Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins

Learning the language of antibody hypervariability

Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP's transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.

Inhibition of BAK-mediated apoptosis by the BH3-only protein BNIP5

BCL-2 family proteins regulate apoptosis by initiating mitochondrial outer membrane permeabilization (MOMP). Activation of the MOMP effectors BAX and BAK is controlled by the interplay of anti-apoptotic BCL-2 proteins (e.g., MCL-1) and pro-apoptotic BH3-only proteins (e.g., BIM). Using a genome-wide CRISPR-dCas9 transactivation screen we identified BNIP5 as an inhibitor of BAK-, but not BAX-induced apoptosis. BNIP5 blocked BAK activation in different cell types and in response to various cytotoxic therapies. The BH3 domain of BNIP5 was both necessary and sufficient to block BAK activation. Mechanistically, the BH3 domain of BNIP5 acts as a selective BAK activator, but a poor de-repressor of complexes between BAK and pro-survival BCL-2 family proteins. By promoting the binding of activated BAK to MCL-1 or BCL-xL, BNIP5 inhibits apoptosis when BAX is absent. Based on our observations, BNIP5 can act functionally as an anti-apoptotic BH3-only protein.

Crystal structure of Bak bound to the BH3 domain of Bnip5, a noncanonical BH3 domain-containing protein

The activation or inactivation of B-cell lymphoma-2 (Bcl-2) antagonist/killer (Bak) is critical for controlling mitochondrial outer membrane permeabilization-dependent apoptosis. Its pro-apoptotic activity is controlled by intermolecular interactions with the Bcl-2 homology 3 (BH3) domain, which is accommodated in the hydrophobic pocket of Bak. Bcl-2-interacting protein 5 (Bnip5) is a noncanonical BH3 domain-containing protein that interacts with Bak. Bnip5 is characterized by its controversial effects on the regulation of the pro-apoptotic activity of Bak. In the present study, we determined the crystal structure of Bak bound to Bnip5 BH3. The intermolecular association appeared to be typical at first glance, but we found that it is maintained by tight hydrophobic interactions together with hydrogen/ionic bonds, which accounts for their high binding affinity with a dissociation constant of 775 nM. Structural analysis of the complex showed that Bnip5 interacts with Bak in a manner similar to that of the Bak-activating pro-apoptotic factor peroxisomal testis-enriched protein 1, particularly in the destabilization of the intramolecular electrostatic network of Bak. Our structure is considered to reflect the initial point of drastic and consecutive conformational and stoichiometric changes in Bak induced by Bnip5 BH3, which helps in explaining the effects of Bnip5 in regulating Bak-mediated apoptosis.

Surfaces: a software to quantify and visualize interactions within and between proteins and ligands

SUMMARY

Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here, we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent or better correlations with experimental data as computationally expensive methods based on molecular dynamics.

AVAILABILITY AND IMPLEMENTATION

All scripts are available at https://github.com/NRGLab/Surfaces. Surface's documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html.

Structural basis for proapoptotic activation of Bak by the noncanonical BH3-only protein Pxt1

Bak is a critical executor of apoptosis belonging to the Bcl-2 protein family. Bak contains a hydrophobic groove where the BH3 domain of proapoptotic Bcl-2 family members can be accommodated, which initiates its activation. Once activated, Bak undergoes a conformational change to oligomerize, which leads to mitochondrial destabilization and the release of cytochrome c into the cytosol and eventual apoptotic cell death. In this study, we investigated the molecular aspects and functional consequences of the interaction between Bak and peroxisomal testis-specific 1 (Pxt1), a noncanonical BH3-only protein exclusively expressed in the testis. Together with various biochemical approaches, this interaction was verified and analyzed at the atomic level by determining the crystal structure of the Bak-Pxt1 BH3 complex. In-depth biochemical and cellular analyses demonstrated that Pxt1 functions as a Bak-activating proapoptotic factor, and its BH3 domain, which mediates direct intermolecular interaction with Bak, plays a critical role in triggering apoptosis. Therefore, this study provides a molecular basis for the Pxt1-mediated novel pathway for the activation of apoptosis and expands our understanding of the cell death signaling coordinated by diverse BH3 domain-containing proteins.

