Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

An automated high-throughput screening method for the identification of high-yield, soluble protein variants using cell-free expression and systematic truncation

A highly automated method for rapidly identifying soluble protein variants with good expression yields has been developed. This method is based on a commercially available in vitro protein expression system. It consists of two polymerase chain reactions (PCR) followed by in vitro protein expression and protein quantification by dot blot. The PCR protocols have been improved and optimized to allow automation using commercial fluid handling devices. A PCR primer design program has also been implemented to streamline protein variant design. This automated protocol is highly reliable and has tremendously improved the throughput of expression screening as compared to conventional cell-based methods and manual in vitro methods. We have applied this method to 32 problematic targets from the TB Structural Genomics Consortium. Experimental results of these studies are reported.     

Accurate computer-based design of a new backbone conformation in the second turn of protein L.

The rational design of loops and turns is a key step towards creating proteins with new functions. We used a computational design procedure to create new backbone conformations in the second turn of protein L. The Protein Data Bank was searched for alternative turn conformations, and sequences optimal for these turns in the context of protein L were identified using a Monte Carlo search procedure and an energy function that favors close packing. Two variants containing 12 and 14 mutations were found to be as stable as wild-type protein L. The crystal structure of one of the variants has been solved at a resolution of 1.9 A, and the backbone conformation in the second turn is remarkably close to that of the in silico model (1.1 A RMSD) while it differs significantly from that of wild-type protein L (the turn residues are displaced by an average of 7.2 A). The folding rates of the redesigned proteins are greater than that of the wild-type protein and in contrast to wild-type protein L the second beta-turn appears to be formed at the rate limiting step in folding.     

High-resolution structural validation of the computational redesign of human U1A protein

Achieving atomic-level resolution in the computational design of a protein structure remains a challenging problem despite recent progress. Rigorous experimental tests are needed to improve protein design algorithms, yet studies of the structure and dynamics of computationally designed proteins are very few. The NMR structure and backbone dynamics of a redesigned protein of 96 amino acids are compared here with the design target, human U1A protein. We demonstrate that the redesigned protein reproduces the target structure to within the uncertainty of the NMR coordinates, even as 65 out of 96 amino acids were simultaneously changed by purely computational methods. The dynamics of the backbone of the redesigned protein also mirror those of human U1A, suggesting that the protein design algorithm captures the shape of the potential energy landscape in addition to the local energy minimum.     

Improved Pruning algorithms and Divide-and-Conquer strategies for Dead-End Elimination, with application to protein design

MOTIVATION: Structure-based protein redesign can help engineer proteins with desired novel function. Improving computational efficiency while still maintaining the accuracy of the design predictions has been a major goal for protein design algorithms. The combinatorial nature of protein design results both from allowing residue mutations and from the incorporation of protein side-chain flexibility. Under the assumption that a single conformation can model protein folding and binding, the goal of many algorithms is the identification of the Global Minimum Energy Conformation (GMEC). A dominant theorem for the identification of the GMEC is Dead-End Elimination (DEE). DEE-based algorithms have proven capable of eliminating the majority of candidate conformations, while guaranteeing that only rotamers not belonging to the GMEC are pruned. However, when the protein design process incorporates rotameric energy minimization, DEE is no longer provably-accurate. Hence, with energy minimization, the minimized-DEE (MinDEE) criterion must be used instead. RESULTS: In this paper, we present provably-accurate improvements to both the DEE and MinDEE criteria. We show that our novel enhancements result in a speedup of up to a factor of more than 1000 when applied in redesign for three different proteins: Gramicidin Synthetase A, plastocyanin, and protein G. AVAILABILITY: Contact authors for source code.     

Compact archaeal variant of heme A synthase

The N- and C-terminal halves of the heme A synthase polypeptide of Bacillus subtilis, and many other organisms, are homologous. This indicates that these enzyme proteins originate from a tandem duplication and fusion event of a gene encoding a protein half as large. The ape1694 gene of the hyperthermophilic archaeon Aeropyrum pernix encodes a protein that is similar to the hypothetical small primordial protein. We demonstrate that this A. pernix protein is a heat-stable membrane bound heme A synthase designated cCtaA. The case of cCtaA is unusual in evolution in that the primordial-like protein has not become extinct and apparently carries out the same function as the twice as large more diversified heme A synthase protein variant found in most cytochrome a-containing organisms.     

