An amino acid code for β‐sheet packing structure

To understand the relationship between protein sequence and structure, this work extends the knob‐socket model in an investigation of β‐sheet packing. Over a comprehensive set of β‐sheet folds, the contacts between residues were used to identify packing cliques: sets of residues that all contact each other. These packing cliques were then classified based on size and contact order. From this analysis, the two types of four‐residue packing cliques necessary to describe β‐sheet packing were characterized. Both occur between two adjacent hydrogen bonded β‐strands. First, defining the secondary structure packing within β‐sheets, the combined socket or XY:HG pocket consists of four residues i, i+2 on one strand and j, j+2 on the other. Second, characterizing the tertiary packing between β‐sheets, the knob‐socket XY:H+B consists of a three‐residue XY:H socket (i, i+2 on one strand and j on the other) packed against a knob B residue (residue k distant in sequence). Depending on the packing depth of the knob B residue, two types of knob‐sockets are found: side‐chain and main‐chain sockets. The amino acid composition of the pockets and knob‐sockets reveal the sequence specificity of β‐sheet packing. For β‐sheet formation, the XY:HG pocket clearly shows sequence specificity of amino acids. For tertiary packing, the XY:H+B side‐chain and main‐chain sockets exhibit distinct amino acid preferences at each position. These relationships define an amino acid code for β‐sheet structure and provide an intuitive topological mapping of β‐sheet packing. Proteins 2014; 82:2128–2140. © 2014 Wiley Periodicals, Inc.     

Effect of using suboptimal alignments in template-based protein structure prediction

Computational protein structure prediction remains a challenging task in protein bioinformatics. In the recent years, the importance of template-based structure prediction is increasing because of the growing number of protein structures solved by the structural genomics projects. To capitalize the significant efforts and investments paid on the structural genomics projects, it is urgent to establish effective ways to use the solved structures as templates by developing methods for exploiting remotely related proteins that cannot be simply identified by homology. In this work, we examine the effect of using suboptimal alignments in template-based protein structure prediction. We showed that suboptimal alignments are often more accurate than the optimal one, and such accurate suboptimal alignments can occur even at a very low rank of the alignment score. Suboptimal alignments contain a significant number of correct amino acid residue contacts. Moreover, suboptimal alignments can improve template-based models when used as input to Modeller. Finally, we use suboptimal alignments for handling a contact potential in a probabilistic way in a threading program, SUPRB. The probabilistic contacts strategy outperforms the partly thawed approach, which only uses the optimal alignment in defining residue contacts, and also the re-ranking strategy, which uses the contact potential in re-ranking alignments. The comparison with existing methods in the template-recognition test shows that SUPRB is very competitive and outperforms existing methods.     

CONFOLD: Residue‐residue contact‐guided ab initio protein folding

Predicted protein residue–residue contacts can be used to build three‐dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two‐stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM‐score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM‐score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/ . Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.     

BuildBeta—A system for automatically constructing beta sheets

We describe a method that can thoroughly sample a protein conformational space given the protein primary sequence of amino acids and secondary structure predictions. Specifically, we target proteins with β‐sheets because they are particularly challenging for ab initio protein structure prediction because of the complexity of sampling long‐range strand pairings. Using some basic packing principles, inverse kinematics (IK), and β‐pairing scores, this method creates all possible β‐sheet arrangements including those that have the correct packing of β‐strands. It uses the IK algorithms of ProteinShop to move α‐helices and β‐strands as rigid bodies by rotating the dihedral angles in the coil regions. Our results show that our approach produces structures that are within 4–6 Å RMSD of the native one regardless of the protein size and β‐sheet topology although this number may increase if the protein has long loops or complex α‐helical regions. Proteins 2010. © Published 2009 Wiley‐Liss, Inc.     

Exploring the effects of sparse restraints on protein structure prediction

One of the main barriers to accurate computational protein structure prediction is searching the vast space of protein conformations. Distance restraints or inter‐residue contacts have been used to reduce this search space, easing the discovery of the correct folded state. It has been suggested that about 1 contact for every 12 residues may be sufficient to predict structure at fold level accuracy. Here, we use coarse‐grained structure‐based models in conjunction with molecular dynamics simulations to examine this empirical prediction. We generate sparse contact maps for 15 proteins of varying sequence lengths and topologies and find that given perfect secondary‐structural information, a small fraction of the native contact map (5%‐10%) suffices to fold proteins to their correct native states. We also find that different sparse maps are not equivalent and we make several observations about the type of maps that are successful at such structure prediction. Long range contacts are found to encode more information than shorter range ones, especially for α and αβ‐proteins. However, this distinction reduces for β‐proteins. Choosing contacts that are a consensus from successful maps gives predictive sparse maps as does choosing contacts that are well spread out over the protein structure. Additionally, the folding of proteins can also be used to choose predictive sparse maps. Overall, we conclude that structure‐based models can be used to understand the efficacy of structure‐prediction restraints and could, in future, be tuned to include specific force‐field interactions, secondary structure errors and noise in the sparse maps.     

Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignments

Using a recently developed protein folding algorithm, a prediction of the tertiary structure of the KIX domain of the CREB binding protein is described. The method incorporates predicted secondary and tertiary restraints derived from multiple sequence alignments in a reduced protein model whose conformational space is explored by Monte Carlo dynamics. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that was modified for the presence of predicted U-turns, i.e., regions where the chain reverses global direction. Tertiary restraints are obtained via a two-step process: First, seed side-chain contacts are identified from a correlated mutation analysis, and then, a threading-based algorithm expands the number of these seed contacts. Blind predictions indicate that the KIX domain is a putative three-helix bundle, although the chirality of the bundle could not be uniquely determined. The expected root-mean-square deviation for the correct chirality of the KIX domain is between 5.0 and 6.2 Å. This is to be compared with the estimate of 12.9 Å that would be expected by a random prediction, using the model of F. Cohen and M. Sternberg (J. Mol. Biol. 138:321–333, 1980). Proteins 30:287–294, 1998. © 1998 Wiley-Liss, Inc.     

Mitofusin‐2 knockdown increases ER–mitochondria contact and decreases amyloid β‐peptide production

Mitochondria are physically and biochemically in contact with other organelles including the endoplasmic reticulum (ER). Such contacts are formed between mitochondria‐associated ER membranes (MAM), specialized subregions of ER, and the outer mitochondrial membrane (OMM). We have previously shown increased expression of MAM‐associated proteins and enhanced ER to mitochondria Ca²⁺ transfer from ER to mitochondria in Alzheimer's disease (AD) and amyloid β‐peptide (Aβ)‐related neuronal models. Here, we report that siRNA knockdown of mitofusin‐2 (Mfn2), a protein that is involved in the tethering of ER and mitochondria, leads to increased contact between the two organelles. Cells depleted in Mfn2 showed increased Ca²⁺ transfer from ER to mitchondria and longer stretches of ER forming contacts with OMM. Interestingly, increased contact resulted in decreased concentrations of intra‐ and extracellular Aβ₄₀ and Aβ₄₂. Analysis of γ‐secretase protein expression, maturation and activity revealed that the low Aβ concentrations were a result of impaired γ‐secretase complex function. Amyloid‐β precursor protein (APP), β‐site APP‐cleaving enzyme 1 and neprilysin expression as well as neprilysin activity were not affected by Mfn2 siRNA treatment. In summary, our data shows that modulation of ER–mitochondria contact affects γ‐secretase activity and Aβ generation. Increased ER–mitochondria contact results in lower γ‐secretase activity suggesting a new mechanism by which Aβ generation can be controlled.     

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position–specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα‐Cα atoms. First, using a rigorous leave‐one‐protein‐out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state‐of‐the‐art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/ . Proteins 2016; 84:332–348. © 2016 Wiley Periodicals, Inc.     

Towards accurate residue-residue hydrophobic contact prediction for alpha helical proteins via integer linear optimization

A new optimization-based method is presented to predict the hydrophobic residue contacts in alpha-helical proteins. The proposed approach uses a high resolution distance dependent force field to calculate the interaction energy between different residues of a protein. The formulation predicts the hydrophobic contacts by minimizing the sum of these contact energies. These residue contacts are highly useful in narrowing down the conformational space searched by protein structure prediction algorithms. The proposed algorithm also offers the algorithmic advantage of producing a rank ordered list of the best contact sets. This model was tested on four independent alpha-helical protein test sets and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) obtained using the presented method was approximately 66% for single domain proteins. The average true positive and false positive distances were also calculated for each protein test set and they are 8.87 and 14.67 A, respectively.     

