Publications

A detailed understanding of the molecular mechanisms whereby ubiquitin (Ub) recognizes enzymes in the Ub proteasome system is crucial for understanding the biological function of Ub. Many structures of Ub complexes have been solved and, in most cases, reveal a large structural epitope on a common face of the Ub molecule. However, owing to the generally weak nature of these interactions, it has been difficult to map in detail the functional contributions of individual Ub side chains to affinity and specificity. Here we took advantage of Ub variants (Ubvs) that bind tightly to particular Ub-specific proteases (USPs) and used phage display and saturation scanning mutagenesis to comprehensively map functional epitopes within the structural epitopes. We found that Ubvs that bind to USP2 or USP21 contain a remarkably similar core functional epitope, or "hot spot," consisting mainly of positions that are conserved as the wild type sequence, but also some positions that prefer mutant sequences. The Ubv core functional epitope contacts residues that are conserved in the human USP family, and thus it is likely important for the interactions of Ub across many family members.

The molecular interactions between macrophage colony-stimulating factor (M-CSF) and the tyrosine kinase receptor c-FMS play a key role in the immune response, bone metabolism, and the development of some cancers. Because no x-ray structure is available for the human M-CSF . c-FMS complex, the binding epitope for this complex is largely unknown. Our goal was to identify the residues that are essential for binding of the human M-CSF to c-FMS. For this purpose, we used a yeast surface display (YSD) approach. We expressed a combinatorial library of monomeric M-CSF (M-CSFM) single mutants and screened this library to isolate variants with reduced affinity for c-FMS using FACS. Sequencing yielded a number of single M-CSFM variants with mutations both in the direct binding interface and distant from the binding site. In addition, we used computational modeling to map the identified mutations onto the M-CSFM structure and to classify the mutations into three groups as follows: those that significantly decrease protein stability; those that destroy favorable intermolecular interactions; and those that decrease affinity through allosteric effects. To validate the YSD and computational data, M-CSFM and three variants were produced as soluble proteins; their affinity and structure were analyzed; and very good correlations with both YSD data and computational predictions were obtained. By identifying the M-CSFM residues critical for M-CSF . c-FMS interactions, we have laid down the basis for a deeper understanding of the M-CSF . c-FMS signaling mechanism and for the development of target-specific therapeutic agents with the ability to sterically occlude the M-CSF.c-FMS binding interface.

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.

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.

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.

Manipulations of PPIs (protein-protein interactions) are important for many biological applications such as synthetic biology and drug design. Combinatorial methods have been traditionally used for such manipulations, failing, however, to explain the effects achieved. We developed a computational method for prediction of changes in free energy of binding due to mutation that bring about deeper understanding of the molecular forces underlying binding interactions. Our method could be used for computational scanning of binding interfaces and subsequent analysis of the interfacial sequence optimality. The computational method was validated in two biological systems. Computational saturated mutagenesis of a high-affinity complex between an enzyme AChE (acetylcholinesterase) and a snake toxin Fas (fasciculin) revealed the optimal nature of this interface with only a few predicted affinity-enhancing mutations. Binding measurements confirmed high optimality of this interface and identified a few mutations that could further improve interaction fitness. Computational interface scanning of a medium-affinity complex between TIMP-2 (tissue inhibitor of metalloproteinases-2) and MMP (matrix metalloproteinase) 14 revealed a non-optimal nature of the binding interface with multiple mutations predicted to stabilize the complex. Experimental results corroborated our computational predictions, identifying a large number of mutations that improve the binding affinity for this interaction and some mutations that enhance binding specificity. Overall, our computational protocol greatly facilitates the discovery of affinity- and specificity-enhancing mutations and thus could be applied for design of potent and highly specific inhibitors of any PPI.

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.

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.

Computational prediction of stabilizing mutations into monomeric proteins has become an almost ordinary task. Yet, computational stabilization of protein-protein complexes remains a challenge. Design of protein-protein interactions (PPIs) is impeded by the absence of an energy function that could reliably reproduce all favorable interactions between the binding partners. In this work, we present three energy functions: one function that was trained on monomeric proteins, while the other two were optimized by different techniques to predict side-chain conformations in a dataset of PPIs. The performances of these energy functions are evaluated in three different tasks related to design of PPIs: predicting side-chain conformations in PPIs, recovering native binding-interface sequences, and predicting changes in free energy of binding due to mutations. Our findings show that both functions optimized on side-chain repacking in PPIs are more suitable for PPI design compared to the function trained on monomeric proteins. Yet, no function performs best at all three tasks. Comparison of the three energy functions and their performances revealed that (1) burial of polar atoms should not be penalized significantly in PPI design as in single-protein design and (2) contribution of electrostatic interactions should be increased several-fold when switching from single-protein to PPI design. In addition, the use of a softer van der Waals potential is beneficial in cases when backbone flexibility is important. All things considered, we define an energy function that captures most of the nuances of the binding energetics and hence, should be used in future for design of PPIs.