Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?

Evolution of Interactions in the Protein Solution As Induced by Mono and Multivalent Ions

The evolution of interactions in the bovine serum albumin (BSA) protein solution on addition of mono and multivalent (di, tri and tetra) counterions has been studied using small-angle neutron scattering (SANS), dynamic light scattering (DLS) and ζ-potential measurements. It is found that in the presence of mono and divalent counterions, protein behavior can be well explained by DLVO theory, combining the contributions of screened Coulomb repulsion with the van der Waals attraction. The addition of mono or divalent salts in protein solution reduces the repulsive barrier and hence the overall interaction becomes attractive, but the system remains in one-phase for the entire concentration range of the salts, added in the system. However, contrary to DLVO theory, the protein solution undergoes a reentrant phase transition from one-phase to a two-phase system and then back to the one-phase system in the presence of tri and tetravalent counterions. The results show that tri and tetravalent (unlike mono and divalent) counterions induce short-range attraction between the protein molecules, leading to the transformation from one-phase to two-phase system. The two-phase is characterized by the fractal structure of protein aggregates. The excess condensation of these higher-valent counterions in the double layer around the BSA causes the reversal of charge of the protein molecules resulting into reentrant of the one-phase, at higher salt concentrations. The complete phase behavior with mono and multivalent ions has been explained in terms of the interplay of electrostatic repulsion and ion-induced short-range attraction between the protein molecules.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Molecular architecture of a cylindrical self-assembly at human centrosomes

The cell is constructed by higher-order structures and organelles through complex interactions among distinct structural constituents. The centrosome is a membraneless organelle composed of two microtubule-derived structures called centrioles and an amorphous mass of pericentriolar material. Super-resolution microscopic analyses in various organisms revealed that diverse pericentriolar material proteins are concentrically localized around a centriole in a highly organized manner. However, the molecular nature underlying these organizations remains unknown. Here we show that two human pericentriolar material scaffolds, Cep63 and Cep152, cooperatively generate a heterotetrameric α-helical bundle that functions in conjunction with its neighboring hydrophobic motifs to self-assemble into a higher-order cylindrical architecture capable of recruiting downstream components, including Plk4, a key regulator for centriole duplication. Mutations disrupting the self-assembly abrogate Plk4-mediated centriole duplication. Because pericentriolar material organization is evolutionarily conserved, this work may offer a paradigm for investigating the assembly and function of centrosomal scaffolds in various organisms.

The centrosome is a membraneless organelle composed of two centrioles and an amorphous pericentriolar material but the overall centrosome organizations remains unknown. Here authors show that two scaffold proteins, Cep63 and Cep152, self-assemble into a higher-order cylindrical architecture capable of recruiting downstream components, including Plk4.

Construction and characterization of an infectious cDNA clone of potato virus S developed from selected populations that survived genetic bottlenecks

BACKGROUND

Infectious cDNA clones are a powerful tool for studies on RNA viruses using reverse genetics. Potato virus S (PVS) is a carlavirus with a worldwide distribution. Although the complete genome sequences of many PVS isolates have been reported, the construction of an infectious cDNA clone of PVS is yet to be reported. The aim of this study is the development and molecular characterization of an infectious cDNA clone of PVS.

METHODS

A full-length cDNA clone pPVS-H-FL-AB was constructed by connecting eight cDNA clones of PVS isolate H95. Capped RNA transcripts from pPVS-H-FL-AB and a modified clone pPVS-H-FL-H, containing the consensus genome sequence of PVS-H95, proved to be non-infectious. Therefore, a full-length cDNA clone pPVS-H-FL-β was reconstructed from PVS-H00, isolated from PVS-H95 populations by repeating a single local lesion isolation in Chenopodium quinoa three times; PVS-H00 appeared to be a selected variant that survived genetic bottlenecks. The sequence of cDNA clone pPVS-H-FL-β was determined as the genome sequence of PVS-H00 and compared with the consensus sequence of PVS-H95 genome.