Improving accuracy of protein contact prediction using balanced network deconvolution

Residue contact map is essential for protein three‐dimensional structure determination. But most of the current contact prediction methods based on residue co‐evolution suffer from high false‐positives as introduced by indirect and transitive contacts (i.e., residues A–B and B–C are in contact, but A–C are not). Built on the work by Feizi et al. (Nat Biotechnol 2013; 31:726–733), which demonstrated a general network model to distinguish direct dependencies by network deconvolution, this study presents a new balanced network deconvolution (BND) algorithm to identify optimized dependency matrix without limit on the eigenvalue range in the applied network systems. The algorithm was used to filter contact predictions of five widely used co‐evolution methods. On the test of proteins from three benchmark datasets of the 9th critical assessment of protein structure prediction (CASP9), CASP10, and PSICOV (precise structural contact prediction using sparse inverse covariance estimation) database experiments, the BND can improve the medium‐ and long‐range contact predictions at the L/5 cutoff by 55.59% and 47.68%, respectively, without additional central processing unit cost. The improvement is statistically significant, with a P‐value < 5.93 × 10^?3 in the Student's t‐test. A further comparison with the ab initio structure predictions in CASPs showed that the usefulness of the current co‐evolution‐based contact prediction to the three‐dimensional structure modeling relies on the number of homologous sequences existing in the sequence databases. BND can be used as a general contact refinement method, which is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/BND/ . Proteins 2015; 83:485–496. © 2014 Wiley Periodicals, Inc.     

Folding of Top7 in unbiased all‐atom Monte Carlo simulations

For computational studies of protein folding, proteins with both helical and β‐sheet secondary structure elements are very challenging, as they expose subtle biases of the physical models. Here, we present reproducible folding of a 92 residue α/β protein (residues 3–94 of Top7, PDB ID: 1QYS) in computer simulations starting from random initial conformations using a transferable physical model which has been previously shown to describe the folding and thermodynamic properties of about 20 other smaller proteins of different folds. Top7 is a de novo designed protein with two α‐helices and a five stranded β‐sheet. Experimentally, it is known to be unusually stable for its size, and its folding transition distinctly deviates from the two‐state behavior commonly seen in natural single domain proteins. In our all‐atom implicit solvent parallel tempering Monte Carlo simulations, Top7 shows a rapid transition to a group of states with high native‐like secondary structure, and a much slower subsequent transition to the native state with a root mean square deviation of about 3.5 Å from the experimentally determined structure. Consistent with experiments, we find Top7 to be thermally extremely stable, although the simulations also find a large number of very stable non‐native states with high native‐like secondary structure. Proteins 2013; 81:1446–1456. © 2013 Wiley Periodicals, Inc.     

Analysis and prediction of the packing of α-helices against a β-sheet in the tertiary structure of globular proteins

The packing of α-helices and β-sheets in six $\text{α}\text{β}$ proteins (e.g. flavodoxin) has been analysed. The results provide the basis for a computer algorithm to predict the tertiary structure of an $\text{α}\text{β}$ protein from its amino acid sequence and actual assignment of secondary structure.The packing of an individual α-helix against a β-sheet generally involves two adjacent ± 4 rows of non-polar residues on the α-helix at the positions i, i + 4, i + 8, i + 1, i + 5, i + 9. The pattern of interacting β-sheet residues results from the twisted nature of the sheet surface and the attendant rotation of the side-chains. At a more detailed level, four of the α-helical residues (i + 1, i + 4, i + 5 and i + 8) form a diamond that surrounds one particular β-sheet residue, generally isoleucine, leucine or valine. In general, the α-helix sits 10 Å above the sheet and lies parallel to the strand direction.The prediction follows a combinational approach. First, a list of possible β-sheet structures (10⁶ to 10¹⁴) is constructed by the generation of all β-sheet topologies and β-strand alignments. This list is reduced by constraints on topology and the location of non-polar residues to mediate the sheet/helix packing, and then rank-ordered on the extent of hydrogen bonding. This algorithm was uniformly applied to 16 $\text{α}\text{β}$ domains in 13 proteins. For every structure, one member of the reduced list was close to the crystal structure; the root-mean-square deviation between equivalenced C^α atoms averaged 5.6 Å for 100 residues. For the $\text{α}\text{β}$ proteins with pure parallel β-sheets, the total number of structures comparable to or better than the native in terms of hydrogen bonds was between 1 and 148. For proteins with mixed β-sheets, the worst case is glyceraldehyde-3-phosphate dehydrogenase, where as many as 3800 structures would have to be sampled. The evolutionary significance of these results as well as the potential use of a combinatorial approach to the protein folding problem are discussed.     

Improving prediction of helix–helix packing in membrane proteins using predicted contact numbers as restraints

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α‐helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue–residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best‐sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best‐sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix–helix packing. Proteins 2017; 85:1212–1221. © 2017 Wiley Periodicals, Inc.     

