Steric and thermodynamic limits of design for the incorporation of large unnatural amino acids in aminoacyl‐tRNA synthetase enzymes

Orthogonal aminoacyl‐tRNA synthetase/tRNA pairs from archaea have been evolved to facilitate site specific in vivo incorporation of unnatural amino acids into proteins in Escherichia coli. Using this approach, unnatural amino acids have been successfully incorporated with high translational efficiency and fidelity. In this study, CHARMM‐based molecular docking and free energy calculations were used to evaluate rational design of specific protein–ligand interactions for aminoacyl‐tRNA synthetases. A series of novel unnatural amino acid ligands were docked into the p‐benzoyl‐L ‐phenylalanine tRNA synthetase, which revealed that the binding pocket of the enzyme does not provide sufficient space for significantly larger ligands. Specific binding site residues were mutated to alanine to create additional space to accommodate larger target ligands, and then mutations were introduced to improve binding free energy. This approach was used to redesign binding sites for several different target ligands, which were then tested against the standard 20 amino acids to verify target specificity. Only the synthetase designed to bind Man‐α‐O‐Tyr was predicted to be sufficiently selective for the target ligand and also thermodynamically stable. Our study suggests that extensive redesign of the tRNA synthatase binding pocket for large bulky ligands may be quite thermodynamically unfavorable. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Surface‐based protein binding pocket similarity

Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three‐dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all‐by‐all comparisons two sets of human protein kinases, a very diverse set of ATP‐bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence‐based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation. Proteins 2011; © 2011 Wiley‐Liss, Inc.     

Combining features in a graphical model to predict protein binding sites

Large efforts have been made in classifying residues as binding sites in proteins using machine learning methods. The prediction task can be translated into the computational challenge of assigning each residue the label binding site or non‐binding site. Observational data comes from various possibly highly correlated sources. It includes the structure of the protein but not the structure of the complex. The model class of conditional random fields (CRFs) has previously successfully been used for protein binding site prediction. Here, a new CRF‐approach is presented that models the dependencies of residues using a general graphical structure defined as a neighborhood graph and thus our model makes fewer independence assumptions on the labels than sequential labeling approaches. A novel node feature “change in free energy” is introduced into the model, which is then denoted by ΔF‐CRF. Parameters are trained with an online large‐margin algorithm. Using the standard feature class relative accessible surface area alone, the general graph‐structure CRF already achieves higher prediction accuracy than the linear chain CRF of Li et al. ΔF‐CRF performs significantly better on a large range of false positive rates than the support‐vector‐machine‐based program PresCont of Zellner et al. on a homodimer set containing 128 chains. ΔF‐CRF has a broader scope than PresCont since it is not constrained to protein subgroups and requires no multiple sequence alignment. The improvement is attributed to the advantageous combination of the novel node feature with the standard feature and to the adopted parameter training method. Proteins 2015; 83:844–852. © 2015 Wiley Periodicals, Inc.     

Multiple solvent crystal structures of ribonuclease A: An assessment of the method

The multiple solvent crystal structures (MSCS) method uses organic solvents to map the surfaces of proteins. It identifies binding sites and allows for a more thorough examination of protein plasticity and hydration than could be achieved by a single structure. The crystal structures of bovine pancreatic ribonuclease A (RNAse A) soaked in the following organic solvents are presented: 50% dioxane, 50% dimethylformamide, 70% dimethylsulfoxide, 70% 1,6‐hexanediol, 70% isopropanol, 50% R,S,R‐bisfuran alcohol, 70% t‐butanol, 50% trifluoroethanol, or 1.0M trimethylamine‐N‐oxide. This set of structures is compared with four sets of crystal structures of RNAse A from the protein data bank (PDB) and with the solution NMR structure to assess the validity of previously untested assumptions associated with MSCS analysis. Plasticity from MSCS is the same as from PDB structures obtained in the same crystal form and deviates only at crystal contacts when compared to structures from a diverse set of crystal environments. Furthermore, there is a good correlation between plasticity as observed by MSCS and the dynamic regions seen by NMR. Conserved water binding sites are identified by MSCS to be those that are conserved in the sets of structures taken from the PDB. Comparison of the MSCS structures with inhibitor‐bound crystal structures of RNAse A reveals that the organic solvent molecules identify key interactions made by inhibitor molecules, highlighting ligand binding hot‐spots in the active site. The present work firmly establishes the relevance of information obtained by MSCS. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Screening and docking chemical ligands onto pocket cavities of a protease for designing a biocatalyst

