Protein Structure Recognition Research Articles

Many locks to one key: N-acetylneuraminic acid binding to proteins.

Sialic acids play crucial roles in cell surface glycans of both eukaryotic and prokaryotic organisms, mediating various biological processes, including cell-cell interactions, development, immune response, oncogenesis and host-pathogen interactions. This review focuses on the β-anomeric form of N-acetylneuraminic acid (Neu5Ac), particularly its binding affinity towards various proteins, as elucidated by solved protein structures. Specifically, we delve into the binding mechanisms of Neu5Ac to proteins involved in sequestering and transporting Neu5Ac in Gram-negative bacteria, with implications for drug design targeting these proteins as antimicrobial agents. Unlike the initial assumptions, structural analyses revealed significant variability in the Neu5Ac binding pockets among proteins, indicating diverse evolutionary origins and binding modes. By comparing these findings with existing structures from other systems, we can effectively highlight the intricate relationship between protein structure and Neu5Ac recognition, emphasizing the need for tailored drug design strategies to inhibit Neu5Ac-binding proteins across bacterial species.

Structure Prediction, Evaluation, and Validation of GPR18 Lipid Receptor Using Free Programs

The GPR18 receptor, often referred to as the N-arachidonylglycine receptor, although assigned (along with GPR55 and GPR119) to the new class A GPCR subfamily-lipid receptors, officially still has the status of a class A GPCR orphan. While its signaling pathways and biological significance have not yet been fully elucidated, increasing evidence points to the therapeutic potential of GPR18 in relation to immune, neurodegenerative, and cancer processes to name a few. Therefore, it is necessary to understand the interactions of potential ligands with the receptor and the influence of particular structural elements on their activity. Thus, given the lack of an experimentally solved structure, the goal of the present study was to obtain a homology model of the GPR18 receptor in the inactive state, meeting all requirements in terms of protein structure quality and recognition of active ligands. To increase the reliability and precision of the predictions, different contemporary protein structure prediction methods and software were used and compared herein. To test the usability of the resulting models, we optimized and compared the selected structures followed by the assessment of the ability to recognize known, active ligands. The stability of the predicted poses was then evaluated by means of molecular dynamics simulations. On the other hand, most of the best-ranking contemporary CADD software/platforms for its full usability require rather expensive licenses. To overcome this down-to-earth obstacle, the overarching goal of these studies was to test whether it is possible to perform the thorough CADD experiments with high scientific confidence while using only license-free/academic software and online platforms. The obtained results indicate that a wide range of freely available software and/or academic licenses allow us to carry out meaningful molecular modelling/docking studies.

Nanopore Technology for the Application of Protein Detection.

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

Anomaly Detection-Based Recognition of Near-Native Protein Structures

The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational approaches for determining functionally-relevant structures. The majority of structures generated in silico are not relevant. Discriminating relevant/native protein structures from non-native ones is an outstanding challenge in computational structural biology. Inherently, this is a recognition problem that can be addressed under the umbrella of machine learning. In this paper, based on the premise that near-native structures are effectively anomalies, we build on the concept of anomaly detection in machine learning. We propose methods that automatically select relevant subsets, as well as methods that select a single structure to offer as prediction. Evaluations are carried out on benchmark datasets and demonstrate that the proposed methods advance the state of the art. The presented results motivate further building on and adapting concepts and techniques from machine learning to improve recognition of near-native structures in protein structure prediction.

Discriminative margin-sensitive autoencoder for collective multi-view disease analysis

