SARS CoV-2 Main Protease

The three-dimensional conformations of a protein influence its function and select for the ligands it can interact with. The total free energy change during protein-ligand complex formation includes enthalphic and entropic components, which together report on the binding affinity and conformational states of the complex. However, determining the entropic contribution is computationally burdensome. Here, we apply kinematic flexibility analysis (KFA) to efficiently estimate vibrational frequencies from static protein and protein-ligand structures. The vibrational frequencies, in turn, determine the vibrational entropies of the structures and their complexes. Our estimates of the vibrational entropy change caused by ligand binding compare favorably to values obtained from a dynamic Normal Mode Analysis (NMA). Higher correlation factors can be achieved by increasing the distance cutoff in the potential energy model. Furthermore, we apply our new method to analyze the entropy changes of the SARS CoV-2 main protease when binding with different ligand inhibitors, which is relevant for the design of potential drugs.

Epistasis at the SARS-CoV-2 Receptor-Binding Domain Interface and the Propitiously Boring Implications for Vaccine Escape

At the time of this writing, December 2021, potential emergence of vaccine escape variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a grave global concern. The interface between the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein and the host receptor (ACE2) overlaps the binding site of principal neutralizing antibodies (NAb), limiting the repertoire of viable mutations. Nonetheless, variants with multiple RBD mutations have risen to dominance. Nonadditive, epistatic relationships among RBD mutations are apparent, and assessing the impact of such epistasis on the mutational landscape, particularly the risk of vaccine escape, is crucial. We employed protein structure modeling using Rosetta to compare the effects of all single mutants at the RBD-NAb and RBD-ACE2 interfaces for the wild type and Delta, Gamma, and Omicron variants. Overall, epistasis at the RBD interface appears to be limited, and the effects of most multiple mutations are additive. Epistasis at the Delta variant interface weakly stabilizes NAb interaction relative to ACE2 interaction, whereas in Gamma, epistasis more substantially destabilizes NAb interaction. Despite bearing many more RBD mutations, the epistatic landscape of Omicron closely resembles that of Gamma. Thus, although Omicron poses new risks not observed with Delta, structural constraints on the RBD appear to hamper continued evolution toward more complete vaccine escape. The modest ensemble of mutations relative to the wild type that are currently known to reduce vaccine efficacy is likely to contain the majority of all possible escape mutations for future variants, predicting the continued efficacy of the existing vaccines. IMPORTANCE Emergence of vaccine escape variants of SARS-CoV-2 is arguably the most pressing problem during the COVID-19 pandemic as vaccines are distributed worldwide. We employed a computational approach to assess the risk of antibody escape resulting from mutations in the receptor-binding domain of the spike protein of the wild-type SARS-CoV-2 virus as well as the Delta, Gamma, and Omicron variants. The efficacy of the existing vaccines against Omicron could be substantially reduced relative to the wild type, and the potential for vaccine escape is of grave concern. Our results suggest that although Omicron poses new evolutionary risks not observed for Delta, structural constraints on the RBD make continued evolution toward more complete vaccine escape from either Delta or Omicron unlikely. The modest set of escape-enhancing mutations already identified for the wild type likely include the majority of all possible mutations with this effect.

Interleukin-2 superkines by computational design

While computational engineering of therapeutic proteins is a desirable goal, in practice the optimization of protein–protein interactions requires substantial experimental intervention. We present here a computational approach that focuses on stabilizing core protein structures rather than engineering the protein–protein interface. Using this approach, we designed thermostabilized interleukin-2 (IL-2) variants that bind tightly to their receptor without experimental optimization, mimicking the properties of the yeast-display engineered IL-2 variant “super-2.” Our results suggest that structure-guided stabilization may be a general method for in silico affinity maturation of protein–protein interactions.

Affinity maturation of protein–protein interactions is an important approach in the development of therapeutic proteins such as cytokines. Typical experimental strategies involve targeting the cytokine-receptor interface with combinatorial libraries and then selecting for higher-affinity variants. Mutations to the binding scaffold are usually not considered main drivers for improved affinity. Here we demonstrate that computational design can provide affinity-enhanced variants of interleukin-2 (IL-2) “out of the box” without any requirement for interface engineering. Using a strategy of global IL-2 structural stabilization targeting metastable regions of the three-dimensional structure, rather than the receptor binding interfaces, we computationally designed thermostable IL-2 variants with up to 40-fold higher affinity for IL-2Rβ without any library-based optimization. These IL-2 analogs exhibited CD25-independent activities on T and natural killer (NK) cells both in vitro and in vivo, mimicking the properties of the IL-2 superkine “super-2” that was engineered through yeast surface display [A. M. Levin et al., Nature, 484, 529–533 (2012)]. Structure-guided stabilization of cytokines is a powerful approach to affinity maturation with applications to many cytokine and protein–protein interactions.

