Electrostatic complementarity at protein/protein interfaces

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations

The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.

Thrombin activation of the factor XI dimer is a multistaged process for each subunit

BACKGROUND

Factor (F)XI can be activated by proteases, including thrombin and FXIIa. The interactions of these enzymes with FXI are transient in nature and therefore difficult to study.

OBJECTIVES

To identify the binding interface between thrombin and FXI and understand the dynamics underlying FXI activation.

METHODS

Crosslinking mass spectrometry was used to localize the binding interface of thrombin on FXI. Molecular dynamics simulations were applied to investigate conformational changes enabling thrombin-mediated FXI activation after binding. The proposed trajectory of activation was examined with nanobody 1C10, which was previously shown to inhibit thrombin-mediated activation of FXI.

RESULTS

We identified a binding interface of thrombin located on the light chain of FXI involving residue Pro520. After this initial interaction, FXI undergoes conformational changes driven by binding of thrombin to the apple 1 domain in a secondary step to allow migration toward the FXI cleavage site. The 1C10 binding site on the apple 1 domain supports this proposed trajectory of thrombin. We validated the results with known mutation sites on FXI. As Pro520 is conserved in prekallikrein (PK), we hypothesized and showed that thrombin can bind PK, even though it cannot activate PK.

CONCLUSION

Our investigations show that the activation of FXI is a multistaged procedure. Thrombin first binds to Pro520 in FXI; thereafter, it migrates toward the activation site by engaging the apple 1 domain. This detailed analysis of the interaction between thrombin and FXI paves a way for future interventions for bleeding or thrombosis.

The electrostatic landscape of MHC-peptide binding revealed using inception networks

Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.

Deep Drug Discovery of Mac Domain of SARS-CoV-2 (WT) Spike Inhibitors: Using Experimental ACE2 Inhibition TR-FRET Assay, Screening, Molecular Dynamic Simulations and Free Energy Calculations

SARS-CoV-2 exploits the homotrimer transmembrane Spike glycoproteins (S protein) during host cell invasion. The Omicron XBB subvariant, delta, and prototype SARS-CoV-2 receptor-binding domain show similar binding strength to hACE2 (human Angiotensin-Converting Enzyme 2). Here we utilized multiligand virtual screening to identify small molecule inhibitors for their efficacy against SARS-CoV-2 virus using QPLD, pseudovirus ACE2 Inhibition -Time Resolved Forster/Fluorescence energy transfer (TR-FRET) Assay Screening, and Molecular Dynamics simulations (MDS). Three hundred and fifty thousand compounds were screened against the macrodomain of the nonstructural protein 3 of SARS-CoV-2. Using TR-FRET Assay, we filtered out two of 10 compounds that had no reported activity in in vitro screen against Spike S1: ACE2 binding assay. The percentage inhibition at 30 µM was found to be 79% for "Compound F1877-0839" and 69% for "Compound F0470-0003". This first of its kind study identified "FILLY" pocket in macrodomains. Our 200 ns MDS revealed stable binding poses of both leads. They can be used for further development of preclinical candidates.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Genetic predisposition to porto-sinusoidal vascular disorder: A functional genomic-based, multigenerational family study

BACKGROUND AND AIMS

Porto-sinusoidal vascular disorder (PSVD) is a group of liver vascular diseases featuring lesions encompassing the portal venules and sinusoids unaccompanied by cirrhosis, irrespective of the presence/absence of portal hypertension. It can occur secondary to coagulation disorders or insult by toxic agents. However, the cause of PSVD remains unknown in most cases. Hereditary cases of PSVD are exceptionally rare, but they are of particular interest and may unveil genetic alterations and molecular mechanisms associated with the disease.

APPROACH AND RESULTS

We performed genome sequencing of four patients and two healthy individuals of a large multigenerational Lebanese family with PSVD and identified a heterozygous deleterious variant (c.547C>T, p.R183W) of FCH and double SH3 domains 1 ( FCHSD1 ), an uncharacterized gene, in patients. This variant segregated with the disease, and its pattern of inheritance was suggestive of autosomal dominant with variable expressivity. RNA structural modelling of human FCHSD1 suggests that the C-to-T substitution at position 547, corresponding to FCHSD1R183W , may increase both messenger RNA (mRNA) and protein stability and its interaction with MTOR-associated protein, LST8 homolog, a key protein of the mechanistic target of rapamycin (mTOR pathway). These predictions were substantiated by biochemical analyses, which showed that FCHSD1R183W induced high FCHSD1 mRNA stability, overexpression of FCHSD1 protein, and an increase in mTORC1 activation. This human FCHSD1 variant was introduced into mice through CRISPR/Cas9 genome editing. Nine out of the 15 mice carrying the human FCHSD1R183W variant mimicked the phenotype of human PSVD, including splenomegaly and enlarged portal vein.

CONCLUSIONS

Aberrant FCHSD1 structure and function leads to mTOR pathway overactivation and may cause PSVD.

MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

Protein interactions are key in vital biological processes. In many cases, particularly in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions in other proteins. The flexible nature of peptides enables the rapid yet specific regulation of important functions in cells, such as their life cycle. Consequently, knowledge of the molecular details of peptide-protein interactions is crucial for understanding and altering their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has led to a leap in accuracy for the computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact, as well as its accuracy in modeling the resulting interaction complexes, are benchmarked against established methods. We find that AlphaFold-Multimer predicts the structure of peptide-protein complexes with acceptable or better quality (DockQ ≥0.23) for 66 of the 112 complexes investigated-25 of which were high quality (DockQ ≥0.8). This is a massive improvement on previous methods with 23 or 47 acceptable models and only four or eight high quality models, when using energy-based docking or interaction templates, respectively. In addition, AlphaFold-Multimer can be used to predict whether a peptide and a protein will interact. At 1% false positives, AlphaFold-Multimer found 26% of the possible interactions with a precision of 85%, the best among the methods benchmarked. However, the most interesting result is the possibility of improving AlphaFold by randomly perturbing the neural network weights to force the network to sample more of the conformational space. This increases the number of acceptable models from 66 to 75 and improves the median DockQ from 0.47 to 0.55 (17%) for first ranked models. The best possible DockQ improves from 0.58 to 0.72 (24%), indicating that selecting the best possible model is still a challenge. This scheme of generating more structures with AlphaFold should be generally useful for many applications involving multiple states, flexible regions, and disorder.

Evolution of homo-oligomerization of methionine S-adenosyltransferases is replete with structure-function constrains

Homomers are prevalent in bacterial proteomes, particularly among core metabolic enzymes. Homomerization is often key to function and regulation, and interfaces that facilitate the formation of homomeric enzymes are subject to intense evolutionary change. However, our understanding of the molecular mechanisms that drive evolutionary variation in homomeric complexes is still lacking. How is the diversification of protein interfaces linked to variation in functional regulation and structural integrity of homomeric complexes? To address this question, we studied quaternary structure evolution of bacterial methionine S-adenosyltransferases (MATs)-dihedral homotetramers formed along a large and conserved dimeric interface harboring two active sites, and a small, recently evolved, interdimeric interface. Here, we show that diversity in the physicochemical properties of small interfaces is directly linked to variability in the kinetic stability of MAT quaternary complexes and in modes of their functional regulation. Specifically, hydrophobic interactions within the small interface of Escherichia coli MAT render the functional homotetramer kinetically stable yet impose severe aggregation constraints on complex assembly. These constraints are alleviated by electrostatic interactions that accelerate dimer-dimer assembly. In contrast, Neisseria gonorrhoeae MAT adopts a nonfunctional dimeric state due to the low hydrophobicity of its small interface and the high flexibility of its active site loops, which perturbs small interface integrity. Remarkably, in the presence of methionine and ATP, N. gonorrhoeae MAT undergoes substrate-induced assembly into a functional tetrameric state. We suggest that evolution acts on the interdimeric interfaces of MATs to tailor the regulation of their activity and stability to unique organismal needs.

Omicron Spike Protein Has a Positive Electrostatic Surface That Promotes ACE2 Recognition and Antibody Escape

High transmissibility is a hallmark of the Omicron variant of SARS-CoV-2. Understanding the molecular determinants of Omicron’s transmissibility will impact development of intervention strategies. Here we map the electrostatic potential surface of the Spike protein to show that major SARS-CoV-2 variants have accumulated positive charges in solvent-exposed regions of the Spike protein, especially its ACE2-binding interface. Significantly, the Omicron Spike-ACE2 complex has complementary electrostatic surfaces. In contrast, interfaces between Omicron and neutralizing antibodies tend to have similar positively charged surfaces. Structural modeling demonstrates that the electrostatic property of Omicron’s Spike receptor binding domain (S RBD) plays a role in enhancing ACE2 recognition and destabilizing Spike-antibody complexes. Specifically, the Omicron S RBD has favorable electrostatic interaction energy with ACE2 that is 3-5 times greater than the Delta variant over a range of 20 Å, implying efficient recognition of host receptors. Computed binding affinities of six representative S RBD-antibody complexes show that Omicron can escape most antibodies targeting the ACE2-binding region of S RBD. Interestingly, a straightforward assessment of the electrostatic surfaces of 18 neutralizing antibodies correctly predicted the Omicron escape status of 80% of cases. Collectively, our structural analysis implies that Omicron S RBD interaction interfaces have been optimized to simultaneously promote access to human ACE2 receptors and evade antibodies. These findings suggest that electrostatic interactions are a major contributing factor for increased Omicron transmissibility relative to other variants.

Protein-protein interaction of RdRp with its co-factor NSP8 and NSP7 to decipher the interface hotspot residues for drug targeting: A comparison between SARS-CoV-2 and SARS-CoV

In this study we explored the molecular mechanism of RdRp (Non-Structural Protein, NSP12) interaction with its co-factors NSP7 and NSP8 which is the main toolbox for RNA replication and transcription of SARS-CoV-2 and SARS-CoV. The replication complex is a heterotetramer consists of one NSP12, one NSP7 and two NSP8. Extensive molecular dynamics (MD) simulations were applied on both the heterotetramer complexes to generate the conformations and were used to estimate the MMPBSA binding free energy (BFE) and per-residue energy decomposition of NSP12-NSP8 and NSP12-NSP7 and NSP7-NSP8 complexes. The BFE of SARS-CoV-2 heterotetramer complex with its corresponding partner protein was significantly higher as compared to SARS-CoV. Interface hotspot residues were predicted using different methods implemented in KFC (Knowledge-based FADA and Contracts), HotRegion and Robetta web servers. Per-residue energy decomposition analysis showed that the predicted interface hotspot residues contribute more energy towards the formation of complexes and most of the predicted hotspot residues are clustered together. However, there is a slight difference in the residue-wise energy contribution in the interface NSPs on heterotetramer viral replication complex of both coronaviruses. While the overall replication complex of SARS-CoV-2 was found to be slightly flexible as compared to SARS-CoV. This difference in terms of structural flexibility/stability and energetic characteristics of interface residues including hotspots at PPI interface in the viral replication complexes may be the reason of higher rate of RNA replication of SARS-CoV-2 as compared to SARS-CoV. Overall, the interaction profile at PPI interface such as, interface area, hotspot residues, nature of bonds and energies between NSPs, may provide valuable insights in designing of small molecules or peptide/peptidomimetic ligands which can fit into the PPI interface to disrupt the interaction.

