RPS: Repeats in Protein Sequences

Repeats are two or more contiguous segments of amino acid residues that are believed to have arisen as a result of intragenic duplication, recombination and mutation events. These repeats can be utilized for protein structure prediction and can provide insights into the protein evolution and phylogenetic relationship. Therefore, to aid structural biologists and phylogeneticists in their research, a computing resource (a web server and a database), Repeats in Protein Sequences (RPS), has been created. Using RPS, users can obtain useful information regarding identical, similar and distant repeats (of varying lengths) in protein sequences. In addition, users can check the frequency of occurrence of the repeats in sequence databases such as the Genome Database, PIR and SWISS-PROT and among the protein sequences available in the Protein Data Bank archive. Furthermore, users can view the three-dimensional structure of the repeats using the Java visualization plug-in Jmol. The proposed computing resource can be accessed over the World Wide Web at http://bioserver1.physics.iisc.ernet.in/rps/.

Lysozyme: A model protein for amyloid research

Ever since lysozyme was discovered by Fleming in 1922, this protein has emerged as a model for investigations on protein structure and function. Over the years, several high-resolution structures have yielded a wealth of structural data on this protein. Extensive studies on folding of lysozyme have shown how different regions of this protein dynamically interact with one another. Data is also available from numerous biotechnological studies wherein lysozyme has been employed as a model protein for recovering active recombinant protein from inclusion bodies using small molecules like L-arginine. A variety of conditions have been developed in vitro to induce fibrillation in hen lysozyme. They include (a) acidic pH at elevated temperature, (b) concentrated solutions of ethanol, (c) moderate concentrations of guanidinium hydrochloride at moderate temperature, and (d) alkaline pH at room temperature. This review aims to bring together similarities and differences in aggregation mechanisms, morphology of aggregates, and related issues that arise using the different conditions mentioned above to improve our understanding. The alkaline pH condition (pH 12.2), discovered and studied extensively in our lab, shall receive special attention. More than a decade ago, it was revealed that mutations in human lysozyme can cause accumulation of large quantities of amyloid in liver, kidney, and other regions of gastrointestinal tract. Understanding the mechanism of lysozyme aggregation will probably have therapeutic implications for the treatment of systemic nonneuropathic amyloidosis. Numerous studies have begun to focus attention on inhibition of lysozyme aggregation using antibody or small molecules. The enzymatic activity of lysozyme presents a convenient handle to quantify the native population of lysozyme in a sample where aggregation has been inhibited. The rich information available on lysozyme coupled with the multiple conditions that have been successful in inducing/inhibiting its aggregation in vitro makes lysozyme an ideal model protein to investigate amyloidogenesis.

Protein Model Discrimination Using Mutational Sensitivity Derived from Deep Sequencing

A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of similar to 1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (Rank Score), which correlated with the residue depth, and identify active-site residues. Using these correlations, similar to 98% of correct models of CcdB (RMSD <= 4 angstrom) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout.

Network properties of protein-decoy structures

Convergence of the vast sequence space of proteins into a highly restricted fold/conformational space suggests a simple yet unique underlying mechanism of protein folding that has been the subject of much debate in the last several decades. One of the major challenges related to the understanding of protein folding or in silico protein structure prediction is the discrimination of non-native structures/decoys from the native structure. Applications of knowledge-based potentials to attain this goal have been extensively reported in the literature. Also, scoring functions based on accessible surface area and amino acid neighbourhood considerations were used in discriminating the decoys from native structures. In this article, we have explored the potential of protein structure network (PSN) parameters to validate the native proteins against a large number of decoy structures generated by diverse methods. We are guided by two principles: (a) the PSNs capture the local properties from a global perspective and (b) inclusion of non-covalent interactions, at all-atom level, including the side-chain atoms, in the network construction accommodates the sequence dependent features. Several network parameters such as the size of the largest cluster, community size, clustering coefficient are evaluated and scored on the basis of the rank of the native structures and the Z-scores. The network analysis of decoy structures highlights the importance of the global properties contributing to the uniqueness of native structures. The analysis also exhibits that the network parameters can be used as metrics to identify the native structures and filter out non-native structures/decoys in a large number of data-sets; thus also has a potential to be used in the protein `structure prediction' problem.

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a ID sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods. (C) 2012 Elsevier Masson SAS. All rights reserved.