RESULTS

All Nicotiana occidentalis plants inoculated with ≥0.2 μg capped RNA transcripts from pPVS-H-FL-β developed symptoms on upper leaves, as observed with PVS-H00 inoculation. Similar levels of viral genomic and subgenomic RNAs and coat protein were detected in systemically infected leaves. Sequence comparison of PVS-H95 and PVS-H00 revealed 370 nucleotide polymorphisms (4.4% of the entire genome sequence), causing 91 amino acid substitutions in six open reading frames (ORFs). The infectivity of chimeric RNAs derived from recombinants between the two cDNA clones revealed that the lack of infectivity of pPVS-H-FL-H transcripts was due to ORF1, which encodes replicase and harbors 80 amino acid substitutions compared with pPVS-H-FL-β. Approximately 71.3% amino acid substitutions in replicase were located within the variable region of unknown function between the putative methyltransferase and ovarian tumor-like protease domains.

CONCLUSIONS

This is the first report of the development of an infectious cDNA clone of PVS. Our analyses suggest that PVS population within a plant exists as quasispecies and the replicase sequence diversity of PVS obstruct the construction of a full-length infectious cDNA clone.

Bioluminescence Resonance Energy Transfer as a Method to Study Protein-Protein Interactions: Application to G Protein Coupled Receptor Biology

The bioluminescence resonance energy transfer (BRET) approach involves resonance energy transfer between a light-emitting enzyme and fluorescent acceptors. The major advantage of this technique over biochemical methods is that protein-protein interactions (PPI) can be monitored without disrupting the natural environment, frequently altered by detergents and membrane preparations. Thus, it is considered as one of the most versatile technique for studying molecular interactions in living cells at "physiological" expression levels. BRET analysis has been applied to study many transmembrane receptor classes including G-protein coupled receptors (GPCR). It is well established that these receptors may function as dimeric/oligomeric forms and interact with multiple effectors to transduce the signal. Therefore, they are considered as attractive targets to identify PPI modulators. In this review, we present an overview of the different BRET systems developed up to now and their relevance to identify inhibitors/modulators of protein⁻protein interaction. Then, we introduce the different classes of agents that have been recently developed to target PPI, and provide some examples illustrating the use of BRET-based assays to identify and characterize innovative PPI modulators in the field of GPCRs biology. Finally, we discuss the main advantages and the limits of BRET approach to characterize PPI modulators.

Insight Into the Binding Mechanism of p53/pDIQ-MDMX/MDM2 With the Interaction Entropy Method

The study of the p53-MDMX/MDM2 binding sites is a research hotspot for tumor drug design. The inhibition of p53-targeted MDMX/MDM2 has become an effective approach in anti-tumor drug development. In this paper, a theoretically rigorous and computationally accurate method, namely, the interaction entropy (IE) method, combined with the polarized protein-specific charge (PPC) force field, is used to explore the difference in the binding mechanism between p53-MDMX and p53-MDM2. The interaction of a 12mer peptide inhibitor (pDIQ), which is similar to p53 in structure, with MDMX/MDM2 is also studied. The results demonstrate that p53/pDIQ with MDM2 generates a stronger interaction than with MDMX. Compared to p53, pDIQ has larger binding free energies with MDMX and MDM2. According to the calculated binding free energies, the differences in the binding free energy among the four complexes that are obtained from the combination of PPC and IE are more consistent with the experimental values than with the results from the combination of the non-polarizable AMBER force field and IE. In addition, according to the decomposition of the binding free energy, the van der Waals (vdW) interactions are the main driving force for the binding of the four complexes. They are also the main source of the weaker binding affinity of p53/pDIQ-MDMX relative to p53/pDIQ-MDM2. Compared with p53-MDMX/MDM2, according to the analysis of the residue decomposition, the predicated total residue contributions are higher in pDIQ-MDMX/MDM2 than in p53-MDMX/MDM2, which explains why pDIQ has higher binding affinity than p53 with MDMX/MDM2. The current study provides theoretical guidance for understanding the binding mechanisms and designing a potent dual inhibitor that is targeted to MDMX/MDM2.