High-resolution structure prediction of a circular permutation loop

Methods for rapid and reliable design and structure prediction of linker loops would facilitate a variety of protein engineering applications. Circular permutation, in which the existing termini of a protein are linked by the polypeptide chain and new termini are created, is one such application that has been employed for decreasing proteolytic susceptibility and other functional purposes. The length and sequence of the linker can impact the expression level, solubility, structure and function of the permuted variants. Hence it is desirable to achieve atomic‐level accuracy in linker design. Here, we describe the use of RosettaRemodel for design and structure prediction of circular permutation linkers on a model protein. A crystal structure of one of the permuted variants confirmed the accuracy of the computational prediction, where the all‐atom rmsd of the linker region was 0.89 Å between the model and the crystal structure. This result suggests that RosettaRemodel may be generally useful for the design and structure prediction of protein loop regions for circular permutations or other structure‐function manipulations.     

Green fluorescent protein: a perspective

A brief personal perspective is provided for green fluorescent protein (GFP), covering the period 1994-2011. The topics discussed are primarily those in which my research group has made a contribution and include structure and function of the GFP polypeptide, the mechanism of fluorescence emission, excited state protein transfer, the design of ratiometric fluorescent protein biosensors and an overview of the fluorescent proteins derived from coral reef animals. Structure-function relationships in photoswitchable fluorescent proteins and nonfluorescent chromoproteins are also briefly covered.     

A Brief History of De Novo Protein Design: Minimal,Rational, and Computational

Protein design has come of age, but how will it mature? In the 1980s and the 1990s, the primary motivation for de novo protein design was to test our understanding of the informational aspect of the protein-folding problem; i.e., how does protein sequence determine protein structure and function? This necessitated minimal and rational design approaches whereby the placement of each residue in a design was reasoned using chemical principles and/or biochemical knowledge. At that time, though with some notable exceptions, the use of computers to aid design was not widespread. Over the past two decades, the tables have turned and computational protein design is firmly established. Here, I illustrate this progress through a timeline of de novo protein structures that have been solved to atomic resolution and deposited in the Protein Data Bank. From this, it is clear that the impact of rational and computational design has been considerable: More-complex and more-sophisticated designs are being targeted with many being resolved to atomic resolution. Furthermore, our ability to generate and manipulate synthetic proteins has advanced to a point where they are providing realistic alternatives to natural protein functions for applications both in vitro and in cells. Also, and increasingly, computational protein design is becoming accessible to non-specialists. This all begs the questions: Is there still a place for minimal and rational design approaches? And, what challenges lie ahead for the burgeoning field of de novo protein design as a whole?     

Computational Design of the β-Sheet Surface of a Red Fluorescent Protein Allows Control of Protein Oligomerization

Computational design has been used with mixed success for the design of protein surfaces, with directed evolution heretofore providing better practical solutions than explicit design. Directed evolution, however, requires a tractable high-throughput screen because the random nature of mutation does not enrich for desired traits. Here we demonstrate the successful design of the β-sheet surface of a red fluorescent protein (RFP), enabling control over its oligomerization. To isolate the problem of surface design, we created a hybrid RFP from DsRed and mCherry with a stabilized protein core that allows for monomerization without loss of fluorescence. We designed an explicit library for which 93 of 96 (97%) of the protein variants are soluble, stably fluorescent, and monomeric. RFPs are heavily used in biology, but are natively tetrameric, and creating RFP monomers has proven extremely difficult. We show that surface design and core engineering are separate problems in RFP development and that the next generation of RFP markers will depend on improved methods for core design.     

VitAL: Viterbi Algorithm for de novo Peptide Design

Background

Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.

Methodology/Principal Findings

A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.

Conclusions/Significance

The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.     

Fast gap‐free enumeration of conformations and sequences for protein design

Despite significant successes in structure‐based computational protein design in recent years, protein design algorithms must be improved to increase the biological accuracy of new designs. Protein design algorithms search through an exponential number of protein conformations, protein ensembles, and amino acid sequences in an attempt to find globally optimal structures with a desired biological function. To improve the biological accuracy of protein designs, it is necessary to increase both the amount of protein flexibility allowed during the search and the overall size of the design, while guaranteeing that the lowest‐energy structures and sequences are found. DEE/A*‐based algorithms are the most prevalent provable algorithms in the field of protein design and can provably enumerate a gap‐free list of low‐energy protein conformations, which is necessary for ensemble‐based algorithms that predict protein binding. We present two classes of algorithmic improvements to the A* algorithm that greatly increase the efficiency of A*. First, we analyze the effect of ordering the expansion of mutable residue positions within the A* tree and present a dynamic residue ordering that reduces the number of A* nodes that must be visited during the search. Second, we propose new methods to improve the conformational bounds used to estimate the energies of partial conformations during the A* search. The residue ordering techniques and improved bounds can be combined for additional increases in A* efficiency. Our enhancements enable all A*‐based methods to more fully search protein conformation space, which will ultimately improve the accuracy of complex biomedically relevant designs. Proteins 2015; 83:1859–1877. © 2015 Wiley Periodicals, Inc.     