A detailed study of the trypsin surface has been carried out to gain insight into its biological functions and interactions which helped to determine the binding specificity. Twenty-four cavity pockets were automatically identified on trypsin from PDB file entry 1AUJ using CASTp (Computed Atlas of Surface Topography of proteins). Molecular docking was exploited as an efficient in silico screening tool for studying protein–ligand interactions. A systematic docking study using Autodock 3.05 has been performed on the five largest binding pockets in trypsin. A set of ten putative chemical ligands was used to dock into selected binding pockets. Docking of ligands into the five largest pockets in trypsin showed that 1,10-phenanthroline and ethanolamine preferentially bound at pocket 24 and benzamidine at pocket 22. Thermodynamically, we also found that ethanol, propanol, propandiol and phosphoethanolamine preferentially bound at pocket 21 whereas p-aminobenzamidine, phenylacetic acid and phenylalanine interacted mainly at pocket 20 based on their lowest interaction free energy.     

The interplay between effector binding and allostery in an engineered protein switch

The protein design rules for engineering allosteric regulation are not well understood. A fundamental understanding of the determinants of ligand binding in an allosteric context could facilitate the design and construction of versatile protein switches and biosensors. Here, we conducted extensive in vitro and in vivo characterization of the effects of 285 unique point mutations at 15 residues in the maltose‐binding pocket of the maltose‐activated β‐lactamase MBP317‐347. MBP317‐347 is an allosteric enzyme formed by the insertion of TEM‐1 β‐lactamase into the E. coli maltose binding protein (MBP). We find that the maltose‐dependent resistance to ampicillin conferred to the cells by the MBP317‐347 switch gene (the switch phenotype) is very robust to mutations, with most mutations slightly improving the switch phenotype. We identified 15 mutations that improved switch performance from twofold to 22‐fold, primarily by decreasing the catalytic activity in the absence of maltose, perhaps by disrupting interactions that cause a small fraction of MBP in solution to exist in a partially closed state in the absence of maltose. Other notable mutations include K15D and K15H that increased maltose affinity 30‐fold and Y155K and Y155R that compromised switching by diminishing the ability of maltose to increase catalytic activity. The data also provided insights into normal MBP physiology, as select mutations at D14, W62, and F156 retained high maltose affinity but abolished the switch's ability to substitute for MBP in the transport of maltose into the cell. The results reveal the complex relationship between ligand binding and allostery in this engineered switch.     

Lipopolysaccharide binding to the periplasmic protein LptA

Lipopolysaccharide (LPS) and the periplasmic protein, LptA, are two essential components of Gram‐negative bacteria. LPS, also known as endotoxin, is found asymmetrically distributed in the outer leaflet of the outer membrane of Gram‐negative bacteria such as Escherichia coli and plays a role in the organism's natural defense in adverse environmental conditions. LptA is a member of the lipopolysaccharide transport protein (Lpt) family, which also includes LptC, LptDE, and LptBFG₂, that functions to transport LPS through the periplasm to the outer leaflet of the outer membrane after MsbA flips LPS across the inner membrane. It is hypothesized that LPS binds to LptA to cross the periplasm and that the acyl chains of LPS bind to the central pocket of LptA. The studies described here are the first to comprehensively characterize and quantitate the binding of LPS by LptA. Using site‐directed spin‐labeling electron paramagnetic resonance (EPR) spectroscopy, data were collected for 15 spin‐labeled residues in and around the proposed LPS binding pocket on LptA to observe the mobility changes caused by the presence of exogenous LPS and identify the binding location of LPS to LptA. The EPR data obtained suggest a 1:1 ratio for the LPS:LptA complex and allow the first calculation of dissociation constants for the LptA–LPS interaction. The results indicate that the entire protein is affected by LPS binding, the N‐terminus unfolds in the presence of LPS, and a mutant LptA protein unable to form oligomers has an altered affinity for LPS.     

Prediction of structures of zinc‐binding proteins through explicit modeling of metal coordination geometry

Metal ions play an essential role in stabilizing protein structures and contributing to protein function. Ions such as zinc have well‐defined coordination geometries, but it has not been easy to take advantage of this knowledge in protein structure prediction efforts. Here, we present a computational method to predict structures of zinc‐binding proteins given knowledge of the positions of zinc‐coordinating residues in the amino acid sequence. The method takes advantage of the “atom‐tree” representation of molecular systems and modular architecture of the Rosetta3 software suite to incorporate explicit metal ion coordination geometry into previously developed de novo prediction and loop modeling protocols. Zinc cofactors are tethered to their interacting residues based on coordination geometries observed in natural zinc‐binding proteins. The incorporation of explicit zinc atoms and their coordination geometry in both de novo structure prediction and loop modeling significantly improves sampling near the native conformation. The method can be readily extended to predict protein structures bound to other metal and/or small chemical cofactors with well‐defined coordination or ligation geometry.     