Long-range electrostatic interactions significantly modulate the affinity of dynein for microtubules

The dynein family of microtubule minus-end-directed motor proteins drives diverse functions in eukaryotic cells, including cell division, intracellular transport, and flagellar beating. Motor protein processivity, which characterizes how far a motor walks before detaching from its filament, depends on the interaction between its microtubule-binding domain (MTBD) and the microtubule. Dynein's MTBD switches between high- and low-binding affinity states as it steps. Significant structural and functional data show that specific salt bridges within the MTBD and between the MTBD and the microtubule govern these affinity state shifts. However, recent computational work suggests that nonspecific, long-range electrostatic interactions between the MTBD and the microtubule may also play an important role in the processivity of dynein. To investigate this hypothesis, we mutated negatively charged amino acids remote from the dynein MTBD-microtubule-binding interface to neutral residues and measured the binding affinity using microscale thermophoresis and optical tweezers. We found a significant increase in the binding affinity of the mutated MTBDs for microtubules. Furthermore, we found that charge screening by free ions in solution differentially affected the binding and unbinding rates of MTBDs to microtubules. Together, these results demonstrate a significant role for long-range electrostatic interactions in regulating dynein-microtubule affinity. Moreover, these results provide insight into the principles that potentially underlie the biophysical differences between molecular motors with various processivities and protein-protein interactions more generally.

A Charge Variant of Bevacizumab Offers Enhanced FcRn-Dependent Pharmacokinetic Half-Life and Efficacy

BACKGROUND

Lysine variants of monoclonal antibodies (mAbs) result from incomplete clipping of the C-terminal lysine residues of the heavy chain. Although the structure of the lysine variants has been determined for several mAb products, a detailed study that investigates the impact of lysine charge variants on PK/PD and preclinical safety is yet to be published.

OBJECTIVE

An in-depth investigation of the impact of C- terminal lysine clipping of mAbs on safety and efficacy for bevacizumab charge variants.

METHOD

Charge variant isolation using semi-preparative chromatography is followed by a comparative analysis of FcRn binding, pharmacokinetics, and pharmacodynamics in relevant animal models.

RESULTS

K1 variant exhibited improved FcRn binding affinity (4-fold), half-life (1.3-fold), and anti-tumor activity (1.3-fold) as compared to the K0 (main) product. However, the K2 variant, even though exhibited higher FcRn affinity (2-fold), displayed lower half-life (1.6-fold) and anti-tumor activity at medium and low doses. Differential proteomic analysis revealed that seven pathways (such as glycolysis, gluconeogenesis, carbon metabolism, synthesis of amino acids) were significantly enriched. Higher efficacy of the K1 variant is likely due to higher bioavailability of the drug, leading to complete downregulation of the pathways that facilitate catering of the energy requirements of the proliferating tumor cells. On the contrary, the K2 variant exhibits a shorter half-life, resulting only in partial reduction in the metabolic/energy requirements of the growing tumor cells.

CONCLUSION

Overall, we conclude that the mAb half-life, dosage, and efficacy of a biotherapeutic product are significantly impacted by the charge variant profile of a biotherapeutic product.

RosettaSurf-A surface-centric computational design approach

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

High affinity protein surface binding through co-engineering of nanoparticles and proteins

Control over supramolecular recognition between proteins and nanoparticles (NPs) is of fundamental importance in therapeutic applications and sensor development. Most NP-protein binding approaches use 'tags' such as biotin or His-tags to provide high affinity; protein surface recognition provides a versatile alternative strategy. Generating high affinity NP-protein interactions is challenging however, due to dielectric screening at physiological ionic strengths. We report here the co-engineering of nanoparticles and protein to provide high affinity binding. In this strategy, 'supercharged' proteins provide enhanced interfacial electrostatic interactions with complementarily charged nanoparticles, generating high affinity complexes. Significantly, the co-engineered protein-nanoparticle assemblies feature high binding affinity even at physiologically relevant ionic strength conditions. Computational studies identify both hydrophobic and electrostatic interactions as drivers for these high affinity NP-protein complexes.

Serine pseudoproteases in physiology and disease

Serine proteases (SPs) constitute a very important family of enzymes, both physiologically and pathologically. The effects produced by these proteins have been explained by their proteolytic activity. However, the discovery of pharmacologically active SP molecules that show no enzymatic activity, as the so-called pseudo SPs or SP homologs (SPHs), has exposed a profoundly neglected possibility of nonenzymatic functions of these SP molecules. In this review, the most thoroughly described SPHs are presented. The main physiological domains in which SPHs operate appear to be in reproduction, embryonic development, immune response, host defense, and hemostasis. Hitherto unexplained actions of SPs should therefore be considered also as the result of the ligand-like attributes of SPs. The gain of a novel function by an SPH is a consequence of specific amino acid replacements that have resulted in a novel interaction interface or a 'catalytic trap'. Unraveling the SP/SPH interactome will provide a description of previously unknown physiological functions of SPs/SPHs, aiding the creation of innovative medical approaches.

Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, "hot spots", as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ-TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

New quinoxaline-based VEGFR-2 inhibitors: design, synthesis, and antiproliferative evaluation with in silico docking, ADMET, toxicity, and DFT studies

A new series of 3-methylquinoxaline-based derivatives having the same essential pharmacophoric features as VEGFR-2 inhibitors have been synthesized and evaluated for their antiproliferative activities against two human cancer cell lines, MCF-7 and HepG-2. Compounds 15b and 17b demonstrated a significant antiproliferative effect with IC₅₀ ranging from 2.3 to 5.8 μM. An enzymatic assay was carried out for all the tested candidates against VEGFR-2. Compound 17b was the most potent VEGFR-2 inhibitor (IC₅₀ = 2.7 nM). Mechanistic investigation including cell cycle arrest and apoptosis was performed for compound 17b against HepG-2 cells, and the results revealed that 17b induced cell apoptosis and arrested cell cycle in the G2/M phase. Moreover, apoptosis analyses were conducted for compound 17b to evaluate its apoptotic potential. The results showed upregulation in caspase-3 and caspase-9 levels, and improving the Bax/Bcl-2 ratio by more than 10-fold. Docking studies were performed to determine the possible interaction with the VEGFR-2 active site. Further docking studies were carried out for compound 17b against cytochrome P450 to present such compounds as non-inhibitors. In silico ADMET, toxicity, and physico-chemical properties revealed that most of the synthesized members have acceptable values of drug-likeness. Finally, DFT studies were carried out to calculate the thermodynamic, molecular orbital and electrostatic potential properties.

Assessment of protein-protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

Plausible blockers of Spike RBD in SARS-CoV2-molecular design and underlying interaction dynamics from high-level structural descriptors

Abstract

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently “reaction-prone.” The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic “hydrophobic ↔ polar” mutations serve as a potential breakthrough.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00894-021-04779-0.

Integrative proteomics identifies thousands of distinct, multi-epitope, and high-affinity nanobodies

The antibody immune response is essential for the survival of mammals. However, we still lack a systematic understanding of the antibody repertoire. Here, we developed a proteomic strategy to survey, at an unprecedented scale, the landscape of antigen-engaged, circulating camelid heavy-chain antibodies, whose minimal binding fragments are called VHH antibodies or nanobodies. The sensitivity and robustness of this approach were validated with three antigens spanning orders of magnitude in immune responses; thousands of distinct, high-affinity nanobody families were reliably identified and quantified. Using high-throughput structural modeling, cross-linking mass spectrometry, mutagenesis, and deep learning, we mapped and analyzed the epitopes of >100,000 antigen-nanobody complexes. Our results revealed a surprising diversity of ultrahigh-affinity camelid nanobodies for specific antigen binding on various dominant epitope clusters. Nanobodies utilize both shape and charge complementarity to enable highly selective antigen binding. Interestingly, we found that nanobody-antigen binding can mimic conserved intracellular protein-protein interactions. A record of this paper's Transparent Peer Review process is included in the Supplemental information.

Molecular forces governing protein-protein interaction: Structure-function relationship of complexes protein in the food industry

The application of protein-protein interaction (PPI) has been widely used in various industries, such as food, nutraceutical, and pharmaceutical. A deeper understanding of PPI is needed, and the molecular forces governing proteins and their interaction must be explained. The design of new structures with improved functional properties, e.g., solubility, emulsion, and gelation, has been fueled by the development of structural and colloidal building blocks. In this review, the molecular forces of protein structures are discussed, followed by the relationship between molecular force and structure, ways of a bind of proteins together in solution or at the interface, and functional properties. A more detailed look is thus taken at the relationship between the various influencing factors on molecular forces involved in PPI. These factors include protein properties, such as types, concentration, and mixing ratio, and solvent conditions, such as ionic strength and pH. This review also summarizes methods tha1t are capable of identifying molecular forces in protein and PPI, as well as characterizing protein structure.

Docking-based identification of small-molecule binding sites at protein-protein interfaces

Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein–protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein–protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein–ligand docking. The proposed strategy will be useful for many protein–protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.

The antioxidant function of Sco proteins depends on a critical surface-exposed residue

BACKGROUND

Besides their role in copper metabolism, Sco proteins from different organisms have been shown to play a defensive role against oxidative stress. In the present study, we set out to identify crucial amino acid residues for the antioxidant activity.

METHODS

Native and mutated Sco proteins from human, Arabidopsis thaliana and the yeast Kluyveromyces lactis were expressed in the model organism Saccharomyces cerevisiae. The oxidative stress resistance of the respective transformants was determined by growth and lipid peroxidation assays.

RESULTS

A functionally important site, located 15 amino acids downstream of the well-conserved copper binding CxxxC motif, was identified. Mutational analysis revealed that a positive charge at this position has a detrimental effect on the antioxidant capacity. Bioinformatic analysis predicts that this site is surface-exposed, and according to Co-IP data it is required for binding of proteins that are connected to known antioxidant pathways.

CONCLUSION

This study shows that the antioxidant capacity of eukaryotic Sco proteins is conserved and depends on the presence of functional site(s) rather than the extent of overall sequence homology.