Network properties of decoys and CASP predicted models: a comparison with native protein structures

Protein structure space is believed to consist of a finite set of discrete folds, unlike the protein sequence space which is astronomically large, indicating that proteins from the available sequence space are likely to adopt one of the many folds already observed. In spite of extensive sequence-structure correlation data, protein structure prediction still remains an open question with researchers having tried different approaches (experimental as well as computational). One of the challenges of protein structure prediction is to identify the native protein structures from a milieu of decoys/models. In this work, a rigorous investigation of Protein Structure Networks (PSNs) has been performed to detect native structures from decoys/ models. Ninety four parameters obtained from network studies have been optimally combined with Support Vector Machines (SVM) to derive a general metric to distinguish decoys/models from the native protein structures with an accuracy of 94.11%. Recently, for the first time in the literature we had shown that PSN has the capability to distinguish native proteins from decoys. A major difference between the present work and the previous study is to explore the transition profiles at different strengths of non-covalent interactions and SVM has indeed identified this as an important parameter. Additionally, the SVM trained algorithm is also applied to the recent CASP10 predicted models. The novelty of the network approach is that it is based on general network properties of native protein structures and that a given model can be assessed independent of any reference structure. Thus, the approach presented in this paper can be valuable in validating the predicted structures. A web-server has been developed for this purpose and is freely available at http://vishgraph.mbu.iisc.ernet.in/GraProStr/PSN-QA.html.

Do we see what we should see? Describing non-covalent interactions in protein structures including precision

The power of X-ray crystal structure analysis as a technique is to `see where the atoms are'. The results are extensively used by a wide variety of research communities. However, this `seeing where the atoms are' can give a false sense of security unless the precision of the placement of the atoms has been taken into account. Indeed, the presentation of bond distances and angles to a false precision (i.e. to too many decimal places) is commonplace. This article has three themes. Firstly, a basis for a proper representation of protein crystal structure results is detailed and demonstrated with respect to analyses of Protein Data Bank entries. The basis for establishing the precision of placement of each atom in a protein crystal structure is non-trivial. Secondly, a knowledge base harnessing such a descriptor of precision is presented. It is applied here to the case of salt bridges, i.e. ion pairs, in protein structures; this is the most fundamental place to start with such structure-precision representations since salt bridges are one of the tenets of protein structure stability. Ion pairs also play a central role in protein oligomerization, molecular recognition of ligands and substrates, allosteric regulation, domain motion and alpha-helix capping. A new knowledge base, SBPS (Salt Bridges in Protein Structures), takes these structural precisions into account and is the first of its kind. The third theme of the article is to indicate natural extensions of the need for such a description of precision, such as those involving metalloproteins and the determination of the protonation states of ionizable amino acids. Overall, it is also noted that this work and these examples are also relevant to protein three-dimensional structure molecular graphics software.

Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis

Identification of residue-residue contacts from primary sequence can be used to guide protein structure prediction. Using Escherichia coli CcdB as the test case, we describe an experimental method termed saturation-suppressor mutagenesis to acquire residue contact information. In this methodology, for each of five inactive CcdB mutants, exhaustive screens for suppressors were performed. Proximal suppressors were accurately discriminated from distal suppressors based on their phenotypes when present as single mutants. Experimentally identified putative proximal pairs formed spatial constraints to recover >98% of native-like models of CcdB from a decoy dataset. Suppressor methodology was also applied to the integral membrane protein, diacylglycerol kinase A where the structures determined by X-ray crystallography and NMR were significantly different. Suppressor as well as sequence co-variation data clearly point to the Xray structure being the functional one adopted in vivo. The methodology is applicable to any macromolecular system for which a convenient phenotypic assay exists.

Clemaps: Multiple Alignment Of Protein Structures Based On Conformational Letters

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C-alpha pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.

Capturing protein dynamics with time-resolved luminescence spectroscopy

The presented doctoral research utilizes time-resolved spectroscopy to characterize protein dynamics and folding mechanisms. We resolve millisecond-timescale folding by coupling time-resolved fluorescence energy transfer (trFRET) to a continuous flow microfluidic mixer to obtain intramolecular distance distributions throughout the folding process. We have elucidated the folding mechanisms of two cytochromes---one that exhibits two-state folding (cytochrome cb₅₆₂) and one that has both a kinetic refolding intermediate ensemble and a distinct equilibrium unfolding intermediate (cytochrome c₅₅₂). Our data reveal that the distinct structural features of cytochrome c₅₅₂ contribute to its thermostability.