A novel mechanism of ligand binding and release in the odorant binding protein 20 from the malaria mosquito Anopheles gambiae

Anopheles gambiae mosquitoes that transmit malaria are attracted to humans by the odor molecules that emanate from skin and sweat. Odorant binding proteins (OBPs) are the first component of the olfactory apparatus to interact with odorant molecules, and so present potential targets for preventing transmission of malaria by disrupting the normal olfactory responses of the insect. AgamOBP20 is one of a limited subset of OBPs that it is preferentially expressed in female mosquitoes and its expression is regulated by blood feeding and by the day/night light cycles that correlate with blood‐feeding behavior. Analysis of AgamOBP20 in solution reveals that the apo‐protein exhibits significant conformational heterogeneity but the binding of odorant molecules results in a significant conformational change, which is accompanied by a reduction in the conformational flexibility present in the protein. Crystal structures of the free and bound states reveal a novel pathway for entrance and exit of odorant molecules into the central‐binding pocket, and that the conformational changes associated with ligand binding are a result of rigid body domain motions in α‐helices 1, 4, and 5, which act as lids to the binding pocket. These structures provide new insights into the specific residues involved in the conformational adaptation to different odorants and have important implications in the selection and development of reagents targeted at disrupting normal OBP function.     

Prediction of protein–protein interaction sites from weakly homologous template structures using meta‐threading and machine learning

The identification of protein–protein interactions is vital for understanding protein function, elucidating interaction mechanisms, and for practical applications in drug discovery. With the exponentially growing protein sequence data, fully automated computational methods that predict interactions between proteins are becoming essential components of system‐level function inference. A thorough analysis of protein complex structures demonstrated that binding site locations as well as the interfacial geometry are highly conserved across evolutionarily related proteins. Because the conformational space of protein–protein interactions is highly covered by experimental structures, sensitive protein threading techniques can be used to identify suitable templates for the accurate prediction of interfacial residues. Toward this goal, we developed eFindSite^PPI, an algorithm that uses the three‐dimensional structure of a target protein, evolutionarily remotely related templates and machine learning techniques to predict binding residues. Using crystal structures, the average sensitivity (specificity) of eFindSite^PPI in interfacial residue prediction is 0.46 (0.92). For weakly homologous protein models, these values only slightly decrease to 0.40–0.43 (0.91–0.92) demonstrating that eFindSite^PPI performs well not only using experimental data but also tolerates structural imperfections in computer‐generated structures. In addition, eFindSite^PPI detects specific molecular interactions at the interface; for instance, it correctly predicts approximately one half of hydrogen bonds and aromatic interactions, as well as one third of salt bridges and hydrophobic contacts. Comparative benchmarks against several dimer datasets show that eFindSite^PPI outperforms other methods for protein‐binding residue prediction. It also features a carefully tuned confidence estimation system, which is particularly useful in large‐scale applications using raw genomic data. eFindSite^PPI is freely available to the academic community at http://www.brylinski.org/efindsiteppi . Copyright © 2014 John Wiley & Sons, Ltd.     

Solution NMR structures of the C‐domain of Tetrahymena cytoskeletal protein Tcb2 reveal distinct calcium‐induced structural rearrangements

Tcb2 is a calcium‐binding protein that localizes to the membrane‐associated skeleton of the ciliated protozoan Tetrahymena thermophila with hypothesized roles in ciliary movement, cell cortex signaling, and pronuclear exchange. Tcb2 has also been implicated in a unique calcium‐triggered, ATP‐independent type of contractility exhibited by filamentous networks isolated from the Tetrahymena cytoskeleton. To gain insight into Tcb2's structure‐function relationship and contractile properties, we determined solution NMR structures of its C‐terminal domain in the calcium‐free and calcium‐bound states. The overall architecture is similar to other calcium‐binding proteins, with paired EF‐hand calcium‐binding motifs. Comparison of the two structures reveals that Tcb2‐C's calcium‐induced conformational transition differs from the prototypical calcium sensor calmodulin, suggesting that the two proteins play distinct functional roles in Tetrahymena and likely have different mechanisms of target recognition. Future studies of the full‐length protein and the identification of Tcb2 cellular targets will help establish the molecular basis of Tcb2 function and its unique contractile properties. Proteins 2016; 84:1748–1756. © 2016 Wiley Periodicals, Inc.     

