Binding Pocket Optimization by Computational Protein Design

Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.     

Step‐by‐step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom‐made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high‐throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step‐by‐step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state‐of‐the‐art software is briefly explored.     

Design of protein conformational switches

Protein conformational switches are ubiquitous in nature and often regulate key biological processes. To design new proteins that can switch conformation, protein designers have focused on the two key components of protein switches: the amino acid sequence must be compatible with the multiple target states and there must be a mechanism for perturbing the relative stability of these states. Proteins have been designed that can switch between folded and disordered states, between distinct folded states and between different aggregation states. A variety of trigger mechanisms have been used, including pH shifts, post-translational modification and ligand binding. Recently, computational protein design methods have been applied to switch design. These include algorithms for designing novel ligand-binding sites and simultaneously optimizing a sequence for multiple target structures.     

Computational design of an integrin I domain stabilized in the open high affinity conformation

We have taken a computational approach to design mutations that stabilize a large protein domain of approximately 200 residues in two alternative conformations. Mutations in the hydrophobic core of the alphaMbeta2 integrin I domain were designed to stabilize the crystallographically defined open or closed conformers. When expressed on the cell surface as part of the intact heterodimeric receptor, binding of the designed open and closed I domains to the ligand iC3b, a form of the complement component C3, was either increased or decreased, respectively, compared to wild type. Moreover, when expressed in isolation from other integrin domains using an artificial transmembrane domain, designed open I domains were active in ligand binding, whereas designed closed and wild type I domains were inactive. Comparison to a human expert designed open mutant showed that the computationally designed mutants are far more active. Thus, computational design can be used to stabilize a molecule in a desired conformation, and conformational change in the I domain is physiologically relevant to regulation of ligand binding.     

Gain of native conformation of Aurora A S155R mutant by small molecules

Aurora A is a mitotic serine/threonine kinase protein that is a proposed target of the first-line anticancer drug design. It has been found to be overexpressed in many human cancer cells, including hematological, breast, and colorectal. Here, we focus on a particular somatic mutant S155R of Aurora kinase A protein, whose activity decreases because of loss of interaction with a TPX2 protein that results in ectopic expression of the Aurora kinase A protein, which contributes chromosome instability, centrosome amplification, and oncogenic transformation. The primary target of this study is to select a drug molecule whose binding results in gaining S155R mutant interaction with TPX2. The computational methodology applied in this study involves mapping of hotspots (for uncompetitive binding), virtual screening, protein–ligand docking, postdocking optimization, and protein–protein docking approach. In this study, we screen and validate ZINC968264, which acts as a potential molecule that can improve the loss of function occurred because of mutation (S155R) in Aurora A. Our approaches pave a suitable path to design a potential drug against physiological condition manifested because of S155R mutant in Aurora A.     

OptGraft: A computational procedure for transferring a binding site onto an existing protein scaffold

One of the many challenging tasks of protein design is the introduction of a completely new function into an existing protein scaffold. In this study, we introduce a new computational procedure OptGraft for placing a novel binding pocket onto a protein structure so as its geometry is minimally perturbed. This is accomplished by introducing a two‐level procedure where we first identify where are the most appropriate locations to graft the new binding pocket into the protein fold by minimizing the departure from a set of geometric restraints using mixed‐integer linear optimization. On identifying the suitable locations that can accommodate the new binding pocket, CHARMM energy calculations are employed to identify what mutations in the neighboring residues, if any, are needed to ensure that the minimum energy conformation of the binding pocket conserves the desired geometry. This computational framework is benchmarked against the results available in the literature for engineering a copper binding site into thioredoxin protein. Subsequently, OptGraft is used to guide the transfer of a calcium‐binding pocket from thermitase protein (PDB: 1thm) into the first domain of CD2 protein (PDB:1hng). Experimental characterization of three de novo redesigned proteins with grafted calcium‐binding centers demonstrated that they all exhibit high affinities for terbium (K_d ～ 22, 38, and 55 μM) and can selectively bind calcium over magnesium.     

