The molecular architecture of protein-protein binding sites

Substituted Oligosaccharides as Protein Mimics: Deep Learning Free Energy Landscapes

Protein-protein complexes power the majority of cellular processes. Interfering with the formation of such complexes using well-designed mimics is a difficult, yet actively pursued, research endeavor. Due to the limited availability of results on the conformational preferences of oligosaccharides compared to polypeptides, the former have been much less explored than the latter as protein mimics, despite interesting ADMET characteristics. In this work, the conformational landscapes of a series of 956 substituted glucopyranose oligomers of lengths 3 to 12 designed as protein interface mimics are revealed using microsecond-time-scale, enhanced-sampling molecular dynamics simulations. Deep convolutional networks are trained on these large conformational ensembles, to predict the stability of longer oligosaccharide structures from those of their constituent trimer motifs. Deep generative adversarial networks are then designed to suggest plausible conformations for oligosaccharide mimics of arbitrary length and substituent sequences that can subsequently be used as input to docking simulations. Analyzing the performance of the neural networks also yields insights into the intricate collective effects that dominate oligosaccharide conformational dynamics.

Interfacial residues in protein-protein complexes are in the eyes of the beholder

Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.

Comparative analysis of permanent and transient domain-domain interactions in multi-domain proteins

Protein domains are structural, functional, and evolutionary units. These domains bring out the diversity of functionality by means of interactions with other co-existing domains and provide stability. Hence, it is important to study intra-protein inter-domain interactions from the perspective of types of interactions. Domains within a chain could interact over short timeframes or permanently, rather like protein-protein interactions (PPIs). However, no systematic study has been carried out between two classes, namely permanent and transient domain-domain interactions. In this work, we studied 263 two-domain proteins, belonging to either of these classes and their interfaces on the basis of several factors, such as interface area and details of interactions (number, strength, and types of interactions). We also characterized them based on residue conservation at the interface, correlation of residue motions across domains, its involvement in repeat formation, and their involvement in particular molecular processes. Finally, we could analyze the interactions arising from domains in two-domain monomeric proteins, and we observed significant differences between these two classes of domain interactions and a few similarities. This study will help to obtain a better understanding of structure-function and folding principles of multi-domain proteins.

Nanopore sensors for single molecular protein detection: Research progress based on computer simulations

As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label-free and high-throughput protein sequencing. In recent years, nanopore-based biosensors have been widely used in the detection and analysis of biomolecules such as DNA, RNA, and proteins. At the same time, computer simulations can describe the transport of proteins through nanopores at the atomic level. This paper reviews the applications of nanopore sensors in protein sequencing over the past decade and the solutions to key problems from a computer simulation perspective, with the aim of pointing the way to the future of nanopore protein sequencing.

Analysis of protein-protein interface with incorporating low-frequency molecular interactions in molecular dynamics simulation

Protein-protein interactions are vital for various biological processes such as immune reaction, signal transduction, and viral infection. Molecular Dynamics (MD) simulation is a powerful tool for analyzing non-covalent interactions between two protein molecules. In general, MD simulation studies on the protein-protein interface have focused on the analysis of major and frequent molecular interactions. In this study, we demonstrate that minor interactions with low-frequency need to be incorporated to analyze the molecular interactions in the protein-protein interface more efficiently using the complex of SARS-CoV2-RBD and ACE2 receptor as a model system. It was observed that the dominance of interactions in the MD-simulated structures didn't directly correlate with the interactions in the experimentally determined structure. The interactions from the experimentally determined structure could be reproduced better in the ensemble of MD simulated structures by including the less frequent interactions compared to the norm of choosing only highly frequent interactions. Residue Interaction Networks (RINs) analysis also showed that the critical residues in the protein-protein interface could be more efficiently identified by incorporating low-frequency interactions in MD simulation. It is expected that the approach proposed in this study can be a new way of studying protein-protein interaction through MD simulation.