Slow formation of aggregation‐resistant β‐sheet folding intermediates

Protein folding has been studied extensively for decades, yet our ability to predict how proteins reach their native state from a mechanistic perspective is still rudimentary at best, limiting our understanding of folding‐related processes in vivo and our ability to manipulate proteins in vitro. Here, we investigate the in vitro refolding mechanism of a large β‐helix protein, pertactin, which has an extended, elongated shape. At 55 kDa, this single domain, all‐β‐sheet protein allows detailed analysis of the formation of β‐sheet structure in larger proteins. Using a combination of fluorescence and far‐UV circular dichroism spectroscopy, we show that the pertactin β‐helix refolds remarkably slowly, with multiexponential kinetics. Surprisingly, despite the slow refolding rates, large size, and β‐sheet‐rich topology, pertactin refolding is reversible and not complicated by off‐pathway aggregation. The slow pertactin refolding rate is not limited by proline isomerization, and 30% of secondary structure formation occurs within the rate‐limiting step. Furthermore, site‐specific labeling experiments indicate that the β‐helix refolds in a multistep but concerted process involving the entire protein, rather than via initial formation of the stable core substructure observed in equilibrium titrations. Hence pertactin provides a valuable system for studying the refolding properties of larger, β‐sheet‐rich proteins, and raises intriguing questions regarding the prevention of aggregation during the prolonged population of partially folded, β‐sheet‐rich refolding intermediates. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Folding type-specific secondary structure propensities of amino acids,derived from α-helical, β-sheet, α/β, and α+β proteins of known structures

Folding type-specific secondary structure propensities of 20 naturally occurring amino acids have been derived from α-helical, β-sheet, α/β, and α+β proteins of known structures. These data show that each residue type of amino acids has intrinsic propensities in different regions of secondary structures for different folding types of proteins. Each of the folding types shows markedly different rank ordering, indicating folding type-specific effects on the secondary structure propensities of amino acids. Rigorous statistical tests have been made to validate the folding type-specific effects. It should be noted that α and β proteins have relatively small α-helices and β-strands forming propensities respectively compared with those of α+β and α/β proteins. This may suggest that, with more complex architectures than α and β proteins, α+β and α/β proteins require larger propensities to distinguish from interacting α-helices and β-strands. Our finding of folding type-specific secondary structure propensities suggests that sequence space accessible to each folding type may have differing features. Differing sequence space features might be constrained by topological requirement for each of the folding types. Almost all strong β-sheet forming residues are hydrophobic in character regardless of folding types, thus suggesting the hydrophobicities of side chains as a key determinant of β-sheet structures. In contrast, conformational entropy of side chains is a major determinant of the helical propensities of amino acids, although other interactions such as hydrophobicities and charged interactions cannot be neglected. These results will be helpful to protein design, class-based secondary structure prediction, and protein folding. © 1998 John Wiley & Sons, Inc. Biopoly 45: 35–49, 1998     

TASSER_low‐zsc: An approach to improve structure prediction using low z‐score–ranked templates

In a variety of threading methods, often poorly ranked (low z‐score) templates have good alignments. Here, a new method, TASSER_low‐zsc that identifies these low z‐score–ranked templates to improve protein structure prediction accuracy, is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity (thread_cluster) followed by TASSER modeling, with final models selected by using a TASSER_QA variant. To establish the generality of the approach, templates provided by two threading methods, SP³ and SPARKS², are examined. The SP³ and SPARKS² benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length ≤250 residues, respectively. For SP³ medium and hard targets, using thread_cluster, the TM‐scores of the best template improve by ～4 and 9% over the original set (without low z‐score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by ～7 and 9% over the best model generated with the original template set. Moreover, TASSER_low‐zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low‐ranked templates from SPARKS². The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Solution structures and backbone dynamics of the ribosomal protein S6 and its permutant P54‐55

The ribosomal protein S6 from Thermus thermophilus has served as a model system for the study of protein folding, especially for understanding the effects of circular permutations of secondary structure elements. This study presents the structure of a permutant protein, the 96‐residue P^54‐55, and the structure of its 101‐residue parent protein S6^wt in solution. The data also characterizes the effects of circular permutation on the backbone dynamics of S6. Consistent with crystallographic data on S6^wt, the overall solution structures of both P^54‐55 and S6^wt show a β‐sheet of four antiparallel β‐strands with two α‐helices packed on one side of the sheet. In clear contrast to the crystal data, however, the solution structure of S6^wt reveals a disordered loop in the region between β‐strands 2 and 3 (Leu43‐Phe60) instead of a well‐ordered stretch and associated hydrophobic mini‐core observed in the crystal structure. Moreover, the data for P^54‐55 show that the joined wild‐type N‐ and C‐terminals form a dynamically robust stretch with a hairpin structure that complies with the in silico design. Taken together, the results explain why the loop region of the S6^wt structure is relatively insensitive to mutational perturbations, and why P^54‐55 is more stable than S6^wt: the permutant incision at Lys54‐Asp55 is energetically neutral by being located in an already disordered loop whereas the new hairpin between the wild‐type N‐ and C‐termini is stabilizing.     