Conformational plasticity of the calcium‐binding pocket in the Burkholderia glumae lipase: Remodeling induced by mutation of calcium coordinating residues

Most bacterial lipases bind one or more Ca²⁺ atoms at different locations and are a suitable case of study for investigating structural effects related to calcium binding, depletion, or mutation of calcium‐binding sites. Generally Ca²⁺ in microbial lipases can play a crucial role in the stabilization of the whole three‐dimensional structure by mediating long‐range effects. It has been recently demonstrated that calcium binding influences thermal stability of Burkholderia glumae lipase (BGL) through the restriction of conformational plasticity of specific regions. Moreover, calcium depletion results in a highly cooperative protein unfolding, eliciting protein aggregation. To further shed light on molecular mechanisms and structural features connected to calcium binding in microbial lipases, we present a molecular dynamics investigation, based on multiple‐replica approach at different temperatures, of BGL mutants targeting the calcium‐binding site. It turns out that additional acidic residues, which are conserved in other microbial lipases, help in overcoming effects induced by mutation of D241 Ca²⁺‐coordinating residue, upon rearrangements induced in the calcium binding site. © 2010 Wiley Periodicals, Inc. Biopolymers 95: 117–126, 2011.     

Redesign of LAOBP to bind novel l‐amino acid ligands

Computational protein design is still a challenge for advancing structure‐function relationships. While recent advances in this field are promising, more information for genuine predictions is needed. Here, we discuss different approaches applied to install novel glutamine (Gln) binding into the Lysine/Arginine/Ornithine binding protein (LAOBP) from Salmonella typhimurium. We studied the ligand binding behavior of two mutants: a binding pocket grafting design based on a structural superposition of LAOBP to the Gln binding protein QBP from Escherichia coli and a design based on statistical coupled positions. The latter showed the ability to bind Gln even though the protein was not very stable. Comparison of both approaches highlighted a nonconservative shared point mutation between LAOBP_graft and LAOBP_sca. This context dependent L117K mutation in LAOBP turned out to be sufficient for introducing Gln binding, as confirmed by different experimental techniques. Moreover, the crystal structure of LAOBP_L117K in complex with its ligand is reported.     

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.     

Predicting the disruption by of a protein-ligand interaction

The uranyl cation (UO₂²⁺) can be suspected to interfere with the binding of essential metal cations to proteins, underlying some mechanisms of toxicity. A dedicated computational screen was used to identify UO₂²⁺ binding sites within a set of nonredundant protein structures. The list of potential targets was compared to data from a small molecules interaction database to pinpoint specific examples where UO₂²⁺ should be able to bind in the vicinity of an essential cation, and would be likely to affect the function of the corresponding protein. The C‐reactive protein appeared as an interesting hit since its structure involves critical calcium ions in the binding of phosphorylcholine. Biochemical experiments confirmed the predicted binding site for UO₂²⁺ and it was demonstrated by surface plasmon resonance assays that UO₂²⁺ binding to CRP prevents the calcium‐mediated binding of phosphorylcholine. Strikingly, the apparent affinity of UO₂²⁺ for native CRP was almost 100‐fold higher than that of Ca²⁺. This result exemplifies in the case of CRP the capability of our computational tool to predict effective binding sites for UO₂²⁺ in proteins and is a first evidence of calcium substitution by the uranyl cation in a native protein.     

Cofactor‐induced reversible folding of Flavodoxin‐4 from Lactobacillus acidophilus

Flavodoxins in combination with the flavin mononucleotide (FMN) cofactor play important roles for electron transport in prokaryotes. Here, novel insights into the FMN‐binding mechanism to flavodoxins‐4 were obtained from the NMR structures of the apo‐protein from Lactobacillus acidophilus (YP_193882.1) and comparison of its complex with FMN. Extensive reversible conformational changes were observed upon FMN binding and release. The NMR structure of the FMN complex is in agreement with the crystal structure (PDB ID: 3EDO ) and exhibits the characteristic flavodoxin fold, with a central five‐stranded parallel β–sheet and five α‐helices forming an α/β‐sandwich architecture. The structure differs from other flavoproteins in that helix α2 is oriented perpendicular to the β‐sheet and covers the FMN‐binding site. This helix reversibly unfolds upon removal of the FMN ligand, which represents a unique structural rearrangement among flavodoxins.     