Medical prediction is always collectively determined based on bioimages collected from different sources or various clinical characterizations described from multiple physiological features. Notably, learning intrinsic structures from multiple heterogeneous features is significant but challenging in multi-view disease understanding. Different from existing methods that separately deal with each single view, this paper proposes a discriminative Margin-Sensitive Autoencoder (MSAE) framework for automated Alzheimer’s disease (AD) diagnosis and accurate protein fold recognition. Generally, our MSAE aims to collaboratively explore the complementary properties of multi-view bioimage features in a semantic-sensitive encoder–decoder paradigm, where the discriminative semantic space is explicitly constructed in a margin-scalable regression model. Specifically, we develop a semantic-sensitive autoencoder, where an encoder projects multi-view visual features into the common semantic-aware latent space, and a decoder is exerted as an additional constraint to reconstruct the respective visual features. In particular, the importance of different views is adaptively weighted by self-adjusting learning scheme, such that their underlying correlations and complementary characteristics across multiple views are simultaneously preserved into the latent common representations. Moreover, a flexible semantic space is formulated by a margin-scalable support vector machine to improve the discriminability of the learning model. Importantly, correntropy induced metric is exploited as a robust regularization measurement to better control outliers for effective classification. A half-quadratic minimization and alternating learning strategy are devised to optimize the resulting framework such that each subproblem exists a closed-form solution in each iterative minimization phase. Extensive experimental results performed on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets show that our MSAE can achieve superior performances for both binary and multi-class classification in AD diagnosis, and evaluations on protein folds demonstrate that our method can achieve very encouraging performance on protein structure recognition, outperforming the state-of-the-art methods.

Precision mapping of N- and O-glycoproteins in viral resistant and susceptible strains of Bombyx mori

Protein glycosylation plays important roles in protein structure, function, and immune recognition, among many other activities. One of the major roles of glycans and glycoconjugates on the cell surface is acting as the receptor for outside pathogens such as viruses. During the initial stage of viral replication, viruses interact with cell membrane receptors, which are in many cases glycoproteins. Identifying such glycoproteins is essential to understanding the mechanisms of viral infection, as well as developing antiviral strategies. Silkworm is an important economic insect as well as a model organism for molecular biology, yet current knowledge on its glycoproteome is far from complete due to both analytical challenges and perceived lack of importance. In this study, we performed a large-scale glycoproteomic survey for two silkworm Bombyx mori strains 306 and NB, which are susceptible and resistant to the baculovirus Bombyx mori nucleopolyhedrovirus (BmNPV), respectively. More than 400 silkworm N- and O- glycoproteins were identified with high confidence, demonstrating that this organism employs extensive glycosylation. We mapped some glycoproteins only to the BmNPV susceptible or resistant strain, underlining the potential relationship between glycosylation and viral susceptibility. We predicted O-glycoproteins and O-glycan compositions for the first time for this organism. The variations in glycan site occupancy, as well as glycan diversity between the two silkworm strains, provide an insight into role of glycosylation in viral recognition and infection processes.

Global aspects of viral glycosylation.

Enveloped viruses encompass some of the most common human pathogens causing infections of different severity, ranging from no or very few symptoms to lethal disease as seen with the viral hemorrhagic fevers. All enveloped viruses possess an envelope membrane derived from the host cell, modified with often heavily glycosylated virally encoded glycoproteins important for infectivity, viral particle formation and immune evasion. While N-linked glycosylation of viral envelope proteins is well characterized with respect to location, structure and site occupancy, information on mucin-type O-glycosylation of these proteins is less comprehensive. Studies on viral glycosylation are often limited to analysis of recombinant proteins that in most cases are produced in cell lines with a glycosylation capacity different from the capacity of the host cells. The glycosylation pattern of the produced recombinant glycoproteins might therefore be different from the pattern on native viral proteins. In this review, we provide a historical perspective on analysis of viral glycosylation, and summarize known roles of glycans in the biology of enveloped human viruses. In addition, we describe how to overcome the analytical limitations by using a global approach based on mass spectrometry to identify viral O-glycosylation in virus-infected cell lysates using the complex enveloped virus herpes simplex virus type 1 as a model. We underscore that glycans often pay important contributions to overall protein structure, function and immune recognition, and that glycans represent a crucial determinant for vaccine design. High throughput analysis of glycosylation on relevant glycoprotein formulations, as well as data compilation and sharing is therefore important to identify consensus glycosylation patterns for translational applications.