Epistasis at the SARS-CoV-2 RBD Interface and the Propitiously Boring Implications for Vaccine Escape

At the time of this writing, December 2021, potential emergence of vaccine escape variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a grave global concern. The interface between the receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein and the host receptor (ACE2) overlap with the binding site of principal neutralizing antibodies (NAb), limiting the repertoire of viable mutations. Nonetheless, variants with multiple mutations in the RBD have rose to dominance. Non-additive, epistatic relationships among RBD mutations are apparent, and assessing the impact of such epistasis on the mutational landscape is crucial. Epistasis can substantially increase the risk of vaccine escape and cannot be completely characterized through the study of the wild type (WT) alone. We employed protein structure modeling using Rosetta to compare the effects of all single mutants at the RBD-NAb and RBD-ACE2 interfaces for the WT, Delta, Gamma, and Omicron variants. Overall, epistasis at the RBD interface appears to be limited and the effects of most multiple mutations are additive. Epistasis at the Delta variant interface weakly stabilizes NAb interaction relative to ACE2 interaction, whereas in the Gamma variant, epistasis more substantially destabilizes NAb interaction. Although a small, systematic trend towards NAb destabilization not observed for Delta or Gamma was detected for Omicron, and despite bearing significantly more RBD mutations, the epistatic landscape of the Omicron variant closely resembles that of Gamma. These results suggest that, although Omicron poses new risks not observed with Delta, structural constraints on the RBD hamper continued evolution towards more complete vaccine escape. The modest ensemble of mutations relative to the WT that are currently known to reduce vaccine efficacy is likely to comprise the majority of all possible escape mutations for future variants, predicting continued efficacy of the existing vaccines.

Understanding the impact of missense mutations on the structure and function of the EDA gene in X-linked hypohidrotic ectodermal dysplasia: A bioinformatics approach

X-linked hypohidrotic dysplasia (XLHED), caused by mutations in the EDA gene, is a rare genetic disease that affects the development and function of the teeth, hair, nails, and sweat glands. The structural and functional consequences of caused by an ectodysplasin-A (EDA) mutations on protein phenotype, stability, and posttranslational modifications (PTMs) have not been well investigated. The present investigation involves five missense mutations that cause XLHED (L56P, R155C, P220L, V251M, and V322A) in different domains of EDA (TM, furin, collagen, and tumor necrosis factor [TNF]) from previously published papers. The deleterious nature of EDA mutant variants was identified using several computational algorithm tools. The point mutations induce major drifts in the structural flexibility of EDA mutant variants and have a negative impact on their stability, according to the 3D protein modeling tool assay. Using the molecular docking technique, EDA/EDA variants were docked to 10 EDA interacting partners, retrieved from the STRING database. We found a novel biomarker CD68 by molecular docking analysis, suggesting all five EDA variants had lower affinity for EDAR, EDA2R, and CD68, implying that they would affect embryonic signaling between the ectodermal and mesodermal cell layers. In silico research such as gene ontology, subcellular localization, protein-protein interaction, and PTMs investigations indicates major functional alterations would occur in EDA variants. According to molecular simulations, EDA variants influence the structural conformation, compactness, stiffness, and function of the EDA protein. Further studies on cell line and animal models might be useful in determining their specific roles in functional annotations.

Conformational dynamics of auto-inhibition in the ER calcium sensor STIM1

The dimeric ER Ca²⁺ sensor STIM1 controls store-operated Ca²⁺ entry (SOCE) through the regulated binding of its CRAC activation domain (CAD) to Orai channels in the plasma membrane. In resting cells, the STIM1 CC1 domain interacts with CAD to suppress SOCE, but the structural basis of this interaction is unclear. Using single-molecule Förster resonance energy transfer (smFRET) and protein crosslinking approaches, we show that CC1 interacts dynamically with CAD in a domain-swapped configuration with an orientation predicted to sequester its Orai-binding region adjacent to the ER membrane. Following ER Ca²⁺ depletion and release from CAD, cysteine crosslinking indicates that the two CC1 domains become closely paired along their entire length in the active Orai-bound state. These findings provide a structural basis for the dual roles of CC1: sequestering CAD to suppress SOCE in resting cells and propelling it toward the plasma membrane to activate Orai and SOCE after store depletion.

Polyclonal antibody responses to HIV Env immunogens resolved using cryoEM

Engineered ectodomain trimer immunogens based on BG505 envelope glycoprotein are widely utilized as components of HIV vaccine development platforms. In this study, we used rhesus macaques to evaluate the immunogenicity of several stabilized BG505 SOSIP constructs both as free trimers and presented on a nanoparticle. We applied a cryoEM-based method for high-resolution mapping of polyclonal antibody responses elicited in immunized animals (cryoEMPEM). Mutational analysis coupled with neutralization assays were used to probe the neutralization potential at each epitope. We demonstrate that cryoEMPEM data can be used for rapid, high-resolution analysis of polyclonal antibody responses without the need for monoclonal antibody isolation. This approach allowed to resolve structurally distinct classes of antibodies that bind overlapping sites. In addition to comprehensive mapping of commonly targeted neutralizing and non-neutralizing epitopes in BG505 SOSIP immunogens, our analysis revealed that epitopes comprising engineered stabilizing mutations and of partially occupied glycosylation sites can be immunogenic.

