Engineered variants of the Ras effector protein RASSF5 (NORE1A) promote anticancer activities in lung adenocarcinoma.

Within the superfamily of small GTPases, Ras appears to be the master regulator of such processes as cell cycle progression, cell division, and apoptosis. Several oncogenic Ras mutations at amino acid positions 12, 13, and 61 have been identified that lose their ability to hydrolyze GTP, giving rise to constitutive signaling and eventually development of cancer. While disruption of the Ras/effector interface is an attractive strategy for drug design to prevent this constitutive activity, inhibition of this interaction using small molecules is impractical due to the absence of a cavity to which such molecules could bind. However, proteins and especially natural Ras effectors that bind to the Ras/effector interface with high affinity could disrupt Ras/effector interactions and abolish procancer pathways initiated by Ras oncogene. Using a combination of computational design and in vitro evolution, we engineered high-affinity Ras-binding proteins starting from a natural Ras effector, RASSF5 (NORE1A), which is encoded by a tumor suppressor gene. Unlike previously reported Ras oncogene inhibitors, the proteins we designed not only inhibit Ras-regulated procancer pathways, but also stimulate anticancer pathways initiated by RASSF5. We show that upon introduction into A549 lung carcinoma cells, the engineered RASSF5 mutants decreased cell viability and mobility to a significantly greater extent than WT RASSF5. In addition, these mutant proteins induce cellular senescence by increasing acetylation and decreasing phosphorylation of p53. In conclusion, engineered RASSF5 variants provide an attractive therapeutic strategy able to oppose cancer development by means of inhibiting of procancer pathways and stimulating anticancer processes.

A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning.

Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy" interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.

Two roles for the yeast transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and SAGA.

Deletions within genes coding for subunits of the transcription coactivator SAGA caused strong genome-wide defects in transcription and SAGA-mediated chromatin modifications. In contrast, rapid SAGA depletion produced only modest transcription defects at 13% of protein-coding genes - genes that are generally more sensitive to rapid TFIID depletion. However, transcription of these 'coactivator-redundant' genes is strongly affected by rapid depletion of both factors, showing the overlapping functions of TFIID and SAGA at this gene set. We suggest that this overlapping function is linked to TBP-DNA recruitment. The remaining 87% of expressed genes that we term 'TFIID-dependent' are highly sensitive to rapid TFIID depletion and insensitive to rapid SAGA depletion. Genome-wide mapping of SAGA and TFIID found binding of both factors at many genes independent of gene class. Promoter analysis suggests that the distinction between the gene classes is due to multiple components rather than any single regulatory factor or promoter sequence motif.

RAS/Effector Interactions from Structural and Biophysical Perspective.

RAS is a molecular switch that regulates a large number of pathways through interactions with many effector proteins. Most RAS/effector complexes are short-lived, demonstrating fast association and fast dissociation rate and Kds ranging from 10(-8)-10(-5) M, compatible with the signaling function of these interactions in the cell. RAS effectors share little sequence homology but all contain an RAS binding domain that exhibits ubiquitin fold. All effectors bind to the same epitope on RAS by forming an intermolecular beta sheet and creating a number of favorable hydrogen bonds and salt bridges across the binding interface. Several hot-spots on both RAS and effector molecules constitute a general recognition mode. RAS/effector interactions occur only when RAS is found in the active, GTP-bound state, and are disrupted upon GTP hydrolysis, most probably due to increased flexibility of the RAS molecule. Recent NMR studies demonstrate how in the presence of multiple binding partners, RAS prefers certain effectors to others. The hierarchy of these interactions could be altered for RAS oncogenic mutants, thus perturbing the network of the downstream signaling. Insights obtained through biophysical and structural studies of effectors interacting with RAS and its mutants establish the basic principles that could be used for designing drugs in RAS-associated diseases.

Synthetic peptides mimicking the binding site of human acetylcholinesterase for its inhibitor fasciculin 2.

Molecules capable of mimicking protein binding and/or functional sites present useful tools for a range of biomedical applications, including the inhibition of protein-ligand interactions. Such mimics of protein binding sites can currently be generated through structure-based design and chemical synthesis. Computational protein design could be further used to optimize protein binding site mimetics through rationally designed mutations that improve intermolecular interactions or peptide stability. Here, as a model for the study, we chose an interaction between human acetylcholinesterase (hAChE) and its inhibitor fasciculin-2 (Fas) because the structure and function of this complex is well understood. Structure-based design of mimics of the hAChE binding site for Fas yielded a peptide that binds to Fas at micromolar concentrations. Replacement of hAChE residues known to be essential for its interaction with Fas with alanine, in this peptide, resulted in almost complete loss of binding to Fas. Computational optimization of the hAChE mimetic peptide yielded a variant with slightly improved affinity to Fas, indicating that more rounds of computational optimization will be required to obtain peptide variants with greatly improved affinity for Fas. CD spectra in the absence and presence of Fas point to conformational changes in the peptide upon binding to Fas. Furthermore, binding of the optimized hAChE mimetic peptide to Fas could be inhibited by hAChE, providing evidence for a hAChE-specific peptide-Fas interaction.

How structure defines affinity in protein-protein interactions.

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.

