De novo backbone scaffolds for protein design

In recent years, there have been significant advances in the field of computational protein design including the successful computational design of enzymes based on backbone scaffolds from experimentally solved structures. It is likely that large‐scale sampling of protein backbone conformations will become necessary as further progress is made on more complicated systems. Removing the constraint of having to use scaffolds based on known protein backbones is a potential method of solving the problem. With this application in mind, we describe a method to systematically construct a large number of de novo backbone structures from idealized topological forms in a top–down hierarchical approach. The structural properties of these novel backbone scaffolds were analyzed and compared with a set of high‐resolution experimental structures from the protein data bank (PDB). It was found that the Ramachandran plot distribution and relative γ‐ and β‐turn frequencies were similar to those found in the PDB. The de novo scaffolds were sequence designed with RosettaDesign, and the energy distributions and amino acid compositions were comparable with the results for redesigned experimentally solved backbones. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

De novo design of biocatalysts

The challenging field of de novo enzyme design is beginning to produce exciting results. The application of powerful computational methods to functional protein design has recently succeeded at engineering target activities. In addition, efforts in directed evolution continue to expand the transformations that can be accomplished by existing enzymes. The engineering of completely novel catalytic activity requires traversing inactive sequence space in a fitness landscape, a feat that is better suited to computational design. Optimizing activity, which can include subtle alterations in backbone conformation and protein motion, is better suited to directed evolution, which is highly effective at scaling fitness landscapes towards maxima. Improved rational design efforts coupled with directed evolution should dramatically improve the scope of de novo enzyme design.     

De novo ligand design to an ensemble of protein structures

We describe a combinatorial method for de novo ligand design to an ensemble of receptor structures. Receptor conformations, protonation states, and structural water molecules are considered consistently within the framework of de novo ligand design. The method relies on Monte Carlo optimization to search the space of ligand structures, conformations, and rigid-body movements as well as receptor models. The method is applied to an ensemble of HIV protease and human collagenase receptor models. Ligand structures generated de novo exhibit the correct hydrogen-bonding pattern in the core of the active site, with hydrophobic groups extending into the receptor S1 and S1' pocket space. Furthermore, it is shown that known ligands are recovered in the correct binding mode and in the native, most tightly binding receptor model.     

De novo protein design using pairwise potentials and a genetic algorithm.

One of the major goals of molecular biology is to understand how protein chains fold into a unique 3-dimensional structure. Given this knowledge, perhaps the most exciting prospect will be the possibility of designing new proteins to perform designated tasks, an application that could prove to be of great importance in medicine and biotechnology. It is possible that effective protein design may be achieved without the requirement for a full understanding of the protein folding process. In this paper a simple method is described for designing an amino acid sequence to fit a given 3-dimensional structure. The compatibility of a designed sequence with a given fold is assessed by means of a set of statistically determined potentials (including interresidue pairwise and solvation terms), which have been previously applied to the problem of protein fold recognition. In order to generate sequences that best fit the fold, a genetic algorithm is used, whereby the sequence is optimized by a stochastic search in the style of natural selection.     

De novo protein design. II. Plasticity in sequence space.

It is generally accepted that many different protein sequences have similar folded structures, and that there is a relatively high probability that a new sequence possesses a previously observed fold. An indirect consequence of this is that protein design should define the sequence space accessible to a given structure, rather than providing a single optimized sequence. We have recently developed a new approach for protein sequence design, which optimizes the complete sequence of a protein based on the knowledge of its backbone structure, its amino acid composition and a physical energy function including van der Waals interactions, electrostatics, and environment free energy. The specificity of the designed sequence for its template backbone is imposed by keeping the amino acid composition fixed. Here, we show that our procedure converges in sequence space, albeit not to the native sequence of the protein. We observe that while polar residues are well conserved in our designed sequences, non-polar amino acids at the surface of a protein are often replaced by polar residues. The designed sequences provide a multiple alignment of sequences that all adopt the same three-dimensional fold. This alignment is used to derive a profile matrix for chicken triose phosphate isomerase, TIM. The matrix is found to recognize significantly the native sequence for TIM, as well as closely related sequences. Possible application of this approach to protein fold recognition is discussed.     

De novo design of VEGFR-2 tyrosine kinase inhibitors based on a linked-fragment approach

Vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitors have been demonstrated to possess substantial antitumor activity. VEGFR-2 tyrosine kinase inhibitors are crucial for development of antitumor drugs. Based on the crystal structure of VEGFR-2 tyrosine kinase, a linked-fragment strategy was employed to design novel VEGFR-2 tyrosine kinase inhibitors, and 1000 compounds were generated in this process. Absorption, distribution, metabolism, excretion and toxicity (ADMET) were used to screen the 1000 compounds, and 59 compounds were acceptable. Scaffold hopping was then used for further screening, and only four compounds were obtained in this way. Then, the binding energy of the four molecules to VEGFR-2 tyrosine kinase was calculated using molecular docking, and their values were found to be lower than that of Sorafenib. Finally, molecular dynamics simulations were performed on the complex of the compound with the lowest binding energy with VEGFR-2 tyrosine kinase, and the binding model was analyzed. At the end, four chemical entities with novel structures were obtained, and were suggested for experimental testing in future studies.     