Diarylethene moiety as an enthalpy-entropy switch: photoisomerizable stapled peptides for modulating p53/MDM2 interaction

Analogs of the known inhibitor (peptide pDI) of the p53/MDM2 protein-protein interaction are reported, which are stapled by linkers bearing a photoisomerizable diarylethene moiety. The corresponding photoisomers possess significantly different affinities to the p53-interacting domain of the human MDM2. Apparent dissociation constants are in the picomolar-to-low nanomolar range for those isomers with diarylethene in the "open" configuration, but up to eight times larger for the corresponding "closed" isomers. Spectroscopic, structural, and computational studies showed that the stapling linkers of the peptides contribute to their binding. Calorimetry revealed that the binding of the "closed" isomers is mostly enthalpy-driven, whereas the "open" photoforms bind to the protein stronger due to their increased binding entropy. The results suggest that conformational dynamics of the protein-peptide complexes may explain the differences in the thermodynamic profiles of the binding.

Epitopes of Protein Binders Are Related to the Structural Flexibility of a Target Protein Surface

Protein binders including antibodies are known not to bind to random sites of target proteins, and their functional effectiveness mainly depends on the binding region, called the epitope. For the development of protein binders with desired functions, it is thus critical to understand which surface region protein binders prefer (or do not prefer) to bind. The current methods for epitope prediction focus on static indicators such as structural geometry or amino acid propensity, whereas protein binding events are in fact a consequence of dynamic interactions. Here, we demonstrate that the preference for a binding site by protein binders is strongly related to the structural flexibility of a target protein surface. Molecular dynamics simulations on unbound forms of antigen structures revealed that the antigen surface in direct contact with antibodies is less flexible than the rest of the surface. This tendency was shown to be similar in other non-antibody protein binders such as affibody, DARPin, monobody, and repebody. We also found that the relatedness of epitopes to the structural flexibility of a target protein surface is dependent on the secondary structure elements of paratopes. Monobody and repebody, whose binding sites are composed of β-strands, distinctively prefer to bind to a relatively more rigid region of a target protein. These observations enabled us to develop a simple epitope prediction method which shows a comparable performance to the commonly used ones.

ATP-Magnesium Coordination: Protein Structure-Based Force Field Evaluation and Corrections

In the numerous molecular recognition and catalytic processes across biochemistry involving adenosine triphosphate (ATP), the common bioactive form is its magnesium chelate, ATP·Mg2+. In aqueous solution, two chelation geometries predominate, distinguished by bidentate and tridentate Mg2+-phosphate coordination. These are approximately isoenergetic but separated by a high energy barrier. Force field-based atomistic simulation studies of this complex require an accurate representation of its structure and energetics. Here we focused on the energetics of ATP·Mg2+ coordination. Applying an enhanced sampling scheme to circumvent prohibitively slow sampling of transitions between coordination modes, we observed striking contradictions between Amber and CHARMM force field descriptions, most prominently in opposing predictions of the favored coordination mode. Through further configurational free energy calculations, conducted against a diverse set of ATP·Mg2+-protein complex structures to supplement otherwise limited experimental data, we quantified systematic biases for each force field. The force field calculations were strongly predictive of experimentally observed coordination modes, enabling additive corrections to the coordination free energy that deliver close agreement with experiment. We reassessed the applicability of the thus corrected force field descriptions of ATP·Mg2+ for biomolecular simulation and observed that, while the CHARMM parameters display an erroneous preference for overextended triphosphate configurations that will affect many common biomolecular simulation applications involving ATP, the force field energy landscapes broadly agree with experimental measurements of solution geometry and the distribution of ATP·Mg2+ structures found in the Protein Data Bank. Our force field evaluation and correction approach, based on maximizing consistency with the large and heterogeneous collection of structural information encoded in the PDB, should be broadly applicable to many other systems.

Molecular dynamics simulation studies on the specific regulation of PTPN18 to the HER2 phospho-peptides

The specific regulation of PTPN18 protein to three HER2 phospho-peptides has been studied by molecular dynamics simulations and free energy calculations. The results revealed that the three HER2 phospho-peptides binding to the PTPN18 catalytic domain is energetically favorable due to substrate specificity of PTPN18, and moreover, the PTPN18 protein have significantly higher affinity to pY1248 peptide (-45.22 kcal/mol) than that of pY1112 (-25.3 kcal/mol) and pY1196 (-31.86 kcal/mol) peptides. Further, the binding of HER2 phospho-peptides to PTPN18 have also caused the closure of WPD-loop with the decrease of the centroid distances between the P-loop and the WPD loop. The WPD-loop closure of PTPN18 relates directly to the new hydrogen bond and hydrophobic interaction formations between the residues Tyr62, Asp64, Val65, Ala231, Arg235, and Ala273 in PTPN18 and Tyr(PO3) in the HER2 phospho-peptides, which suggests that these key residues would contribute to the specific regulation of PTPN18 to the substrates. The correlation analysis revealed the allosteric communication networks from the pY binding loop to the WPD loop through the structural change and the residue interactions in PTPN18. These results will be helpful to understand the specific regulation through the allosteric communication network in the PTPN18 catalytic domain.