GENERAL SIGNIFICANCE

These findings provide an insight into the conserved functional sites of eukaryotic Sco proteins that are crucial for combating oxidative stress. This capacity is probably not due to an enzymatic activity but rather is indirectly mediated by interaction with other proteins.

Molecular dynamics studies of dog prion protein wild-type and its D159N mutant

Prion diseases (e.g. 'mad cow' disease in cattle, chronic wasting disease in deer and elk, Creutzfeldt-Jakob disease in humans) have been a major public health concern affecting humans and almost all animals. However, dogs are strongly resistant to prion diseases. Recently, through transgenic techniques, it was reported that the single (surface) residue D159 is sufficient to confer protection against protein conformational change and pathogenesis, thus provides conformational stability for dog prion protein. This made a big breakthrough in dog prion protein research field. For dog prion protein, another advancement is the produce of its NMR structure in 2005. However, all these breakthroughs are still short of enough structural informatics of dog prion protein. This paper studies dog prion protein wild-type and D159N mutant through molecular dynamics (MD) techniques. Our MD results reveal sufficient structural informatics on the residue at position 159 to understand the mechanism underlying the resistance to prion diseases of dogs. The structural informatics of this paper should be very useful for the medicinal treatment of prion diseases.Communicated by Ramaswamy H. Sarma.

Qualitative as well as quantitative analysis of interactions present in chlorine and bromine substituted aromatic organic crystals: A DFT linked Crystal Explorer study

The effect of noncovalent interactions in shaping a crystal structure is explored qualitatively as well as quantitatively in a DFT linked Crystal Explorer (CE) study of nine different Chlorine and Bromine substituted benzene derivatives. The qualitative approach to analyze interactions is based on Hirshfeld surface that locates electronic charge distribution on the surface, quantitative estimation is obtained by linking DFT computations withCE.In the halogen substituted benzene derivatives considered here, in addition to conventional hydrogen and halogen bonding other interactions such as those between Chlorine-Hydrogen, Bromine-Hydrogen, Bromine-Oxygen have been deciphered. The molecular crystal structure of a variety of halogen substituted aromatic molecules has been rationalized and attributed to specific interactions.

Antibody Structure and Function: The Basis for Engineering Therapeutics

Antibodies and antibody-derived macromolecules have established themselves as the mainstay in protein-based therapeutic molecules (biologics). Our knowledge of the structure–function relationships of antibodies provides a platform for protein engineering that has been exploited to generate a wide range of biologics for a host of therapeutic indications. In this review, our basic understanding of the antibody structure is described along with how that knowledge has leveraged the engineering of antibody and antibody-related therapeutics having the appropriate antigen affinity, effector function, and biophysical properties. The platforms examined include the development of antibodies, antibody fragments, bispecific antibody, and antibody fusion products, whose efficacy and manufacturability can be improved via humanization, affinity modulation, and stability enhancement. We also review the design and selection of binding arms, and avidity modulation. Different strategies of preparing bispecific and multispecific molecules for an array of therapeutic applications are included.

Physicochemical determinants of antibody-protein interactions

Antibodies are specialized proteins generated by immune system for high specificity and affinity binding to target antigens. Because of their essential roles in immune system, antibodies have been successfully developed and engineered as biopharmaceuticals for treatment of various diseases. Analysis of antibody-protein interactions is always required to get detailed information on effectivity of such antibody-based therapeutics. Although physicochemical rules cannot be generalized for every antibody-protein interaction, there are some features which should be taken into account during antibody development and engineering efforts. In this chapter, physicochemical analysis of antibody paratope-protein epitope interactions will be discussed to highlight important characteristics. First, paratope and non-paratope regions of antibodies will be described and important roles of these regions on binding and biophysical features of antibodies will be discussed. Then, general features of epitope regions of protein antigens will be introduced along with several computational/experimental tools to identify them. Lastly, a rising star of antibody biopharmaceuticals, nanobodies, will be described to show importance of next-generation antibody fragment based biopharmaceuticals in drug development.

Structural Analysis of PfSec62-Autophagy Interacting Motifs (AIM) and PfAtg8 Interactions for Its Implications in RecovER-phagy in Plasmodium falciparum

Autophagy is a degradative pathway associated with many pathological and physiological processes crucial for cell survival. During ER stress, while selective autophagy occurs via ER-phagy, the re-establishment of physiologic ER homeostasis upon resolution of a transient ER stress is mediated by recovER-phagy. Recent studies demonstrated that recovER-phagy is governed via association of Sec62 as an ER-resident autophagy receptor through its autophagy interacting motifs (AIM)/LC3-interacting region (LIR) toAtg8/LC3. Atg8 is an autophagy protein, which is central to autophagosome formation and maturation. Plasmodium falciparum Atg8 (PfAtg8) has both autophagic and non-autophagic functions critical for parasite survival. Since Plasmodium also has Sec62 in the ER membrane and is prone to ER stress due to drastic transformation during their complex intraerythrocytic cycle; hence, we initiated the studies to check whether recovER-phagy occurs in the parasite. To achieve this, a comprehensive study based on the computational approaches was carried out. This study embarks upon identification of AIM sequences in PfSec62 by carrying out peptide-protein docking simulations and comparing the interactions of these AIMs with PfAtg8, based on the molecular dynamic simulations. Detailed analysis is based on electrostatic surface complementarity, peptide-protein interaction strength, mapping of non-covalent bond interactions and rupture force calculated from steered MD simulations. Potential mean forces and unbinding free energies (ΔG_dissociation) using Jarzynski's equality were also computed for the AIM/LIR motif complexes with PfAtg8/HsLC3 autophagy proteins to understand their dissociation free energy profiles and thereby their binding affinities and stability of the peptide-protein complexes. Through this study, we predict Sec62 mediated recovER-phagy in Plasmodium falciparum, which might open new avenues to explore novel drug targets for antimalarial drug discovery.

