BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization

Structural modeling, energetic analysis and molecular design of a π-stacking system at the complex interface of pediatric respiratory syncytial virus nucleocapsid with the C-terminal peptide of phosphoprotein

Human respiratory syncytial virus (RSV) is a primary cause of lower respiratory tract infections and hospital visits during infancy and childhood. The RSV phosphoprotein (P) is a major polymerase cofactor that interacts with nucleoprotein (N) to promote the recognition of ribonucleoprotein complex (RNP) by viral RNA polymerase. The binding pocket of N protein is chemically diverse, in or around which a number of aromatic and charged amino acid residues are observed. Previously, a nonapeptide segment (P peptide, ²³³DNDLSLEDF²⁴¹) representing the C-terminal tail of P protein was identified to mediate the N-P interaction with a moderate affinity, in which the Phe241 at the end of P's C-terminus plays a critical role in the binding of P peptide to N protein. Here, we found that the side-chain aromatic phenyl moiety of P Phe241 residue can form short- and long-range cation-π interactions with N Arg132 and Arg150 residues, respectively, as well as T-shaped and parallel-displaced π-π stackings with N Tyr135 and His151 residues, respectively, which co-define a geometrically satisfactory π-stacking system at the complex interface of N protein with P peptide, thus largely stabilizing the complex architecture. The stacking effect was further optimized by systematically mutating the P Phe241 residue to other natural and non-natural aromatic amino acids with diverse chemical substitutions at the phenyl moiety to examine their structural and energetic effects on π-stacking system and on protein-peptide binding. The electron-donating mutations at the phenyl moiety of P Phe241 residue can effectively enhance the π-stacking system and then promote peptide binding, whereas the bulky and positively charged mutations would considerably impair the peptide potency by introducing steric hindrance and electrostatic repulsion. The [Tyr]P, [Thp]P and [Fph]P mutants were determined to have an increased affinity relative to wild-type P peptide, which could be used as self-inhibitory peptides to competitively disrupt the native interaction between N and P proteins.

Rational design of stapled helical peptides as antidiabetic PPARγ antagonists to target coactivator site by decreasing unfavorable entropy penalty instead of increasing favorable enthalpy contribution

Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor belonging to the nuclear hormone receptor and has been exploited as a well-established druggable target for the treatment of diabetes mellitus (DM). Traditionally, small-molecule compounds have been developed to attack at the ligand site and Ser273 phosphorylation site of PPARγ. In this study, we derived helical peptide segments from the LXXLL motif region of coactivator proteins as antidiabetic PPARγ antagonists, which were expected to competitively disrupt the native interaction between PPARγ and its cognate coactivators by rebinding at PPARγ coactivator site. Structural analysis, dynamics simulation and energetics dissection revealed that these peptides cannot be well folded into active helical structure when splitting from the protein context of their parent coactivators and exhibit a large flexibility and intrinsic disorder in the free state, which would, therefore, incur a considerable entropy penalty upon rebinding to PPARγ. Hydrocarbon stapling strategy was employed to constrain these free coactivator peptides into ordered helical conformation, thus largely minimizing unfavorable entropy penalty but having only a moderate effect on favorable enthalpy contribution. The computational findings were further substantiated by fluorescence-based assays; the binding affinity of three potent SRC1, NCoA6 and p300 coactivator peptides to PPARγ was observed to be improved by 7.2-fold, 4.2-fold and 5.7-fold upon the stapling, which were also measured to have an efficient competitive potency with their unstapled counterparts for PPARγ coactivator site, with CC₅₀ = 0.096, 0.12 and 0.18 μM, respectively.

Molecular design and rational optimization of synergistic effect between the two wings of a roughly orthogonal cation-π-π stacking system at nasopharyngeal carcinoma YAP1-TEAD4 parallel Helix-Helix interaction interface

