On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Sucrose modulates insulin amyloid-like fibril formation: effect on the aggregation mechanism and fibril morphology

Sucrose modifies the human insulin fibrillation pathways, affecting the fibril morphology.

Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes

Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.

Protein-protein interactions: scoring schemes and binding affinity

Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.

Mitochondrial ROS, uncoupled from ATP synthesis, determine endothelial activation for both physiological recruitment of patrolling cells and pathological recruitment of inflammatory cells

Mitochondrial reactive oxygen species (mtROS) are signaling molecules, which drive inflammatory cytokine production and T cell activation. In addition, cardiovascular diseases, cancers, and autoimmune diseases all share a common feature of increased mtROS level. Both mtROS and ATP are produced as a result of electron transport chain activity, but it remains enigmatic whether mtROS could be generated independently from ATP synthesis. A recent study shed light on this important question and found that, during endothelial cell (EC) activation, mtROS could be upregulated in a proton leak-coupled, but ATP synthesis-uncoupled manner. As a result, EC could upregulate mtROS production for physiological EC activation without compromising mitochondrial membrane potential and ATP generation, and consequently without causing mitochondrial damage and EC death. Thus, a novel pathophysiological role of proton leak in driving mtROS production was uncovered for low grade EC activation, patrolling immunosurveillance cell trans-endothelial migration and other signaling events without compromising cellular survival. This new working model explains how mtROS could be increasingly generated independently from ATP synthesis and endothelial damage or death. Mapping the connections among mitochondrial metabolism, physiological EC activation, patrolling cell migration, and pathological inflammation is significant towards the development of novel therapies for inflammatory diseases and cancers.

Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols

Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson-Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔGelec ) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔGelec . We argue that the sensitivity of the absolute ΔΔGelec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM-27, AMBER-94, and OPLS-AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔGelec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔGelec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.

Could Bat Cell Temperature and Filovirus Filament Length Explain the Emergence of Ebola Virus in Mammals? Predictions of a Thermodynamic Model

The host reservoir of Zaire ebolavirus (EBOV) remains elusive. One suggestion is that EBOV emerges in mammals when the precursor virus jumps from mayflies (or other riverine insects) to insectivorous bats. However, this does not fit with the current view that filoviruses cannot infect arthropods. Here, it is first argued that the evidence that arthropods are refractory is not definitive. Second, it is proposed that a combination of filovirus filament length and the high temperature (~42°C) experienced by an insect virus ingested by a flying bat, together with the large number of insects eaten by bats (e.g. during an ephemeral mass emergence of mayflies), facilitate jumping the species barrier. The length of a filovirus filament is related to the number of genome copies (GC). Predictions from a preliminary thermodynamic model developed here suggest that filament length could greatly affect EBOV infectivity to mammalian cells with infectivity peaking for filaments of a certain length. Importantly, the infectivity to mammals of even short filaments may be more than one million-fold higher than that for the single GC virion. Third, it is proposed that at the high temperature within the bat, the phospholipid phosphatidylserine in the virus envelope promotes filament formation through fusion of single GC particles within the ingested insect, thus hugely increasing their infectivity to bats. Forth, according to the thermodynamic model, increasing the temperature from 27°C (insect cell temperature at average air temperature in Guinea, West Africa) to 42°C (bat) could increase the affinity of the filaments for bat cells by 1-2 orders of magnitude, while having no effect on the binding affinity of the single GC virions. The thermodynamic model developed here is supported by the counterintuitive observation that high glycoprotein densities on the EBOV surface reduce its infectivity in contrast to other viruses such as HIV.

Linking in domain-swapped protein dimers

The presence of knots has been observed in a small fraction of single-domain proteins and related to their thermodynamic and kinetic properties. The exchanging of identical structural elements, typical of domain-swapped proteins, makes such dimers suitable candidates to validate the possibility that mutual entanglement between chains may play a similar role for protein complexes. We suggest that such entanglement is captured by the linking number. This represents, for two closed curves, the number of times that each curve winds around the other. We show that closing the curves is not necessary, as a novel parameter G', termed Gaussian entanglement, is strongly correlated with the linking number. Based on 110 non redundant domain-swapped dimers, our analysis evidences a high fraction of chains with a significant intertwining, that is with |G'| > 1. We report that Nature promotes configurations with negative mutual entanglement and surprisingly, it seems to suppress intertwining in long protein dimers. Supported by numerical simulations of dimer dissociation, our results provide a novel topology-based classification of protein-swapped dimers together with some preliminary evidence of its impact on their physical and biological properties.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking

Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

Cell-Binding Assays for Determining the Affinity of Protein-Protein Interactions: Technologies and Considerations

Determining the equilibrium-binding affinity (Kd) of two interacting proteins is essential not only for the biochemical study of protein signaling and function but also for the engineering of improved protein and enzyme variants. One common technique for measuring protein-binding affinities uses flow cytometry to analyze ligand binding to proteins presented on the surface of a cell. However, cell-binding assays require specific considerations to accurately quantify the binding affinity of a protein-protein interaction. Here we will cover the basic assumptions in designing a cell-based binding assay, including the relevant equations and theory behind determining binding affinities. Further, two major considerations in measuring binding affinities-time to equilibrium and ligand depletion-will be discussed. As these conditions have the potential to greatly alter the Kd, methods through which to avoid or minimize them will be provided. We then outline detailed protocols for performing direct- and competitive-binding assays against proteins displayed on the surface of yeast or mammalian cells that can be used to derive accurate Kd values. Finally, a comparison of cell-based binding assays to other types of binding assays will be presented.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

Human dihydrofolate reductase and thymidylate synthase form a complex in vitro and co-localize in normal and cancer cells

Enzymes involved in thymidylate biosynthesis, thymidylate synthase (TS), and dihydrofolate reductase (DHFR) are well-known targets in cancer chemotherapy. In this study, we demonstrated for the first time, that human TS and DHFR form a strong complex in vitro and co-localize in human normal and colon cancer cell cytoplasm and nucleus. Treatment of cancer cells with methotrexate or 5-fluorouracil did not affect the distribution of either enzyme within the cells. However, 5-FU, but not MTX, lowered the presence of DHFR-TS complex in the nucleus by 2.5-fold. The results may suggest the sequestering of TS by FdUMP in the cytoplasm and thereby affecting the translocation of DHFR-TS complex to the nucleus. Providing a strong likelihood of DHFR-TS complex formation in vivo, the latter complex is a potential new drug target in cancer therapy. In this paper, known 3D structures of human TS and human DHFR, and some protozoan bifunctional DHFR-TS structures as templates, are used to build an in silico model of human DHFR-TS complex structure, consisting of one TS dimer and two DHFR monomers. This complex structure may serve as an initial 3D drug target model for prospective inhibitors targeting interfaces between the DHFR and TS enzymes.

Characterization of molecular interactions between Escherichia coli RNA polymerase and topoisomerase I by molecular simulations

Escherichia coli topoisomerase I (EctopoI), a type IA DNA topoisomerase, relaxes the negative DNA supercoiling generated by RNA polymerase (RNAP) during transcription elongation. Due to the lack of structural information on the complex, the exact nature of the RNAP-EctopoI interactions remains unresolved. Herein, we report for the first time, the structure-based modeling of the RNAP-EctopoI interactions using computational methods. Our results predict that the salt bridge as well as hydrogen bond interactions are responsible for the formation and stabilization of the RNAP-EctopoI complex. Our investigations provide molecular insights for understanding how EctopoI interacts with RNAP, a critical step for preventing hypernegative DNA supercoiling during transcription.

Recent Advances in Computational Models for the Study of Protein-Peptide Interactions

We review computational models and software tools in current use for the study of protein-peptide interactions. Peptides and peptide derivatives are growing in interest as therapeutic agents to target protein-protein interactions. Protein-protein interactions are pervasive in biological systems and are responsible for the regulation of critical functions within the cell. Mutations or dysregulation of expression can alter the network of interactions among proteins and cause diseases such as cancer. Protein-protein binding interfaces, which are often large, shallow, and relatively feature-less, are difficult to target with small-molecule inhibitors. Peptide derivatives based on the binding motifs present in the target protein complex are increasingly drawing interest as superior alternatives to conventional small-molecule inhibitors. However, the design of peptide-based inhibitors also presents novel challenges. Peptides are more complex and more flexible than standard medicinal compounds. They also tend to form more extended and more complex interactions with their protein targets. Computational modeling is increasingly being employed to supplement synthetic and biochemical work to offer guidance and energetic and structural insights. In this review, we discuss recent in silico structure-based and physics-based approaches currently employed to model protein-peptide interactions with a few examples of their applications.