Rapid Evaluation of Staple Placement in Stabilized α Helices Using Bacterial Surface Display

There are a wealth of proteins involved in disease that cannot be targeted by current therapeutics because they are inside cells, inaccessible to most macromolecules, and lack small-molecule binding pockets. Stapled peptides, where two amino acids are covalently linked, form a class of macrocycles that have the potential to penetrate cell membranes and disrupt intracellular protein-protein interactions. However, their discovery relies on solid-phase synthesis, greatly limiting queries into their complex design space involving amino acid sequence, staple location, and staple chemistry. Here, we use stabilized peptide engineering by Escherichia coli display (SPEED), which utilizes noncanonical amino acids and click chemistry for stabilization, to rapidly screen staple location and linker structure to accelerate peptide design. After using SPEED to confirm hotspots in the mdm2-p53 interaction, we evaluated different staple locations and staple chemistry to identify several novel nanomolar and sub-nanomolar antagonists. Next, we evaluated SPEED in the B cell lymphoma 2 (Bcl-2) protein family, which is responsible for regulating apoptosis. We report that novel staple locations modified in the context of BIM, a high affinity but nonspecific naturally occurring peptide, improve its specificity against the highly homologous proteins in the Bcl-2 family. These compounds demonstrate the importance of screening linker location and chemistry in identifying high affinity and specific peptide antagonists. Therefore, SPEED can be used as a versatile platform to evaluate multiple design criteria for stabilized peptide engineering.

Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization

Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.

Structural Details of BH3 Motifs and BH3-Mediated Interactions: an Updated Perspective

Apoptosis is a mechanism of programmed cell death crucial in organism development, maintenance of tissue homeostasis, and several pathogenic processes. The B cell lymphoma 2 (BCL2) protein family lies at the core of the apoptotic process, and the delicate balance between its pro- and anti-apoptotic members ultimately decides the cell fate. BCL2 proteins can bind with each other and several other biological partners through the BCL2 homology domain 3 (BH3), which has been also classified as a possible Short Linear Motif and whose distinctive features remain elusive even after decades of studies. Here, we aim to provide an updated overview of the structural features characterizing BH3s and BH3-mediated interactions (with a focus on human proteins), elaborating on the plasticity of BCL2 proteins and the motif properties. We also discussed the implication of these findings for the discovery of interactors of the BH3-binding groove of BCL2 proteins and the design of mimetics for therapeutic purposes.

Dual RNA Sequencing Reveals Key Events When Different Giardia Life Cycle Stages Interact With Human Intestinal Epithelial Cells In Vitro