Alteration of the C-terminal ligand specificity of the erbin PDZ domain by allosteric mutational effects.

Modulation of protein binding specificity is important for basic biology and for applied science. Here we explore how binding specificity is conveyed in PDZ (postsynaptic density protein-95/discs large/zonula occludens-1) domains, small interaction modules that recognize various proteins by binding to an extended C terminus. Our goal was to engineer variants of the Erbin PDZ domain with altered specificity for the most C-terminal position (position 0) where a Val is strongly preferred by the wild-type domain. We constructed a library of PDZ domains by randomizing residues in direct contact with position 0 and in a loop that is close to but does not contact position 0. We used phage display to select for PDZ variants that bind to 19 peptide ligands differing only at position 0. To verify that each obtained PDZ domain exhibited the correct binding specificity, we selected peptide ligands for each domain. Despite intensive efforts, we were only able to evolve Erbin PDZ domain variants with selectivity for the aliphatic C-terminal side chains Val, Ile and Leu. Interestingly, many PDZ domains with these three distinct specificities contained identical amino acids at positions that directly contact position 0 but differed in the loop that does not contact position 0. Computational modeling of the selected PDZ domains shows how slight conformational changes in the loop region propagate to the binding site and result in different binding specificities. Our results demonstrate that second-sphere residues could be crucial in determining protein binding specificity.

A single-tube assembly of DNA using the transfer-PCR (TPCR) platform.

DNA cloning is a basic methodology employed for multiple applications in all life-science disciplines. In order to facilitate DNA cloning we developed Transfer-PCR (TPCR), a novel approach that integrates in a single tube, PCR amplification of the target DNA from an origin vector and its subsequent integration into the destination vector. TPCR can be applied for incorporation of DNA fragments into any desired position within a circular plasmid without the need for purification of the intermediate PCR product and without the use of any commercial kit. TPCR reaction is most efficient within a narrow range of primer concentrations. Adaptation of the TPCR should facilitate, simplify, and significantly reduce time and costs for DNA assembly, as well as protein engineering studies. In the current publication we describe a detailed protocol for implementation of the TPCR method for DNA assembly.

Computational methods for controlling binding specificity.

Learning to control, protein-binding specificity is useful for both fundamental and applied biology. In fundamental research, better understanding of complicated signaling networks could be achieved through engineering of regulator proteins to bind to only a subset of their effector proteins. In applied research such as drug design, nonspecific binding remains a major reason for failure of many drug candidates. However, developing antibodies that simultaneously inhibit several disease-associated pathways are a rising trend in pharmaceutical industry. Binding specificity could be manipulated experimentally through various display technologies that allow us to select desired binders from a large pool of candidate protein sequences. We developed an alternative approach for controlling binding specificity based on computational protein design. We can enhance binding specificity of a protein by computationally optimizing its sequence for better interactions with one target and worse interaction with alternative target(s). Moreover, we can design multispecific proteins that simultaneously interact with a predefined set of proteins. Unlike combinatorial techniques, our computational methods for manipulating binding specificity are fast, low cost and in principle are able to consider an unlimited number of desired and undesired binding partners.

Transfer-PCR (TPCR): a highway for DNA cloning and protein engineering.

DNA cloning and protein engineering are basic methodologies employed for various applications in all life-science disciplines. Manipulations of DNA however, could be a lengthy process that slows down subsequent experiments. To facilitate both DNA cloning and protein engineering, we present Transfer-PCR (TPCR), a novel approach that integrates in a single tube, PCR amplification of the target DNA from an origin vector and its subsequent integration into the destination vector. TPCR can be applied for incorporation of DNA fragments into any desired position within a circular plasmid without the need for purification of the intermediate PCR product and without the use of any commercial kit. Using several examples, we demonstrate the applicability of the TPCR platform for both DNA cloning and for multiple-site targeted mutagenesis. In both cases, we show that the TPCR reaction is most efficient within a narrow range of primer concentrations. In mutagenesis, TPCR is primarily advantageous for generation of combinatorial libraries of targeted mutants but could be also applied to generation of variants with specific multiple mutations throughout the target gene. Adaptation of the TPCR platform should facilitate, simplify and significantly reduce time and costs for diverse protein structure and functional studies.

Multispecific recognition: mechanism, evolution, and design.

Accumulating evidence shows that many particular proteins have evolved to bind multiple targets, including other proteins, peptides, DNA, and small molecule substrates. Multispecific recognition might be not only common but also necessary for the robustness of signaling and metabolic networks in the cell. It is also important for the immune response and for regulation of transcription and translation. Multispecificity presents an apparent paradox: How can a protein encoded by a single sequence accommodate numerous targets? Analysis of sequences and structures of multispecific proteins revealed a number of mechanisms that achieve multispecificity. Interestingly, similar mechanisms appear in antibody-antigen, T-cell receptor-peptide, protein-DNA, enzyme-substrate, and protein-protein complexes. Directed evolution and protein design experiments with multispecific proteins offer some interesting insights into the evolution of such proteins and help in the dissection of molecular interactions that mediate multispecificity. Understanding the basic principles governing multispecificity could greatly assist in the unraveling of various complex processes in the cell. In addition, through manipulation of functional multispecificity, novel proteins could be created for use in various biotechnological and biomedical applications.