Optimization of protein-protein docking for predicting Fc-protein interactions

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.

Destabilizing an interacting motif strengthens the association of a designed ankyrin repeat protein with tubulin

Affinity maturation by random mutagenesis and selection is an established technique to make binding molecules more suitable for applications in biomedical research, diagnostics and therapy. Here we identified an unexpected novel mechanism of affinity increase upon in vitro evolution of a tubulin-specific designed ankyrin repeat protein (DARPin). Structural analysis indicated that in the progenitor DARPin the C-terminal capping repeat (C-cap) undergoes a 25° rotation to avoid a clash with tubulin upon binding. Additionally, the C-cap appears to be involved in electrostatic repulsion with tubulin. Biochemical and structural characterizations demonstrated that the evolved mutants achieved a gain in affinity through destabilization of the C-cap, which relieves the need of a DARPin conformational change upon tubulin binding and removes unfavorable interactions in the complex. Therefore, this specific case of an order-to-disorder transition led to a 100-fold tighter complex with a subnanomolar equilibrium dissociation constant, remarkably associated with a 30% decrease of the binding surface.

Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes

Reliable prediction of binding affinity changes (ΔΔG) upon mutations in protein complexes relies not only on the performance of computational methods but also on the availability and quality of experimental data. Binding affinity changes can be measured by various experimental methods with different accuracies and limitations. To understand the impact of these on the prediction of binding affinity change, we present the Database of binding Affinity Change Upon Mutation (DACUM), a database of 1872 binding affinity changes upon single-point mutations, a subset of the SKEMPI database (Moal,I.H. and Fernández-Recio,J. Bioinformatics, 2012;28:2600-2607) extended with information on the experimental methods used for ΔΔG measurements. The ΔΔG data were classified into different data sets based on the experimental method used and the position of the mutation (interface and non-interface). We tested the prediction performance of the original HADDOCK score, a newly trained version of it and mutation Cutoff Scanning Matrix (Pires,D.E.V., Ascher,D.B. and Blundell,T.L. Bioinformatics 2014;30:335-342), one of the best reported ΔΔG predictors so far, on these various data sets. Our results demonstrate a strong impact of the experimental methods on the performance of binding affinity change predictors for protein complexes. This underscores the importance of properly considering and carefully choosing experimental methods in the development of novel binding affinity change predictors. The DACUM database is available online at https://github.com/haddocking/DACUM.

Techniques for the Analysis of Protein-Protein Interactions in Vivo

Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.

Interplay between binding affinity and kinetics in protein-protein interactions

To clarify the interplay between the binding affinity and kinetics of protein-protein interactions, and the possible role of intrinsically disordered proteins in such interactions, molecular simulations were carried out on 20 protein complexes. With bias potential and reweighting techniques, the free energy profiles were obtained under physiological affinities, which showed that the bound-state valley is deep with a barrier height of 12 - 33 RT. From the dependence of the affinity on interface interactions, the entropic contribution to the binding affinity is approximated to be proportional to the interface area. The extracted dissociation rates based on the Arrhenius law correlate reasonably well with the experimental values (Pearson correlation coefficient R = 0.79). For each protein complex, a linear free energy relationship between binding affinity and the dissociation rate was confirmed, but the distribution of the slopes for intrinsically disordered proteins showed no essential difference with that observed for ordered proteins. A comparison with protein folding was also performed. Proteins 2016; 84:920-933. © 2016 Wiley Periodicals, Inc.

Designer protein delivery: From natural to engineered affinity-controlled release systems

Exploiting binding affinities between molecules is an established practice in many fields, including biochemical separations, diagnostics, and drug development; however, using these affinities to control biomolecule release is a more recent strategy. Affinity-controlled release takes advantage of the reversible nature of noncovalent interactions between a therapeutic protein and a binding partner to slow the diffusive release of the protein from a vehicle. This process, in contrast to degradation-controlled sustained-release formulations such as poly(lactic-co-glycolic acid) microspheres, is controlled through the strength of the binding interaction, the binding kinetics, and the concentration of binding partners. In the context of affinity-controlled release--and specifically the discovery or design of binding partners--we review advances in in vitro selection and directed evolution of proteins, peptides, and oligonucleotides (aptamers), aided by computational design.