Comparison of Huggins Coefficients and Osmotic Second Virial Coefficients of Buffered Solutions of Monoclonal Antibodies

The Huggins coefficient kH is a well-known metric for quantifying the increase in solution viscosity arising from intermolecular interactions in relatively dilute macromolecular solutions, and there has been much interest in this solution property in connection with developing improved antibody therapeutics. While numerous kH measurements have been reported for select monoclonal antibodies (mAbs) solutions, there has been limited study of kH in terms of the fundamental molecular interactions that determine this property. In this paper, we compare measurements of the osmotic second virial coefficient B22, a common metric of intermolecular and interparticle interaction strength, to measurements of kH for model antibody solutions. This comparison is motivated by the seminal work of Russel for hard sphere particles having a short-range "sticky" interparticle interaction, and we also compare our data with known results for uncharged flexible polymers having variable excluded volume interactions because proteins are polypeptide chains. Our observations indicate that neither the adhesive hard sphere model, a common colloidal model of globular proteins, nor the familiar uncharged flexible polymer model, an excellent model of intrinsically disordered proteins, describes the dependence of kH of these antibodies on B22. Clearly, an improved understanding of protein and ion solvation by water as well as dipole-dipole and charge-dipole effects is required to understand the significance of kH from the standpoint of fundamental protein-protein interactions. Despite shortcomings in our theoretical understanding of kH for antibody solutions, this quantity provides a useful practical measure of the strength of interprotein interactions at elevated protein concentrations that is of direct significance for the development of antibody formulations that minimize the solution viscosity.

Flexibility of enzymatic transitions as a hallmark of optimized enzyme steady-state kinetics and thermodynamics

We investigate the relations between the enzyme kinetic flexibility, the rate of entropy production, and the Shannon information entropy in a steady-state enzyme reaction. All these quantities are maximized with respect to enzyme rate constants. We show that the steady-state, which is characterized by the most flexible enzymatic transitions between the enzyme conformational states, coincides with the global maxima of the Shannon information entropy and the rate of entropy production. This steady-state of an enzyme is referred to as globally optimal. This theoretical approach is then used for the analysis of the kinetic and the thermodynamic performance of the enzyme triose-phosphate isomerase. The analysis reveals that there exist well-defined maxima of the kinetic flexibility, the rate of entropy production, and the Shannon information entropy with respect to any arbitrarily chosen rate constant of the enzyme and that these maxima, calculated from the measured kinetic rate constants for the triose-phosphate isomerase are lower, however of the same order of magnitude, as the maxima of the globally optimal state of the enzyme. This suggests that the triose-phosphate isomerase could be a well, but not fully evolved enzyme, as it was previously claimed. Herein presented theoretical investigations also provide clear evidence that the flexibility of enzymatic transitions between the enzyme conformational states is a requirement for the maximal Shannon information entropy and the maximal rate of entropy production.

Interplay among Structural Stability, Plasticity, and Energetics Determined by Conformational Attuning of Flexible Loops in PD-1

The dynamics and plasticity of the PD-1/PD-L1 axis are the bottlenecks for the discovery of small-molecule antagonists to perturb this interaction interface significantly. Understanding the process of this protein-protein interaction (PPI) is of fundamental biological interest in structure-based drug designing. Food and Drug Administration (FDA)-approved anti-PD-1 monoclonal antibodies (mAbs) are the first-in-class with distinct binding modes to access this axis clinically; however, their mechanistic aspects remain elusive. Here, we have unveiled the interactive interfaces with PD-L1 and mAbs to investigate the native plasticity of PD-1 at global (structural and dynamical) and local (residue side-chain orientations) levels. We found that the structural stability and coordinated C_α movements are increased in the presence of PD-1's binding partners. The rigorous analysis of these PPIs using computational biophysical approaches revealed PD-1's intrinsic plasticity, its concerted loops' movement (BC, FG, and CC'), distal side-chain motions, and the thermodynamic landscape, which are perturbed remarkably from its unbound to bound states. Based on intra-/inter-residues' contact networks and energetics, the hot-spots have been identified that were found to be essential to arrest the dynamical motions of PD-1 significantly for the rational design of therapeutic agents by mimicking the mAbs mechanism.

