Predicting fold novelty based on ProtoNet hierarchical classification

MOTIVATION: Structural genomics projects aim to solve a large number of protein structures with the ultimate objective of representing the entire protein space. The computational challenge is to identify and prioritize a small set of proteins with new, currently unknown, superfamilies or folds. RESULTS: We develop a method that assigns each protein a likelihood of it belonging to a new, yet undetermined, structural superfamily. The method relies on a variant of ProtoNet, an automatic hierarchical classification scheme of all protein sequences from SwissProt. Our results show that proteins that are remote from solved structures in the ProtoNet hierarchy are more likely to belong to new superfamilies. The results are validated against SCOP releases from recent years that account for about half of the solved structures known to date. We show that our new method and the representation of ProtoNet are superior in detecting new targets, compared to our previous method using ProtoMap classification. Furthermore, our method outperforms PSI-BLAST search in detecting potential new superfamilies.     

Consistent dissection of the protein interaction network by combining global and local metrics

We propose a new network decomposition method to systematically identify protein interaction modules in the protein interaction network. Our method incorporates both a global metric and a local metric for balance and consistency. We have compared the performance of our method with several earlier approaches on both simulated and real datasets using different criteria, and show that our method is more robust to network alterations and more effective at discovering functional protein modules.     

Optimisation of OPLS–UA force-field parameters for protein systems using protein data bank

We have previously proposed a method for refining force-field parameters of protein systems, which consists of minimising the summation of the square of the force acting on each atom in the proteins with the structures from the protein data bank (PDB). The results showed that the modified force-field parameters for all-atom model gave structures more consistent with the experimental implications than the original force fields. In this work, we applied this method and a new method to the OPLS–UA force field. In the new method, we perform a minimisation of the average of the root-mean-square deviation of various protein structures from the native structure. We selected some torsion-energy parameters for this optimisation, and 100 molecules from the PDB were used. The results imply that the new force-field parameters gave structures of two peptides more consistent with the experimental implications for the secondary structure-forming tendencies than the original OPLS–UA force field.     

Evaluating Protein Similarity from Coarse Structures

To unscramble the relationship between protein function and protein structure, it is essential to assess the protein similarity from different aspects. Although many methods have been proposed for protein structure alignment or comparison, alternative similarity measures are still strongly demanded due to the requirement of fast screening and query in large-scale structure databases. In this paper, we first formulate a novel representation of a protein structure, i.e., Feature Sequence of Surface (FSS). Then, a new score scheme is developed to measure the similarity between two representations. To verify the proposed method, numerical experiments are conducted in four different protein data sets. We also classify SARS coronavirus to verify the effectiveness of the new method. Furthermore, preliminary results of fast classification of the whole CATH v2.5.1 database based on the new macrostructure similarity are given as a pilot study. We demonstrate that the proposed approach to measure the similarities between protein structures is simple to implement, computationally efficient, and surprisingly fast. In addition, the method itself provides a new and quantitative tool to view a protein structure.     

Determination method of the balance of the secondary-structure-forming tendencies of force fields

We propose a new method of optimisation of backbone torsion-energy parameters in the force field for molecular simulations of protein systems. This method is based on the idea of balancing the secondary-structure-forming tendencies, namely, those of α-helix and β-sheet structures. We perform a minimisation of the backbone dihedral angle-based root-mean-square deviation of the helix and β structure regions in many protein structures. As an example, we optimised the backbone torsion-energy parameters of AMBER parm96 force field using 100 protein molecules from the Protein Data Bank. We then performed folding simulations of α-helical and β-hairpin peptides, using the optimised force field. The results imply that the new force-field parameters give structures more consistent with the experimental implications than the original AMBER parm96 force field.     