A method for measuring binding constants using unpurified in vivo biotinylated ligands

Obtaining accurate kinetics and steady-state binding constants for biomolecular interactions normally requires pure and homogeneous protein preparations. Furthermore, in many cases, one of the ligands must be labeled. Over the past decade, several technologies have been introduced that allow for the measurement of kinetics constants for multiple different interactions in parallel. One such technology is bio-layer interferometry (BLI), which has been used to develop systems that can measure up to 96 biomolecular interactions simultaneously. However, despite the ever-increasing throughput of the tools available for measuring protein-protein interactions, the preparation of pure protein still remains a bottleneck in the process of producing high-quality kinetics data. Here, we show that high-quality binding data can be obtained using soluble lysate fractions containing protein that has been biotinylated in vivo using BirA and then applied to BLI sensors without further purification. Furthermore, we show that BirA ligase does not necessarily need to be co-overexpressed with the protein of interest for biotinylation of the biotin acceptor peptide to occur, suggesting that the activity of endogenous BirA in Escherichia coli is sufficient for producing enough biotinylated protein for a binding experiment.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Metal ion-modulated self-assembly of pseudo-suit[3]anes using crown ether-based terpyridine metalloprisms

The host–guest inclusion rates of two metallo-supramolecular pseudo-suit[3]anes are modulated by metal–terpyridine interactions in the metallo-suits.

Role of the XPA protein in the NER pathway: A perspective on the function of structural disorder in macromolecular assembly

Lack of structure is often an essential functional feature of protein domains. The coordination of macromolecular assemblies in DNA repair pathways is yet another task disordered protein regions are highly implicated in. Here I review the available experimental and computational data and within this context discuss the functional role of structure and disorder in one of the essential scaffolding proteins in the nucleotide excision repair (NER) pathway, namely Xeroderma pigmentosum complementation group A (XPA). From the analysis of the current knowledge, in addition to protein–protein docking and secondary structure prediction results presented for the first time herein, a mechanistic framework emerges, where XPA builds the NER pre-incision complex in a modular fashion, as “beads on a string”, where the protein–protein interaction “beads”, or modules, are interconnected by disordered link regions. This architecture is ideal to avoid the expected steric hindrance constraints of the DNA expanded bubble. Finally, the role of the XPA structural disorder in binding affinity modulation and in the sequential binding of NER core factors in the pre-incision complex is also discussed.

Antibody Binding Selectivity: Alternative Sets of Antigen Residues Entail High-Affinity Recognition

Understanding the relationship between protein sequence and molecular recognition selectivity remains a major challenge. The antibody fragment scFv1F4 recognizes with sub nM affinity a decapeptide (sequence 6TAMFQDPQER15) derived from the N-terminal end of human papilloma virus E6 oncoprotein. Using this decapeptide as antigen, we had previously shown that only the wild type amino-acid or conservative replacements were allowed at positions 9 to 12 and 15 of the peptide, indicating a strong binding selectivity. Nevertheless phenylalanine (F) was equally well tolerated as the wild type glutamine (Q) at position 13, while all other amino acids led to weaker scFv binding. The interfaces of complexes involving either Q or F are expected to diverge, due to the different physico-chemistry of these residues. This would imply that high-affinity binding can be achieved through distinct interfacial geometries. In order to investigate this point, we disrupted the scFv-peptide interface by modifying one or several peptide positions. We then analyzed the effect on binding of amino acid changes at the remaining positions, an altered susceptibility being indicative of an altered role in complex formation. The 23 starting variants analyzed contained replacements whose effects on scFv1F4 binding ranged from minor to drastic. A permutation analysis (effect of replacing each peptide position by all other amino acids except cysteine) was carried out on the 23 variants using the PEPperCHIP® Platform technology. A comparison of their permutation patterns with that of the wild type peptide indicated that starting replacements at position 11, 12 or 13 modified the tolerance to amino-acid changes at the other two positions. The interdependence between the three positions was confirmed by SPR (Biacore® technology). Our data demonstrate that binding selectivity does not preclude the existence of alternative high-affinity recognition modes.

Minimalistic predictor of protein binding energy: contribution of solvation factor to protein binding

It has long been known that solvation plays an important role in protein-protein interactions. Here, we use a minimalistic solvation-based model for predicting protein binding energy to estimate quantitatively the contribution of the solvation factor in protein binding. The factor is described by a simple linear combination of buried surface areas according to amino-acid types. Even without structural optimization, our minimalistic model demonstrates a predictive power comparable to more complex methods, making the proposed approach the basis for high throughput applications. Application of the model to a proteomic database shows that receptor-substrate complexes involved in signaling have lower affinities than enzyme-inhibitor and antibody-antigen complexes, and they differ by chemical compositions on interfaces. Also, we found that protein complexes with components that come from the same genes generally have lower affinities than complexes formed by proteins from different genes, but in this case the difference originates from different interface areas. The model was implemented in the software PYTHON, and the source code can be found on the Shakhnovich group webpage: http://faculty.chemistry.harvard.edu/shakhnovich/software.

