Side-Chain Conformational Preferences Govern Protein-Protein Interactions

Folding of Class IIa HDAC Derived Peptides into α-helices Upon Binding to Myocyte Enhancer Factor-2 in Complex with DNA

Interaction of transcription factor myocyte enhancer factor-2 (MEF2) family members with class IIa histone deacetylases (HDACs) has been implicated in a wide variety of diseases. Though considerable knowledge on this topic has been accumulated over the years, a high resolution and detailed analysis of the binding mode of multiple class IIa HDAC derived peptides with MEF2D is still lacking. To fulfil this gap, we report here the crystal structure of MEF2D in complex with double strand DNA and four different class IIa HDAC derived peptides, namely HDAC4, HDAC5, HDAC7 and HDAC9. All class IIa HDAC derived peptides form extended amphipathic α-helix structures that fit snugly in the hydrophobic groove of MEF2D domain. Binding mode of class IIa HDAC derived peptides to MEF2D is very similar and occur primarily through nonpolar interactions mediated by highly conserved branched hydrophobic amino acids. Further studies revealed that class IIa HDAC derived peptides are unstructured in solution and appear to adopt a folded α-helix structure only upon binding to MEF2D. Comparison of our peptide-protein complexes with previously characterized structures of MEF2 bound to different co-activators and co-repressors, highlighted both differences and similarities, and revealed the adaptability of MEF2 in protein-protein interactions. The elucidation of the three-dimensional structure of MEF2D in complex with multiple class IIa HDAC derived peptides provide not only a better understanding of the molecular basis of their interactions but also have implications for the development of novel antagonist.

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here we introduce a modular strategy to generate conformations based on "backmapping" from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

Recent Advances in Photoinduced Modification of Amino Acids, Peptides, and Proteins

The chemical modification of biopolymers like peptides and proteins is a key technology to access vaccines and pharmaceuticals. Similarly, the tunable derivatization of individual amino acids is important as they are key building blocks of biomolecules, bioactive natural products, synthetic polymers, and innovative materials. The high diversity of functional groups present in amino acid-based molecules represents a significant challenge for their selective derivatization Recently, visible light-mediated transformations have emerged as a powerful strategy for achieving chemoselective biomolecule modification. This technique offers numerous advantages over other methods, including a higher selectivity, mild reaction conditions and high functional-group tolerance. This review provides an overview of the most recent methods covering the photoinduced modification for single amino acids and site-selective functionalization in peptides and proteins under mild and even biocompatible conditions. Future challenges and perspectives are discussed beyond the diverse types of photocatalytic transformations that are currently available.

Role of tungsten disulfide quantum dots in specific protein-protein interactions at air-water interface

The intriguing network of antibody-antigen (Ab-Ag) interactions is highly governed by environmental perturbations and the nature of biomolecular interaction. Protein-protein interactions (PPIs) have potential applications in developing protein-adsorption-based sensors and nano-scale materials. Therefore, characterizing PPIs in the presence of a nanomaterial at the molecular level becomes imperative. The present work involves the investigation of antiferritin-ferritin (Ab-Ag) protein interactions under the influence of tungsten disulfide quantum dots (WS2 QDs). Isothermal calorimetry and contact angle measurements validated the strong influence of WS2 QDs on Ab-Ag interactions. The interfacial signatures of nano-bio-interactions were evaluated using sum frequency generation vibration spectroscopy (SFG-VS) at the air-water interface. Our SFG results reveal a variation in the tilt angle of methyl groups by ∼12° ± 2° for the Ab-Ag system in the presence of WS2 QDs. The results illustrated an enhanced ordering of water molecules in the presence of QDs, which underpins the active role of interfacial water molecules during nano-bio-interactions. We have also witnessed a differential impact of QDs on Ab-Ag by raising the concentration of the Ab-Ag combination, which showcased an increased inter-molecular interaction among the Ab and Ag molecules and a minimal influence on the methyl tilt angle. These findings suggest the formation of stronger and ordered Ab-Ag complexes upon introducing WS2 QDs in the aqueous medium and signify the potentiality of WS2 QDs relevant to protein-based sensing assays.