Protein Receptors Evolved from Homologous Cohesion Modules That Self-Associated and Are Encoded by Interactive Networked Genes

Previously, it was proposed that protein receptors evolved from self-binding peptides that were encoded by self-interacting gene segments (inverted repeats) widely dispersed in the genome. In addition, self-association of the peptides was thought to be mediated by regions of amino acid sequence similarity. To extend these ideas, special features of receptors have been explored, such as their degree of homology to other proteins, and the arrangement of their genes for clues about their evolutionary origins and dynamics in the genome. As predicted, BLASTP searches for homologous proteins detected a greater number of unique hits for queries with receptor sequences than for sequences of randomly-selected, non-receptor proteins. This suggested that the building blocks (cohesion modules) for receptors were duplicated, dispersed, and maintained in the genome, due to structure/function relationships discussed here. Furthermore, the genes coding for a representative panel of receptors participated in a larger number of gene-gene interactions than for randomly-selected genes. This could conceivably reflect a greater evolutionary conservation of the receptor genes, with their more extensive integration into networks along with inherent properties of the genes themselves. In support of the latter possibility, some receptor genes were located in active areas of adaptive gene relocation/amalgamation to form functional blocks of related genes. It is suggested that adaptive relocation might allow for their joint regulation by common promoters and enhancers, and affect local chromatin structural domains to facilitate or repress gene expression. Speculation is included about the nature of the coordinated communication between receptors and the genes that encode them.

Deciphering the impacts of composition of extracellular polymeric substances on sludge dewaterability: An often overlooked role of amino acids

An investigation was conducted for waste activated sludge pretreated by different methods (e.g., ultrasonic, thermal, ozone, and acid/alkaline) in order to establish correlations between amino acids and parameters related to sludge dewaterability (e.g., capillary suction time (CST), specific resistance to filtration (SRF), proteins (PN) and polysaccharides (PS) in different fractions of extracellular polymeric substances (EPS), zeta potential, and particle sizes). The results indicated that glycine, serine, and threonine were the key identified amino acids correlated with parameters related to sludge dewaterability. To be exemplified, glycine showed positive correlations with the normalized CST (regression coefficient (R) = 0.72, p < 0.05), the normalized SRF (R = 0.74, p < 0.05), PN in soluble EPS (R = 0.89, p < 0.05), PS in soluble EPS (R = 0.56, p < 0.05), tryptophan-like PN in soluble EPS (R = 0.60, p < 0.05), and tryptophan-like PN in loosely-bound EPS (R = 0.58, p < 0.05). After adding extra glycine, serine, and threonine into sludge samples, sludge dewaterability was deteriorated. The hydrophilic functional groups of CO and C-OH were found to be more predominant in sludge with the presence of these amino acids. The Lewis acid-base interaction predominated in determining the net attraction among sludge flocs. Moreover, the presence of glycine, serine, and threonine resulted in high repulsive hydrophilic interaction, which deteriorated sludge dewaterability. This study emphasized the importance of amino acids in sludge dewatering and amino acids might be incorporated into parameters reflecting sludge dewaterability.

Positive charge in the complementarity-determining regions of synthetic nanobody prevents aggregation

In the past, specificity and affinity were the priority for synthetic antibody library. However, therapeutic antibodies need good stability for medical use. Through carefully adjust the chemical diversity in CDRs, one hopes to design a synthetic antibody library with good developability. Here we thoroughly analyzed 296 nanobody sequences and structures, constructed a fully-functional synthetic nanobody library, evaluated the relationship between aggregation and isoelectric point, and found that high-pI nanobodies were more resistant to aggregation than low-pIs. As we used the same framework for constructing the library, CDRs charge played a crucial role in mediating nanobody aggregation. We also analyzed the theoretical pI of 296 nanobodies from PDB, about 75% had basic pI, only 25% were acidic. Those results provided useful guidelines for designing next-generation synthetic nanobody libraries and for identifying potent and safe nanobody therapeutics.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

A grafted peptidomimetic for EGFR heterodimerization inhibition: Implications in NSCLC models