Exploring designability of electrostatic complementarity at an antigen-antibody interface directed by mutagenesis, biophysical analysis, and molecular dynamics simulations

Antibodies protect organisms from a huge variety of foreign antigens. Antibody diversity originates from both genetic and structural levels. Antigen recognition relies on complementarity between antigen-antibody interfaces. Recent methodological advances in structural biology and the accompanying rapid increase of the number of crystal structures of proteins have enabled atomic-level manipulation of protein structures to effect alterations in function. In this study, we explored the designability of electrostatic complementarity at an antigen-antibody interface on the basis of a crystal structure of the complex. We designed several variants with altered charged residues at the interface and characterized the designed variants by surface plasmon resonance, circular dichroism, differential scanning calorimetry, and molecular dynamics simulations. Both successes and failures of the structure-based design are discussed. The variants that compensate electrostatic interactions can restore the interface complementarity, enabling the cognate antigen-antibody binding. Retrospectively, we also show that these mutational effects could be predicted by the simulations. Our study demonstrates the importance of charged residues on the physical properties of this antigen-antibody interaction and suggests that computational approaches can facilitate design of antibodies that recognize a weakly immunogenic antigen.

Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Inner-View of Nanomaterial Incited Protein Conformational Changes: Insights into Designable Interaction

Nanoparticle bioreactivity critically depends upon interaction between proteins and nanomaterials (NM). The formation of the "protein corona" (PC) is the effect of such nanoprotein interactions. PC has a wide usage in pharmaceuticals, drug delivery, medicine, and industrial biotechnology. Therefore, a detailed in-vitro, in-vivo, and in-silico understanding of nanoprotein interaction is fundamental and has a genuine contemporary appeal. NM surfaces can modify the protein conformation during interaction, or NMs themselves can lead to self-aggregations. Both phenomena can change the whole downstream bioreactivity of the concerned nanosystem. The main aim of this review is to understand the mechanistic view of NM-protein interaction and recapitulate the underlying physical chemistry behind the formation of such complicated macromolecular assemblies, to provide a critical overview of the different models describing NM induced structural and functional modification of proteins. The review also attempts to point out the current limitation in understanding the field and highlights the future scopes, involving a plausible proposition of how artificial intelligence could be aided to explore such systems for the prediction and directed design of the desired NM-protein interactions.

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

The influence of like-charge attraction on the structure and dynamics of ionic liquids: NMR chemical shifts, quadrupole coupling constants, rotational correlation times and failure of Stokes–Einstein–Debye

The formation of clusters of like-charge influences the structure and dynamics of ionic liquids.

CPdock: the complementarity plot for docking of proteins: implementing multi-dielectric continuum electrostatics

The complementarity plot (CP) is an established validation tool for protein structures, applicable to both globular proteins (folding) as well as protein-protein complexes (binding). It computes the shape and electrostatic complementarities (S_m, E_m) for amino acid side-chains buried within the protein interior or interface and plots them in a two-dimensional plot having knowledge-based probabilistic quality estimates for the residues as well as for the whole structure. The current report essentially presents an upgraded version of the plot with the implementation of the advanced multi-dielectric functionality (as in Delphi version 6.2 or higher) in the computation of electrostatic complementarity to make the validation tool physico-chemically more realistic. The two methods (single- and multi-dielectric) agree decently in their resultant E_m values, and hence, provisions for both methods have been kept in the software suite. So to speak, the global electrostatic balance within a well-folded protein and/or a well-packed interface seems only marginally perturbed by the choice of different internal dielectric values. However, both from theoretical as well as practical grounds, the more advanced multi-dielectric version of the plot is certainly recommended for potentially producing more reliable results. The report also presents a new methodology and a variant plot, namely CP_dock, based on the same principles of complementarity specifically designed to be used in the docking of proteins. The efficacy of the method to discriminate between good and bad docked protein complexes has been tested on a recent state-of-the-art docking benchmark. The results unambiguously indicate that CP_dock can indeed be effective in the initial screening phase of a docking scoring pipeline before going into more sophisticated and computationally expensive scoring functions. CP_dock has been made available at https://github.com/nemo8130/CPdock . Graphical Abstract An example showing the efficacy of CP_dock to be used in the initial screening phase of a protein-protein docking scoring pipeline.

Dynamics of Hydration Water Plays a Key Role in Determining the Binding Thermodynamics of Protein Complexes

Interfacial waters are considered to play a crucial role in protein–protein interactions, but in what sense and why are they important? Here, using molecular dynamics simulations and statistical thermodynamic analyses, we demonstrate distinctive dynamic characteristics of the interfacial water and investigate their implications for the binding thermodynamics. We identify the presence of extraordinarily slow (~1,000 times slower than in bulk water) hydrogen-bond rearrangements in interfacial water. We rationalize the slow rearrangements by introducing the “trapping” free energies, characterizing how strongly individual hydration waters are captured by the biomolecular surface, whose magnitude is then traced back to the number of water–protein hydrogen bonds and the strong electrostatic field produced at the binding interface. We also discuss the impact of the slow interfacial waters on the binding thermodynamics. We find that, as expected from their slow dynamics, the conventional approach to the water-mediated interaction, which assumes rapid equilibration of the waters’ degrees of freedom, is inadequate. We show instead that an explicit treatment of the extremely slow interfacial waters is critical. Our results shed new light on the role of water in protein–protein interactions, highlighting the need to consider its dynamics to improve our understanding of biomolecular bindings.