Leucine Motifs Stabilize Residual Helical Structure in Disordered Proteins

Many examples are known of regions of intrinsically disordered proteins that fold into α-helices upon binding to their targets. These helical binding motifs (HBMs) can be partially helical also in the unbound state, and this so-called residual structure can affect binding affinity and kinetics. To investigate the underlying mechanisms governing the formation of residual helical structure, we assembled a dataset of experimental helix contents of 65 peptides containing HBM that fold-upon-binding. The average residual helicity is 17% and increases to 60% upon target binding. The helix contents of residual and target-bound structures do not correlate, however the relative location of helix elements in both states shows a strong overlap. Compared to the general disordered regions, HBMs are enriched in amino acids with high helix preference and these residues are typically involved in target binding, explaining the overlap in helix positions. In particular, we find that leucine residues and leucine motifs in HBMs are the major contributors to helix stabilization and target-binding. For the two model peptides, we show that substitution of leucine motifs to other hydrophobic residues (valine or isoleucine) leads to reduction of residual helicity, supporting the role of leucine as helix stabilizer. From the three hydrophobic residues only leucine can efficiently stabilize residual helical structure. We suggest that the high occurrence of leucine motifs and a general preference for leucine at binding interfaces in HBMs can be explained by its unique ability to stabilize helical elements.

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Reduction of 2-H-substituted pyrrolinium cations: the carbon-carbon single bond in air stable 2,2'-bipyrrolidines as a two-electron-source

Reduction of 2-H-substituted pyrrolinium cations via initially formed secondary radicals results in either dimerisation or H-abstracted products, while the outcome depends on the N-substituents. The resultant central carbon-carbon single bond in the dimerised 2,2'-bipyrrolidine derivatives can be oxidised chemically and electrochemically. The notably air and moisture-stable dimers were subsequently utilised as a source of two electrons in various chemical transformations.

Sequence Tendency for the Interaction between Low-Complexity Intrinsically Disordered Proteins

Reversible interaction between intrinsically disordered proteins (IDPs) is considered as the driving force for liquid-liquid phase separation (LLPS), while the detailed description of such a transient interaction process still remains a challenge. And the mechanisms underlying the behavior of IDP interaction, for example, the possible relationship with its inherent conformational fluctuations and sequence features, remain elusive. Here, we use atomistic molecular dynamics (MD) simulation to investigate the reversible association of the LAF-1 RGG domain, the IDP with ultra-low LLPS concentration (0.06 mM). We find that the duration of the association between two RGG domains is highly heterogeneous, and the sustained associations essentially dominate the IDP interaction. More interestingly, such sustained associations are mediated by a finite region, that is, the C-terminal region 138-168 (denoted as a contact-prone region). We noticed that such sequence tendency is attributed to the extended conformation of the RGG domain during its inherent conformational fluctuations. Hence, our results suggest that there is a certain region in this low-complexity IDP which can essentially dominate their interaction and should be also important to the LLPS. And the inherent conformational fluctuations are actually essential for the emergence of such a hot region of IDP interaction. The importance of this hot region to LLPS is verified by experiment.

Exploiting Sequence-Dependent Rotamer Information in Global Optimization of Proteins

Rotamers, namely amino acid side chain conformations common to many different peptides, can be compiled into libraries. These rotamer libraries are used in protein modeling, where the limited conformational space occupied by amino acid side chains is exploited. Here, we construct a sequence-dependent rotamer library from simulations of all possible tripeptides, which provides rotameric states dependent on adjacent amino acids. We observe significant sensitivity of rotamer populations to sequence and find that the library is successful in locating side chain conformations present in crystal structures. The library is designed for applications with basin-hopping global optimization, where we use it to propose moves in conformational space. The addition of rotamer moves significantly increases the efficiency of protein structure prediction within this framework, and we determine parameters to optimize efficiency.

Anchor Residues Govern Binding and Folding of an Intrinsically Disordered Domain

Minimal protein mimics have yielded novel classes of protein-protein interaction inhibitors; however, this success has not been extended to targeting intrinsically disordered proteins, which represent a significant proportion of important therapeutic targets. We sought to determine the requirements for binding an intrinsically disordered region (IDR) by its native binding partner as a prelude to developing minimal protein mimics that regulate IDR interactions. Our analysis reinforces the hypothesis that IDRs reside on a fulcrum between unfolded and folded states and that a handful of key binding residues on partner protein surfaces dictate their folding. Our studies also suggest that minimal mimics of protein surfaces may not offer specific ligands for IDRs and that it would be more judicious to target the globular protein partners of IDRs.

Molecular analysis and therapeutic applications of human serum albumin-fatty acid interactions

Human serum albumin (hSA) is the major carrier protein for fatty acids (FAs) in plasma. Its ability to bind multiple FA moieties with moderate to high affinity has inspired the use of FA conjugation as a safe and natural platform to generate long-lasting therapeutics with enhanced pharmacokinetic properties and superior efficacy. In this frame, the choice of the FA is crucial and a comprehensive elucidation of the molecular interactions of FAs with hSA cannot be left out of consideration. To this intent, we report here a comparative analysis of the binding mode of different FA moieties with hSA. The choice among different albumin-binding FAs and how this influence the pharmacokinetics properties of a broad spectrum of therapeutic molecules will be discussed including a critical description of some clinically relevant FA conjugated therapeutics.

