The geometric influence on the Cys2His2 zinc finger domain and functional plasticity.

The Cys2His2 zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity. Here, we use a synthetic approach to exhaustively investigate binding geometry, one of the dominant influences on adjacent finger function. By screening over 28 billion protein-DNA interactions in various geometric contexts we find the plasticity of the most common natural geometry enables more functional amino acid combinations across all targets. Further, residues that define this geometry are enriched in genomes where zinc fingers are prevalent and specificity transitions would be limited in alternative geometries. Finally, these results demonstrate an exhaustive synthetic screen can produce an accurate model of domain function while providing mechanistic insight that may have assisted in the domains expansion.

Strategies to Develop Inhibitors of Motif-Mediated Protein-Protein Interactions as Drug Leads.

Protein-protein interactions are fundamental for virtually all functions of the cell. A large fraction of these interactions involve short peptide motifs, and there has been increased interest in targeting them using peptide-based therapeutics. Peptides benefit from being specific, relatively safe, and easy to produce. They are also easy to modify using chemical synthesis and molecular biology techniques. However, significant challenges remain regarding the use of peptides as therapeutic agents. Identification of peptide motifs is difficult, and peptides typically display low cell permeability and sensitivity to enzymatic degradation. In this review, we outline the principal high-throughput methodologies for motif discovery and describe current methods for overcoming pharmacokinetic and bioavailability limitations.

Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5 blind challenge.

Predicting the impact of mutations on proteins remains an important problem. As part of the CAGI5 frataxin challenge, we evaluate the accuracy with which Provean, FoldX, and ELASPIC can predict changes in the Gibbs free energy of a protein using a limited data set of eight mutations. We find that different methods have distinct strengths and limitations, with no method being strictly superior to other methods on all metrics. ELASPIC achieves the highest accuracy while also providing a web interface which simplifies the evaluation and analysis of mutations. FoldX is slightly less accurate than ELASPIC but is easier to run locally, as it does not depend on external tools or datasets. Provean achieves reasonable results while being computational less expensive than the other methods and not requiring a structure of the protein. In addition to methods submitted to the CAGI5 community experiment, and with the aim to inform about other methods with high accuracy, we also evaluate predictions made by Rosetta's ddg_monomer protocol, Rosetta's cartesian_ddg protocol, and thermodynamic integration calculations using Amber package. ELASPIC still achieves the highest accuracy, while Rosetta's catesian_ddg protocol appears to perform best in capturing the overall trend in the data.

Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge.

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant (ΔΔGH2O) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the ΔΔGH2O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.

Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration.

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.

Pooled screening for antiproliferative inhibitors of protein-protein interactions.

Protein-protein interactions (PPIs) are emerging as a promising new class of drug targets. Here, we present a novel high-throughput approach to screen inhibitors of PPIs in cells. We designed a library of 50,000 human peptide-binding motifs and used a pooled lentiviral system to express them intracellularly and screen for their effects on cell proliferation. We thereby identified inhibitors that drastically reduced the viability of a pancreatic cancer line (RWP1) while leaving a control line virtually unaffected. We identified their target interactions computationally, and validated a subset in experiments. We also discovered their potential mechanisms of action, including apoptosis and cell cycle arrest. Finally, we confirmed that synthetic lipopeptide versions of our inhibitors have similarly specific and dosage-dependent effects on cancer cell growth. Our screen reveals new drug targets and peptide drug leads, and it provides a rich data set covering phenotypes for the inhibition of thousands of interactions.

Motif mediated protein-protein interactions as drug targets.

Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery.

Two-state dynamics of the SH3-SH2 tandem of Abl kinase and the allosteric role of the N-cap.

The regulation and localization of signaling enzymes is often mediated by accessory modular domains, which frequently function in tandems. The ability of these tandems to adopt multiple conformations is as important for proper regulation as the individual domain specificity. A paradigmatic example is Abl, a ubiquitous tyrosine kinase of significant pharmacological interest. SH3 and SH2 domains inhibit Abl by assembling onto the catalytic domain, allosterically clamping it in an inactive state. We investigate the dynamics of this SH3-SH2 tandem, using microsecond all-atom simulations and differential scanning calorimetry. Our results indicate that the Abl tandem is a two-state switch, alternating between the conformation observed in the structure of the autoinhibited enzyme and another configuration that is consistent with existing scattering data for an activated form. Intriguingly, we find that the latter is the most probable when the tandem is disengaged from the catalytic domain. Nevertheless, an amino acid stretch preceding the SH3 domain, the so-called N-cap, reshapes the free-energy landscape of the tandem and favors the interaction of this domain with the SH2-kinase linker, an intermediate step necessary for assembly of the autoinhibited complex. This allosteric effect arises from interactions between N-cap and the SH2 domain and SH3-SH2 connector, which involve a phosphorylation site. We also show that the SH3-SH2 connector plays a determinant role in the assembly equilibrium of Abl, because mutations thereof hinder the engagement of the SH2-kinase linker. These results provide a thermodynamic rationale for the involvement of N-cap and SH3-SH2 connector in Abl regulation and expand our understanding of the principles of modular domain organization.