Bisphosphonate ligand mediated ultrasensitive capacitive protein sensor: complementary match of supramolecular and dynamic chemistry

A powerful polymeric protein sensor was constructed by microcontact imprinting taking advantage of the specific interaction between a bisphosphonate binding monomer and lysine/arginine residues on the surface of trypsin.

Antigen binding allosterically promotes Fc receptor recognition

A key question in immunology is whether antigen recognition and Fc receptor (FcR) binding are allosterically linked. This question is also relevant for therapeutic antibody design. Antibody Fab and Fc domains are connected by flexible unstructured hinge region. Fc chains have conserved glycosylation sites at Asn297, with each conjugated to a core heptasaccharide and forming biantennary Fc glycan. The glycans modulate the Fc conformations and functions. It is well known that the antibody Fab and Fc domains and glycan affect antibody activity, but whether these elements act independently or synergistically is still uncertain. We simulated four antibody complexes: free antibody, antigen-bound antibody, FcR-bound antibody, and an antigen-antibody-FcR complex. We found that, in the antibody's "T/Y" conformation, the glycans, and the Fc domain all respond to antigen binding, with the antibody population shifting to two dominant clusters, both with the Fc-receptor binding site open. The simulations reveal that the Fc-glycan-receptor complexes also segregate into two conformational clusters, one corresponding to the antigen-free antibody-FcR baseline binding, and the other with an antigen-enhanced antibody-FcR interaction. Our study confirmed allosteric communications in antibody-antigen recognition and following FcR activation. Even though we observed allosteric communications through the IgG domains, the most important mechanism that we observed is the communication via population shift, stimulated by antigen binding and propagating to influence FcR recognition.

Refining evERdock: Improved selection of good protein-protein complex models achieved by MD optimization and use of multiple conformations

A method for evaluating binding free energy differences of protein-protein complex structures generated by protein docking was recently developed by some of us. The method, termed evERdock, combined short (2 ns) molecular dynamics (MD) simulations in explicit water and solution theory in the energy representation (ER) and succeeded in selecting the near-native complex structures from a set of decoys. In the current work, we performed longer (up to 100 ns) MD simulations before employing ER analysis in order to further refine the structures of the decoy set with improved binding free energies. Moreover, we estimated the binding free energies for each complex structure based on an average value from five individual MD snapshots. After MD simulations, all decoys exhibit a decrease in binding free energy, suggesting that proper equilibration in explicit solvent resulted in more favourably bound complexes. During the MD simulations, non-native structures tend to become unstable and in some cases dissociate, while near-native structures maintain a stable interface. The energies after the MD simulations show an improved correlation between similarity criteria (such as interface root-mean-square distance) to the native (crystal) structure and the binding free energy. In addition, calculated binding free energies show sensitivity to the number of contacts, which was demonstrated to reflect the relative stability of structures at earlier stages of the MD simulation. We therefore conclude that the additional equilibration step along with the use of multiple conformations can make the evERdock scheme more versatile under low computational cost.

Dithiocarbamate-inspired side chain stapling chemistry for peptide drug design

A novel peptide stapling strategy based on the dithiocarbamate chemistry linking the side chains of residues Lys(i) and Cys(i + 4) of unprotected peptides is developed.