Transient association between proteins elicits alteration of dynamics at sites far away from interfaces

Proteins are known to undergo structural changes upon binding to partner proteins. However, the prevalence, extent, location, and function of change in protein dynamics due to transient protein-protein interactions is not well documented. Here, we have analyzed a dataset of 58 protein-protein complexes of known three-dimensional structure and structures of their corresponding unbound forms to evaluate dynamics changes induced by binding. Fifty-five percent of cases showed significant dynamics change away from the interfaces. This change is not always accompanied by an observed structural change. Binding of protein partner is found to alter inter-residue communication within the tertiary structure in about 90% of cases. Also, residue motions accessible to proteins in unbound form were not always maintained in the bound form. Further analyses revealed functional roles for the distant site where dynamics change was observed. Overall, the results presented here strongly suggest that alteration of protein dynamics due to binding of a partner protein commonly occurs.

Why are ACE2 binding coronavirus strains SARS-CoV/SARS-CoV-2 wild and NL63 mild?

Coronaviruses are responsible for several epidemics, including the 2002 SARS, 2012 MERS, and COVID‐19. The emergence of recent COVID‐19 pandemic due to SARS‐CoV‐2 virus in December 2019 has resulted in considerable research efforts to design antiviral drugs and other therapeutics against coronaviruses. In this context, it is crucial to understand the biophysical and structural features of the major proteins that are involved in virus‐host interactions. In the current study, we have compared spike proteins from three strains of coronaviruses NL63, SARS‐CoV, and SARS‐CoV, known to bind human angiotensin‐converting enzyme 2 (ACE2), in terms of sequence/structure conservation, hydrophobic cluster formation and importance of binding site residues. The study reveals that the severity of coronavirus strains correlates positively with the interaction area, surrounding hydrophobicity and interaction energy and inversely correlate with the flexibility of the binding interface. Also, we identify the conserved residues in the binding interface of spike proteins in all three strains. The systematic point mutations show that these conserved residues in the respective strains are evolutionarily favored at their respective positions. The similarities and differences in the spike proteins of the three viruses indicated in this study may help researchers to deeply understand the structural behavior, binding site properties and etiology of ACE2 binding, accelerating the screening of potential lead molecules and the development/repurposing of therapeutic drugs.

Entropy of Proteins Using Multiscale Cell Correlation

A new multiscale method is presented to calculate the entropy of proteins from molecular dynamics simulations. Termed Multiscale Cell Correlation (MCC), the method decomposes the protein into sets of rigid-body units based on their covalent-bond connectivity at three levels of hierarchy: molecule, residue, and united atom. It evaluates the vibrational and topographical entropy from forces, torques, and dihedrals at each level, taking into account correlations between sets of constituent units that together make up a larger unit at the coarser length scale. MCC gives entropies in close agreement with normal-mode analysis and smaller than those using quasiharmonic analysis as well as providing much faster convergence. Moreover, MCC provides an insightful decomposition of entropy at each length scale and for each type of amino acid according to their solvent exposure and whether they are terminal residues. While the residue entropy depends weakly on solvent exposure, there is greater variation in entropy components for larger, more polar amino acids, which have increased conformational entropy but reduced vibrational entropy with greater solvent exposure.

Binding Free Energies of Conformationally Disordered Peptides Through Extensive Sampling and End-Point Methods

The ability to obtain binding free energies from molecular simulation techniques provides a valuable support to the interpretation and design of experiments. Among all methods available, the most widely used equilibrium free energy methods range from highly accurate and computationally expensive perturbation theory-based methods, such as free energy perturbation (FEP), or thermodynamic integration (TI), through end-point methods, such as molecular mechanics with generalized Born and surface area solvation (MM/GBSA) or MM/PBSA, when the Poisson-Boltzmann method is used instead of GB, and linear interaction energy (LIE) methods, to scoring functions, which are relatively simple empirical functions widely used as part of molecular docking protocols. Because the use of FEP and TI approaches is restricted to cases where the perturbation leading from an initial to final state is negligible or minimal, their application to cases where large conformational changes are involved between bound and unbound states is rather complex, if not prohibitive in terms of convergence. Here we describe a protocol that involves the use of extensive conformational sampling through molecular dynamics (MD) in combination with end-point methods (MM/GB(PB)SA) with an additional quasi-harmonic entropy component, for the calculation of the relative binding free energies of highly flexible, or intrinsically disordered, peptides to a structured receptor.