A new biochemical engineering approach to the fractional precipitation of proteins

A biochemical engineering framework for optimizing the design and operation of fractional protein precipitation has been developed. The method utilizes a fractionation diagram to represent the purification of a product protein relative to total contaminating protein. The purification factor for a single or double-cut fractional precipitation is obtained as the gradient of an appropriate operating tie-line. A computer algorithm has been devised to maximize the tie-line gradient for a given yield enabling a plot of optimum purification factor versus yield to be constructed. The recovery of the enzyme alcohol dehydrogenase from clarified bakers homogenate using saturated ammonium sulphate has been examined. Fractionation and purification versus yield diagrams were used to investigate the effects of such process parameters as pH, temperature, and initial total protein concentration on fractionation efficiency. The results are discussed in terms of the underlying solubility and mixing phenomena and the industrial application of fractional precipitation.     

Novel forms of chemical protein diversity -- in nature and in the laboratory

Novel chemical variants of proteins have been found in nature, including potent 'microprotein' natural products and folded protein molecules that contain a cyclic polypeptide chain. Researchers have used chemical synthesis and genetic methods to make these proteins and more: protein catenanes, neoglycoproteins, and artificial protein molecules with novel architectures or made from novel building blocks. De novo design has taken a big step forward with the accurate design and construction of proteins with complex molecular structure. A variety of non-coded amino acids and other building blocks has been used to make increasingly sophisticated protein molecular devices for use as biosensors and for the study of signal transduction inside living cells.     

Inhibitors of the p53/hdm2 protein–protein interaction—path to the clinic

A growing number of the elements identified in intracellular signaling events that affect cell growth and transformation are proteins that physically interact with each other via domains or specifically recognized amino acid sequences. Some of these intracellular protein–protein interactions are attractive targets for anticancer targeted therapy, but progress in this field has been compromised by the paucity of compounds with suitable biological profiles and pharmacological properties. This Letter covers salient achievements in the identification and development of inhibitors of the p53–hdm2 protein–protein interaction, and highlights different screening techniques and structure-based design approaches that may be brought to bear on the discovery and development of inhibitors of other therapeutically relevant intracellular protein–protein interactions.     

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality.     

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.     

Underwater Adhesive of Marine Organisms as the Vital Link Between Biological Science and Material Science

Marine sessile organisms naturally attach themselves to diverse materials in water by a technique that has so far remained unreproducible. Recent studies on the holdfast of marine sessile organisms have revealed natural concepts that are currently beyond our understanding with respect to the molecular design and macroscopic range. The combination of valuable and practical natural design of biotic adhesives as biomolecular materials, together with continuing efforts towards mimetic design, hold the promise of revolution for future materials. This review focuses on recent advances in the study of barnacle underwater cement, a protein complex whose constituents and the properties of individual components are being uncovered. A comparison is made with the model systems used by the mussel and tubeworm.     

Multistate approaches in computational protein design

Computational protein design (CPD) is a useful tool for protein engineers. It has been successfully applied towards the creation of proteins with increased thermostability, improved binding affinity, novel enzymatic activity, and altered ligand specificity. Traditionally, CPD calculations search and rank sequences using a single fixed protein backbone template in an approach referred to as single-state design (SSD). While SSD has enjoyed considerable success, certain design objectives require the explicit consideration of multiple conformational and/or chemical states. Cases where a "multistate" approach may be advantageous over the SSD approach include designing conformational changes into proteins, using native ensembles to mimic backbone flexibility, and designing ligand or oligomeric association specificities. These design objectives can be efficiently tackled using multistate design (MSD), an emerging methodology in CPD that considers any number of protein conformational or chemical states as inputs instead of a single protein backbone template, as in SSD. In this review article, recent examples of the successful design of a desired property into proteins using MSD are described. These studies employing MSD are divided into two categories-those that utilized multiple conformational states, and those that utilized multiple chemical states. In addition, the scoring of competing states during negative design is discussed as a current challenge for MSD.