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.

Few proteins carry out their tasks in isolation. Instead, proteins combine with each other in complicated ways that can be affected by either the natural genetic variation that occurs among people or by disease causing mutations such as those that occur in cancer or in genetic disorders. To understand how these mutations affect our health, it is necessary to understand how mutations can affect the strength of the interactions that bind proteins together. This is a difficult task to do in a laboratory on a large scale and scientists are increasingly turning to computational methods to predict these effects in advance. We show that by looking at the multiple alignments of similar protein-protein complex structures at the interface regions, new constraints based on the evolution of the three dimensional structures of proteins can be made to predict which mutations are compatible with two proteins interacting and which are not.

Potential of mean force analysis of the self-association of leucine-rich transmembrane α-helices: difference between atomistic and coarse-grained simulations

Interaction of transmembrane (TM) proteins is important in many biological processes. Large-scale computational studies using coarse-grained (CG) simulations are becoming popular. However, most CG model parameters have not fully been calibrated with respect to lateral interactions of TM peptide segments. Here, we compare the potential of mean forces (PMFs) of dimerization of TM helices obtained using a MARTINI CG model and an atomistic (AT) Berger lipids-OPLS/AA model (AT(OPLS)). For helical, tryptophan-flanked, leucine-rich peptides (WL15 and WALP15) embedded in a parallel configuration in an octane slab, the AT(OPLS) PMF profiles showed a shallow minimum (with a depth of approximately 3 kJ/mol; i.e., a weak tendency to dimerize). A similar analysis using the CHARMM36 all-atom model (AT(CHARMM)) showed comparable results. In contrast, the CG analysis generally showed steep PMF curves with depths of approximately 16-22 kJ/mol, suggesting a stronger tendency to dimerize compared to the AT model. This CG > AT discrepancy in the propensity for dimerization was also seen for dilauroylphosphatidylcholine (DLPC)-embedded peptides. For a WL15 (and WALP15)/DLPC bilayer system, AT(OPLS) PMF showed a repulsive mean force for a wide range of interhelical distances, in contrast to the attractive forces observed in the octane system. The change from the octane slab to the DLPC bilayer also mitigated the dimerization propensity in the CG system. The dimerization energies of CG (AALALAA)3 peptides in DLPC and dioleoylphosphatidylcholine bilayers were in good agreement with previous experimental data. The lipid headgroup, but not the length of the lipid tails, was a key causative factor contributing to the differences between octane and DLPC. Furthermore, the CG model, but not the AT model, showed high sensitivity to changes in amino acid residues located near the lipid-water interface and hydrophobic mismatch between the peptides and membrane. These findings may help interpret CG and AT simulation results on membrane proteins.

Proteomics in evolutionary ecology

Evolutionary ecologists are traditionally gene-focused, as genes propagate phenotypic traits across generations and mutations and recombination in the DNA generate genetic diversity required for evolutionary processes. As a consequence, the inheritance of changed DNA provides a molecular explanation for the functional changes associated with natural selection. A direct focus on proteins on the other hand, the actual molecular agents responsible for the expression of a phenotypic trait, receives far less interest from ecologists and evolutionary biologists. This is partially due to the central dogma of molecular biology that appears to define proteins as the 'dead-end of molecular information flow' as well as technical limitations in identifying and studying proteins and their diversity in the field and in many of the more exotic genera often favored in ecological studies. Here we provide an overview of a newly forming field of research that we refer to as 'Evolutionary Proteomics'. We point out that the origins of cellular function are related to the properties of polypeptide and RNA and their interactions with the environment, rather than DNA descent, and that the critical role of horizontal gene transfer in evolution is more about coopting new proteins to impact cellular processes than it is about modifying gene function. Furthermore, post-transcriptional and post-translational processes generate a remarkable diversity of mature proteins from a single gene, and the properties of these mature proteins can also influence inheritance through genetic and perhaps epigenetic mechanisms. The influence of post-transcriptional diversification on evolutionary processes could provide a novel mechanistic underpinning for elements of rapid, directed evolutionary changes and adaptations as observed for a variety of evolutionary processes. Modern state-of the art technologies based on mass spectrometry are now available to identify and quantify peptides, proteins, protein modifications and protein interactions of interest with high accuracy and assess protein diversity and function. Therefore, proteomic technologies can be viewed as providing evolutionary biologist with exciting novel opportunities to understand very early events in functional variation of cellular molecular machinery that are acting as part of evolutionary processes.

Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high-quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top 10 docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall and r=0.72 for the rigid complexes.

Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

High-resolution protein-protein docking by global optimization: recent advances and future challenges

A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.

AlphaSpace: Fragment-Centric Topographical Mapping To Target Protein-Protein Interaction Interfaces

Inhibition of protein–protein interactions (PPIs) is emerging as a promising therapeutic strategy despite the difficulty in targeting such interfaces with drug-like small molecules. PPIs generally feature large and flat binding surfaces as compared to typical drug targets. These features pose a challenge for structural characterization of the surface using geometry-based pocket-detection methods. An attractive mapping strategy—that builds on the principles of fragment-based drug discovery (FBDD)—is to detect the fragment-centric modularity at the protein surface and then characterize the large PPI interface as a set of localized, fragment-targetable interaction regions. Here, we introduce AlphaSpace, a computational analysis tool designed for fragment-centric topographical mapping (FCTM) of PPI interfaces. Our approach uses the alpha sphere construct, a geometric feature of a protein’s Voronoi diagram, to map out concave interaction space at the protein surface. We introduce two new features—alpha-atom and alpha-space—and the concept of the alpha-atom/alpha-space pair to rank pockets for fragment-targetability and to facilitate the evaluation of pocket/fragment complementarity. The resulting high-resolution interfacial map of targetable pocket space can be used to guide the rational design and optimization of small molecule or biomimetic PPI inhibitors.

Modeling for (physical) biologists: an introduction to the rule-based approach

Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624) while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation).

Mechanotransduction Mechanisms for Intraventricular Diastolic Vortex Forces and Myocardial Deformations: Part 2

Epigenetic mechanisms are fundamental in cardiac adaptations, remodeling, reverse remodeling, and disease. A primary goal of translational cardiovascular research is recognizing whether disease-related changes in phenotype can be averted by eliminating or reducing the effects of environmental epigenetic risks. There may be significant medical benefits in using gene-by-environment interaction knowledge to prevent or reverse organ abnormalities and disease. This survey proposes that "environmental" forces associated with diastolic RV/LV rotatory flows exert important, albeit still unappreciated, epigenetic actions influencing functional and morphological cardiac adaptations. Mechanisms analogous to Murray's law of hydrodynamic shear-induced endothelial cell modulation of vascular geometry are likely to link diastolic vortex-associated shear, torque and "squeeze" forces to RV/LV adaptations. The time has come to explore a new paradigm in which such forces play a fundamental epigenetic role, and to work out how heart cells react to them. Findings from various imaging modalities, computational fluid dynamics, molecular cell biology and cytomechanics are considered. The following are examined, among others: structural dynamics of myocardial cells (endocardium, cardiomyocytes, and fibroblasts), cytoskeleton, nucleoskeleton, and extracellular matrix; mechanotransduction and signaling; and mechanical epigenetic influences on genetic expression. To help integrate and focus relevant pluridisciplinary research, rotatory RV/LV filling flow is placed within a working context that has a cytomechanics perspective. This new frontier in cardiac research should uncover versatile mechanistic insights linking filling vortex patterns and attendant forces to variable expressions of gene regulation in RV/LV myocardium. In due course, it should reveal intrinsic homeostatic arrangements that support ventricular myocardial function and adaptability.

Detection and characterization of nonspecific, sparsely populated binding modes in the early stages of complexation

A method is proposed to study protein-ligand binding in a system governed by specific and nonspecific interactions. Strong associations lead to narrow distributions in the proteins configuration space; weak and ultraweak associations lead instead to broader distributions, a manifestation of nonspecific, sparsely populated binding modes with multiple interfaces. The method is based on the notion that a discrete set of preferential first-encounter modes are metastable states from which stable (prerelaxation) complexes at equilibrium evolve. The method can be used to explore alternative pathways of complexation with statistical significance and can be integrated into a general algorithm to study protein interaction networks. The method is applied to a peptide-protein complex. The peptide adopts several low-population conformers and binds in a variety of modes with a broad range of affinities. The system is thus well suited to analyze general features of binding, including conformational selection, multiplicity of binding modes, and nonspecific interactions, and to illustrate how the method can be applied to study these problems systematically. The equilibrium distributions can be used to generate biasing functions for simulations of multiprotein systems from which bulk thermodynamic quantities can be calculated.