Entropy Hotspots for the Binding of Intrinsically Disordered Ligands to a Receptor Domain

Proline-rich motifs (PRMs) are widely used for mediating protein-protein interactions with weak binding affinities. Because they are intrinsically disordered when unbound, conformational entropy plays a significant role for the binding. However, residue-level differences of the entropic contribution in the binding of different ligands remain not well understood. We use all-atom molecular dynamics simulation and the maximal information spanning tree formalism to analyze conformational entropy associated with the binding of two PRMs, one from the Abl kinase and the other from the nonstructural protein 1 of the 1918 Spanish influenza A virus, to the N-terminal SH3 (nSH3) domain of the CrkII protein. Side chains of the stably folded nSH3 experience more entropy change upon ligand binding than the backbone, whereas PRMs involve comparable but heterogeneous entropy changes among the backbone and side chains. In nSH3, two conserved nonpolar residues forming contacts with the PRM experience the largest side-chain entropy loss. In contrast, the C-terminal charged residues of PRMs that form polar contacts with nSH3 experience the greatest side-chain entropy loss, although their "fuzzy" nature is attributable to the backbone that remains relatively flexible. Thus, residues that form high-occupancy contacts between nSH3 and PRM do not reciprocally contribute to entropy loss. Furthermore, certain surface residues of nSH3 distal to the interface with PRMs gain entropy, indicating a nonlocal effect of ligand binding. Comparing between the PRMs from cAbl and nonstructural protein 1, the latter involves a larger side-chain entropy loss and forms more contacts with nSH3. Consistent with experiments, this indicates stronger binding of the viral ligand at the expense of losing the flexibility of side chains, whereas the backbone experiences less entropy loss. The entropy "hotspots" as identified in this study will be important for tuning the binding affinity of various ligands to a receptor.

Deciphering the impact of missense mutations on structure and dynamics of SMAD4 protein involved in pathogenesis of gall bladder cancer

Gall bladder cancer (GBC) is the most common malignancy of biliary tract cancer associated with high mortality rate and poor prognosis due to lack of suitable biomarkers. In this study, we explored the structural and functional effects of different missense mutations occurs in SMAD4 that was associated with the development of GBC. We utilized in silico methods to predict the harmful effects of nonsynonymous missense mutations and monitored the stability of protein. We found that all mutations (D351N, G352E, R361C, R361H, E526Q) associated with SMAD4 were deleterious in nature resulting in the formation of deformed or unstable protein structure. Molecular dynamics simulation studies revealed how these mutations affect protein stability, structure, conformation and function. We observed, different mutants increase the compactness and rigidity of SMAD4 protein, alter secondary structure composition, decrease the surface area and protein-ligand interaction and affect its conformation. Findings of current work indicated that the analyzed mutations might affect the structure of protein and its caliber to interact with other molecules, which probably related to functional impairment of SMAD4 upon D351N, G352E, R361C, R361H, E526Q mutations and their involvement in cancer. Hence, the present study has significance of rational drug design and further increase our understanding of GBC development.Communicated by Ramaswamy H. Sarma.

In silico analysis of PFN1 related to amyotrophic lateral sclerosis

Profilin 1 (PFN1) protein plays key roles in neuronal growth and differentiation, membrane trafficking, and regulation of the actin cytoskeleton. Four natural variants of PFN1 were described as related to ALS, the most common adult-onset motor neuron disorder. However, the pathological mechanism of PFN1 in ALS is not yet completely understood. The goal of this work is to thoroughly analyze the effects of the ALS-related mutations on PFN1 structure and function using computational simulations. Here, PhD-SNP, PMUT, PolyPhen-2, SIFT, SNAP, SNPS&GO, SAAP, nsSNPAnalyzer, SNPeffect4.0 and I-Mutant2.0 were used to predict the functional and stability effects of PFN1 mutations. ConSurf was used for the evolutionary conservation analysis, and GROMACS was used to perform the MD simulations. The mutations C71G, M114T, and G118V, but not E117G, were predicted as deleterious by most of the functional prediction algorithms that were used. The stability prediction indicated that the ALS-related mutations could destabilize PFN1. The ConSurf analysis indicated that the mutation C71G, M114T, E117G, and G118V occur in highly conserved positions. The MD results indicated that the studied mutations could affect the PFN1 flexibility at the actin and PLP-binding domains, and consequently, their intermolecular interactions. It may be therefore related to the functional impairment of PFN1 upon C71G, M114T, E117G and G118V mutations, and their involvement in ALS development. We also developed a database, SNPMOL (http://www.snpmol.org/), containing the results presented on this paper for biologists and clinicians to exploit PFN1 and its natural variants.

Computational studies of protein-protein dissociation by statistical potential and coarse-grained simulations: a case study on interactions between colicin E9 endonuclease and immunity proteins

Proteins carry out their diverse functions in cells by forming interactions with each other. The dynamics of these interactions are quantified by the measurement of association and dissociation rate constants. Relative to the efforts made to model the association of biomolecules, little has been studied to understand the principles of protein complex dissociation. Using the interaction between colicin E9 endonucleases and immunity proteins as a test system, here we develop a coarse-grained simulation method to explore the dissociation mechanisms of protein complexes. The interactions between proteins in the complex are described by the knowledge-based potential that was constructed by the statistics from available protein complexes in the structural database. Our study provides the supportive evidences to the dual recognition mechanism for the specificity of binding between E9 DNase and immunity proteins, in which the conserved residues of helix III of Im2 and Im9 proteins act as the anchor for binding, while the sequence variations in helix II make positive or negative contributions to specificity. Beyond that, we further suggest that this binding specificity is rooted in the process of complex dissociation instead of association. While we increased the flexibility of protein complexes, we further found that they are less prone to dissociation, suggesting that conformational fluctuations of protein complexes play important functional roles in regulating their binding and dissociation. Our studies therefore bring new insights to the molecule mechanisms of protein-protein interactions, while the method can serve as a new addition to a suite of existing computational tools for the simulations of protein complexes.