Giardia intestinalis is a protozoan parasite causing diarrheal disease, giardiasis, after extracellular infection of humans and other mammals’ intestinal epithelial cells (IECs) of the upper small intestine. The parasite has two main life cycle stages: replicative trophozoites and transmissive cysts. Differentiating parasites (encysting cells) and trophozoites have recently been shown to be present in the same regions of the upper small intestine, whereas most mature cysts are found further down in the intestinal system. To learn more about host-parasite interactions during Giardia infections, we used an in vitro model of the parasite’s interaction with host IECs (differentiated Caco-2 cells) and Giardia WB trophozoites, early encysting cells (7 h), and cysts. Dual RNA sequencing (Dual RNAseq) was used to identify differentially expressed genes (DEGs) in both Giardia and the IECs, which might relate to establishing infection and disease induction. In the human cells, the largest gene expression changes were found in immune and MAPK signaling, transcriptional regulation, apoptosis, cholesterol metabolism and oxidative stress. The different life cycle stages of Giardia induced a core of similar DEGs but at different levels and there are many life cycle stage-specific DEGs. The metabolic protein PCK1, the transcription factors HES7, HEY1 and JUN, the peptide hormone CCK and the mucins MUC2 and MUC5A are up-regulated in the IECs by trophozoites but not cysts. Cysts specifically induce the chemokines CCL4L2, CCL5 and CXCL5, the signaling protein TRKA and the anti-bacterial protein WFDC12. The parasite, in turn, up-regulated a large number of hypothetical genes, high cysteine membrane proteins (HCMPs) and oxidative stress response genes. Early encysting cells have unique DEGs compared to trophozoites (e.g. several uniquely up-regulated HCMPs) and interaction of these cells with IECs affected the encystation process. Our data show that different life cycle stages of Giardia induce different gene expression responses in the host cells and that the IECs in turn differentially affect the gene expression in trophozoites and early encysting cells. This life cycle stage-specific host-parasite cross-talk is an important aspect to consider during further studies of Giardia’s molecular pathogenesis.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Estimating the binding affinities of protein-protein interactions (PPIs) is crucial to understand protein function and design new functional proteins. Since the experimental measurement in wet-labs is labor-intensive and time-consuming, fast and accurate in silico approaches have received much attention. Although considerable efforts have been made in this direction, predicting the effects of mutations on the protein-protein binding affinity is still a challenging research problem. In this work, we introduce GeoPPI, a novel computational approach that uses deep geometric representations of protein complexes to predict the effects of mutations on the binding affinity. The geometric representations are first learned via a self-supervised learning scheme and then integrated with gradient-boosting trees to accomplish the prediction. We find that the learned representations encode meaningful patterns underlying the interactions between atoms in protein structures. Also, extensive tests on major benchmark datasets show that GeoPPI has made an important improvement over the existing methods in predicting the effects of mutations on the binding affinity.

Alterations of the interactome of Bcl-2 proteins in breast cancer at the transcriptional, mutational and structural level

Apoptosis is an essential defensive mechanism against tumorigenesis. Proteins of the B-cell lymphoma-2 (Bcl-2) family regulate programmed cell death by the mitochondrial apoptosis pathway. In response to intracellular stress, the apoptotic balance is governed by interactions of three distinct subgroups of proteins; the activator/sensitizer BH3 (Bcl-2 homology 3)-only proteins, the pro-survival, and the pro-apoptotic executioner proteins. Changes in expression levels, stability, and functional impairment of pro-survival proteins can lead to an imbalance in tissue homeostasis. Their overexpression or hyperactivation can result in oncogenic effects. Pro-survival Bcl-2 family members carry out their function by binding the BH3 short linear motif of pro-apoptotic proteins in a modular way, creating a complex network of protein-protein interactions. Their dysfunction enables cancer cells to evade cell death. The critical role of Bcl-2 proteins in homeostasis and tumorigenesis, coupled with mounting insight in their structural properties, make them therapeutic targets of interest. A better understanding of gene expression, mutational profile, and molecular mechanisms of pro-survival Bcl-2 proteins in different cancer types, could help to clarify their role in cancer development and may guide advancement in drug discovery. Here, we shed light on the pro-survival Bcl-2 proteins in breast cancer using different bioinformatic approaches, linking -omics with structural data. We analyzed the changes in the expression of the Bcl-2 proteins and their BH3-containing interactors in breast cancer samples. We then studied, at the structural level, a selection of interactions, accounting for effects induced by mutations found in the breast cancer samples. We find two complexes between the up-regulated Bcl2A1 and two down-regulated BH3-only candidates (i.e., Hrk and Nr4a1) as targets associated with reduced apoptosis in breast cancer samples for future experimental validation. Furthermore, we predict L99R, M75R as damaging mutations altering protein stability, and Y120C as a possible allosteric mutation from an exposed surface to the BH3-binding site.

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.

Peptide design by optimization on a data-parameterized protein interaction landscape

Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein-peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x_L, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x_L, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2-binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.