Two major pharmacological hurdles severely limit the widespread use of small peptides as therapeutics: poor proteolytic stability and membrane permeability. Importantly, low aqueous solubility also impedes the development of peptides for clinical use. Various elaborate side chain stapling chemistries have been developed for α-helical peptides to circumvent this problem, with considerable success in spite of inevitable limitations. Here we report a novel peptide stapling strategy based on the dithiocarbamate chemistry linking the side chains of residues Lys(i) and Cys(i + 4) of unprotected peptides and apply it to a series of dodecameric peptide antagonists of the p53-inhibitory oncogenic proteins MDM2 and MDMX. Crystallographic studies of peptide–MDM2/MDMX complexes structurally validated the chemoselectivity of the dithiocarbamate staple bridging Lys and Cys at (i, i + 4) positions. One dithiocarbamate-stapled PMI derivative, ^DTCPMI, showed a 50-fold stronger binding to MDM2 and MDMX than its linear counterpart. Importantly, in contrast to PMI and its linear derivatives, the ^DTCPMI peptide actively traversed the cell membrane and killed HCT116 tumor cells in vitro by activating the tumor suppressor protein p53. Compared with other known stapling techniques, our solution-based DTC stapling chemistry is simple, cost-effective, regio-specific and environmentally friendly, promising an important new tool for the development of peptide therapeutics with improved pharmacological properties including aqueous solubility, proteolytic stability and membrane permeability.

Hydrophobic Surface Enhances Electrostatic Interaction in Water

A high dielectric constant is one of the peculiar properties of liquid water, indicating that the electrostatic interaction between charged substances is largely reduced in water. We show by molecular dynamics simulation that the dielectric constant of water is decreased near the hydrophobic surface. We further show that the decrease in the dielectric constant is due to both the decreased water density and the reduced water dipole correlation in the direction perpendicular to the surface. We finally demonstrate that electrostatic interaction in water is actually strengthened near the hydrophobic surface.

Immunomodulators targeting the PD-1/PD-L1 protein-protein interaction: From antibodies to small molecules

Cancer immunotherapy has made great strides in the recent decade, especially in the area of immune checkpoint blockade. The outstanding efficacy, prolonged durability of effect, and rapid assimilation of anti-PD-1 and anti-PD-L1 monoclonal antibodies in clinical practice have been nothing short of a medical breakthrough in the treatment of numerous malignancies. The major advantages of these therapeutic antibodies over their small molecule counterparts have been their high binding affinity and target specificity. However, antibodies do have their flaws including immune-related toxicities, inadequate pharmacokinetics and tumor penetration, and high cost burden to manufacturers and consumers. These limitations hinder broader clinical applications of the antibodies and have heightened interests in developing the alternative small molecule platform that includes peptidomimetics and peptides to target the PD-1/PD-L1 immune checkpoint system. The progress on these small molecule alternatives has been relatively slow compared to that of the antibodies. Fortunately, recent structural studies of the interactions among PD-1, PD-L1, and their respective antibodies have revealed key hotspots on PD-1 and PD-L1 that may facilitate drug discovery efforts for small molecule immunotherapeutics. This review is intended to discuss key concepts in immuno-oncology, describe the successes and shortcomings of PD-1/PD-L1 antibody-based therapies, and to highlight the recent development of small molecule inhibitors of the PD-1/PD-L1 protein-protein interaction.

The Mitochondrial-Encoded Peptide MOTS-c Translocates to the Nucleus to Regulate Nuclear Gene Expression in Response to Metabolic Stress

Cellular homeostasis is coordinated through communication between mitochondria and the nucleus, organelles that each possess their own genomes. Whereas the mitochondrial genome is regulated by factors encoded in the nucleus, the nuclear genome is currently not known to be actively controlled by factors encoded in the mitochondrial DNA. Here, we show that MOTS-c, a peptide encoded in the mitochondrial genome, translocates to the nucleus and regulates nuclear gene expression following metabolic stress in a 5'-adenosine monophosphate-activated protein kinase (AMPK)-dependent manner. In the nucleus, MOTS-c regulated a broad range of genes in response to glucose restriction, including those with antioxidant response elements (ARE), and interacted with ARE-regulating stress-responsive transcription factors, such as nuclear factor erythroid 2-related factor 2 (NFE2L2/NRF2). Our findings indicate that the mitochondrial and nuclear genomes co-evolved to independently encode for factors to cross-regulate each other, suggesting that mitonuclear communication is genetically integrated.