Among the lung cancers, approximately 85% are histologically classified as non-small-cell lung cancer (NSCLC), a leading cause of cancer deaths worldwide. Epidermal growth factor receptors (EGFRs) are known to play a crucial role in lung cancer. HER2 overexpression is detected by immunohistochemistry in 2.4%-38% of NSCLC samples. EGFRs have been targeted with three generations of tyrosine kinase inhibitors (TKIs), and drug resistance has become a major issue; HER2 dimerization with EGFR also plays a major role in the development of resistance to TKI therapy. We have designed grafted peptides to bind to the HER2 extracellular domain (ECD) and inhibit protein-protein interactions of EGFR:HER2 and HER2:HER3. A sunflower trypsin inhibitor (SFTI-1) template was used to graft a peptidomimetic compound. Among several grafted peptides, SFTI-G5 exhibited antiproliferative activity in HER2-positive NSCLC cell lines such as Calu-3 cells with an IC50 value of 0.073 μM. SFTI-G5 was shown to bind to ECD of HER2 and inhibit EGFR:HER2 and HER2:HER3 dimerization and inhibit the phosphorylation of HER2 and downstream signaling proteins. As a proof-of-concept, the in vivo activity of SFTI-G5 was evaluated in two NSCLC mouse models. SFTI-G5 was able to inhibit tumor growth in both models. Furthermore, SFTI-G5 was shown to inhibit EGFR dimerization in tissue samples obtained from in vivo models. These grafted peptides can be used as novel dual inhibitors of EGFR dimerization in NSCLC.

Recent advances in β-catenin/BCL9 protein-protein interaction inhibitors

Wnt/β-catenin signaling is crucial both in normal embryonic development and throughout the life of an organism. Moreover, aberrant Wnt signaling has been associated with various diseases, especially cancer and fibrosis. Recent research suggests that direct targeting of the β-catenin/BCL9 protein-protein interaction (PPI) is a promising strategy to block the Wnt pathway. Progress in understanding the cocrystalline complex and mechanism of action of the β-catenin/BCL9 interaction facilitates the discovery process of its inhibitors, but only a few inhibitors have been reported. In this review, the discovery and development of β-catenin/BCL9 PPI inhibitors in the areas of drug design, structure-activity relationships and biological and biochemical properties are summarized. In addition, perspectives for the future development of β-catenin/BCL9 PPI inhibitors are explored.

Activity Mapping the Acyl Carrier Protein: Elongating Ketosynthase Interaction in Fatty Acid Biosynthesis

Elongating ketosynthases (KSs) catalyze carbon-carbon bond-forming reactions during the committed step for each round of chain extension in both fatty acid synthases (FASs) and polyketide synthases (PKSs). A small α-helical acyl carrier protein (ACP) shuttles fatty acyl intermediates between enzyme active sites. To accomplish this task, the ACP relies on a series of dynamic interactions with multiple partner enzymes of FAS and associated FAS-dependent pathways. Recent structures of the Escherichia coli FAS ACP, AcpP, in covalent complexes with its two cognate elongating KSs, FabF and FabB, provide high-resolution details of these interfaces, but a systematic analysis of specific interfacial interactions responsible for stabilizing these complexes has not yet been undertaken. Here, we use site-directed mutagenesis with both in vitro and in vivo activity analyses to quantitatively evaluate these contacting surfaces between AcpP and FabF. We delineate the FabF interface into three interacting regions and demonstrate the effects of point mutants, double mutants, and region deletion variants. Results from these analyses reveal a robust and modular FabF interface capable of tolerating seemingly critical interface mutations with only the deletion of an entire region significantly compromising activity. Structure and sequence analyses of FabF orthologs from related type II FAS pathways indicate significant conservation of type II FAS KS interface residues and, overall, support its delineation into interaction regions. These findings strengthen our mechanistic understanding of molecular recognition events between ACPs and FAS enzymes and provide a blueprint for engineering ACP-dependent biosynthetic pathways.