We have also investigated intrachain contact dynamics in unfolded cytochrome cb₅₆₂ by monitoring electron transfer, which occurs as the heme collides with a ruthenium photosensitizer, covalently bound to residues along the polypeptide. Intrachain diffusion for chemically denatured proteins proceeds on the microsecond timescale with an upper limit of 0.1 microseconds. The power-law dependence (slope = -1.5) of the rate constants on the number of peptide bonds between the heme and Ru complex indicate that cytochrome cb₅₆₂ is minimally frustrated.

In addition, we have explored the pathway dependence of electron tunneling rates between metal sites in proteins. Our research group has converted cytochrome b₅₆₂ to a c-type cytochrome with the porphyrin covalently bound to cysteine sidechains. We have investigated the effects of the changes to the protein structure (i.e., increased rigidity and potential new equatorial tunneling pathways) on the electron transfer rates, measured by transient absorption, in a series of ruthenium photosensitizer-modified proteins.

Spectroscopic characterization of protein-wrapped single-wall carbon nanotubes and quantification of their cellular uptake in multiple cell generations

We study the spectral characteristics of bovine serum albumin (BSA) protein conjugated single-wall carbon nanotubes (SWNTs), and quantify their uptake by macrophages. The binding of BSA onto the SWNT surface is found to change the protein structure and to increase the doping of the nanotubes. The G-band Raman intensity follows a well-defined power law for SWNT concentrations of up to 33 μg ml-1 in aqueous solutions. Subsequently, in vitro experiments demonstrate that incubation of BSA-SWNT complexes with macrophages affects neither the cellular growth nor the cellular viability over multiple cell generations. Using wide spot Raman spectroscopy as a fast, non-destructive method for statistical quantification, we observe that macrophages effectively uptake BSA-SWNT complexes, with the average number of nanotubes internalized per cell remaining relatively constant over consecutive cell generations. The number of internalized SWNTs is found to be ∼30 × 106 SWNTs/cell for a 60 mm-2 seeding density and ∼100 × 10 6 SWNTs/cell for a 200 mm-2 seeding density. Our results show that BSA-functionalized SWNTs are an efficient molecular transport system with low cytotoxicity maintained over multiple cell generations. © 2013 IOP Publishing Ltd.

Estimation of individual phases from the four-phase structure invariants in the single isomorphous replacement case

The probability distribution of the four-phase invariants in the case of single isomorphous replacement has been developed to estimate some individual phases. An example of its application to obtain the phases having special values of 0, pi or +/-pi /2 is given for a known protein structure in space group P2(1)2(1)2(1). The phasing procedure includes the determination of starting phases and an iterative calculation. The initial values of starting phases, which are required by the formula, can be obtained from the estimate of one-phase seminvariants and by specifying the origin and enantiomorph. In addition, the calculations lead to two sets of possible phases for each type of reflection by assigning arbitrarily an initial phase value. The present method provides a possibility for the multisolution technique to increase greatly the number of known phases while keeping the number of the trials quite small.

Refining near-native protein-protein docking decoys by local resampling and energy minimization

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 angstrom or more and only 7% increased by 0.5 angstrom or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed.

Predicting protein interaction interfaces from protein sequences: Case studies of subtilisin and phycocyanin

Identification of protein interaction interfaces is very important for understanding the molecular mechanisms underlying biological phenomena. Here, we present a novel method for predicting protein interaction interfaces from sequences by using PAM matrix (PIFPAM). Sequence alignments for interacting proteins were constructed and parsed into segments using sliding windows. By calculating distance matrix for each segment, the correlation coefficients between segments were estimated. The interaction interfaces were predicted by extracting highly correlated segment pairs from the correlation map. The predictions achieved an accuracy 0.41-0.71 for eight intraprotein interaction examples, and 0.07-0.60 for four interprotein interaction examples. Compared with three previously published methods, PIFPAM predicted more contacting site pairs for 11 out of the 12 example proteins, and predicted at least 34% more contacting site pairs for eight proteins of them. The factors affecting the predictions were also analyzed. Since PIFPAM uses only the alignments of the two interacting proteins as input, it is especially useful when no three-dimensional protein structure data are available.

Analysis of Sequence Periodicity in E. coli Proteins: Empirical Investigation of the ?Duplication and Divergence? Theory of Protein Evolution

Gatherer, D., and McEwan, N.R. (2003). Analysis of sequence periodicity in E. coli proteins: empirical investigation of the 'duplication and divergence' theory of protein evolution. Journal of Molecular Evolution 57, 149-158. RAE2008