Phage display identification of high-affinity ligands for human TSG101-UEV: A structural and thermodynamic study of PTAP recognition.

The ubiquitin E2 variant domain of TSG101 (TSG101-UEV) plays a pivotal role in protein sorting and virus budding by recognizing PTAP motifs within ubiquitinated proteins. Disrupting TSG101-UEV/PTAP interactions has emerged as a promising strategy for the development of novel host-oriented antivirals with a broad spectrum of action. Nonetheless, finding inhibitors with good properties as therapeutic agents remains a challenge since the key determinants of binding affinity and specificity are still poorly understood. Here we present a detailed thermodynamic, structural, and dynamic characterization viral PTAP Late domain recognition by TSG101-UEV, combining isothermal titration calorimetry, X-ray diffraction structural studies, molecular dynamics simulations, and computational analysis of intramolecular communication pathways. Our analysis highlights key contributions from conserved hydrophobic contacts and water-mediated hydrogen bonds at the PTAP binding interface. We have identified additional electrostatic hotspots adjacent to the core motif that modulate affinity. Using competitive phage display screening we have improved affinity by 1-2 orders of magnitude, producing novel peptides with low micromolar affinities that combine critical elements found in the best natural binders. Molecular dynamics simulations revealed that optimized peptides engage new pockets on the UEV domain surface. This study provides a comprehensive view of the molecular forces directing TSG101-UEV recognition of PTAP motifs, revealing that binding is governed by conserved structural elements yet tuneable through targeted optimization. These insights open new venues to design inhibitors targeting TSG101-dependent pathways with potential application as novel broad-spectrum antivirals.

Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease.

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.

Phage display identification of nanomolar ligands for human NEDD4-WW3: Energetic and dynamic implications for the development of broad-spectrum antivirals.

The recognition of PPxY viral Late domains by the third WW domain of the human HECT-E3 ubiquitin ligase NEDD4 (NEDD4-WW3) is essential for the budding of many viruses. Blocking these interactions is a promising strategy to develop broad-spectrum antivirals. As all WW domains, NEDD4-WW3 is a challenging therapeutic target due to the low binding affinity of its natural interactions, its high conformational plasticity, and its complex thermodynamic behavior. In this work, we set out to investigate whether high affinity can be achieved for monovalent ligands binding to the isolated NEDD4-WW3 domain. We show that a competitive phage-display set-up allows for the identification of high-affinity peptides showing inhibitory activity of viral budding. A detailed biophysical study combining calorimetry, nuclear magnetic resonance, and molecular dynamic simulations reveals that the improvement in binding affinity does not arise from the establishment of new interactions with the domain, but is associated to conformational restrictions imposed by a novel C-terminal -LFP motif in the ligand, unprecedented in the PPxY interactome. These results, which highlight the complexity of WW domain interactions, provide valuable insight into the key elements for high binding affinity, of interest to guide virtual screening campaigns for the identification of novel therapeutics targeting NEDD4-WW3 interactions.

Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver.

Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that uses ProteinSolver, a pre-trained graph convolutional neural network, to quickly generate thousands of sequences matching a specific protein topology. We describe computational approaches that can be used to evaluate the generated sequences, and we show how select sequences can be validated experimentally. For complete details on the use and execution of this protocol, please refer to Strokach et al. (2020).

Fast and Flexible Protein Design Using Deep Graph Neural Networks.

Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proof-of-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at http://design.proteinsolver.org and https://gitlab.com/ostrokach/proteinsolver. A record of this paper's transparent peer review process is included in the Supplemental Information.

Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions.

The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein's amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.

Binding site plasticity in viral PPxY Late domain recognition by the third WW domain of human NEDD4.

The recognition of PPxY viral Late domains by the third WW domain of the HECT-E3 ubiquitin ligase NEDD4 (hNEDD4-WW3) is essential for the completion of the budding process of numerous enveloped viruses, including Ebola, Marburg, HTLV1 or Rabies. hNEDD4-WW3 has been validated as a promising target for the development of novel host-oriented broad spectrum antivirals. Nonetheless, finding inhibitors with good properties as therapeutic agents remains a challenge since the key determinants of binding affinity and specificity are still poorly understood. We present here a detailed structural and thermodynamic study of the interactions of hNEDD4-WW3 with viral Late domains combining isothermal titration calorimetry, NMR structural determination and molecular dynamics simulations. Structural and energetic differences in Late domain recognition reveal a highly plastic hNEDD4-WW3 binding site that can accommodate PPxY-containing ligands with varying orientations. These orientations are mostly determined by specific conformations adopted by residues I859 and T866. Our results suggest a conformational selection mechanism, extensive to other WW domains, and highlight the functional relevance of hNEDD4-WW3 domain conformational flexibility at the binding interface, which emerges as a key element to consider in the search for potent and selective inhibitors of therapeutic interest.