Use of designed sequences in protein structure recognition

BackgroundKnowledge of the protein structure is a pre-requisite for improved understanding of molecular function. The gap in the sequence-structure space has increased in the post-genomic era. Grouping related protein sequences into families can aid in narrowing the gap. In the Pfam database, structure description is provided for part or full-length proteins of 7726 families. For the remaining 52% of the families, information on 3-D structure is not yet available. We use the computationally designed sequences that are intermediately related to two protein domain families, which are already known to share the same fold. These strategically designed sequences enable detection of distant relationships and here, we have employed them for the purpose of structure recognition of protein families of yet unknown structure.ResultsWe first measured the success rate of our approach using a dataset of protein families of known fold and achieved a success rate of 88%. Next, for 1392 families of yet unknown structure, we made structural assignments for part/full length of the proteins. Fold association for 423 domains of unknown function (DUFs) are provided as a step towards functional annotation.ConclusionThe results indicate that knowledge-based filling of gaps in protein sequence space is a lucrative approach for structure recognition. Such sequences assist in traversal through protein sequence space and effectively function as ‘linkers’, where natural linkers between distant proteins are unavailable.ReviewersThis article was reviewed by Oliviero Carugo, Christine Orengo and Srikrishna Subramanian.

Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility

This paper deals with the relations among structural, topological, and chemical properties of the E.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coli protein is initially represented according to its known folded 3D shape. This step consists in representing the available E.Coli proteins in terms of graphs. We first analyze those graphs by considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlying both shortest paths and vertex degrees. Results confirm the general architectural principles of proteins. Successively, we focus on the statistical properties of a representation of such graphs in terms of vectors composed of several numerical features, which we extracted from their structural representation. We found that protein size is the main discriminator for the solubility, while however there are other factors that help explaining the solubility degree. We finally analyze such data through a novel one-class classifier, with the aim of discriminating among very and poorly soluble proteins. Results are encouraging and consolidate the potential of pattern recognition techniques when employed to describe complex biological systems.

Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction.

The DOcking decoy-based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance-dependent atom-pair interactions. To optimize the atom-pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand-receptor systems (or just pairs). Thus, a total of 8609 ligand-receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand-receptor systems, 1000 evenly sampled docking decoys with 0-10 Å interface root-mean-square-deviation (iRMSD) were generated with a method used before for protein-protein docking. A neural network-based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel-like energy landscape for the interaction between these hypothetical ligand-receptor systems. Thus, our method hierarchically models the overall funnel-like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom-pair-based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation-dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand-receptor systems and their decoys are freely available at http://agknapp.chemie.fu-berlin.de/doop/.

RAPHAEL: recognition, periodicity and insertion assignment of solenoid protein structures

Repeat proteins form a distinct class of structures where folding is greatly simplified. Several classes have been defined, with solenoid repeats of periodicity between ca. 5 and 40 being the most challenging to detect. Such proteins evolve quickly and their periodicity may be rapidly hidden at sequence level. From a structural point of view, finding solenoids may be complicated by the presence of insertions or multiple domains. To the best of our knowledge, no automated methods are available to characterize solenoid repeats from structure. Here we introduce RAPHAEL, a novel method for the detection of solenoids in protein structures. It reliably solves three problems of increasing difficulty: (1) recognition of solenoid domains, (2) determination of their periodicity and (3) assignment of insertions. RAPHAEL uses a geometric approach mimicking manual classification, producing several numeric parameters that are optimized for maximum performance. The resulting method is very accurate, with 89.5% of solenoid proteins and 97.2% of non-solenoid proteins correctly classified. RAPHAEL periodicities have a Spearman correlation coefficient of 0.877 against the manually established ones. A baseline algorithm for insertion detection in identified solenoids has a Q(2) value of 79.8%, suggesting room for further improvement. RAPHAEL finds 1931 highly confident repeat structures not previously annotated as solenoids in the Protein Data Bank records.

What is the best reference state for designing statistical atomic potentials in protein structure prediction?

Many statistical potentials were developed in last two decades for protein folding and protein structure recognition. The major difference of these potentials is on the selection of reference states to offset sampling bias. However, since these potentials used different databases and parameter cutoffs, it is difficult to judge what the best reference states are by examining the original programs. In this study, we aim to address this issue and evaluate the reference states by a unified database and programming environment. We constructed distance-specific atomic potentials using six widely-used reference states based on 1022 high-resolution protein structures, which are applied to rank modeling in six sets of structure decoys. The reference state on random-walk chain outperforms others in three decoy sets while those using ideal-gas, quasi-chemical approximation and averaging sample stand out in one set separately. Nevertheless, the performance of the potentials relies on the origin of decoy generations and no reference state can clearly outperform others in all decoy sets. Further analysis reveals that the statistical potentials have a contradiction between the universality and pertinence, and optimal reference states should be extracted based on specific application environments and decoy spaces.