Knowledge-based Fragment Binding Prediction

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE''s ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.     

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph‐theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.     

In silico modeling and experimental evidence of coagulant protein interaction with precursors for nanoparticle functionalization

The design of novel protein–nanoparticle hybrid systems has applications in many fields of science ranging from biomedicine, catalysis, water treatment, etc. The main barrier in devising such tool is lack of adequate information or poor understanding of protein–ligand chemistry. Here, we establish a new strategy based on computational modeling for protein and precursor linkers that can decorate the nanoparticles. Moringa oleifera (MO_2.1) seed protein that has coagulation and antimicrobial properties was used. Superparamagnetic nanoparticles (SPION) with precursor ligands were used for the protein–ligand interaction studies. The molecular docking studies reveal that there are two binding sites, one is located at the core binding site; tetraethoxysilane (TEOS) or 3-aminopropyl trimethoxysilane (APTES) binds to this site while the other one is located at the side chain residues where trisodium citrate (TSC) or Si₆₀ binds to this site. The protein–ligand distance profile analysis explains the differences in functional activity of the decorated SPION. Experimentally, TSC-coated nanoparticles showed higher coagulation activity as compared to TEOS- and APTES-coated SPION. To our knowledge, this is the first report on in vitro experimental data, which endorses the computational modeling studies as a powerful tool to design novel precursors for functionalization of nanomaterials; and develop interface hybrid systems for various applications.     

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.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

Computational design of novel protein–protein interactions – An overview on methodological approaches and applications

Protein–protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a fundamental challenge to our understanding of molecular interactions. The capability to robustly engineer PPIs has immense potential for the development of novel synthetic biology tools and protein-based therapeutics. Over the last decades, many efforts in this area have relied purely on experimental approaches, but more recently, computational protein design has made important contributions. Template-based approaches utilize known PPIs and transplant the critical residues onto heterologous scaffolds. De novo design instead uses computational methods to generate novel binding motifs, allowing for a broader scope of the sites engaged in protein targets. Here, we review successful design cases, giving an overview of the methodological approaches used for templated and de novo PPI design.     

Computational protein design promises to revolutionize protein engineering

Natural evolution has produced an astounding array of proteins that perform the physical and chemical functions required for life on Earth. Although proteins can be reengineered to provide altered or novel functions, the utility of this approach is limited by the difficulty of identifying protein sequences that display the desired properties. Recently, advances in the field of computational protein design (CPD) have shown that molecular simulation can help to predict sequences with new and improved functions. In the past few years, CPD has been used to design protein variants with optimized specificity of binding to DNA, small molecules, peptides, and other proteins. Initial successes in enzyme design highlight CPD's unique ability to design function de novo. The use of CPD for the engineering of potential therapeutic agents has demonstrated its strength in real-life applications.     

A computational tool to predict the evolutionarily conserved protein–protein interaction hot-spot residues from the structure of the unbound protein

Identifying hot-spot residues – residues that are critical to protein–protein binding – can help to elucidate a protein’s function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein–protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36–57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein–protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins.     

Computational design of protein-small molecule interfaces

The computational design of proteins that bind small molecule ligands is one of the unsolved challenges in protein engineering. It is complicated by the relatively small size of the ligand which limits the number of intermolecular interactions. Furthermore, near-perfect geometries between interacting partners are required to achieve high binding affinities. For apolar, rigid small molecules the interactions are dominated by short-range van der Waals forces. As the number of polar groups in the ligand increases, hydrogen bonds, salt bridges, cation–π, and π–π interactions gain importance. These partial covalent interactions are longer ranged, and additionally, their strength depends on the environment (e.g. solvent exposure). To assess the current state of protein-small molecule interface design, we benchmark the popular computer algorithm Rosetta on a diverse set of 43 protein–ligand complexes. On average, we achieve sequence recoveries in the binding site of 59% when the ligand is allowed limited reorientation, and 48% when the ligand is allowed full reorientation. When simulating the redesign of a protein binding site, sequence recovery among residues that contribute most to binding was 52% when slight ligand reorientation was allowed, and 27% when full ligand reorientation was allowed. As expected, sequence recovery correlates with ligand displacement.     