Conformational entropy of a single peptide controlled under force governs protease recognition and catalysis

An immense repertoire of protein chemical modifications catalyzed by enzymes is available as proteomics data. Quantifying the impact of the conformational dynamics of the modified peptide remains challenging to understand the decisive kinetics and amino acid sequence specificity of these enzymatic reactions in vivo, because the target peptide must be disordered to accommodate the specific enzyme-binding site. Here, we were able to control the conformation of a single-molecule peptide chain by applying mechanical force to activate and monitor its specific cleavage by a model protease. We found that the conformational entropy impacts the reaction in two distinct ways. First, the flexibility and accessibility of the substrate peptide greatly increase upon mechanical unfolding. Second, the conformational sampling of the disordered peptide drives the specific recognition, revealing force-dependent reaction kinetics. These results support a mechanism of peptide recognition based on conformational selection from an ensemble that we were able to quantify with a torsional free-energy model. Our approach can be used to predict how entropy affects site-specific modifications of proteins and prompts conformational and mechanical selectivity.

Local and global anatomy of antibody-protein antigen recognition

Deciphering antibody-protein antigen recognition is of fundamental and practical significance. We constructed an antibody structural dataset, partitioned it into human and murine subgroups, and compared it with nonantibody protein-protein complexes. We investigated the physicochemical properties of regions on and away from the antibody-antigen interfaces, including net charge, overall antibody charge distributions, and their potential role in antigen interaction. We observed that amino acid preference in antibody-protein antigen recognition is entropy driven, with residues having low side-chain entropy appearing to compensate for the high backbone entropy in interaction with protein antigens. Antibodies prefer charged and polar antigen residues and bridging water molecules. They also prefer positive net charge, presumably to promote interaction with negatively charged protein antigens, which are common in proteomes. Antibody-antigen interfaces have large percentages of Tyr, Ser, and Asp, but little Lys. Electrostatic and hydrophobic interactions in the Ag binding sites might be coupled with Fab domains through organized charge and residue distributions away from the binding interfaces. Here we describe some features of antibody-antigen interfaces and of Fab domains as compared with nonantibody protein-protein interactions. The distributions of interface residues in human and murine antibodies do not differ significantly. Overall, our results provide not only a local but also a global anatomy of antibody structures.

Molecular rigidity and enthalpy-entropy compensation in DNA melting

Enthalpy-entropy compensation (EEC) is observed in diverse molecular binding processes of importance to living systems and manufacturing applications, but this widely occurring phenomenon is not sufficiently understood from a molecular physics standpoint. To gain insight into this fundamental problem, we focus on the melting of double-stranded DNA (dsDNA) since measurements exhibiting EEC are extensive for nucleic acid complexes and existing coarse-grained models of DNA allow us to explore the influence of changes in molecular parameters on the energetic parameters by using molecular dynamics simulations. Previous experimental and computational studies have indicated a correlation between EEC and changes in molecular rigidity in certain binding-unbinding processes, and, correspondingly, we estimate measures of DNA molecular rigidity under a wide range of conditions, along with resultant changes in the enthalpy and entropy of binding. In particular, we consider variations in dsDNA rigidity that arise from changes of intrinsic molecular rigidity such as varying the associative interaction strength between the DNA bases, the length of the DNA chains, and the bending stiffness of the individual DNA chains. We also consider extrinsic changes of molecular rigidity arising from the addition of polymer additives and geometrical confinement of DNA between parallel plates. All our computations confirm EEC and indicate that this phenomenon is indeed highly correlated with changes in molecular rigidity. However, two distinct patterns relating to how DNA rigidity influences the entropy of association emerge from our analysis. Increasing the intrinsic DNA rigidity increases the entropy of binding, but increases in molecular rigidity from external constraints decreases the entropy of binding. EEC arises in numerous synthetic and biological binding processes and we suggest that changes in molecular rigidity might provide a common origin of this ubiquitous phenomenon in the mutual binding and unbinding of complex molecules.