Mapping Protein-Protein Interaction Interface Peptides with Jun-Fos Assisted Phage Display and Deep Sequencing

Protein-protein interactions govern many cellular processes, and identifying binding interaction sites on proteins can facilitate the discovery of inhibitors to block such interactions. Here we identify peptides from a randomly fragmented plasmid encoding the β-lactamase inhibitory protein (BLIP) and the Lac repressor (LacI) that represent regions of protein-protein interactions. We utilized a Jun-Fos-assisted phage display system that has previously been used to screen cDNA and genomic libraries to identify antibody antigens. Affinity selection with polyclonal antibodies against LacI or BLIP resulted in the rapid enrichment of in-frame peptides from various regions of the proteins. Further, affinity selection with β-lactamase enriched peptides that encompass regions of BLIP previously shown to contribute strongly to the binding energy of the BLIP/β-lactamase interaction, i.e., hotspot residues. Further, one of the regions enriched by affinity selection encompassed a disulfide-constrained region of BLIP that forms part of the BLIP interaction surface in the native complex that we show also binds to β-lactamase as a disulfide-constrained macrocycle peptide with a K_D of ∼1 μM. Fragmented open reading frame (ORF) libraries may efficiently identify such naturally constrained peptides at protein-protein interaction interfaces. With sufficiently deep coverage of ORFs by peptide-coding inserts, phage display and deep sequencing can provide detailed information on the domains or peptides that contribute to an interaction. Such information should enable the design of potentially therapeutic macrocycles or peptidomimetics that block the interaction.

A Novel Index of Contact Frequency from Noise Protein-Protein Interaction Data Help for Accurate Interface Residue Pair Prediction

Protein-protein interactions are important for most biological processes and have been studied for decades. However, the detailed formation mechanism of protein-protein interaction interface is still ambiguous, which makes it difficult to accurately predict the protein-protein interaction interface residue pairs. Here, we extract the interface residue-residue contacts from the decoys in the ZDOCK protein-protein complex decoy set with RMSD mostly larger than 3 Å. To accurately compute the interface residue-residue contacts, we define a new constant called interface residue pairs frequency, which counts the atom contact numbers between two interface residues. We normalize interface residue pairs frequency to pick out the top residue-residue pairs from all the possible pairs preferential to be on correct protein-protein interaction interface. When tested on 37 protein dimers from the decoy set where most decoys are incorrect, our method successfully predicts 30 protein dimers with a success rate of up to 81.1%. Higher accuracy than some other state-of-the-art methods confirmed the performance of our method.

Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets

Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

Synthesis of Oligomeric and Monomeric Functionalized Graphene Oxides and a Comparison of Their Abilities to Perform as Protein Ligands and Enzyme Inhibitors

Graphene oxide (GO) is a versatile, monomolecular layered nanomaterial that possesses various oxygen-containing functionality on its large surface. These characteristics allow GO to interact with a variety of materials and to be applied towards a number of areas. The strength and selectivity of these interactions can be improved significantly through further functionalization. In this paper, we describe the functionalization of GO and its application as a protein ligand and an enzyme inhibitor. The work reported in this paper details how chymotrypsin inhibition can be improved using GO functionalized with a monomeric and oligomer layer of tyrosine. The results indicated that the mono- and oligo-functionalized systems performed extremely well, with Ki values nearly four times better than GO alone. Our original premise was that the oligomeric system would bind better because of the length of the oligomeric arms and potential for a high degree of flexibility. However, the results clearly showed that the shorter monomeric system was the better ligand/inhibitor. This was due to weaker intramolecular interactions between the aromatic side chains of tyrosine and the aromatic surface of GO. Although these are possible for both systems, they are cooperative and therefore stronger for the oligomeric functionalized GO. As such, the protein must compete and overcome these cooperative intramolecular interactions before it can bind to the functionalized GO, whereas the tyrosines on the surface of the monomeric system interact with the surface of GO through a significantly weaker monovalent interaction, but interact cooperatively with the protein surface.

Infinite Assembly of Folded Proteins in Evolution, Disease, and Engineering

Mutations and changes in a protein's environment are well known for their potential to induce misfolding and aggregation, including amyloid formation. Alternatively, such perturbations can trigger new interactions that lead to the polymerization of folded proteins. In contrast to aggregation, this process does not require misfolding and, to highlight this difference, we refer to it as agglomeration. This term encompasses the amorphous assembly of folded proteins as well as the polymerization in one, two, or three dimensions. We stress the remarkable potential of symmetric homo-oligomers to agglomerate even by single surface point mutations, and we review the double-edged nature of this potential: how aberrant assemblies resulting from agglomeration can lead to disease, but also how agglomeration can serve in cellular adaptation and be exploited for the rational design of novel biomaterials.