Protein-Ligand Interaction Energy-Based Entropy Calculations: Fundamental Challenges For Flexible Systems

Entropy calculations represent one of the most challenging steps in obtaining the binding free energy in biomolecular systems. A novel computationally effective approach (IE) was recently proposed to calculate the entropy based on the computation of protein-ligand interaction energy directly from molecular dynamics (MD) simulations. We present a study focused on the application of this method to flexible molecular systems and compare its performance with well-established normal mode (NM) and quasiharmonic (QH) entropy calculation approaches. Our results demonstrated that the IE method is intended for calculating entropy change for binding partners in fixed conformations, as by the original definition of IE, and is not applicable to the molecular complexes in which the interacting partners undergo significant conformational changes during the binding process.

De novo coiled-coil peptides as scaffolds for disrupting protein-protein interactions

Homo- and hetero-dimeric coiled coils as scaffolds for the presentation of α-helical protein-binding motifs.

Protein–protein interactions (PPIs) play pivotal roles in the majority of biological processes. Therefore, improved approaches to target and disrupt PPIs would provide tools for chemical biology and leads for therapeutic development. PPIs with α-helical components are appealing targets given that the secondary structure is well understood and can be mimicked or stabilised to render small-molecule and constrained-peptide-based inhibitors. Here we present a strategy to target α-helix-mediated PPIs that exploits de novo coiled-coil assemblies and test this using the MCL-1/NOXA-B PPI. First, computational alanine scanning is used to identify key α-helical residues from NOXA-B that contribute to the interface. Next, these residues are grafted onto the exposed surfaces of de novo designed homodimeric or heterodimeric coiled-coil peptides. The resulting synthetic peptides selectively inhibit a cognate MCL-1/BID complex in the mid-nM range. Furthermore, the heterodimeric system affords control as inhibition occurs only when both the grafted peptide and its designed partner are present. This establishes proof of concept for exploiting peptides stabilised in de novo coiled coils as inhibitors of PPIs. This dependence on supramolecular assembly introduces new possibilities for regulation and control.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Development of a new benchmark for assessing the scoring functions applicable to protein–protein interactions

Aim: Scoring functions are important component of protein–protein docking methods. They need to be evaluated on high-quality benchmarks to reveal their strengths and weaknesses. Evaluation results obtained on such benchmarks can provide valuable guidance for developing more advanced scoring functions. Methodology & results: In our comparative assessment of scoring functions for protein–protein interactions benchmark, the performance of a scoring function was characterized by ‘docking power’ and ‘scoring power’. A high-quality dataset of 273 protein–protein complexes was compiled and employed in both tests. Four scoring functions, including FASTCONTACT, ZRANK, dDFIRE and ATTRACT were tested as demonstration. ZRANK and ATTRACT exhibited encouraging performance in the docking power test. However, all four scoring functions failed badly in the scoring power test. Conclusion: Our comparative assessment of scoring functions for protein–protein interaction benchmark is created especially for assessing the scoring functions applicable to protein–protein interactions. It is different from other benchmarks for assessing protein–protein docking methods. Our benchmark is available to the public at www.pdbbind-cn.org/download/CASF-PPI/ .

Local and global anatomy of antibody-protein antigen recognition

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

Cooperativity Principles in Self-Assembled Nanomedicine

Nanomedicine is a discipline that applies nanoscience and nanotechnology principles to the prevention, diagnosis, and treatment of human diseases. Self-assembly of molecular components is becoming a common strategy in the design and syntheses of nanomaterials for biomedical applications. In both natural and synthetic self-assembled nanostructures, molecular cooperativity is emerging as an important hallmark. In many cases, interplay of many types of noncovalent interactions leads to dynamic nanosystems with emergent properties where the whole is bigger than the sum of the parts. In this review, we provide a comprehensive analysis of the cooperativity principles in multiple self-assembled nanostructures. We discuss the molecular origin and quantitative modeling of cooperative behaviors. In selected systems, we describe the examples on how to leverage molecular cooperativity to design nanomedicine with improved diagnostic precision and therapeutic efficacy in medicine.