Influence of ligand binding on structure and thermostability of human α1‐acid glycoprotein

Ligand binding of neutral progesterone, basic propranolol, and acidic warfarin to human α₁‐acid glycoprotein (AGP) was investigated by Raman spectroscopy. The binding itself is characterized by a uniform conformational shift in which a tryptophan residue is involved. Slight differences corresponding to different contacts of the individual ligands inside the β‐barrel are described. Results are compared with in silico ligand docking into the available crystal structure of deglycosylated AGP using quantum/molecular mechanics. Calculated binding energies are ?18.2, ?14.5, and ?11.5 kcal/mol for warfarin, propranolol, and progesterone, respectively. These calculations are consistent with Raman difference spectroscopy; nevertheless, minor discrepancies in the precise positions of the ligands point to structural differences between deglycosylated and native AGP. Thermal dynamics of AGP with/without bounded warfarin was followed by Raman spectroscopy in a temperature range of 10–95 °C and analyzed by principal component analysis. With increasing temperature, a slight decrease of α‐helical content is observed that coincides with an increase in β‐sheet content. Above 45 °C, also β‐strands tend to unfold, and the observed decrease in β‐sheet coincides with an increase of β‐turns accompanied by a conformational shift of the nearby disulfide bridge from high‐energy trans‐gauche‐trans to more relaxed gauche‐gauche‐trans. This major rearrangement in the vicinity of the bridge is not only characterized by unfolding of the β‐sheet but also by subsequent ligand release. Hereby, ligand binding alters the protein dynamics, and the more rigid protein–ligand complex shows an improved thermal stability, a finding that contributes to the reported chaperone‐like function of AGP. Copyright © 2015 John Wiley & Sons, Ltd.     

PAIRpred: Partner‐specific prediction of interacting residues from sequence and structure

We present a novel partner‐specific protein–protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter‐protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding‐associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/ . Proteins 2014; 82:1142–1155. © 2013 Wiley Periodicals, Inc.     

β‐sheet breaker peptide inhibitor of Alzheimer's amyloidogenesis with increased blood–brain barrier permeability and resistance to proteolytic degradation in plasma

Short synthetic peptides homologous to the central region of Aβ but bearing proline residues as β‐sheet blockers have been shown in vitro to bind to Aβ with high affinity, partially inhibit Aβ fibrillogenesis, and redissolve preformed fibrils. While short peptides have been used extensively as therapeutic drugs in medicine, two important problems associated with their use in central nervous system diseases have to be addressed: (a) rapid proteolytic degradation in plasma, and (b) poor blood–brain barrier (BBB) permeability. Recently, we have demonstrated that the covalent modification of proteins with the naturally occurring polyamines significantly increases their permeability at the BBB. We have extended this technology to iAβ11, an 11‐residue β‐sheet breaker peptide that inhibits Aβ fibrillogenesis, by covalently modifying this peptide with the polyamine, putrescine (PUT), and evaluating its plasma pharmacokinetics and BBB permeability. After a single intravenous bolus injection in rats, both ¹²⁵I‐YiAβ11 and ¹²⁵I‐PUT‐YiAβ11 showed rapid degradation in plasma as determined by trichloroacetic acid (TCA) precipitation and paper chromatography. By switching to the all d ‐enantiomers of YiAβ11 and PUT‐YiAβ11, significant protection from degradation by proteases in rat plasma was obtained with only 1.9% and 5.7% degradation at 15 min after intravenous bolus injection, respectively. The permeability coefficient × surface area product at the BBB was five‐ sevenfold higher in the cortex and hippocampus for the ¹²⁵I‐PUT‐d ‐YiAβ11 compared to the ¹²⁵I‐d ‐YiAβ11, with no significant difference in the residual plasma volume. In vitro assays showed that PUT‐d ‐YiAβ11 retains its ability to partially inhibit Aβ fibrillogenesis and dissolve preformed amyloid fibrils. Because of its five‐ to sevenfold increase in permeability at the BBB and its resistance to proteolysis in the plasma, this polyamine‐modified β‐sheet breaker peptide may prove to be an effective inhibitor of amyloidogenesis in vivo and, hence, an important therapy for Alzheimer's disease. © 1999 John Wiley & Sons, Inc. J Neurobiol 39: 371–382, 1999