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.

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.

Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design

Protein dimerization controls many physiological processes in the body. Proteins form homo-, hetero-, or oligomerization in the cellular environment to regulate the cellular processes. Any deregulation of these processes may result in a disease state. Protein-protein interactions (PPIs) can be inhibited by antibodies, small molecules, or peptides, and inhibition of PPI has therapeutic value. PPI drug discovery research has steadily increased in the last decade, and a few PPI inhibitors have already reached the pharmaceutical market. Several PPI inhibitors are in clinical trials. With advancements in structural and molecular biology methods, several methods are now available to study protein homo- and heterodimerization and their inhibition by drug-like molecules. Recently developed methods to study PPI such as proximity ligation assay and enzyme-fragment complementation assay that detect the PPI in the cellular environment are described with examples. At present, the methods used to design PPI inhibitors can be classified into three major groups: (1) structure-based drug design, (2) high-throughput screening, and (3) fragment-based drug design. In this chapter, we have described some of the experimental methods to study PPIs and their inhibition. Examples of homo- and heterodimers of proteins, their structural and functional aspects, and some of the inhibitors that have clinical importance are discussed. The design of PPI inhibitors of epidermal growth factor receptor heterodimers and CD2-CD58 is discussed in detail.

Antibody-Based Protective Immunity against Helminth Infections: Antibody Phage Display Derived Antibodies against BmR1 Antigen

Helminth parasite infections are significantly impacting global health, with more than two billion infections worldwide with a high morbidity rate. The complex life cycle of the nematodes has made host immune response studies against these parasites extremely difficult. In this study, we utilized two phage antibody libraries; the immune and naïve library were used to identify single chain fragment variable (scFv) clones against a specific filarial antigen (BmR1). The V-gene analysis of isolated scFv clones will help shed light on preferential VDJ gene segment usage against the filarial BmR1 antigen in healthy and infected states. The immune library showed the usage of both lambda and kappa light chains. However, the naïve library showed preferential use of the lambda family with different amino acid distributions. The binding characteristics of the scFv clones identified from this work were analyzed by immunoassay and immunoaffinity pull down of BmR1. The work highlights the antibody gene usage pattern of a naïve and immune antibody library against the same antigen as well as the robust nature of the enriched antibodies for downstream applications.

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.

Packing in protein cores

Proteins are biological polymers that underlie all cellular functions. The first high-resolution protein structures were determined by x-ray crystallography in the 1960s. Since then, there has been continued interest in understanding and predicting protein structure and stability. It is well-established that a large contribution to protein stability originates from the sequestration from solvent of hydrophobic residues in the protein core. How are such hydrophobic residues arranged in the core; how can one best model the packing of these residues, and are residues loosely packed with multiple allowed side chain conformations or densely packed with a single allowed side chain conformation? Here we show that to properly model the packing of residues in protein cores it is essential that amino acids are represented by appropriately calibrated atom sizes, and that hydrogen atoms are explicitly included. We show that protein cores possess a packing fraction of [Formula: see text], which is significantly less than the typically quoted value of 0.74 obtained using the extended atom representation. We also compare the results for the packing of amino acids in protein cores to results obtained for jammed packings from discrete element simulations of spheres, elongated particles, and composite particles with bumpy surfaces. We show that amino acids in protein cores pack as densely as disordered jammed packings of particles with similar values for the aspect ratio and bumpiness as found for amino acids. Knowing the structural properties of protein cores is of both fundamental and practical importance. Practically, it enables the assessment of changes in the structure and stability of proteins arising from amino acid mutations (such as those identified as a result of the massive human genome sequencing efforts) and the design of new folded, stable proteins and protein-protein interactions with tunable specificity and affinity.