Diversity-Oriented Synthesis of ERα Modulators via Mitsunobu Macrocyclization

The diversity of cyclic peptides was expanded by elaborating Mitsunobu macrocyclization, tethering various hydroxy acid building blocks with different N^ε-amine substituents. This new strategy was then applied in synthesizing peptidomimetic estrogen receptor modulator (PERM) analogs on the solid support. The PERM analogs exhibited increased serum peptidase stability, cell penetration, and estrogen receptor α binding affinity. Studying diversity-oriented methods for preparing azacyclopeptides provides a new tool for macrocycle construction and further structural information for optimizing ERα modulators for ER positive breast cancers.

Promiscuity mapping of the S100 protein family using a high-throughput holdup assay

S100 proteins are small, typically homodimeric, vertebrate-specific EF-hand proteins that establish Ca²⁺-dependent protein-protein interactions in the intra- and extracellular environment and are overexpressed in various pathologies. There are about 20 distinct human S100 proteins with numerous potential partner proteins. Here, we used a quantitative holdup assay to measure affinity profiles of most members of the S100 protein family against a library of chemically synthetized foldamers. The profiles allowed us to quantitatively map the binding promiscuity of each member towards the foldamer library. Since the library was designed to systematically contain most binary natural amino acid side chain combinations, the data also provide insight into the promiscuity of each S100 protein towards all potential naturally occurring S100 partners in the human proteome. Such information will be precious for future drug design to interfere with S100 related pathologies.

Characterization of Hydrophilic α-Helical Hot Spots on the Protein-Protein Interaction Interfaces for the Design of α-Helix Mimetics

The cooperativity index, K_c, was developed to examine the binding synergy between hot spots of the ligand-protein. For the first time, the convergence of the side-chain spatial arrangements of hydrophilic α-helical hot spots Thr, Tyr, Asp, Asn, Ser, Cys, and His in protein-protein interaction (PPI) complex structures was disclosed and quantified by developing novel clustering models. In-depth analyses revealed the driving force for the protein-protein binding conformation convergence of hydrophilic α-helical hot spots. This observation allows deriving pharmacophore models to design new mimetics for hydrophilic α-helical hot spots. A computational protocol was developed to search amino acid analogues and small-molecule mimetics for each hydrophilic α-helical hot spot. As a pilot study, diverse building blocks of commercially available nonstandard L-type α-amino acids and the phenyl ring-containing small-molecule fragments were obtained, which serve as a fragment collection to mimic hydrophilic α-helical hot spots for the improvement of binding affinity, selectivity, physicochemical properties, and synthesis accessibility of α-helix mimetics.

Peptide Tethering: Pocket-Directed Fragment Screening for Peptidomimetic Inhibitor Discovery

Constrained peptides have proven to be a rich source of ligands for protein surfaces, but are often limited in their binding potency. Deployment of nonnatural side chains that access unoccupied crevices on the receptor surface offers a potential avenue to enhance binding affinity. We recently described a computational approach to create topographic maps of protein surfaces to guide the design of nonnatural side chains [J. Am. Chem. Soc. 2017, 139, 15560]. The computational method, AlphaSpace, was used to predict peptide ligands for the KIX domain of the p300/CBP coactivator. KIX has been the subject of numerous ligand discovery strategies, but potent inhibitors of its interaction with transcription factors remain difficult to access. Although the computational approach provided a significant enhancement in the binding affinity of the peptide, fine-tuning of nonnatural side chains required an experimental screening method. Here we implement a peptide-tethering strategy to screen fragments as nonnatural side chains on conformationally defined peptides. The combined computational-experimental approach offers a general framework for optimizing peptidomimetics as inhibitors of protein-protein interactions.

