Jaume Bonet

Multivalent antibodies: when design surpasses evolution

Evolutionary pressure has selected antibodies as key immune molecules acting against foreign pathogens. The development of monoclonal antibody technology has allowed their widespread use in research, real-time diagnosis and treatment of multiple diseases, including cancer. However, compared with hematologic malignancies, solid tumors have often proven to be relatively resistant to antibody-based therapies. In an attempt to improve the tumor-targeting efficacy of antibodies, new formats with modified, multivalent properties have been generated. Initially, these formats imitated the structure of native IgG, creating mostly monospecific, bivalent antibodies. Recently, novel trivalent antibodies have been developed to maximize tumor targeting capabilities through enhanced biodistribution and functional affinity. We review recent advances in the engineering of multivalent antibodies and further discuss their promise as agents for in vivo diagnostics and therapy.

CDK2-dependent activation of PARP-1 is required for hormonal gene regulation in breast cancer cells

Eukaryotic gene regulation implies that transcription factors gain access to genomic information via poorly understood processes involving activation and targeting of kinases, histone-modifying enzymes, and chromatin remodelers to chromatin. Here we report that progestin gene regulation in breast cancer cells requires a rapid and transient increase in poly-(ADP)-ribose (PAR), accompanied by a dramatic decrease of cellular NAD that could have broad implications in cell physiology. This rapid increase in nuclear PARylation is mediated by activation of PAR polymerase PARP-1 as a result of phosphorylation by cyclin-dependent kinase CDK2. Hormone-dependent phosphorylation of PARP-1 by CDK2, within the catalytic domain, enhances its enzymatic capabilities. Activated PARP-1 contributes to the displacement of histone H1 and is essential for regulation of the majority of hormone-responsive genes and for the effect of progestins on cell cycle progression. Both global chromatin immunoprecipitation (ChIP) coupled with deep sequencing (ChIP-seq) and gene expression analysis show a strong overlap between PARP-1 and CDK2. Thus, progestin gene regulation involves a novel signaling pathway that connects CDK2-dependent activation of PARP-1 with histone H1 displacement. Given the multiplicity of PARP targets, this new pathway could be used for the pharmacological management of breast cancer.

In Vivo Tumor Targeting and Imaging with Engineered Trivalent Antibody Fragments Containing Collagen-Derived Sequences

There is an urgent need to develop new and effective agents for cancer targeting. In this work, a multivalent antibody is characterized in vivo in living animals. The antibody, termed “trimerbody”, comprises a single-chain antibody (scFv) fragment connected to the N-terminal trimerization subdomain of collagen XVIII NC1 by a flexible linker. As indicated by computer graphic modeling, the trimerbody has a tripod-shaped structure with three highly flexible scFv heads radially outward oriented. Trimerbodies are trimeric in solution and exhibited multivalent binding, which provides them with at least a 100-fold increase in functional affinity than the monovalent scFv. Our results also demonstrate the feasibility of producing functional bispecific trimerbodies, which concurrently bind two different ligands. A trimerbody specific for the carcinoembryonic antigen (CEA), a classic tumor-associated antigen, showed efficient tumor targeting after systemic administration in mice bearing CEA-positive tumors. Importantly, a trimerbody that recognizes an angiogenesis-associated laminin epitope, showed excellent tumor localization in several cancer types, including fibrosarcomas and carcinomas. These results illustrate the potential of this new antibody format for imaging and therapeutic applications, and suggest that some laminin epitopes might be universal targets for cancer targeting.

Characterization of protein hubs by inferring interacting motifs from protein interactions

The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.