Protein side‐chain modeling with a protein‐dependent optimized rotamer library

Despite years of effort, the problem of predicting the conformations of protein side chains remains a subject of inquiry. This problem has three major issues, namely defining the conformations that a side chain may adopt within a protein, developing a sampling procedure for generating possible side‐chain packings, and defining a scoring function that can rank these possible packings. To solve the former of these issues, most procedures rely on a rotamer library derived from databases of known protein structures. We introduce an alternative method that is free of statistics. We begin with a rotamer library that is based only on stereochemical considerations; this rotamer library is then optimized independently for each protein under study. We show that this optimization step restores the diversity of conformations observed in native proteins. We combine this protein‐dependent rotamer library (PDRL) method with the self‐consistent mean field (SCMF) sampling approach and a physics‐based scoring function into a new side‐chain prediction method, SCMF–PDRL. Using two large test sets of 831 and 378 proteins, respectively, we show that this new method compares favorably with competing methods such as SCAP, OPUS‐Rota, and SCWRL4 for energy‐minimized structures. Proteins 2014; 82:2000–2017. © 2014 Wiley Periodicals, Inc.     

Analysis and prediction of functional sub-types from protein sequence alignments

The increasing number and diversity of protein sequence families requires new methods to define and predict details regarding function. Here, we present a method for analysis and prediction of functional sub-types from multiple protein sequence alignments. Given an alignment and set of proteins grouped into sub-types according to some definition of function, such as enzymatic specificity, the method identifies positions that are indicative of functional differences by comparison of sub-type specific sequence profiles, and analysis of positional entropy in the alignment. Alignment positions with significantly high positional relative entropy correlate with those known to be involved in defining sub-types for nucleotidyl cyclases, protein kinases, lactate/malate dehydrogenases and trypsin-like serine proteases. We highlight new positions for these proteins that suggest additional experiments to elucidate the basis of specificity. The method is also able to predict sub-type for unclassified sequences. We assess several variations on a prediction method, and compare them to simple sequence comparisons. For assessment, we remove close homologues to the sequence for which a prediction is to be made (by a sequence identity above a threshold). This simulates situations where a protein is known to belong to a protein family, but is not a close relative of another protein of known sub-type. Considering the four families above, and a sequence identity threshold of 30 %, our best method gives an accuracy of 96 % compared to 80 % obtained for sequence similarity and 74 % for BLAST. We describe the derivation of a set of sub-type groupings derived from an automated parsing of alignments from PFAM and the SWISSPROT database, and use this to perform a large-scale assessment. The best method gives an average accuracy of 94 % compared to 68 % for sequence similarity and 79 % for BLAST. We discuss implications for experimental design, genome annotation and the prediction of protein function and protein intra-residue distances.     

Efficient identification of critical residues based only on protein structure by network analysis

Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at http://bis.ifc.unam.mx/jamming/. In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins.     

Site-specific conjugation of oligonucleotides to the C-terminus of recombinant protein by expressed protein ligation

We developed a convenient method for synthesizing homogeneous DNA-protein conjugates. The method is based on expressed protein ligation of intein-fusion proteins and oligonucleotides derivatized with a cysteine. A range of cysteinyl oligonucleotides were synthesized by using a new reagent 1 and were successfully applied to expressed protein ligation to attach the oligonucleotides specifically at the C-terminus of a recombinant protein.     

Minimal nanodisc without exogenous lipids for stabilizing membrane proteins in detergent-free buffer

Membrane protein stabilization after detergent solubilization presents drawbacks for structural and biophysical studies, in particular that of a reduced stability in detergent micelles. Therefore, alternative methods are required for efficient stabilization. Lipid nanodisc made with the membrane scaffold protein MSP is a valuable system but requires a fine optimization of the lipid to protein ratio. We present here the use of the scaffold protein MSP without added lipids as a minimal system to stabilize membrane proteins. We show that this method is applicable to α-helical and β-strands transmembrane proteins. This method allowed cryo-electron microscopy structural study of the bacterial transporter MexB. A protein quantification indicates that MexB is stabilized by two MSP proteins. This simplified and efficient method proposes a new advance in harnessing the MSP potential to stabilize membrane proteins.     

Two-step purification of low-content cellular protein using protein-imprinted polymers

We introduce a new method, based on molecular imprinting, for purification of low-content cellular protein. This is a combination method that uses two types of protein-imprinting polymers (PIPs) synthesized with limited-length polymer chains that contain randomly distributed recognition sites, namely assistant recognition polymer chains, and uses cloned bacterial protein as a template. The low-content cellular target protein was purified from cell extract by this method. This is believed to be the first time that low-content cellular protein has been purified by using PIPs and with only two steps.     

CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data

MOTIVATION: Identifying candidate genes associated with a given phenotype or trait is an important problem in biological and biomedical studies. Prioritizing genes based on the accumulated information from several data sources is of fundamental importance. Several integrative methods have been developed when a set of candidate genes for the phenotype is available. However, how to prioritize genes for phenotypes when no candidates are available is still a challenging problem. RESULTS: We develop a new method for prioritizing genes associated with a phenotype by Combining Gene expression and protein Interaction data (CGI). The method is applied to yeast gene expression data sets in combination with protein interaction data sets of varying reliability. We found that our method outperforms the intuitive prioritizing method of using either gene expression data or protein interaction data only and a recent gene ranking algorithm GeneRank. We then apply our method to prioritize genes for Alzheimer's disease. AVAILABILITY: The code in this paper is available upon request.     

Weak protein–protein interactions revealed by immiscible filtration assisted by surface tension

Biological mechanisms are often mediated by transient interactions between multiple proteins. The isolation of intact protein complexes is essential to understanding biochemical processes and an important prerequisite for identifying new drug targets and biomarkers. However, low-affinity interactions are often difficult to detect. Here, we use a newly described method called immiscible filtration assisted by surface tension (IFAST) to isolate proteins under defined binding conditions. This method, which gives a near-instantaneous isolation, enables significantly higher recovery of transient complexes compared to current wash-based protocols, which require reequilibration at each of several wash steps, resulting in protein loss. The method moves proteins, or protein complexes, captured on a solid phase through one or more immiscible-phase barriers that efficiently exclude the passage of nonspecific material in a single operation. We use a previously described polyol-responsive monoclonal antibody to investigate the potential of this new method to study protein binding. In addition, difficult-to-isolate complexes involving the biologically and clinically important Wnt signaling pathway were isolated. We anticipate that this simple, rapid method to isolate intact, transient complexes will enable the discoveries of new signaling pathways, biomarkers, and drug targets.     

Chemical control of protein stability and function in living mice

Conditional control of protein function in vivo offers great potential for deconvoluting the roles of individual proteins in complicated systems. We recently developed a method in which a small protein domain, termed a destabilizing domain, confers instability to fusion protein partners in cultured cells. Instability is reversed when a cell-permeable small molecule binds this domain. Here we describe the use of this system to regulate protein function in living mammals. We show regulation of secreted proteins and their biological activity with conditional secretion of an immunomodulatory cytokine, resulting in tumor burden reduction in mouse models. Additionally, we use this approach to control the function of a specific protein after systemic delivery of the gene that encodes it to a tumor, suggesting uses for enhancing the specificity and efficacy of targeted gene-based therapies. This method represents a new strategy to regulate protein function in living organisms with a high level of control.     

Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization

MOTIVATION: Determining locations of protein expression is essential to understand protein function. Advances in green fluorescence protein (GFP) fusion proteins and automated fluorescence microscopy allow for rapid acquisition of large collections of protein localization images. Recognition of these cell images requires an automated image analysis system. Approaches taken by previous work concentrated on designing a set of optimal features and then applying standard machine-learning algorithms. In fact, trends of recent advances in machine learning and computer vision can be applied to improve the performance. One trend is the advances in multiclass learning with error-correcting output codes (ECOC). Another trend is the use of a large number of weak detectors with boosting for detecting objects in images of real-world scenes. RESULTS: We take advantage of these advances to propose a new learning algorithm, AdaBoost.ERC, coupled with weak and strong detectors, to improve the performance of automatic recognition of protein subcellular locations in cell images. We prepared two image data sets of CHO and Vero cells and downloaded a HeLa cell image data set in the public domain to evaluate our new method. We show that AdaBoost.ERC outperforms other AdaBoost extensions. We demonstrate the benefit of weak detectors by showing significant performance improvements over classifiers using only strong detectors. We also empirically test our method's capability of generalizing to heterogeneous image collections. Compared with previous work, our method performs reasonably well for the HeLa cell images. AVAILABILITY: CHO and Vero cell images, their corresponding feature sets (SSLF and WSLF), our new learning algorithm, AdaBoost.ERC, and Supplementary Material are available at http://aiia.iis.sinica.edu.tw/     

Pulse proteolysis: a simple method for quantitative determination of protein stability and ligand binding