Effect of Terminal-Group Functionality on the Ability of Dendrimers to Bind Proteins

It is known that dendrimers can bind proteins with good selectively. This selectivity comes about from an optimization based on matching the size of the dendrimer with the size of the protein's interfacial binding area. In this paper, we report how this selectivity can be moderated by the functionality on the surface of the dendrimer. Specifically, we describe the synthesis of amino acid functionalized dendrimers and the effect of functionality on the dendrimer's ability to bind and inhibit the enzymatic protein, chymotrypsin. The results show how dendrimer binding can be increased or decreased depending on the terminal functionality. These results will allow new ligands to be designed and synthesized, possessing increased and selective protein-binding abilities.

Combining ancestral sequence reconstruction with protein design to identify an interface hotspot in a key metabolic enzyme complex

It is important to identify hotspot residues that determine protein-protein interactions in interfaces of macromolecular complexes. We have applied a combination of ancestral sequence reconstruction and protein design to identify hotspots within imidazole glycerol phosphate synthase (ImGPS). ImGPS is a key metabolic enzyme complex, which links histidine and de novo purine biosynthesis and consists of the cyclase subunit HisF and the glutaminase subunit HisH. Initial fluorescence titration experiments showed that HisH from Zymomonas mobilis (zmHisH) binds with high affinity to the reconstructed HisF from the last universal common ancestor (LUCA-HisF) but not to HisF from Pyrobaculum arsenaticum (paHisF), which differ by 103 residues. Subsequent titration experiments with a reconstructed evolutionary intermediate linking LUCA-HisF and paHisF and inspection of the subunit interface of a contemporary ImGPS allowed us to narrow down the differences crucial for zmHisH binding to nine amino acids of HisF. Homology modeling and in silico mutagenesis studies suggested that at most two of these nine HisF residues are crucial for zmHisH binding. These computational results were verified by experimental site-directed mutagenesis, which finally enabled us to pinpoint a single amino acid residue in HisF that is decisive for high-affinity binding of zmHisH. Our work shows that the identification of protein interface hotspots can be very efficient when reconstructed proteins with different binding properties are included in the analysis. Proteins 2017; 85:312-321. © 2016 Wiley Periodicals, Inc.

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

Background

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

Results

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

Conclusions

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

Availability

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

Electronic supplementary material

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

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

Insights into the mechanism of human papillomavirus E2-induced procaspase-8 activation and cell death

High-risk human papillomavirus (HR-HPV) E2 protein, the master regulator of viral life cycle, induces apoptosis of host cell that is independent of its virus-associated regulatory functions. E2 protein of HR-HPV18 has been found to be involved in novel FADD-independent activation of caspase-8, however, the molecular basis of this unique non-death-fold E2-mediated apoptosis is poorly understood. Here, with an interdisciplinary approach that involves in silico, mutational, biochemical and biophysical probes, we dissected and characterized the E2-procasapse-8 binding interface. Our data demonstrate direct non-homotypic interaction of HPV18 E2 transactivation domain (TAD) with α2/α5 helices of procaspase-8 death effector domain-B (DED-B). The observed interaction mimics the homotypic DED-DED complexes, wherein the conserved hydrophobic motif of procaspase-8 DED-B (F122/L123) occupies a groove between α2/α3 helices of E2 TAD. This interaction possibly drives DED oligomerization leading to caspase-8 activation and subsequent cell death. Furthermore, our data establish a model for E2-induced apoptosis in HR-HPV types and provide important clues for designing E2 analogs that might modulate procaspase-8 activation and hence apoptosis.

Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2.

Discrete structural features among interface residue-level classes

BACKGROUND

Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding.

RESULTS

Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes.

CONCLUSIONS

Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.