Using Peptidomimetics and Constrained Peptides as Valuable Tools for Inhibiting Protein⁻Protein Interactions

Protein–protein interactions (PPIs) are tremendously important for the function of many biological processes. However, because of the structure of many protein–protein interfaces (flat, featureless and relatively large), they have largely been overlooked as potential drug targets. In this review, we highlight the current tools used to study the molecular recognition of PPIs through the use of different peptidomimetics, from small molecules and scaffolds to peptides. Then, we focus on constrained peptides, and in particular, ways to constrain α-helices through stapling using both one- and two-component techniques.

Charged Surface Regulates the Molecular Interactions of Electrostatically Repulsive Peptides by Inducing Oriented Alignment

Regulation of molecular orientation of charged dipeptides and involved interactions by electrostatic repulsion from like-charged surfaces were studied using all-atom molecular dynamics simulations. It was found that a charged surface can induce oriented alignment of like-charged peptides, and the oriented alignment leads to enhanced electrostatic repulsion between the peptide molecules. The findings are consistent with previous experimental results about the inhibition of charged protein aggregation using like-charged ion-exchange resin. Furthermore, the simulations provided molecular insights into this process, and demonstrated the distinct regulation effect of like-charged surfaces on the molecular interactions between peptides that possess an electric dipole structure. Both the charged surface and the electric dipole structure of peptides were confirmed to be crucial for the regulation. The research is expected to facilitate the rational design of surfaces or devices to regulate the behavior of amphoteric molecules such as proteins for both in vivo and in vitro applications, which would contribute to the regulation of protein-protein interactions and its application in life science and biotechnology.

Hidden long evolutionary memory in a model biochemical network

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

PROXiMATE: a database of mutant protein-protein complex thermodynamics and kinetics

Summary

We have developed PROXiMATE, a database of thermodynamic data for more than 6000 missense mutations in 174 heterodimeric protein-protein complexes, supplemented with interaction network data from STRING database, solvent accessibility, sequence, structural and functional information, experimental conditions and literature information. Additional features include complex structure visualization, search and display options, download options and a provision for users to upload their data.

Availability and implementation

The database is freely available at http://www.iitm.ac.in/bioinfo/PROXiMATE/ . The website is implemented in Python, and supports recent versions of major browsers such as IE10, Firefox, Chrome and Opera.

Contact

gromiha@iitm.ac.in.

Supplementary information

Supplementary data are available at Bioinformatics online.

Macrocyclic α helical peptide therapeutic modality: A perspective of learnings and challenges

Macrocyclic α-helical peptides have emerged as a compelling new therapeutic modality to tackle targets confined to the intracellular compartment. Within the scope of hydrocarbon-stapling there has been significant progress to date, including the first stapled α-helical peptide to enter into clinical trials. The principal design concept of stapled α-helical peptides is to mimic a cognate (protein) ligand relative to binding its target via an α-helical interface. However, it was the proclivity of such stapled α-helical peptides to exhibit cell permeability and proteolytic stability that underscored their promise as unique macrocyclic peptide drugs for intracellular targets. This perspective highlights key learnings as well as challenges in basic research with respect to structure-based design, innovative chemistry, cell permeability and proteolytic stability that are essential to fulfill the promise of stapled α-helical peptide drug development.

