Julia M. Shifman

Exploring the origins of binding specificity through the computational redesign of calmodulin

Calmodulin (CaM) is a second messenger protein that has evolved to bind tightly to a variety of targets and, as such, exhibits low binding specificity. We redesigned CaM by using a computational protein design algorithm to improve its binding specificity for one of its targets, smooth muscle myosin light chain kinase (smMLCK). Residues in or near the CaM/smMLCK binding interface were optimized; CaM interactions with alternative targets were not directly considered in the optimization. The predicted CaM sequences were constructed and tested for binding to a set of eight targets including smMLCK. The best CaM variant, obtained from a calculation that emphasized intermolecular interactions, showed up to a 155-fold increase in binding specificity. The increase in binding specificity was not due to improved binding to smMLCK, but due to decreased binding to the alternative targets. This finding is consistent with the fact that the sequence of wild-type CaM is nearly optimal for interactions with numerous targets.

Ca2+/calmodulin-dependent protein kinase II (CaMKII) is activated by calmodulin with two bound calciums

Changes in synaptic strength that underlie memory formation in the CNS are initiated by pulses of Ca2+ flowing through NMDA-type glutamate receptors into postsynaptic spines. Differences in the duration and size of the pulses determine whether a synapse is potentiated or depressed after repetitive synaptic activity. Calmodulin (CaM) is a major Ca2+ effector protein that binds up to four Ca2+ ions. CaM with bound Ca2+ can activate at least six signaling enzymes in the spine. In fluctuating cytosolic Ca2+, a large fraction of free CaM is bound to fewer than four Ca2+ ions. Binding to targets increases the affinity of CaMs remaining Ca2+-binding sites. Thus, initial binding of CaM to a target may depend on the targets affinity for CaM with only one or two bound Ca2+ ions. To study CaM-dependent signaling in the spine, we designed mutant CaMs that bind Ca2+ only at the two N-terminal or two C-terminal sites by using computationally designed mutations to stabilize the inactivated Ca2+-binding domains in the “closed” Ca2+-free conformation. We have measured their interactions with CaMKII, a major Ca2+/CaM target that mediates initiation of long-term potentiation. We show that CaM with two Ca2+ ions bound in its C-terminal lobe not only binds to CaMKII with low micromolar affinity but also partially activates kinase activity. Our results support the idea that competition for binding of CaM with two bound Ca2+ ions may influence significantly the outcome of local Ca2+ signaling in spines and, perhaps, in other signaling pathways.

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.

Computational Design of Calmodulin Mutants with up to 900-Fold Increase in Binding Specificity

Calmodulin (CaM) is a ubiquitous second messenger protein that regulates a variety of structurally and functionally diverse targets in response to changes in Ca(2+) concentration. CaM-dependent protein kinase II (CaMKII) and calcineurin (CaN) are the prominent CaM targets that play an opposing role in many cellular functions including synaptic regulation. Since CaMKII and CaN compete for the available Ca(2+)/CaM, the differential affinity of these enzymes for CaM is crucial for achieving a balance in Ca(2+) signaling. We used the computational protein design approach to modify CaM binding specificity for these two targets. Starting from the X-ray structure of CaM in complex with the CaM-binding domain of CaMKII, we optimized CaM interactions with CaMKII by introducing mutations into the CaM sequence. CaM optimization was performed with a protein design program, ORBIT, using a modified energy function that emphasized intermolecular interactions in the sequence selection procedure. Several CaM variants were experimentally constructed and tested for binding to the CaMKII and CaN peptides using the surface plasmon resonance technique. Most of our CaM mutants demonstrated small increase in affinity for the CaMKII peptide and substantial decrease in affinity for the CaN peptide compared to that of wild-type CaM. Our best CaM design exhibited an about 900-fold increase in binding specificity towards the CaMKII peptide, becoming the highest specificity switch achieved in any protein-protein interface through the computational protein design approach. Our results show that computational redesign of protein-protein interfaces becomes a reliable method for altering protein binding affinity and specificity.

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.

Tradeoff Between Stability and Multispecificity in the Design of Promiscuous Proteins

Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions.

What makes Ras an efficient molecular switch: a computational, biophysical, and structural study of Ras-GDP interactions with mutants of Raf.

Ras is a small GTP-binding protein that is an essential molecular switch for a wide variety of signaling pathways including the control of cell proliferation, cell cycle progression and apoptosis. In the GTP-bound state, Ras can interact with its effectors, triggering various signaling cascades in the cell. In the GDP-bound state, Ras looses its ability to bind to known effectors. The interaction of the GTP-bound Ras (Ras(GTP)) with its effectors has been studied intensively. However, very little is known about the much weaker interaction between the GDP-bound Ras (Ras(GDP)) and Ras effectors. We investigated the factors underlying the nucleotide-dependent differences in Ras interactions with one of its effectors, Raf kinase. Using computational protein design, we generated mutants of the Ras-binding domain of Raf kinase (Raf) that stabilize the complex with Ras(GDP). Most of our designed mutations narrow the gap between the affinity of Raf for Ras(GTP) and Ras(GDP), producing the desired shift in binding specificity towards Ras(GDP). A combination of our best designed mutation, N71R, with another mutation, A85K, yielded a Raf mutant with a 100-fold improvement in affinity towards Ras(GDP). The Raf A85K and Raf N71R/A85K mutants were used to obtain the first high-resolution structures of Ras(GDP) bound to its effector. Surprisingly, these structures reveal that the loop on Ras previously termed the switch I region in the Ras(GDP).Raf mutant complex is found in a conformation similar to that of Ras(GTP) and not Ras(GDP). Moreover, the structures indicate an increased mobility of the switch I region. This greater flexibility compared to the same loop in Ras(GTP) is likely to explain the natural low affinity of Raf and other Ras effectors to Ras(GDP). Our findings demonstrate that an accurate balance between a rigid, high-affinity conformation and conformational flexibility is required to create an efficient and stringent molecular switch.

Dead‐end elimination for multistate protein design

Multistate protein design is the task of predicting the amino acid sequence that is best suited to selectively and stably fold to one state out of a set of competing structures. Computationally, it entails solving a challenging optimization problem. Therefore, notwithstanding the increased interest in multistate design, the only implementations reported are based on either genetic algorithms or Monte Carlo methods. The dead‐end elimination (DEE) theorem cannot be readily transfered to multistate design problems despite its successful application to single‐state protein design. In this article we propose a variant of the standard DEE, called type‐dependent DEE. Our method reduces the size of the conformational space of the multistate design problem, while provably preserving the minimal energy conformational assignment for any choice of amino acid sequence. Type‐dependent DEE can therefore be used as a preprocessing step in any computational multistate design scheme. We demonstrate the applicability of type‐dependent DEE on a set of multistate design problems and discuss its strength and limitations.

Triathlon for energy functions: Who is the winner for design of protein–protein interactions?

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. Proteins 2011;

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.