The Yes-associated protein-1 (YAP1) is an essential regulator of human Hippo signaling pathway and functions through interaction with TEA domain-4 (TEAD4) transcription factor involved in the tumorigenesis of nasopharyngeal cancer. Previously, a parallel helix-helix interaction (PHHI) was identified as the key hotspot at YAP1-TEAD4 complex interface and has been exploited as an attractive druggable target to disrupt the complex. In this study, we investigated a roughly orthogonal cation-π-π stacking system across the crystal PHHI packing interface by integrating computational modeling and binding assay, which forms between one YAP1 helical residue Phe69 and two TEAD4 helical residues Phe373/Lys376. A synergistic effect between cation-π and π-π interactions was observed; they separately represent two wings of the stacking system. The π-electron is primarily responsible for the synergistic effect. Combination between diverse aromatic/charged amino acids. as well as neutral alanine on the cation-π-π stacking, revealed that the presence of aromatic tryptophan and charged arginine at, respectively, the residues 373 and 376 of TEAD4 helix can considerably improve PHHI binding affinity by ~6-fold, whereas neutral alanine substitution on each residue and on both would reduce the affinity significantly, confirming a strong synergistic effect involved in the roughly orthogonal cation-π-π stacking system at YAP1-TEAD4 PHHI interface.

Molecular insight into the systematic affinity and selectivity of partner recognition sites between the WW1 and WW2 domains of human KIBRA neuroprotein

Human KIBRA, a member of the WWC family proteins, is an upstream regulator of the Salvador/Warts/Hippo (SWH) signaling pathway and predominately expressed in nervous system. The protein has two functionally regulatory domains WW1 and WW2 at N-terminal region, which recognize and bind to the PY-motif segments of their partner proteins to serve as a signaling scaffold role in the SWH pathway. The two domains are highly conserved, but their downstream ligands and biological functions may not be fully consistent. In this study, we attempted to systematically profile the PY-motif affinity to and selectivity between KIBRA WW1 and WW2 domains involved in partner recognition sites. Ontology mining was used to enrich the KIBRA-interacting proteins in literature libraries, from which a variety of PY-motif peptide segments were identified, and their binding behavior to each domain was then analyzed by integrating computational modeling and experimental assay. Most PY-motif peptides were found to interact potently with WW1 and WW2, but they generally only exhibit a moderate or modest selectivity between the two domains. Subsequently, several representative peptides were further examined in detail to elucidate the molecular mechanism underlying their affinity and selectivity. It is revealed that the middle motif region of PY-motif peptides is primarily responsible for the affinity and stability of peptide binding, but only contributes marginally to peptide selectivity. Instead, the N-terminal region and, particularly, C-terminal region of PY-motif peptides play a crucial role in the selectivity. Hydrophobic contacts and hydrogen bonds confer stability and specificity to the domain-peptide interaction, respectively.

Structure-based analysis and rational design of human peroxiredoxin-1's C-terminus-derived peptides to target sulfiredoxin-1 in pancreatic cancer

Human peroxiredoxin (PRX) family of antioxidant enzymes reduces hydrogen peroxide and alkyl hydroperoxide involved in the redox signaling, among which the widely documented PRX1 is a versatile molecule regulating cell growth, differentiation and apoptosis, and has been implicated in the tumorigensis of pancreatic cancer. In this study, we systematically examined the complex crystal structure of PRX1 with its cognate interacting partner sulfiredoxin-1 (SRX1) at molecular level, and found that the PRX1-SRX1 association is a typical peptide-mediated protein-protein interaction, where a 18-mer C-terminal tail (CTT) segment of PRX1 was identified to be primarily responsible for the interaction, which contributes ~80% and ~ 55% to the total binding potency of SRX1 to PRX1 monomer and homodimer, respectively. We also demonstrated that the SRX1 exhibits a strong global selectivity for PRX1 CTT tail over other PRX family proteins. Next, the intermolecular interaction between PRX1 CTT tail and SRX1 was investigated at structural, energetic and dynamic levels, from which a 9-mer core region of PRX1 CTT tail was defined as the SRX1-binding hotspot. Biophysical assays substantiated that the CTT and CTTc peptides (out of PRX1 protein context) can bind in an independent manner and possess a close affinity to SRX1. Based on the CTTc sketch a computational combinatorial library consisting of 216 designed peptide derivatives was rationally generated, from which the top-5 hits were found to have comparable affinity with CTT peptide and improved affinity relative to CTTc peptide. They can be used as structurally reduced lead molecular entities to further develop new peptidic agents for therapeutic purpose to disrupt the native PRX1-SRX1 interaction by competing with PRX1 CTT tail for the peptide-binding pocket of SRX1.