De novo enzyme design using Rosetta3

The Rosetta de novo enzyme design protocol has been used to design enzyme catalysts for a variety of chemical reactions, and in principle can be applied to any arbitrary chemical reaction of interest. The process has four stages: 1) choice of a catalytic mechanism and corresponding minimal model active site, 2) identification of sites in a set of scaffold proteins where this minimal active site can be realized, 3) optimization of the identities of the surrounding residues for stabilizing interactions with the transition state and primary catalytic residues, and 4) evaluation and ranking the resulting designed sequences. Stages two through four of this process can be carried out with the Rosetta package, while stage one needs to be done externally. Here, we demonstrate how to carry out the Rosetta enzyme design protocol from start to end in detail using for illustration the triosephosphate isomerase reaction.     

De novo protein design. I. In search of stability and specificity.

We have developed a fully automated protein design strategy that works on the entire sequence of the protein and uses a full atom representation. At each step of the procedure, an all-atom model of the protein is built using the template protein structure and the current designed sequence. The energy of the model is used to drive a Monte Carlo optimization in sequence space: random moves are either accepted or rejected based on the Metropolis criterion. We rely on the physical forces that stabilize native protein structures to choose the optimum sequence. Our energy function includes van der Waals interactions, electrostatics and an environment free energy. Successful protein design should be specific and generate a sequence compatible with the template fold and incompatible with competing folds. We impose specificity by maintaining the amino acid composition constant, based on the random energy model. The specificity of the optimized sequence is tested by fold recognition techniques. Successful sequence designs for the B1 domain of protein G, for the lambda repressor and for sperm whale myoglobin are presented. We show that each additional term of the energy function improves the performance of our design procedure: the van der Waals term ensures correct packing, the electrostatics term increases the specificity for the correct native fold, and the environment solvation term ensures a correct pattern of buried hydrophobic and exposed hydrophilic residues. For the globin family, we show that we can design a protein sequence that is stable in the myoglobin fold, yet incompatible with the very similar hemoglobin fold.     

Computational design of protein self-assembly

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De novo design of a monomeric three-stranded antiparallel beta-sheet

Here we describe the NMR conformational study of a 20-residue linear peptide designed to fold into a monomeric three-stranded antiparallel beta-sheet in aqueous solution. Experimental and statistical data on amino acid beta-turn and beta-sheet propensities, cross-strand side-chain interactions, solubility criteria, and our previous experience with beta-hairpins were considered for a rational selection of the peptide sequence. Sedimentation equilibrium measurements and NMR dilution experiments provide evidence that the peptide is monomeric. Analysis of 1H and 13C-NMR parameters of the peptide, in particular NOEs and chemical shifts, and comparison with data obtained for two 12-residue peptides encompassing the N- and C-segments of the designed sequence indicates that the 20-residue peptide folds into the expected conformation. Assuming a two-state model, the exchange kinetics between the beta-sheet and the unfolded peptide molecules is in a suitable range to estimate the folding rate on the basis of the NMR linewidths of several resonances. The time constant for the coil-beta-sheet transition is of the order of several microseconds in the designed peptide. Future designs based on this peptide system are expected to contribute greatly to our knowledge of the many factors involved in beta-sheet formation and stability.     

De novo design of a two-stranded coiled-coil switch peptide

The properties and characteristics shared by amyloid fibrils formed from disease and non-disease associated proteins that are unrelated in sequence and structure offer the prospect that model systems can be used to systematically assess the factors that predispose a native protein to form amyloid fibrils. Based on a de novo design approach, we recently reported a unique switch peptide model system, ccbeta, that forms a three-stranded coiled-coil structure at low temperatures and which can be easily converted to amyloid fibrils by increasing the temperature. To simplify the system further, we describe here the redesign of a two-stranded ccbeta coiled-coil variant and its detailed analysis by a variety of biophysical methods. Compared with the original design, the characteristics of the peptide make it even simpler to elucidate and validate fundamental principles of amyloid fibril-formation.     

De novo design of the hydrophobic cores of proteins.