Finding correct protein-protein docking models using ProQDock

Motivation: Protein–protein interactions are a key in virtually all biological processes. For a detailed understanding of the biological processes, the structure of the protein complex is essential. Given the current experimental techniques for structure determination, the vast majority of all protein complexes will never be solved by experimental techniques. In lack of experimental data, computational docking methods can be used to predict the structure of the protein complex. A common strategy is to generate many alternative docking solutions (atomic models) and then use a scoring function to select the best. The success of the computational docking technique is, to a large degree, dependent on the ability of the scoring function to accurately rank and score the many alternative docking models.

Results: Here, we present ProQDock, a scoring function that predicts the absolute quality of docking model measured by a novel protein docking quality score (DockQ). ProQDock uses support vector machines trained to predict the quality of protein docking models using features that can be calculated from the docking model itself. By combining different types of features describing both the protein–protein interface and the overall physical chemistry, it was possible to improve the correlation with DockQ from 0.25 for the best individual feature (electrostatic complementarity) to 0.49 for the final version of ProQDock. ProQDock performed better than the state-of-the-art methods ZRANK and ZRANK2 in terms of correlations, ranking and finding correct models on an independent test set. Finally, we also demonstrate that it is possible to combine ProQDock with ZRANK and ZRANK2 to improve performance even further.

Availability and implementation:http://bioinfo.ifm.liu.se/ProQDock

Contact:bjornw@ifm.liu.se

Supplementary information:Supplementary data are available at Bioinformatics online.

Salt-bridge networks within globular and disordered proteins: characterizing trends for designable interactions

There has been considerable debate about the contribution of salt bridges to the stabilization of protein folds, in spite of their participation in crucial protein functions. Salt bridges appear to contribute to the activity-stability trade-off within proteins by bringing high-entropy charged amino acids into close contacts during the course of their functions. The current study analyzes the modes of association of salt bridges (in terms of networks) within globular proteins and at protein-protein interfaces. While the most common and trivial type of salt bridge is the isolated salt bridge, bifurcated salt bridge appears to be a distinct salt-bridge motif having a special topology and geometry. Bifurcated salt bridges are found ubiquitously in proteins and interprotein complexes. Interesting and attractive examples presenting different modes of interaction are highlighted. Bifurcated salt bridges appear to function as molecular clips that are used to stitch together large surface contours at interacting protein interfaces. The present work also emphasizes the key role of salt-bridge-mediated interactions in the partial folding of proteins containing long stretches of disordered regions. Salt-bridge-mediated interactions seem to be pivotal to the promotion of "disorder-to-order" transitions in small disordered protein fragments and their stabilization upon binding. The results obtained in this work should help to guide efforts to elucidate the modus operandi of these partially disordered proteins, and to conceptualize how these proteins manage to maintain the required amount of disorder even in their bound forms. This work could also potentially facilitate explorations of geometrically specific designable salt bridges through the characterization of composite salt-bridge networks. Graphical abstract ᅟ.

Controlling the kinetic and thermodynamic stability of cationic clusters by the addition of molecules or counterions

We discuss the stability of cationic clusters when adding molecules or counterions, and predict their occurrence in gas phase experiments.

A novel index of protein-protein interface propensity improves interface residue recognition

Background

Protein-protein interface holds important information of protein-protein interactions which play key roles in most biological processes. In the past few years, a lot of efforts have been made to improve interface residue recognition by characterizing protein-protein interfaces and extracting relevant features. However, most previous studies were carried out in a qualitative level, and there are also some inconsistencies between them.

Results

In the present work, to improve interface residue recognition, we built a novel quantitative residue protein-protein interface propensity index (QIPI) and gained a comprehensive picture of protein-protein interface through analyzing protein-protein interfaces on our comprehensive protein-protein interfaces dataset (Astral2.05-40-4506). Furthermore, in order to assess the effect of QIPI in improving the protein-protein interface prediction, we developed an interface residue recognition method SPR (Single domain based Patch Recognition) based on the QIPI. The evaluation results proved that our novel QIPI is able to improve the interface residue recognition.

Conclusions

Through a comprehensive quantitative analysis of protein-protein interface, we constructed a novel quantitative protein-protein interface propensity index (QIPI), which could be easily applied to improve the interface residue recognition and helpful in understanding the protein-protein interface.

Availability

QIPI and SPR are available to non-commercial users at our website: http://www.scbit.org/QIPI/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0351-7) contains supplementary material, which is available to authorized users.

Strong and widespread action of site-specific positive selection in the snake venom Kunitz/BPTI protein family

S1 family of serine peptidases is the largest family of peptidases. They are specifically inhibited by the Kunitz/BPTI inhibitors. Kunitz domain is characterized by the compact 3D structure with the most important inhibitory loops for the inhibition of S1 peptidases. In the present study we analysed the action of site-specific positive selection and its impact on the structurally and functionally important parts of the snake venom Kunitz/BPTI family of proteins. By using numerous models we demonstrated the presence of large numbers of site-specific positively selected sites that can reach between 30-50% of the Kunitz domain. The mapping of the positively selected sites on the 3D model of Kunitz/BPTI inhibitors has shown that these sites are located in the inhibitory loops 1 and 2, but also in the Kunitz scaffold. Amino acid replacements have been found exclusively on the surface, and the vast majority of replacements are causing the change of the charge. The consequence of these replacements is the change in the electrostatic potential on the surface of the Kunitz/BPTI proteins that may play an important role in the precise targeting of these inhibitors into the active site of S1 family of serine peptidases.