Structural definition of the discrete hotspot sites of BMP-2 conformational wrist epitope and rational design of the hotspot-derived osteogenic peptides against chondrocyte senescence

The bone morphogenetic protein-2 (BMP-2) is an essential regulator of bone formation and remodeling, which has also been implicated in the pathogenesis of osteoarthritis and its closely related chondrocyte senescence. The BMP-2 uses a conformational wrist epitope and a linear knuckle epitope to interact with type-I (BMPR-I) and type-II (BMPR-II) receptors, respectively. Previously, the knuckle epitope has been intensely studied, but the wrist epitope still remains largely unexplored due to its discontinuous nature. In the present work, the intermolecular interaction of BMP-2 with BMPR-I was investigated systematically at structural, energetic and dynamic levels. Three discrete hotspots that represent the key BMPR-I recognition sites of BMP-2 were identified; they are spatially dispersed over the two monomers of BMP-2 dimer and totally account for 83.5 % binding potency of BMP-2 to BMPR-I (hotspot 1: residues 49-70 in monomer 1; hotspot 2: residues 24-31 in monomer 2; hotspot 3: residues 88-107 in monomer 2). Therefore, we defined the three discrete hotspot sites as the core region of wrist epitope; their contribution to the binding increases in the order: hotspot 2 < hotspot 3 < hotspot 1. We demonstrated that the primary hotspot 1 site has a native U-shaped conformation in the full-length BMP-2 protein context, but it cannot maintain in the native conformation when split from the context to obtain a free hotspot-1 peptide, thus largely impairing its binding potency to BMPR-I. We further employed disulfide-bonded cyclization and head-to-tail cyclization to constrain the peptide conformation, and found that only the former can effectively constrain the peptide into native conformation, thus considerably improving its binding affinity to BMPR-I, whereas the latter totally disorders the native conformation, thus rendering the peptide as a full nonbinder of BMPR-I.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Rational design and chemical modification of TEAD coactivator peptides to target hippo signaling pathway against gastrointestinal cancers

Human Hippo signaling pathway has been recognized as a new and promising therapeutic target of gastrointestinal cancers, which is regulated by the intermolecular recognition between the TEA domain (TEAD) transcription factor and its prime coactivators. The coactivator proteins adopt two hotspot sites, namely α-helix and Ω-loop, to interact with TEAD. Here, we demonstrate that both the α-helix and Ω-loop peptides cannot maintain in structured state when splitting from the full-length coactivator proteins; they exhibit a large intrinsic disorder in free state that prevents the coactivator peptide recognition by TEAD. Rational design is used to optimize the interfacial residues of coactivator α-helix peptides, which can effectively improve the favorable direct readout effect upon the peptide binding to TEAD. Chemical modification is employed to constrain the free α-helix peptide into native ordered conformation. The method introduces an all-hydrocarbon bridge across i and i + 4 residues to stabilize the helical structure of a free coactivator peptide, which can considerably reduce the unfavorable indirect readout effect upon the peptide binding to TEAD. The all-hydrocarbon bridge is designed to point out of the TEAD-peptide complex interface, which would not disrupt the direct intermolecular interaction between the TEAD and peptide. Therefore, the stapling only improves peptide affinity, but does not alter peptide specificity, to TEAD. Affinity assay confirms that the binding potency of coactivator α-helix peptides is improved substantially by >5-fold upon the rational design and chemical modification. Structural analysis reveals that the optimized/stapled peptides can form diverse nonbonded interactions such as hydrogen bonds and hydrophobic contacts with TEAD, thus conferring stability and specificity to the TEAD-peptide complex systems.

Protein side-chain packing problem: is there still room for improvement?

The protein side-chain packing problem (PSCPP) is an important subproblem of both protein structure prediction and protein design. During the past two decades, a large number of methods have been proposed to tackle this problem. These methods consist of three main components: a rotamer library, a scoring function and a search strategy. The average overall accuracy level obtained by these methods is approximately 87%. Whether a better accuracy level could be achieved remains to be answered. To address this question, we calculated the maximum accuracy level attainable using a simple rotamer library, independently of the energy function or the search method. Using 2883 different structures from the Protein Data Bank, we compared this accuracy level with the accuracy level of five state-of-the-art methods. These comparisons indicated that, for buried residues in the protein, we are already close to the best possible accuracy results. In addition, for exposed residues, we found that a significant gap exists between the possible improvement and the maximum accuracy level achievable with current methods. After determining that an improvement is possible, the next step is to understand what limitations are preventing us from obtaining such an improvement. Previous works on protein structure prediction and protein design have shown that scoring function inaccuracies may represent the main obstacle to achieving better results for these problems. To show that the same is true for the PSCPP, we evaluated the quality of two scoring functions used by some state-of-the-art algorithms. Our results indicate that neither of these scoring functions can guide the search method correctly, thereby reinforcing the idea that efforts to solve the PSCPP must also focus on developing better scoring functions.