Alternative modulation of protein-protein interactions by small molecules

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Protein–protein interactions can be modulated by more than orthosteric disruption.

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Modulator categories: ‘orthosteric versus allosteric’ and ‘disrupting versus stabilising’.

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Interfacial binders exert secondary effects.

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Non-competitive modulation is a way around low affinity molecules.

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Non-competitive modulators require tailored screening strategies.

Protein–protein interactions (PPI) have become increasingly popular drug targets, with a number of promising compounds currently in clinical trials. Recent research shows, that PPIs can be modulated in more ways than direct inhibition, where novel non-competitive modes of action promise a solution for the difficult nature of PPI drug discovery.

Here, we review recently discovered PPI modulators in light of their mode of action and categorise them as disrupting versus stabilising, orthosteric versus allosteric and by their ability to affect the proteins’ dynamics. We also give recent examples of compounds successful in the clinic, analyse their physicochemical properties and discuss how to overcome the hurdles in discovering alternative modes of modulation.

Structural basis for assembly and function of the Nup82 complex in the nuclear pore scaffold

The yeast Nup82 complex forms an unusual asymmetric structure with a dimeric array of subunits that mediate its anchorage to the NPC scaffold and its concomitant interaction with the soluble nucleocytoplasmic transport machinery.

Nuclear pore complexes (NPCs) are huge assemblies formed from ∼30 different nucleoporins, typically organized in subcomplexes. One module, the conserved Nup82 complex at the cytoplasmic face of NPCs, is crucial to terminate mRNA export. To gain insight into the structure, assembly, and function of the cytoplasmic pore filaments, we reconstituted in yeast the Nup82–Nup159–Nsp1–Dyn2 complex, which was suitable for biochemical, biophysical, and electron microscopy analyses. Our integrative approach revealed that the yeast Nup82 complex forms an unusual asymmetric structure with a dimeric array of subunits. Based on all these data, we developed a three-dimensional structural model of the Nup82 complex that depicts how this module might be anchored to the NPC scaffold and concomitantly can interact with the soluble nucleocytoplasmic transport machinery.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Use of fluorescence spectroscopy for quantitative investigations of ubiquitin interactions with the ubiquitin-binding domains of NEMO

Ubiquitin serves as a signal for a variety of cellular processes and its specific interaction with ubiquitin-binding domain (UBD) regulates key cellular events including protein degradation, cell-cycle control, DNA repair, and kinase activation. Several binding mechanisms for isolated UBDs have been reported in recent years. However, little is known about the mechanism through which proteins containing multiple-UBDs achieve specificity for a particular oligomer of polyUb. The NF-κB essential modulator (NEMO, also known IKKγ), which plays a key role in the NF-κB signaling pathway, belongs to the latter family of proteins since it contains two distal NOA (also known UBAN/CC2-LZ/NUB) and ZF UBDs, separated by an unstructured proline-rich linker of about 40 residues in length. Here, we show a new procedure for fast purification of this bipartite domain. We also describe the use of intrinsic fluorescence spectroscopy for quantitative investigations of ubiquitin interactions between two distal ubiquitin-binding domains of NEMO (NOA and ZF). This spectroscopic method has many advantages over other techniques like GST pulldown and Biacore's SPR for monitoring avid interactions between two UBDs, especially when UBDs are located at significant distance from each other within the protein.

Affinity proteomics to study endogenous protein complexes: pointers, pitfalls, preferences and perspectives

Dissecting and studying cellular systems requires the ability to specifically isolate distinct proteins along with the co-assembled constituents of their associated complexes. Affinity capture techniques leverage high affinity, high specificity reagents to target and capture proteins of interest along with specifically associated proteins from cell extracts. Affinity capture coupled to mass spectrometry (MS)-based proteomic analyses has enabled the isolation and characterization of a wide range of endogenous protein complexes. Here, we outline effective procedures for the affinity capture of protein complexes, highlighting best practices and common pitfalls.