Simplifying the Design of Protein-Peptide Interaction Specificity with Sequence-Based Representations of Atomistic Models

Computationally designed peptides targeting protein-protein interaction interfaces are of great interest as reagents for biological research and potential therapeutics. In recent years, it has been shown that detailed structure-based calculations can, in favorable cases, describe relevant determinants of protein-peptide recognition. Yet, despite large increases in available computing power, such accurate modeling of the binding reaction is still largely outside the realm of protein design. The chief limitation is in the large sequence spaces generally involved in protein design problems, such that it is typically infeasible to apply expensive modeling techniques to score each sequence. Toward addressing this issue, we have previously shown that by explicitly evaluating the scores of a relatively small number of sequences, it is possible to synthesize a direct mapping between sequences and scores, such that the entire sequence space can be analyzed extremely rapidly. The associated method, called Cluster Expansion, has been used in a number of studies to design binding affinity and specificity. In this chapter, we provide instructions and guidance for applying this technique in the context of designing protein-peptide interactions to enable the use of more detailed and expensive scoring approaches than is typically possible.

Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design

Peptide reagents with high affinity or specificity for their target protein interaction partner are of utility for many important applications. Optimization of peptide binding by screening large libraries is a proven and powerful approach. Libraries designed to be enriched in peptide sequences that are predicted to have desired affinity or specificity characteristics are more likely to yield success than random mutagenesis. We present a library optimization method in which the choice of amino acids to encode at each peptide position can be guided by available experimental data or structure-based predictions. We discuss how to use analysis of predicted library performance to inform rounds of library design. Finally, we include protocols for more complex library design procedures that consider the chemical diversity of the amino acids at each peptide position and optimize a library score based on a user-specified input model.

Antibody Affinity Maturation by Computational Design

The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.

Epistatic mutations in PUMA BH3 drive an alternate binding mode to potently and selectively inhibit anti-apoptotic Bfl-1

Overexpression of anti-apoptotic Bcl-2 family proteins contributes to cancer progression and confers resistance to chemotherapy. Small molecules that target Bcl-2 are used in the clinic to treat leukemia, but tight and selective inhibitors are not available for Bcl-2 paralog Bfl-1. Guided by computational analysis, we designed variants of the native BH3 motif PUMA that are > 150-fold selective for Bfl-1 binding. The designed peptides potently trigger disruption of the mitochondrial outer membrane in cells dependent on Bfl-1, but not in cells dependent on other anti-apoptotic homologs. High-resolution crystal structures show that designed peptide FS2 binds Bfl-1 in a shifted geometry, relative to PUMA and other binding partners, due to a set of epistatic mutations. FS2 modified with an electrophile reacts with a cysteine near the peptide-binding groove to augment specificity. Designed Bfl-1 binders provide reagents for cellular profiling and leads for developing enhanced and cell-permeable peptide or small-molecule inhibitors.

Mitochondria-Judges and Executioners of Cell Death Sentences

Apoptosis is a form of programmed cell death that is critical for basic human development and physiology. One of the more important surprises in cell biology in the last two decades is the extent to which mitochondria represent a physical point of convergence for many apoptosis-inducing signals in mammalian cells. Mitochondria not only adjudicate the decision of whether or not to commit to cell death, but also release toxic proteins culminating in widespread proteolysis, nucleolysis, and cell engulfment. Interactions among BCL-2 family proteins at the mitochondrial outer membrane control the release of these toxic proteins and, by extension, control cellular commitment to apoptosis. This pathway is particularly relevant to cancer treatment, as most cancer chemotherapies trigger mitochondrial-mediated apoptosis. In this Review, we discuss recent advances in the BCL-2 family interactions, their control by upstream factors, and how the mitochondria itself alters these interactions. We also highlight recent clinical insights into mitochondrial-mediated apoptosis and novel cancer therapies that exploit this pathway.