Linking structural features of protein complexes and biological function

Protein-protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure-function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Flexibility of PCNA-protein interface accommodates differential binding partners

The expanding roles of PCNA in functional assembly of DNA replication and repair complexes motivated investigation of the structural and dynamic properties guiding specificity of PCNA-protein interactions. A series of biochemical and computational analyses were combined to evaluate the PIP Box recognition features impacting complex formation. The results indicate subtle differences in topological and molecular descriptors distinguishing both affinity and stoichiometry of binding among PCNA-peptide complexes through cooperative effects. These features were validated using peptide mimics of p85α and Akt, two previously unreported PCNA binding partners. This study characterizes for the first time a reverse PIP Box interaction with PCNA. Small molecule ligand binding at the PIP Box interaction site confirmed the adaptive nature of the protein in dictating overall shape and implicates allosterism in transmitting biological effects.

New insights into the interdependence between amino acid stereochemistry and protein structure

To successfully design new proteins and understand the effects of mutations in natural proteins, we must understand the geometric and physicochemical principles underlying protein structure. The side chains of amino acids in peptides and proteins adopt specific dihedral angle combinations; however, we still do not have a fundamental quantitative understanding of why some side-chain dihedral angle combinations are highly populated and others are not. Here we employ a hard-sphere plus stereochemical constraint model of dipeptide mimetics to enumerate the side-chain dihedral angles of leucine (Leu) and isoleucine (Ile), and identify those conformations that are sterically allowed versus those that are not as a function of the backbone dihedral angles ϕ and ψ. We compare our results with the observed distributions of side-chain dihedral angles in proteins of known structure. With the hard-sphere plus stereochemical constraint model, we obtain agreement between the model predictions and the observed side-chain dihedral angle distributions for Leu and Ile. These results quantify the extent to which local, geometrical constraints determine protein side-chain conformations.

Role of β-lactamase residues in a common interface for binding the structurally unrelated inhibitory proteins BLIP and BLIP-II

The β-lactamase inhibitory proteins (BLIPs) are a model system for examining molecular recognition in protein-protein interactions. BLIP and BLIP-II are structurally unrelated proteins that bind and inhibit TEM-1 β-lactamase. Both BLIPs share a common binding interface on TEM-1 and make contacts with many of the same TEM-1 surface residues. BLIP-II, however, binds TEM-1 over 150-fold tighter than BLIP despite the fact that it has fewer contact residues and a smaller binding interface. The role of eleven TEM-1 amino acid residues that contact both BLIP and BLIP-II was examined by alanine mutagenesis and determination of the association (k on) and dissociation (k off) rate constants for binding each partner. The substitutions had little impact on association rates and resulted in a wide range of dissociation rates as previously observed for substitutions on the BLIP side of the interface. The substitutions also had less effect on binding affinity for BLIP than BLIP-II. This is consistent with the high affinity and small binding interface of the TEM-1-BLIP-II complex, which predicts per residue contributions should be higher for TEM-1 binding to BLIP-II versus BLIP. Two TEM-1 residues (E104 and M129) were found to be hotspots for binding BLIP while five (L102, Y105, P107, K111, and M129) are hotspots for binding BLIP-II with only M129 as a common hotspot for both. Thus, although the same TEM-1 surface binds to both BLIP and BLIP-II, the distribution of binding energy on the surface is different for the two target proteins, that is, different binding strategies are employed.

DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking

The distance-dependent knowledge-based DrugScore^PPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking “unbound perturbation” (“unbound docking”) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScore^PPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScore^PPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScore^PPI/FRODOCK will be successful.

Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

Druggable protein-protein interactions--from hot spots to hot segments

Protein-Protein Interactions (PPIs) mediate numerous biological functions. As such, the inhibition of specific PPIs has tremendous therapeutic value. The notion that these interactions are 'undruggable' has petered out with the emergence of more and more successful examples of PPI inhibitors, expanding considerably the scope of potential drug targets. The accumulated data on successes in the inhibition of PPIs allow us to analyze the features that are required for such inhibition. Whereas it has been suggested and shown that targeting hot spots at PPI interfaces is a good strategy to achieve inhibition, in this review we focus on the notion that the most amenable interactions for inhibition are those that are mediated by a 'hot segment', a continuous epitope that contributes the majority of the binding energy. This criterion is both useful in guiding future target selection efforts, and in suggesting immediate inhibitory candidates--the dominant peptidic segment that mediates the targeted interaction.