Visualized and Quantitative Conformational Analysis of Peptidomimetics

Protein-protein interactions (PPIs) are fundamentally important and challenging drug targets. Peptidomimetic molecules of various types have been developed to modulate PPIs. A particularly promising drug discovery strategy, structural peptidomimetics, was designed based on special mimicking of side-chain C_α-C_β bonds. It is simple and versatile. Nevertheless, no quantitative method has been established to evaluate its similarity to a target peptide motif. We developed two methods that enable visual, comprehensive, and quantitative analysis of peptidomimetics: peptide conformation distribution (PCD) plot and peptidomimetic analysis (PMA) map. These methods specifically examine multiple side-chain C_α-C_β bonds of a peptide fragment motif and their corresponding bonds (pseudo-C_α-C_β bonds) in a mimetic molecule instead of φ and ψ angles of a single amino acid in the traditional Ramachandran plot. The PCD plot is an alignment-free method, whereas the PMA map is an alignment-based method providing distinctive and complementary analysis. Results obtained from analysis using these two methods indicate our multifacial α-helix mimetic scaffold 12 as an excellent peptidomimetic that can precisely mimic the spatial positioning of side-chain functional groups of α-helix. These methods are useful for visualized and quantified evaluation of peptidomimetics and for the rational design of new mimetic scaffolds.

Proteomimetic surface fragments distinguish targets by function

The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein-protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces.

Bent Into Shape: Folded Peptides to Mimic Protein Structure and Modulate Protein Function

Protein secondary and tertiary structure mimics have served as model systems to probe biophysical parameters that guide protein folding and as attractive reagents to modulate protein interactions. Here we review contemporary methods to reproduce loop, helix, sheet and coiled-coil conformations in short peptides.

Targeting the Side-Chain Convergence of Hydrophobic α-Helical Hot Spots To Design Small-Molecule Mimetics: Key Binding Features for i, i + 3, and i + 7

The conformational convergence of hydrophobic α-helical hot spots was revealed by analyzing α-helix-mediated protein-protein interaction (PPI) complex structures. The pharmacophore models were derived for hydrophobic α-helical hot spots at positions i, i + 3, and i + 7. These provide the foundation for designing generalizable scaffolds that can directly mimic the binding mode of the side chains of α-helical hot spots, offering a new class of small-molecule α-helix mimetics. For the first time, the protocol was developed to identify the PPI targets that have similar binding pockets, allowing evaluation of inhibitor selectivities between α-helix-mediated PPIs. The mimicry efficiency of the previously designed scaffold 1 was disclosed. The close positioning of this small molecule to the additional α-helical hot spots suggests that the decoration of this series of generalizable scaffolds can conveniently reach the binding pockets of additional α-helical hot spots to produce potent small-molecule inhibitors for α-helix-mediated PPIs.

Heterozygous Variants in KMT2E Cause a Spectrum of Neurodevelopmental Disorders and Epilepsy

We delineate a KMT2E-related neurodevelopmental disorder on the basis of 38 individuals in 36 families. This study includes 31 distinct heterozygous variants in KMT2E (28 ascertained from Matchmaker Exchange and three previously reported), and four individuals with chromosome 7q22.2-22.23 microdeletions encompassing KMT2E (one previously reported). Almost all variants occurred de novo, and most were truncating. Most affected individuals with protein-truncating variants presented with mild intellectual disability. One-quarter of individuals met criteria for autism. Additional common features include macrocephaly, hypotonia, functional gastrointestinal abnormalities, and a subtle facial gestalt. Epilepsy was present in about one-fifth of individuals with truncating variants and was responsive to treatment with anti-epileptic medications in almost all. More than 70% of the individuals were male, and expressivity was variable by sex; epilepsy was more common in females and autism more common in males. The four individuals with microdeletions encompassing KMT2E generally presented similarly to those with truncating variants, but the degree of developmental delay was greater. The group of four individuals with missense variants in KMT2E presented with the most severe developmental delays. Epilepsy was present in all individuals with missense variants, often manifesting as treatment-resistant infantile epileptic encephalopathy. Microcephaly was also common in this group. Haploinsufficiency versus gain-of-function or dominant-negative effects specific to these missense variants in KMT2E might explain this divergence in phenotype, but requires independent validation. Disruptive variants in KMT2E are an under-recognized cause of neurodevelopmental abnormalities.

Accurate measurement of dipole/dipole transverse cross-correlated relaxation [Formula: see text] in methylenes and primary amines of uniformly [Formula: see text]-labeled proteins