Understanding the resistance mechanism of penicillin binding protein 1a mutant against cefotaxime using molecular dynamic simulation

Antibiotic resistance is a threatening challenge for global health, as the expansion of resistance to current antibiotics has made serious therapeutic problems. Genome mutations are key evolutionary mechanisms conferring antibiotic resistance in bacterial pathogens. For example, penicillin and cephalosporins resistance is mostly mediated by mutations in penicillin binding proteins to change the affinity of the drug. Accordingly, threonine point mutations were reported to develop antibiotic resistance in various bacterial infections including pneumococcal infections. In this study, conventional molecular dynamics simulations, umbrella sampling simulations and MM/GBSA free energy calculations were applied to figure out how the Threonine to Alanine mutation (T to A) at STMK motif affects the binding of cefotaxime to Penicillin Binding Protein 1a and to reveal the resistance mechanism induced by the T to A mutation. The results obtained from the computational methods demonstrate that the T to A mutation increases the flexibility of the binding pocket and changes its conformation, which leads to increased conformational entropy change (?TΔS) and attenuates the bonds between the ligand and the receptor. In brief, our findings indicate that both of the alterations of the conformational enthalpy and entropy contribute to the T to A-induced resistance in the binding of cefotaxime into penicillin binding protein 1a.     

Exploring additivity effects of double mutations on the binding affinity of protein‐protein complexes

Additivity in binding affinity of protein‐protein complexes refers to the change in free energy of binding (ΔΔG_bind) for double (or multiple) mutations which is approximately equal to the sum of their corresponding single mutation ΔΔG_bind values. In this study, we have explored the additivity effect of double mutants, which shows a linear relationship between the binding affinity of double and sum of single mutants with a correlation of 0.90. However, the comparison of ΔΔG_bind values showed a mean absolute deviation of 0.86 kcal/mol, and 25.6% of the double mutants show a deviation of more than 1 kcal/mol, which are identified as non‐additive. The additivity effects have been analyzed based on the influence of structural features such as accessible surface area, long range order, binding propensity change, surrounding hydrophobicity, flexibility, atomic contacts between the mutations and distance between the 2 mutations. We found that non‐additive mutations tend to be closer to each other and have more contacts. We have also used machine learning methods to discriminate additive and non‐additive mutations using structure‐based features, which showed the accuracies in the range of 0.77–0.92 for protein‐protein complexes belonging to different functions. Further, we have compared the additivity effects of protein stability along with binding affinity and explored the similarities and differences between them. The results obtained in this study provide insights into the effects of various structural features on binding affinity of double mutants, and will aid the development of accurate methods to predict the binding affinity of double mutants.     

Exploring interaction mechanisms of the inhibitor binding to the VP35 IID region of Ebola virus by all atom molecular dynamics simulation method

Ebola viruses (EBOVs) cause an acute and serious illness which is often fatal if untreated, and there is no effective vaccine until now. Multifunctional VP35 is critical for viral replication, RNA silencing suppression and nucleocapsid formation, and it is considered as a future target for the molecular biology technique. In the present work, the binding of inhibitor pyrrole‐based compounds (GA017) to wild‐type (WT), single (K248A, K251A, and I295A), and double (K248A/I295A) mutant VP35 were investigated by all‐atom molecular dynamic (MD) simulations and Molecular Mechanics Generalized Born surface area (MM/GBSA) energy calculation. The calculated results indicate that the binding with GA017 makes the binding pocket more stable and reduces the space of the binding pocket. Moreover, the electrostatic interactions (ΔE_ele) and VDW energy (ΔE_vdw) provide the major forces for affinity binding, and single mutation I295A and double mutation K248A/I295A have great influence on the conformation of the VP35 binding pocket. Interestingly, the residues R300‐G301‐D302 of I295A form a new helix and the sheet formed by the residues V294‐I295‐H296‐I297 disappears in the double mutation K248A/I295A as compared with WT. Moreover, the binding free energy calculations show that I295A and K248A/I295A mutations decrease of absolute binding free energies while K248A and K251A mutations increase absolute binding free energy. Our calculated results are in good agreement with the experimental results that K248A/I295A double mutant results in near‐complete loss of compound binding. The obtained information will be useful for design effective inhibitors for treating Ebola virus. Proteins 2015; 83:2263–2278. © 2015 Wiley Periodicals, Inc.     

Crystal structure of peptidoglycan recognition protein SA in Apis mellifera (Hymenoptera: Apidae)

Peptidoglycan recognition protein SA (PGRP‐SA) is a key pattern recognition receptor in the insect innate immune system. PGRP‐SA can bind to bacterial PGN and activate the Toll pathway, which triggers the expression and release of antimicrobial peptides to prevent bacterial infection. Here, we report the first structure of Apis mellifera PGRP‐SA from Hymenoptera at 1.86 Å resolution. The overall architecture of Am‐PGRP‐SA was similar to the Drosophila PGRP‐SA; however, the residues involved in PGN binding groove were not conserved, and the binding pocket was narrower. This structure gives insight into PGN binding characteristics in honeybees.