Thermodynamic stability is fundamental to the biology of proteins. Information on protein stability is essential for studying protein structure and folding and can also be used indirectly to monitor protein-ligand or protein-protein interactions. While clearly valuable, the experimental determination of a protein's stability typically requires biophysical instrumentation and substantial quantities of purified protein, which has limited the use of this technique as a general laboratory method. We report here a simple new method for determining protein stability by using pulse proteolysis with varying concentrations of denaturant. Pulse proteolysis is designed to digest only the unfolded proteins in an equilibrium mixture of folded and unfolded proteins that relaxes on a time scale longer than the proteolytic pulse. We used this method to study the stabilities of Escherichia coli ribonuclease H and its variants, both in purified form and directly from cell lysates. The DeltaG(unf) degrees values obtained by this technique were in agreement with those determined by traditional methods. We also successfully used this method to monitor the binding of maltose-binding protein to maltose, as well as to rapidly screen cognate ligands for this protein. The simplicity of pulse proteolysis suggests that it is an excellent strategy for the high-throughput determination of protein stability in protein engineering and drug discovery applications.     

Semi-automatic high-throughput determination of plasma protein binding using a 96-well plate filtrate assembly and fast liquid chromatography-tandem mass spectrometry

A semi-automatic, high-throughput method has been developed to rapidly assess plasma protein binding of new chemical entities in drug discovery phase. New chemical entities are mixed with plasma and the unbound fractions are separated from the bound fraction by ultrafiltration in a 96-well filtrate assembly. The unbound fractions are then analyzed by fast liquid chromatography-tandem mass spectrometry (LC-MS/MS). Sample handling is automated by a robotic system. Employing a cocktail approach where multiple new chemical entities are allowed to bind to plasma proteins in the same well has further increased the throughput. We have validated the method with 12 commercially available compounds. The plasma protein binding data obtained by this method are comparable with the literature values. This method enables the determination of protein binding for 32 compounds in one single experiment instead of 1-2 compounds using the conventional methods.     

Fast and accurate side-chain topology and energy refinement (FASTER) as a new method for protein structure optimization

We have developed an original method for global optimization of protein side-chain conformations, called the Fast and Accurate Side-Chain Topology and Energy Refinement (FASTER) method. The method operates by systematically overcoming local minima of increasing order. Comparison of the FASTER results with those of the dead-end elimination (DEE) algorithm showed that both methods produce nearly identical results, but the FASTER algorithm is 100-1000 times faster than the DEE method and scales in a stable and favorable way as a function of protein size. We also show that low-order local minima may be almost as accurate as the global minimum when evaluated against experimentally determined structures. In addition, the new algorithm provides significant information about the conformational flexibility of individual side-chains. We observed that strictly rigid side-chains are concentrated mainly in the core of the protein, whereas highly flexible side-chains are found almost exclusively among solvent-oriented residues.     

PROuST: a comparison method of three-dimensional structures of proteins using indexing techniques.

We present a new method for protein structure comparison that combines indexing and dynamic programming (DP). The method is based on simple geometric features of triplets of secondary structures of proteins. These features provide indexes to a hash table that allows fast retrieval of similarity information for a query protein. After the query protein is matched with all proteins in the hash table producing a list of putative similarities, the dynamic programming algorithm is used to align the query protein with each protein of this list. Since the pairwise comparison with DP is applied only to a small subset of proteins and, furthermore, DP re-uses information that is already computed and stored in the hash table, the approach is very fast even when searching the entire PDB. We have done extensive experimentation showing that our approach achieves results of quality comparable to that of other existing approaches but is generally faster.     

High-throughput dynamic light scattering method for measuring viscosity of concentrated protein solutions

We propose a new method to measure the viscosity of concentrated protein solutions in a high-throughput format. This method measures the apparent hydrodynamic radius of polystyrene beads with known sizes using a dynamic light scattering (DLS) system with a microplate reader. Glycerol solution viscosities obtained by the DLS method were in good agreement with those reported in the literature. Viscosity of the solutions of two monoclonal antibody molecules was acquired using both DLS and cone-and-plate techniques, and the results were comparable. The DLS method described here has the potential to be used in many aspects of protein characterization.