Rapid Optimization of Mcl-1 Inhibitors using Stapled Peptide Libraries Including Non-Natural Side Chains

Alpha helices form a critical part of the binding interface for many protein-protein interactions, and chemically stabilized synthetic helical peptides can be effective inhibitors of such helix-mediated complexes. In particular, hydrocarbon stapling of peptides to generate constrained helices can improve binding affinity and other peptide properties, but determining the best stapled peptide variant often requires laborious trial and error. Here, we describe the rapid discovery and optimization of a stapled-helix peptide that binds to Mcl-1, an antiapoptotic protein that is overexpressed in many chemoresistant cancers. To accelerate discovery, we developed a peptide library synthesis and screening scheme capable of identifying subtle affinity differences among Mcl-1-binding stapled peptides. We used our method to sample combinations of non-natural amino-acid substitutions that we introduced into Mcl-1 inhibitors in the context of a fixed helix-stabilizing hydrocarbon staple that increased peptide helical content and reduced proteolysis. Peptides discovered in our screen contained surprising substitutions at sites that are conserved in natural binding partners. Library-identified peptide M3d is the most potent molecule yet tested for selectively triggering mitochondrial permeabilization in Mcl-1 dependent cell lines. Our library approach for optimizing helical peptide inhibitors can be readily applied to the study of other biomedically important targets.

Designing helical peptide inhibitors of protein-protein interactions

Short helical peptides combine characteristics of small molecules and large proteins and provide an exciting area of opportunity in protein design. A growing number of studies report novel helical peptide inhibitors of protein-protein interactions. New techniques have been developed for peptide design and for chemically stabilizing peptides in a helical conformation, which frequently improves protease resistance and cell permeability. We summarize advances in peptide crosslinking chemistry and give examples of peptide design studies targeting coiled-coil transcription factors, Bcl-2 family proteins, MDM2/MDMX, and HIV gp41, among other targets.

AB-Bind: Antibody binding mutational database for computational affinity predictions

Antibodies (Abs) are a crucial component of the immune system and are often used as diagnostic and therapeutic agents. The need for high-affinity and high-specificity antibodies in research and medicine is driving the development of computational tools for accelerating antibody design and discovery. We report a diverse set of antibody binding data with accompanying structures that can be used to evaluate methods for modeling antibody interactions. Our Antibody-Bind (AB-Bind) database includes 1101 mutants with experimentally determined changes in binding free energies (ΔΔG) across 32 complexes. Using the AB-Bind data set, we evaluated the performance of protein scoring potentials in their ability to predict changes in binding free energies upon mutagenesis. Numerical correlations between computed and observed ΔΔG values were low (r = 0.16-0.45), but the potentials exhibited predictive power for classifying variants as improved vs weakened binders. Performance was evaluated using the area under the curve (AUC) for receiver operator characteristic (ROC) curves; the highest AUC values for 527 mutants with |ΔΔG| > 1.0 kcal/mol were 0.81, 0.87, and 0.88 using STATIUM, FoldX, and Discovery Studio scoring potentials, respectively. Some methods could also enrich for variants with improved binding affinity; FoldX and Discovery Studio were able to correctly rank 42% and 30%, respectively, of the 80 most improved binders (those with ΔΔG < -1.0 kcal/mol) in the top 5% of the database. This modest predictive performance has value but demonstrates the continuing need to develop and improve protein energy functions for affinity prediction.

Design of Specific Peptide-Protein Recognition

Selective targeting of protein-protein interactions in the cell is of great interest in biological research. Computational structure-based design of peptides to bind protein interaction interfaces could provide a potential means of generating such reagents. However, to avoid perturbing off-target interactions, methods that explicitly account for interaction specificity are needed. Further, as peptides often retain considerable flexibility upon association, their binding reaction is computationally demanding to model-a stark limitation for structure-based design. Here we present a protocol for designing peptides that selectively target a given peptide-binding domain, relative to a pre-specified set of possibly related domains. We recently used the method to design peptides that discriminate with high selectivity between two closely related PDZ domains. The framework accounts for the flexibility of the peptide in the binding site, but is efficient enough to quickly analyze trade-offs between affinity and selectivity, enabling the identification of optimal peptides.