Side chains possess a broader conformational space (compared to the backbone) and are directly affected by intra- and intermolecular interactions, hence their dynamics and the corresponding NMR relaxation data are more sensitive and informative. Nevertheless, transverse relaxation in [Formula: see text] ([Formula: see text] or [Formula: see text]) spin systems is predominantly non-measurable in uniformly [Formula: see text]-labeled proteins due to cross-correlation effects. In the present publication, we propose a number of pulse sequences for accurate and precise measurement of the dipole-dipole transverse cross-correlated relaxation rate [Formula: see text], which, similarly to [Formula: see text] measurements, provides information about the amplitudes of intramolecular dynamics. The suggested approach has allowed us to circumvent a number of obstacles that were limiting earlier applications of [Formula: see text]: (1) impossibility of transmission of the central component of the triplet of [Formula: see text] group to [Formula: see text]-acquisition via INEPT has been solved by transmission of the averaged signal of "inner" and "outer" components of the triplet; (2) direct recording of the entire triplets resulting in substantial overlap of side chain signals has been replaced by recording of individual singlets with the use of [Formula: see text]-modulated approach and constant-time evolution; (3) low sensitivity has been enhanced via proton acquisition which required special attention to a zero-quantum coherence evolution. The proposed method expands the set of "dynamics sensors" covering protein side chains and substantially improves the quality and the level of detail of experimental data describing dynamic processes in proteins and protein complexes.

Engineered protein scaffolds as leads for synthetic inhibitors of protein-protein interactions

Rationally designed protein-protein interaction inhibitors mimic interfacial binding epitopes, specifically residues that contribute significantly to binding. However, direct mimicry often does not lead to high affinity ligands because the natural complexes themselves are functionally transient and of low affinity. The mimics typically need to be optimized for potency. Engineered proteins displaying conformationally-defined epitopes may serve as attractive alternatives to natural protein partners as they can be strictly screened for tight binding. The advantage of focused screens with conformationally-defined protein scaffolds is that conservation of the geometry of the natural binding epitopes may preserve binding site specificity while allowing direct mimicry by various synthetic secondary structure scaffolds. Here we review different classes of engineered proteins for their binding epitope geometry and as leads for synthetic secondary and tertiary structure mimics.

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.

Rotamer Libraries for the High-Resolution Design of β-Amino Acid Foldamers

β-Amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating non-natural amino acids typically requires representing their side-chain conformations as sets of discrete rotamers for model refinement and sequence optimization. Such rotamer libraries are key components of several state-of-the-art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of rotamer libraries for β3-amino acids.

Structural Basis of Inhibition of ERα-Coactivator Interaction by High-Affinity N-Terminus Isoaspartic Acid Tethered Helical Peptides

Direct inhibition of the protein-protein interaction of ERα and its endogenous coactivators with a cell permeable stabilized peptide may offer a novel, promising strategy for combating ERα positive breast cancers. Here, we report the co-crystal structure of a helical peptide stabilized by a N-terminal unnatural cross-linked aspartic acid (TD) in complex with the ERα ligand binding domain (LBD). We designed a series of peptides and peptide 6 that showed direct and high-affinity binding to ERα with selective antiproliferative activity in ERα positive breast cancer cells. The co-crystal structure of the TD-stabilized peptide 6 in complex with ERα LBD further demonstrates that it forms an α helical conformation and directly binds at the coactivator binding site of ERα. Further studies showed that peptide 6_W could potently inhibit cellular ERα's transcriptional activity. This approach demonstrates the potential of TD stabilized peptides to modulate various intracellular protein-protein interactions involved in a range of disorders.

MFPred: Rapid and accurate prediction of protein-peptide recognition multispecificity using self-consistent mean field theory

Multispecificity-the ability of a single receptor protein molecule to interact with multiple substrates-is a hallmark of molecular recognition at protein-protein and protein-peptide interfaces, including enzyme-substrate complexes. The ability to perform structure-based prediction of multispecificity would aid in the identification of novel enzyme substrates, protein interaction partners, and enable design of novel enzymes targeted towards alternative substrates. The relatively slow speed of current biophysical, structure-based methods limits their use for prediction and, especially, design of multispecificity. Here, we develop a rapid, flexible-backbone self-consistent mean field theory-based technique, MFPred, for multispecificity modeling at protein-peptide interfaces. We benchmark our method by predicting experimentally determined peptide specificity profiles for a range of receptors: protease and kinase enzymes, and protein recognition modules including SH2, SH3, MHC Class I and PDZ domains. We observe robust recapitulation of known specificities for all receptor-peptide complexes, and comparison with other methods shows that MFPred results in equivalent or better prediction accuracy with a ~10-1000-fold decrease in computational expense. We find that modeling bound peptide backbone flexibility is key to the observed accuracy of the method. We used MFPred for predicting with high accuracy the impact of receptor-side mutations on experimentally determined multispecificity of a protease enzyme. Our approach should enable the design of a wide range of altered receptor proteins with programmed multispecificities.

Effective gene-silencing of siRNAs that contain functionalized spacer linkages within the central region

Short-interfering RNAs containing a variety of functional groups at the central region of the sense strand were synthesized and evaluated.