Toward a Rational Design to Regulate β-Amyloid Fibrillation for Alzheimer's Disease Treatment

The last decades have witnessed a growing global burden of Alzheimer's disease (AD). Evidence indicates that the onset and progression of AD is associated with β-amyloid (Aβ) peptide fibrillation. As such, there is a strong passion with discovering potent Aβ fibrillation inhibitors that can be developed into anti-amyloiddogenic agents for AD treatment. Current challenges that have arisen with this development involve with Aβ oligomer toxicity suppression and Blood Brain Barrier penetration capability. Considering most natural or biological events, one would observe that there is usually a "seed" to direct natural materials to assemble in response to a certain stimulation. Inspired by this, several materials or compounds, including nanoparticle, peptide or peptide mimics, and organic molecules, have been designed for the purpose of redirecting or impeding Aβ aggregation. Achieving these tasks requires comprehensive understanding on (1) initial Aβ assembly into insoluble deposits, (2) main concerns with fibrillation inhibition, and (3) current major methodologies to disrupt the aggregation. Herein, the objective of this review is to address these three areas, and enable the pathway for a promising therapeutic agent design for AD treatment.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction

Background

The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.

Results

In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI).

Conclusions

The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.

Peptides as Potential Therapeutics for Alzheimer's Disease

Intracellular synthesis, folding, trafficking and degradation of proteins are controlled and integrated by proteostasis. The frequency of protein misfolding disorders in the human population, e.g., in Alzheimer's disease (AD), is increasing due to the aging population. AD treatment options are limited to symptomatic interventions that at best slow-down disease progression. The key biochemical change in AD is the excessive accumulation of per-se non-toxic and soluble amyloid peptides (Aβ(1-37/44), in the intracellular and extracellular space, that alters proteostasis and triggers Aβ modification (e.g., by reactive oxygen species (ROS)) into toxic intermediate, misfolded soluble Aβ peptides, Aβ dimers and Aβ oligomers. The toxic intermediate Aβ products aggregate into progressively less toxic and less soluble protofibrils, fibrils and senile plaques. This review focuses on peptides that inhibit toxic Aβ oligomerization, Aβ aggregation into fibrils, or stabilize Aβ peptides in non-toxic oligomers, and discusses their potential for AD treatment.

Selecting Conformational Ensembles Using Residual Electron and Anomalous Density (READ)

Heterogeneous and dynamic biomolecular complexes play a central role in many cellular processes but are poorly understood due to experimental challenges in characterizing their structural ensembles. To address these difficulties, we developed a hybrid methodology that combines X-ray crystallography with ensemble selections typically used in NMR studies to determine structural ensembles of heterogeneous biomolecular complexes. The method, termed READ, for residual electron and anomalous density, enables the visualization of heterogeneous conformational ensembles of complexes within crystals. Here we present a detailed protocol for performing the ensemble selections to construct READ ensembles. From a diverse pool of binding poses, a selection scheme is used to determine a subset of conformations that maximizes agreement with the X-ray data. Overall, READ is a general approach for obtaining a high-resolution view of dynamic protein-protein complexes.

Development of proteolytically stable N-methylated peptide inhibitors of aggregation of the amylin peptide implicated in type 2 diabetes

Islet amyloid polypeptide, also known as amylin, is the main component of the amyloid deposits present in approximately 90% of people with type 2 diabetes mellitus (T2DM). In this disease, amylin aggregates into multimeric β-pleated sheet structures which cause damage to pancreatic islet β-cells. Inhibitors of early-stage amylin aggregation could therefore provide a disease-modifying treatment for T2DM. In this study, overlapping peptides were designed to target the 'binding' region (RLANFLVHSS, residues 11-20) of human amylin, and their effects on amyloid fibril formation were determined by thioflavin-T assay. The first generation peptides showed less than 50% inhibition of aggregation, but a second generation peptide (H₂N-RGANFLVHGR-CONH₂) showed strong inhibitory effects on amylin aggregation, and this was confirmed by negative stain electron microscopy. Cytotoxicity studies revealed that this peptide protected human pancreatic 1.4E7 (ECACC 10070102) insulin-secreting cells from the toxic effects of human amylin. Unlike the retro-inverso version of this peptide, which stimulated aggregation, two N-methylated peptides (H₂N-RGAmNFmLVmHGR-CONH₂ and H₂N-RGANmFLmVHmR-CONH₂) gave very clear dose-dependent inhibition of fibril formation. These two peptides were also stable against a range of different proteolytic enzymes, and in human plasma. These N-methylated peptides could provide a novel treatment for slowing progression of T2DM.