A Multireporter Bacterial 2-Hybrid Assay for the High-Throughput and Dynamic Assay of PDZ Domain-Peptide Interactions.

The accurate determination of protein-protein interactions has been an important focus of molecular biology toward which much progress has been made due to the continuous development of existing and new technologies. However, current methods can have limitations, including scale and restriction to high affinity interactions, limiting our understanding of a large subset of these interactions. Here, we describe a modified bacterial-hybrid assay that employs combined selectable and scalable reporters that enable the sensitive screening of large peptide libraries followed by the sorting of positive interactions by the level of reporter output. We have applied this tool to characterize a set of human and E. coli PDZ domains. Our results are consistent with prior characterization of these proteins, and the improved sensitivity increases our ability to predict known and novel in vivo binding partners. This approach allows for the recovery of a wide range of affinities with a high throughput method that does not sacrifice the scale of the screen.

Allosteric Modulation of Binding Specificity by Alternative Packing of Protein Cores.

Hydrophobic cores are often viewed as tightly packed and rigid, but they do show some plasticity and could thus be attractive targets for protein design. Here we explored the role of different functional pressures on the core packing and ligand recognition of the SH3 domain from human Fyn tyrosine kinase. We randomized the hydrophobic core and used phage display to select variants that bound to each of three distinct ligands. The three evolved groups showed remarkable differences in core composition, illustrating the effect of different selective pressures on the core. Changes in the core did not significantly alter protein stability, but were linked closely to changes in binding affinity and specificity. Structural analysis and molecular dynamics simulations revealed the structural basis for altered specificity. The evolved domains had significantly reduced core volumes, which in turn induced increased backbone flexibility. These motions were propagated from the core to the binding surface and induced significant conformational changes. These results show that alternative core packing and consequent allosteric modulation of binding interfaces could be used to engineer proteins with novel functions.

A PxL motif promotes timely cell cycle substrate dephosphorylation by the Cdc14 phosphatase.

The cell division cycle consists of a series of temporally ordered events. Cell cycle kinases and phosphatases provide key regulatory input, but how the correct substrate phosphorylation and dephosphorylation timing is achieved is incompletely understood. Here we identify a PxL substrate recognition motif that instructs dephosphorylation by the budding yeast Cdc14 phosphatase during mitotic exit. The PxL motif was prevalent in Cdc14-binding peptides enriched in a phage display screen of native disordered protein regions. PxL motif removal from the Cdc14 substrate Cbk1 delays its dephosphorylation, whereas addition of the motif advances dephosphorylation of otherwise late Cdc14 substrates. Crystal structures of Cdc14 bound to three PxL motif substrate peptides provide a molecular explanation for PxL motif recognition on the phosphatase surface. Our results illustrate the sophistication of phosphatase-substrate interactions and identify them as an important determinant of ordered cell cycle progression.

Protein engineering by highly parallel screening of computationally designed variants.

Current combinatorial selection strategies for protein engineering have been successful at generating binders against a range of targets; however, the combinatorial nature of the libraries and their vast undersampling of sequence space inherently limit these methods due to the difficulty in finely controlling protein properties of the engineered region. Meanwhile, great advances in computational protein design that can address these issues have largely been underutilized. We describe an integrated approach that computationally designs thousands of individual protein binders for high-throughput synthesis and selection to engineer high-affinity binders. We show that a computationally designed library enriches for tight-binding variants by many orders of magnitude as compared to conventional randomization strategies. We thus demonstrate the feasibility of our approach in a proof-of-concept study and successfully obtain low-nanomolar binders using in vitro and in vivo selection systems.

Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins.

Alternative splicing acts on transcripts from almost all human multi-exon genes. Notwithstanding its ubiquity, fundamental ramifications of splicing on protein expression remain unresolved. The number and identity of spliced transcripts that form stably folded proteins remain the sources of considerable debate, due largely to low coverage of experimental methods and the resulting absence of negative data. We circumvent this issue by developing a semi-supervised learning algorithm, positive unlabeled learning for splicing elucidation (PULSE; http://www.kimlab.org/software/pulse), which uses 48 features spanning various categories. We validated its accuracy on sets of bona fide protein isoforms and directly on mass spectrometry (MS) spectra for an overall AU-ROC of 0.85. We predict that around 32% of "exon skipping" alternative splicing events produce stable proteins, suggesting that the process engenders a significant number of previously uncharacterized proteins. We also provide insights into the distribution of positive isoforms in various functional classes and into the structural effects of alternative splicing.