Redefining the BH3 Death Domain as a 'Short Linear Motif'

B cell lymphoma-2 (BCL-2)-related proteins control programmed cell death through a complex network of protein–protein interactions mediated by BCL-2 homology 3 (BH3) domains. Given their roles as dynamic linchpins, the discovery of novel BH3-containing proteins has attracted considerable attention. However, without a clearly defined BH3 signature sequence the BCL-2 family has expanded to include a nebulous group of nonhomologous BH3-only proteins, now justified by an intriguing twist. We present evidence that BH3s from both ordered and disordered proteins represent a new class of short linear motifs (SLiMs) or molecular recognition features (MoRFs) and are diverse in their evolutionary histories. The implied corollaries are that BH3s have a broad phylogenetic distribution and could potentially bind to non-BCL-2-like structural domains with distinct functions.

BCL-2 family interactions are mediated by evolutionarily diverse BH3 motifs to regulate apoptosis. Given their key roles, BH3 mimetics are in clinical trials as cancer therapies.

The discovery of novel BH3-only proteins represents a major endeavor in the cell death field. As a result, BH3 motifs are reportedly present in a nebulous conglomerate of different proteins, both structured and intrinsically disordered.

There is no rigorous definition of a BH3 motif. Currently available BH3 signatures are diverse and elusive for predicting new functional BH3-containing proteins.

Redefining the BH3 motif as a new type of short linear motif (SLiM) or molecular recognition feature (MoRF) reconciles many puzzling features of this motif and opens up new avenues for research.

The Bcl-2 family: structures, interactions and targets for drug discovery

Two phylogenetically and structurally distinct groups of proteins regulate stress induced intrinsic apoptosis, the programmed disassembly of cells. Together they form the B cell lymphoma-2 (Bcl-2) family. Bcl-2 proteins appeared early in metazoan evolution and are identified by the presence of up to four short conserved sequence blocks known as Bcl-2 homology (BH) motifs, or domains. The simple BH3-only proteins bear only a BH3-motif and are intrinsically disordered proteins and antagonize or activate the other group, the multi-motif Bcl-2 proteins that have up to four BH motifs, BH1-BH4. Multi-motif Bcl-2 proteins are either pro-survival or pro-apoptotic in action and have remarkably similar α-helical bundle structures that provide a binding groove formed from the BH1, BH2, and BH3-motifs for their BH3-bearing antagonists. In mammals a network of interactions between Bcl-2 members regulates mitochondrial outer membrane permeability (MOMP) and efflux of cytochrome c and other death inducing factors from mitochondria to initiate the apoptotic caspase cascade, but the molecular events leading to MOMP are uncertain. Dysregulation of the Bcl-2 family occurs in many diseases and pathogenic viruses have assimilated pro-survival Bcl-2 proteins to evade immune responses. Their role in disease has made the Bcl-2 family the focus of drug design attempts and clinical trials are showing promise for 'BH3-mimics', drugs that mimic the ability of BH3-only proteins to neutralize selected pro-survival proteins to induce cell death in tumor cells. This review focuses on the structural biology of Bcl-2 family proteins, their interactions and attempts to harness them as targets for drug design.

High-throughput methods for identification of protein-protein interactions involving short linear motifs

Interactions between modular domains and short linear motifs (3–10 amino acids peptide stretches) are crucial for cell signaling. The motifs typically reside in the disordered regions of the proteome and the interactions are often transient, allowing for rapid changes in response to changing stimuli. The properties that make domain-motif interactions suitable for cell signaling also make them difficult to capture experimentally and they are therefore largely underrepresented in the known protein-protein interaction networks. Most of the knowledge on domain-motif interactions is derived from low-throughput studies, although there exist dedicated high-throughput methods for the identification of domain-motif interactions. The methods include arrays of peptides or proteins, display of peptides on phage or yeast, and yeast-two-hybrid experiments. We here provide a survey of scalable methods for domain-motif interaction profiling. These methods have frequently been applied to a limited number of ubiquitous domain families. It is now time to apply them to a broader set of peptide binding proteins, to provide a comprehensive picture of the linear motifs in the human proteome and to link them to their potential binding partners. Despite the plethora of methods, it is still a challenge for most approaches to identify interactions that rely on post-translational modification or context dependent or conditional interactions, suggesting directions for further method development.