De novo phasing with X-ray laser reveals mosquito larvicide BinAB structure

BinAB is a naturally occurring paracrystalline larvicide distributed worldwide to combat the devastating diseases borne by mosquitoes. These crystals are composed of homologous molecules, BinA and BinB, which play distinct roles in the multi-step intoxication process, transforming from harmless, robust crystals, to soluble protoxin heterodimers, to internalized mature toxin, and finally to toxic oligomeric pores. The small size of the crystals-50 unit cells per edge, on average-has impeded structural characterization by conventional means. Here we report the structure of Lysinibacillus sphaericus BinAB solved de novo by serial-femtosecond crystallography at an X-ray free-electron laser. The structure reveals tyrosine- and carboxylate-mediated contacts acting as pH switches to release soluble protoxin in the alkaline larval midgut. An enormous heterodimeric interface appears to be responsible for anchoring BinA to receptor-bound BinB for co-internalization. Remarkably, this interface is largely composed of propeptides, suggesting that proteolytic maturation would trigger dissociation of the heterodimer and progression to pore formation.

Anomalous Dynamics of Water Confined in Protein-Protein and Protein-DNA Interfaces

Confined water often exhibits anomalous properties not observable in the bulk phase. Although water in hydrophobic confinement has been the focus of intense investigation, the behavior of water confined between hydrophilic surfaces, which are more frequently found in biological systems, has not been fully explored. Here, we investigate using molecular dynamics simulations dynamical properties of the water confined in hydrophilic protein-protein and protein-DNA interfaces. We find that the interfacial water exhibits glassy slow relaxations even at 300 K. In particular, the rotational dynamics show a logarithmic decay that was observed in glass-forming liquids at deeply supercooled states. We argue that such slow water dynamics are indeed induced by the hydrophilic binding surfaces, which is in opposition to the picture that the hydration water slaves protein motions. Our results will significantly impact the view on the role of water in biomolecular interactions.

Antigenic Variation of East/Central/South African and Asian Chikungunya Virus Genotypes in Neutralization by Immune Sera

Background

Chikungunya virus (CHIKV) is a re-emerging mosquito-borne virus which causes epidemics of fever, severe joint pain and rash. Between 2005 and 2010, the East/Central/South African (ECSA) genotype was responsible for global explosive outbreaks across India, the Indian Ocean and Southeast Asia. From late 2013, Asian genotype CHIKV has caused outbreaks in the Americas. The characteristics of cross-antibody efficacy and epitopes are poorly understood.

Methodology/Principal Findings

We characterized human immune sera collected during two independent outbreaks in Malaysia of the Asian genotype in 2006 and the ECSA genotype in 2008–2010. Neutralizing capacity was analyzed against representative clinical isolates as well as viruses rescued from infectious clones of ECSA and Asian CHIKV. Using whole virus antigen and recombinant E1 and E2 envelope glycoproteins, we further investigated antibody binding sites, epitopes, and antibody titers. Both ECSA and Asian sera demonstrated stronger neutralizing capacity against the ECSA genotype, which corresponded to strong epitope-antibody interaction. ECSA serum targeted conformational epitope sites in the E1-E2 glycoprotein, and E1-E211K, E2-I2T, E2-H5N, E2-G118S and E2-S194G are key amino acids that enhance cross-neutralizing efficacy. As for Asian serum, the antibodies targeting E2 glycoprotein correlated with neutralizing efficacy, and I2T, H5N, G118S and S194G altered and improved the neutralization profile. Rabbit polyclonal antibody against the N-terminal linear neutralizing epitope from the ECSA sequence has reduced binding capacity and neutralization efficacy against Asian CHIKV. These findings imply that the choice of vaccine strain may impact cross-protection against different genotypes.

Conclusion/Significance

Immune serum from humans infected with CHIKV of either ECSA or Asian genotypes showed differences in binding and neutralization characteristics. These findings have implications for the continued outbreaks of co-circulating CHIKV genotypes and effective design of vaccines and diagnostic serological assays.

Chikungunya virus (CHIKV) has caused large epidemics of fever, rash, and joint pain around the world in recent years. Three different CHIKV genotypes exist. Infection with one genotype is likely to lead to immune protection (or cross-protection) against future infections with a different genotype. However, little is known about the nature of this cross-protection. In this study, we used serum from Malaysian patients infected with CHIKV of either Asian or East/Central/South African (ECSA) genotypes. We compared the ability of the serum antibodies to bind to and neutralize two different viruses, from either Asian or ECSA genotypes. We found that both Asian and ECSA serum were more effective in binding and neutralizing ECSA virus. We identified the key amino acids/epitopes within the E1-E2 surface glycoprotein, and showed that variation of these impacts the efficacy of antiserum in cross-neutralizing different genotypes of CHIKV. We showed how sequence variation of a known linear neutralizing epitope could alter the cross-neutralization efficacy. This study aids understanding of the importance of different circulating genotypes within a country and has implications for the design of vaccines and diagnostic antibody tests.