Influence of Flexible "ω" on the Activity of E. coli RNA Polymerase: A Thermodynamic Analysis

The Escherichia coli RNA polymerase (RNAP) is a multisubunit protein complex containing the smallest subunit, ω. Despite the evolutionary conservation of ω and its role in assembly of RNAP, E. coli mutants lacking rpoZ (codes for ω) are viable due to the association of RNAP with the global chaperone protein GroEL. With an aim to get better insight into the structure and functional role of ω, we isolated a dominant negative mutant of ω (ω₆), which is predominantly α-helical, in contrast to largely unstructured native ω, and then studied its assembly with reconstituted core1 (α₂ββ') by a biophysical approach. The mutant showed higher binding affinity compared to native ω. We observed that the interaction between core1 and ω₆ is driven by highly negative enthalpy and a small but unfavorable negative entropy term. Extensive structural alteration in ω₆ makes it more rigid, the plasticity of the interacting domain formed by ω₆ and core1 is compromised, which may be responsible for the entropic cost. Such tight binding of the structured mutant (ω₆) affects initiation of transcription. However, once preinitiated, the complex elongates the RNA chain efficiently. The initiation of transcription requires recognition of appropriate σ-factors by the core enzyme (core2: α₂ββ'ω). We found that the altered core enzyme (α₂ββ'ω₆) with mutant ω showed a decrease in binding affinity to the σ-factors (σ⁷⁰, σ³² and σ³⁸) compared to that of the core enzyme containing native ω. In the absence of unstructured ω, the association of σ-factors to the core is less efficient, suggesting that the flexible native ω plays a direct role in σ-factor recruitment.

Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data

Background

One goal of structural biology is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available.

Methods

X-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample t test and nonparametric Mann–Whitney U test.

Results

Here we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5 Å) increased the correlation between predicted and experimental affinity from 54 to 68% (p = 4.32x10⁻³). In addition, incorporating information on the experimental conditions under which affinities were measured (pH, temperature and binding assay) had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment.

Conclusions

The results suggest that the accuracy of statistical models for protein-protein affinity prediction may be limited by the information present in databases used to train new models. Improving our capacity to integrate large-scale structural and functional information may be required to substantively advance our understanding of the general principles by which a protein’s structure determines its function.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1533-z) contains supplementary material, which is available to authorized users.

Multilevel Changes in Protein Dynamics upon Complex Formation of the Calcium-Loaded S100A4 with a Nonmuscle Myosin IIA Tail Fragment

Dysregulation of Ca²⁺ -binding S100 proteins plays important role in various diseases. The asymmetric complex of Ca²⁺ -bound S100A4 with nonmuscle myosin IIA has high stability and highly increased Ca²⁺ affinity. Here we investigated the possible causes of this allosteric effect by NMR spectroscopy. Chemical shift-based secondary-structure analysis did not show substantial changes for the complex. Backbone dynamics revealed slow-timescale local motions in the H1 helices of homodimeric S100A4; these were less pronounced in the complex form and might be accompanied by an increase in dimer stability. Different mobilities in the Ca²⁺ -coordinating EF-hand sites indicate that they communicate by an allosteric mechanism operating through changes in protein dynamics; this must be responsible for the elevated Ca²⁺ affinity. These multilevel changes in protein dynamics as conformational adaptation allow S100A4 fine-tuning of its protein-protein interactions inside the cell during Ca²⁺ signaling.

Thermodynamic Basis of Selectivity in the Interactions of Tissue Inhibitors of Metalloproteinases N-domains with Matrix Metalloproteinases-1, -3, and -14

The four tissue inhibitors of metalloproteinases (TIMPs) are potent inhibitors of the many matrixins (MMPs), except that TIMP1 weakly inhibits some MMPs, including MMP14. The broad-spectrum inhibition of MMPs by TIMPs and their N-domains (NTIMPs) is consistent with the previous isothermal titration calorimetric finding that their interactions are entropy-driven but differ in contributions from solvent and conformational entropy (ΔSsolv, ΔSconf), estimated using heat capacity changes (ΔCp). Selective engineered NTIMPs have potential applications for treating MMP-related diseases, including cancer and cardiomyopathy. Here we report isothermal titration calorimetric studies of the effects of selectivity-modifying mutations in NTIMP1 and NTIMP2 on the thermodynamics of their interactions with MMP1, MMP3, and MMP14. The weak inhibition of MMP14 by NTIMP1 reflects a large conformational entropy penalty for binding. The T98L mutation, peripheral to the NTIMP1 reactive site, enhances binding by increasing ΔSsolv but also reduces ΔSconf However, the same mutation increases NTIMP1 binding to MMP3 in an interaction that has an unusual positive ΔCp This indicates a decrease in solvent entropy compensated by increased conformational entropy, possibly reflecting interactions involving alternative conformers. The NTIMP2 mutant, S2D/S4A is a selective MMP1 inhibitor through electrostatic effects of a unique MMP-1 arginine. Asp-2 increases reactive site polarity, reducing ΔCp, but increases conformational entropy to maintain strong binding to MMP1. There is a strong negative correlation between ΔSsolv and ΔSconf for all characterized interactions, but the data for each MMP have characteristic ranges, reflecting intrinsic differences in the structures and dynamics of their free and inhibitor-bound forms.