Supramolecular Chemistry Targeting Proteins

The specific recognition of protein surface elements is a fundamental challenge in the life sciences. New developments in this field will form the basis of advanced therapeutic approaches and lead to applications such as sensors, affinity tags, immobilization techniques, and protein-based materials. Synthetic supramolecular molecules and materials are creating new opportunities for protein recognition that are orthogonal to classical small molecule and protein-based approaches. As outlined here, their unique molecular features enable the recognition of amino acids, peptides, and even whole protein surfaces, which can be applied to the modulation and assembly of proteins. We believe that structural insights into these processes are of great value for the further development of this field and have therefore focused this Perspective on contributions that provide such structural data.

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.

IntPred: a structure-based predictor of protein-protein interaction sites

Motivation

Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.

Results

On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent.

Availability and implementation

IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/.

Supplementary information

Supplementary data are available at Bioinformatics online.

A novel peptide stapling strategy enables the retention of ring-closing amino acid side chains for the Wnt/β-catenin signalling pathway

An alternative all-hydrocarbon stapling approach in which the amino acid side chains are retained at the stapled positions.

The all-hydrocarbon peptide stapling strategy has recently been extensively explored in drug discovery. There remains the potential for improvement regarding the retention of the amino acid side chains at the stapled positions. Herein, we describe a new series of amino acids that not only contain the native side chains, but also carry the alkenyl arms that are needed for the ring-closing stapling chemistry. We incorporate the new amino acids into a β-catenin-binding domain of Axin (469–482) and develop a new category of stapled peptides with the retention of the native side chains. These stapled peptides exhibit high α-helicity, strong proteolytic stability and good cell permeability. Biochemical experiments demonstrate that these stapled peptides can activate β-catenin more efficiently than canonical stapled peptides due to the presence of extra side chains. We expect that the new side-chain-retention stapling method would expand the scope of the all-hydrocarbon stapled peptide strategy by retaining some important peripheral residues for protein–protein interactions or preserving key hydrophilic side chains to improve solubility.

Identification of novel direct protein-protein interactions by irradiating living cells with femtosecond UV laser pulses

The identification of protein-protein interaction networks in living cells is becoming increasingly fundamental to elucidate main biological processes and to understand disease molecular bases on a system-wide level. We recently described a method (LUCK, Laser UV Cross-linKing) to cross-link interacting protein surfaces in living cells by UV laser irradiation. By using this innovative methodology, that does not require any protein modification or cell engineering, here we demonstrate that, upon UV laser irradiation of HeLa cells, a direct interaction between GAPDH and alpha-enolase was "frozen" by a cross-linking event. We validated the occurrence of this direct interaction by co-immunoprecipitation and Immuno-FRET analyses. This represents a proof of principle of the LUCK capability to reveal direct protein interactions in their physiological environment.

Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins

Traditional Protein-Protein Interaction (PPI) networks, which use a node and edge representation, lack some valuable information about the mechanistic details of biological processes. Mapping protein structures to these PPI networks not only provides structural details of each interaction but also helps us to find the mutual exclusive interactions. Yet it is not a comprehensive representation as it neglects the conformational changes of proteins which may lead to different interactions, functions, and downstream signalling. In this study, we proposed a new representation for structural PPI networks inspecting the alternative conformations of proteins. We performed a large-scale study by creating breast cancer metastasis network and equipped it with different conformers of proteins. Our results showed that although 88% of proteins in our network has at least two structures in Protein Data Bank (PDB), only 22% of them have alternative conformations and the remaining proteins have different regions saved in PDB. However, using even this small set of alternative conformations we observed a considerable increase in our protein docking predictions. Our protein-protein interaction predictions increased from 54% to 76% using the alternative conformations. We also showed the benefits of investigating structural data and alternative conformations of proteins through three case studies.