Locating Herpesvirus Bcl-2 Homologs in the Specificity Landscape of Anti-Apoptotic Bcl-2 Proteins

Viral homologs of the anti-apoptotic Bcl-2 proteins are highly diverged from their mammalian counterparts, yet they perform overlapping functions by binding and inhibiting BH3 (Bcl-2 homology 3)-motif-containing proteins. We investigated the BH3 binding properties of the herpesvirus Bcl-2 homologs KSBcl-2, BHRF1, and M11, as they relate to those of the human Bcl-2 homologs Mcl-1, Bfl-1, Bcl-w, Bcl-xL, and Bcl-2. Analysis of the sequence and structure of the BH3 binding grooves showed that, despite low sequence identity, M11 has structural similarities to Bcl-xL, Bcl-2, and Bcl-w. BHRF1 and KSBcl-2 are more structurally similar to Mcl-1 than to the other human proteins. Binding to human BH3-like peptides showed that KSBcl-2 has similar specificity to Mcl-1, and BHRF1 has a restricted binding profile; M11 binding preferences are distinct from those of Bcl-xL, Bcl-2, and Bcl-w. Because KSBcl-2 and BHRF1 are from human herpesviruses associated with malignancies, we screened computationally designed BH3 peptide libraries using bacterial surface display to identify selective binders of KSBcl-2 or BHRF1. The resulting peptides bound to KSBcl-2 and BHRF1 in preference to Bfl-1, Bcl-w, Bcl-xL, and Bcl-2 but showed only modest specificity over Mcl-1. Rational mutagenesis increased specificity against Mcl-1, resulting in a peptide with a dissociation constant of 2.9nM for binding to KSBcl-2 and >1000-fold specificity over other Bcl-2 proteins, as well as a peptide with >70-fold specificity for BHRF1. In addition to providing new insights into viral Bcl-2 binding specificity, this study will inform future work analyzing the interaction properties of homologous binding domains and designing specific protein interaction partners.

Bh3 induced conformational changes in Bcl-Xl revealed by crystal structure and comparative analysis

Apoptosis or programmed cell death is a regulatory process in cells in response to stimuli perturbing physiological conditions. The Bcl-2 family of proteins plays an important role in regulating homeostasis during apoptosis. In the process, the molecular interactions among the three members of this family, the pro-apoptotic, anti-apoptotic and BH3-only proteins at the mitochondrial outer membrane define the fate of a cell. Here, we report the crystal structures of the human anti-apoptotic protein Bcl-XL in complex with BH3-only BID(BH3) and BIM(BH3) peptides determined at 2.0 Å and 1.5 Å resolution, respectively. The BH3 peptides bind to the canonical hydrophobic pocket in Bcl-XL and adopt an alpha helical conformation in the bound form. Despite a similar structural fold, a comparison with other BH3 complexes revealed structural differences due to their sequence variations. In the Bcl-XL-BID(BH3) complex we observed a large pocket, in comparison with other BH3 complexes, lined by residues from helices α1, α2, α3, and α5 located adjacent to the canonical hydrophobic pocket. These results suggest that there are differences in the mode of interactions by the BH3 peptides that may translate into functional differences in apoptotic regulation.

Predicting peptide-mediated interactions on a genome-wide scale

We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome.

Complexes formed between a structured domain on one protein and an unstructured peptide on another are ubiquitous. However, they are often quite difficult to detect experimentally. The development of computational approaches to predict domain-motif interactions is therefore an important goal. We report a method to predict domain-motif interactions using a Bayesian approach to integrate evidence from a variety of sources, including three-dimensional structural and non-structural information. The method was applied to the entire human proteome and showed significant improvement over existing methods. The method was incorporated into PrePPI, a computational pipeline for the prediction of protein-protein interactions that relies heavily on structural information. Approximately 80,000 new interactions were detected. The new PrePPI database provides easy access to about 400,000 human protein-protein interactions and should thus constitute a valuable resource in a variety of biological applications including the characterization of molecular interaction networks and, more generally, in the study of interactions mediated by proteins in families that may not be extensively studied experimentally.

Template-based prediction of protein function

We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.