We have developed and experimentally tested a novel computational approach for the de novo design of hydrophobic cores. A pair of computer programs has been written, the first of which creates a "custom" rotamer library for potential hydrophobic residues, based on the backbone structure of the protein of interest. The second program uses a genetic algorithm to globally optimize for a low energy core sequence and structure, using the custom rotamer library as input. Success of the programs in predicting the sequences of native proteins indicates that they should be effective tools for protein design. Using these programs, we have designed and engineered several variants of the phage 434 cro protein, containing five, seven, or eight sequence changes in the hydrophobic core. As controls, we have produced a variant consisting of a randomly generated core with six sequence changes but equal volume relative to the native core and a variant with a "minimalist" core containing predominantly leucine residues. Two of the designs, including one with eight core sequence changes, have thermal stabilities comparable to the native protein, whereas the third design and the minimalist protein are significantly destabilized. The randomly designed control is completely unfolded under equivalent conditions. These results suggest that rational de novo design of hydrophobic cores is feasible, and stress the importance of specific packing interactions for the stability of proteins. A surprising aspect of the results is that all of the variants display highly cooperative thermal denaturation curves and reasonably dispersed NMR spectra. This suggests that the non-core residues of a protein play a significant role in determining the uniqueness of the folded structure.     

De novo design of the hydrophobic core of ubiquitin.

We have previously reported the development and evaluation of a computational program to assist in the design of hydrophobic cores of proteins. In an effort to investigate the role of core packing in protein structure, we have used this program, referred to as Repacking of Cores (ROC), to design several variants of the protein ubiquitin. Nine ubiquitin variants containing from three to eight hydrophobic core mutations were constructed, purified, and characterized in terms of their stability and their ability to adopt a uniquely folded native-like conformation. In general, designed ubiquitin variants are more stable than control variants in which the hydrophobic core was chosen randomly. However, in contrast to previous results with 434 cro, all designs are destabilized relative to the wild-type (WT) protein. This raises the possibility that beta-sheet structures have more stringent packing requirements than alpha-helical proteins. A more striking observation is that all variants, including random controls, adopt fairly well-defined conformations, regardless of their stability. This result supports conclusions from the cro studies that non-core residues contribute significantly to the conformational uniqueness of these proteins while core packing largely affects protein stability and has less impact on the nature or uniqueness of the fold. Concurrent with the above work, we used stability data on the nine ubiquitin variants to evaluate and improve the predictive ability of our core packing algorithm. Additional versions of the program were generated that differ in potential function parameters and sampling of side chain conformers. Reasonable correlations between experimental and predicted stabilities suggest the program will be useful in future studies to design variants with stabilities closer to that of the native protein. Taken together, the present study provides further clarification of the role of specific packing interactions in protein structure and stability, and demonstrates the benefit of using systematic computational methods to predict core packing arrangements for the design of proteins.     

De novo design and synthesis of HIV-1 integrase inhibitors

Existing AIDS therapies are out of reach for most HIV-infected people in developing countries and, where available, they are limited by their toxicity and their cost. New anti-HIV agents are needed urgently to combat emerging viral resistance and reduce the side effects associated with currently available drugs. Toward this end, LeapFrog, a de novo drug design program was used to design novel, potent, and selective inhibitors of HIV-1 integrase. The designed compounds were synthesized and tested for in vitro inhibition of HIV-1 integrase. Out of the 25 compounds that were designed, and synthesized, four molecules (compounds 23, 26, 43, and 59) showed moderate to low inhibition of HIV-1 integrase for 3'-processing and 3'-strand transfer activities. Nonetheless, these compounds possess structural features not seen in known HIV-1 integrase inhibitors and thus can serve as excellent leads for further optimization of anti-HIV-1 integrase activity.     

De novo design of sequences for nucleic acid structural engineering

An interactive procedure has been developed to assign sequences for the design of nucleic acid secondary structure. The primary goal of the procedure is to facilitate macromolecular architecture studies through the design of branched nucleic acid mono- and oligo-junction constructs in a convenient fashion. The essential feature of the sequence-symmetry minimization algorithm employed is the treatment of short sequences as vocabulary elements whose repetition decreases control over the resulting secondary structure. Both manual and semi-automatic application of this approach are available. The design of linear nucleic acid molecules or molecules containing single-stranded loops or connectors is also possible through application of the procedure.     

ER biogenesis: self-assembly of tubular topology by protein hairpins

The structure of the endoplasmic reticulum (ER) depends on members of the reticulon and DP1/Yop1p families. Two of these proteins are sufficient to form tubular membrane networks from pure phospholipid vesicles, thus revealing a new paradigm of ER morphogenesis.     

DeepTracer-ID: De novo protein identification from cryo-EM maps

The recent revolution in cryo-electron microscopy (cryo-EM) has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust for high-resolution cryo-EM maps: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale three-dimensional protein structure prediction.     

De novo protein structure determination using sparse NMR data

We describe a method for generating moderate to high-resolution protein structures using limited NMR data combined with the ab initio protein structure prediction method Rosetta. Peptide fragments are selected from proteins of known structure based on sequence similarity and consistency with chemical shift and NOE data. Models are built from these fragments by minimizing an energy function that favors hydrophobic burial, strand pairing, and satisfaction of NOE constraints. Models generated using this procedure with 1 NOE constraint per residue are in some cases closer to the corresponding X-ray structures than the published NMR solution structures. The method requires only the sparse constraints available during initial stages of NMR structure determination, and thus holds promise for increasing the speed with which protein solution structures can be determined.