Computational design of protein antigens that interact with the CDR H3 loop of HIV broadly neutralizing antibody 2F5

Rational design of proteins with novel binding specificities and increased affinity is one of the major goals of computational protein design. Epitope‐scaffolds are a new class of antigens engineered by transplanting viral epitopes of predefined structure to protein scaffolds, or by building protein scaffolds around such epitopes. Epitope‐scaffolds are of interest as vaccine components to attempt to elicit neutralizing antibodies targeting the specified epitope. In this study we developed a new computational protocol, MultiGraft Interface, that transplants epitopes but also designs additional scaffold features outside the epitope to enhance antibody‐binding specificity and potentially influence the specificity of elicited antibodies. We employed MultiGraft Interface to engineer novel epitope‐scaffolds that display the known epitope of human immunodeficiency virus 1 (HIV‐1) neutralizing antibody 2F5 and that also interact with the functionally important CDR H3 antibody loop. MultiGraft Interface generated an epitope‐scaffold that bound 2F5 with subnanomolar affinity (K_D = 400 pM) and that interacted with the antibody CDR H3 loop through computationally designed contacts. Substantial structural modifications were necessary to engineer this antigen, with the 2F5 epitope replacing a helix in the native scaffold and with 15% of the native scaffold sequence being modified in the design stage. This epitope‐scaffold represents a successful example of rational protein backbone engineering and protein–protein interface design and could prove useful in the field of HIV vaccine design. MultiGraft Interface can be generally applied to engineer novel binding partners with altered specificity and optimized affinity. Proteins 2014; 82:2770–2782. © 2014 Wiley Periodicals, Inc.     

Landscape of protein–small ligand binding modes

Elucidating the mechanisms of specific small‐molecule (ligand) recognition by proteins is a long‐standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein–ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all‐against‐all comparison of 20,040 protein–ligand complexes provided the landscape of the protein–ligand binding modes and its relationships with protein‐ and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R² = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein–ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.     

The multiple common point set problem and its application to molecule binding pattern detection.

Recognition of binding patterns common to a set of protein structures is important for recognition of function, prediction of binding, and drug design. We consider protein binding sites represented by a set of 3D points with assigned physico-chemical and geometrical properties important for protein-ligand interactions. We formulate the multiple binding site alignment problem as detection of the largest common set of such 3D points. We discuss the computational problem of multiple common point set detection and, particularly, the matching problem in K-partite-epsilon graphs, where K partitions are associated with K structures and edges are defined between epsilon-close points. We show that the K-partite-epsilon matching problem is NP-hard in the Euclidean space with dimension larger than one. Consequently, we show that the largest common point set problem between three point sets is NP-hard. On the practical side, we present a novel computational method, MultiBind, for recognition of binding patterns common to a set of protein structures. It performs a multiple alignment between protein binding sites in the absence of overall sequence, fold, or binding partner similarity. Despite the NP-hardness results, in our applications, we practically overcome the exponential number of multiple alignment combinations by applying an efficient branchand- bound filtering procedure. We show applications of MultiBind to several biological targets. The method recognizes patterns which are responsible for binding small molecules, such as estradiol, ATP/ANP, and transition state analogues.     

The use of chemical design tools to transform proteomics data into drug candidates

The Human Genome Project has fueled the massive information-driven growth of genomics and proteomics and promises to deliver new insights into biology and medicine. Since proteins represent the majority of drug targets, these molecules are the focus of activity in pharmaceutical and biotechnology organizations. In this article, we describe the processes by which computational drug design may be used to exploit protein structural information to create virtual small molecules that may become novel medicines. Experimental protein structure determination, site exploration, and virtual screening provide a foundation for small molecule generation in silico, thus creating the bridge between proteomics and drug discovery.