Molecular dynamics-derived rotamer libraries for d-amino acids within homochiral and heterochiral polypeptides

Computational resources have contributed to the design and engineering of novel proteins by integrating genomic, structural and dynamic aspects of proteins. Non-canonical amino acids, such as d-amino acids, expand the available sequence space for designing and engineering proteins; however, the rotamer libraries for d-amino acids are usually constructed as the mirror images of l-amino acid rotamer libraries, an assumption that has not been tested. To this end, we have performed molecular dynamics (MD) simulations of model host-guest peptide systems containing d-amino acids. Our simulations systematically address the applicability of the mirror image convention as well as the effects of neighboring residue chirality. Rotamer libraries derived from these systems provide realistic rotamer distributions suitable for use in both rational and computational design workflows. Our simulations also address the impact of chirality on the intrinsic conformational preferences of amino acids, providing fundamental insights into the relationship between chirality and biomolecular dynamics. While d-amino acids are rare in naturally occurring proteins, they are used in designed proteins to stabilize a desired conformation, increase bioavailability or confer favorable biochemical and physical attributes. Here, we present d-amino acid rotamer libraries derived from MD simulations of alanine-based host-guest pentapeptides and show how certain residues can deviate from mirror image symmetry. Our simulations directly model d-amino acids as guest residues within the chiral l-Ala and d-Ala pentapeptide series to explicitly incorporate any contributions resulting from the chiralities of neighboring residues.

Scoring of Side-Chain Packings: An Analysis of Weight Factors and Molecular Dynamics Structures

The protein side-chain packing problem (PSCPP) is a central task in computational protein design. The problem is usually modeled as a combinatorial optimization problem, which consists of searching for a set of rotamers, from a given rotamer library, that minimizes a scoring function (SF). The SF is a weighted sum of terms, that can be decomposed in physics-based and knowledge-based terms. Although there are many methods to obtain approximate solutions for this problem, all of them have similar performances and there has not been a significant improvement in recent years. Studies on protein structure prediction and protein design revealed the limitations of current SFs to achieve further improvements for these two problems. In the same line, a recent work reported a similar result for the PSCPP. In this work, we ask whether or not this negative result regarding further improvements in performance is due to (i) an incorrect weighting of the SFs terms or (ii) the constrained conformation resulting from the protein crystallization process. To analyze these questions, we (i) model the PSCPP as a bi-objective combinatorial optimization problem, optimizing, at the same time, the two most important terms of two SFs of state-of-the-art algorithms and (ii) performed a preprocessing relaxation of the crystal structure through molecular dynamics to simulate the protein in the solvent and evaluated the performance of these two state-of-the-art SFs under these conditions. Our results indicate that (i) no matter what combination of weight factors we use the current SFs will not lead to better performances and (ii) the evaluated SFs will not be able to improve performance on relaxed structures. Furthermore, the experiments revealed that the SFs and the methods are biased toward crystallized structures.

Reconstruction of Atomistic Structures from Coarse-Grained Models for Protein-DNA Complexes

While coarse-grained (CG) simulations have widely been used to accelerate structure sampling of large biomolecular complexes, they are unavoidably less accurate and thus the reconstruction of all-atom (AA) structures and the subsequent refinement is desirable. In this study we developed an efficient method to reconstruct AA structures from sampled CG protein-DNA complex models, which attempts to model the protein-DNA interface accurately. First we developed a method to reconstruct atomic details of DNA structures from a three-site per nucleotide CG model, which uses a DNA fragment library. Next, for the protein-DNA interface, we referred to the side chain orientations in the known structure of the target interface when available. The other parts are modeled by existing tools. We confirmed the accuracy of the protocol in various aspects including the structure deviation in the self-reproduction, the base pair reproducibility, atomic contacts at the protein-DNA interface, and feasibility of the posterior AA simulations.

In silico methods for design of biological therapeutics

It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.