ADP-ribose–derived nuclear ATP synthesis by NUDIX5 is required for chromatin remodeling

A nuclear power source in the cell DNA is packaged onto nucleosomes, the principal component of chromatin. This chromatin must be remodeled to allow gene transcription, DNA replication, and DNA repair machineries access to the enclosed DNA. Chromatin-remodeling complexes require high levels of cellular energy to do their job. Wright et al. show that the energy needed to remodel chromatin can be derived from a source, poly-ADP-ribose, in the cell nucleus, rather than by diffusion of ATP from mitochondria in the cytoplasm, the usual powerhouse of the cell. Poly-ADP-ribose is converted to ADP-ribose and then to ATP, which can be used to fuel chromatin remodeling within the nucleus. Science, this issue p. 1221 Energy needed to remodel chromatin to make DNA accessible can be generated in situ in the nucleus from ADP-ribose. Key nuclear processes in eukaryotes, including DNA replication, repair, and gene regulation, require extensive chromatin remodeling catalyzed by energy-consuming enzymes. It remains unclear how the ATP demands of such processes are met in response to rapid stimuli. We analyzed this question in the context of the massive gene regulation changes induced by progestins in breast cancer cells and found that ATP is generated in the cell nucleus via the hydrolysis of poly(ADP-ribose) to ADP-ribose. In the presence of pyrophosphate, ADP-ribose is used by the pyrophosphatase NUDIX5 to generate nuclear ATP. The nuclear source of ATP is essential for hormone-induced chromatin remodeling, transcriptional regulation, and cell proliferation.

ArchDB 2014: structural classification of loops in proteins

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability—raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.

Understanding Protein–Protein Interactions Using Local Structural Features

Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features (loops and domains) to comprehend the molecular mechanisms of PPIs. A paradox in protein-protein binding is to explain how the unbound proteins of a binary complex recognize each other among a large population within a cell and how they find their best docking interface in a short timescale. We use interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation of this classification suggests that the balance between favoring and disfavoring structural features determines if a pair of proteins interacts or not. Our results are in agreement with previous works and support the funnel-like intermolecular energy landscape theory that explains PPIs. We have used these features to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable circumstances.

iLoops: a protein–protein interaction prediction server based on structural features

SUMMARY Protein-protein interactions play a critical role in many biological processes. Despite that, the number of servers that provide an easy and comprehensive method to predict them is still limited. Here, we present iLoops, a web server that predicts whether a pair of proteins can interact using local structural features. The inputs of the server are as follows: (i) the sequences of the query proteins and (ii) the pairs to be tested. Structural features are assigned to the query proteins by sequence similarity. Pairs of structural features (formed by loops or domains) are classified according to their likelihood to favor or disfavor a protein-protein interaction, depending on their observation in known interacting and non-interacting pairs. The server evaluates the putative interaction using a random forest classifier. AVAILABILITY iLoops is available at http://sbi.imim.es/iLoops.php CONTACT baldo.oliva@upf.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Generation and characterization of monospecific and bispecific hexavalent trimerbodies

Here, we describe a new class of multivalent and multispecific antibody-based reagents for therapy. The molecules, termed “trimerbodies,” use a modified version of the N-terminal trimerization region of human collagen XVIII noncollagenous 1 domain flanked by two flexible linkers as trimerizing scaffold. By fusing single-chain variable fragments (scFv) with the same or different specificity to both N- and C-terminus of the trimerizing scaffold domain, we produced monospecific or bispecific hexavalent molecules that were efficiently secreted as soluble proteins by transfected mammalian cells. A bispecific anti-laminin x anti-CD3 N-/C-trimerbody was found to be trimeric in solution, very efficient at recognizing purified plastic-immobilized laminin and CD3 expressed at the surface of T cells, and remarkably stable in human serum. The bispecificity was further demonstrated in T cell activation studies. In the presence of laminin-rich substrate, the bispecific anti-laminin x anti-CD3 N-/C-trimerbody stimulated a high percentage of human T cells to express surface activation markers. These results suggest that the trimerbody platform offers promising opportunities for the development of the next-generation therapeutic antibodies, i.e., multivalent and bispecific molecules with a format optimized for the desired pharmacokinetics and adapted to the pathological context.

Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details

Proteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio‐molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio‐molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein‐protein interactions on modern network medicine and protein function annotation is also explored.