Efficient solutions for the far from most string problem

Computational molecular biology has emerged as one of the most exciting interdisciplinary fields. It has currently benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biochemistry, and computer science. In the past few years it has been shown that a large number of molecular biology problems can be formulated as combinatorial optimization problems, including sequence alignment problems, genome rearrangement problems, string selection and comparison problems, and protein structure prediction and recognition. This paper provides a detailed description of string selection and string comparison problems. For finding good-quality solutions of a particular class of string comparison molecular biology problems, known as the far from most string problem, we propose new heuristics, including a Greedy Randomized Adaptive Search Procedure (GRASP) and a Genetic Algorithm (GA). Computational results indicate that these randomized heuristics find better quality solutions compared with results produced by the best state-of-the-art heuristic approach.

Analogy-based protein structure prediction: II. Testing of substitution matrices and pseudopotentials used to align protein sequences with spatial structures

The results of testing the recognition ability of various amino acid substitution matrices and manifold (both extracted from the literature and of our own design) pseudopotentials intended for the recognition of protein structures and sequence-to-structure alignments are described. The numerical estimates of the recognition ability of various substitution matrices and pseudopotentials were obtained for different levels of protein structure similarity. It is demonstrated that substitution matrices work much better than pseudopotentials at a high degree of sequence similarity of spatially similar proteins; however, some pseudopotentials outdo substitution matrices at a low level of sequence similarity between analogous proteins.

Nonlinear Logistic Discrimination Via Regularized Gaussian Basis Expansions

We consider the problem of constructing multi-class classification methods for analyzing data with complex structure. A nonlinear logistic discriminant model is introduced based on Gaussian basis functions constructed by the self-organizing map. In order to select adjusted parameters, we employ model selection criteria derived from information-theoretic and Bayesian approaches. Numerical examples are conducted to investigate the performance of the proposed multi-class discriminant procedure. Our modeling procedure is also applied to protein structure recognition in life science. The results indicate the effectiveness of our strategy in terms of prediction accuracy.

The effect of Hyperglycemia and Oxidative Stress on the Development and Progress of Vascular Complications in Type 2 Diabetes

The effect of Hyperglycemia and Oxidative Stress on the Development and Progress of Vascular Complications in Type 2 Diabetes Oxidative stress is the result of increased production of free radicals, which impair the cell function and cause many pathological conditions and diseases. The development of diabetes, its course and complications are closely associated with an imbalance in pro-antioxidative cell state and change of redox potential. Prolonged exposure to hyperglycemia is currently considered the major factor of the pathogenesis of atherosclerosis in diabetes. Atherosclerosis is the cause of about 80% of mortality in diabetics, and over 75% of all hospitalized diabetic patients have associated cardiovascular complications. Hyperglycemia induces different vascular tissue damage at the cellular level, which potentially accelerates the atherosclerotic processes. The most significant mechanisms responsible for acceleration of atherosclerotic processes in diabetic patients are: a) non-enzymatic protein and lipid glycosylation which interferes with normal function, in the way that it deranges molecular conformation, impairs enzymatic function, reduces the capacity of breakdown and interferes with recognition of protein structures by receptors; b) interaction of glycosylated proteins with their receptors resulting in induction of oxidative stress and pro-inflammatory reactions; c) polyol pathway; d) hexosamine pathway and e) activation of protein kinase C and impaired growth factor expression.

On some optimization problems in molecular biology

In the last two decades, the study of gene structure and function and molecular genetics have become some of the most prominent sub-fields of molecular biology. Computational molecular biology has emerged as one of the most exciting interdisciplinary fields, riding on the success of the ongoing Human Genome Project, which culminated in the 2001 announcement of the complete sequencing of the human genome. The field has currently benefited from concepts and theoretical results obtained by different scientific research communities, including genetics, biochemistry, and computer science. It is only in the past few years that it has been shown that a large number of molecular biology problems can be formulated as combinatorial optimization problems, including sequence alignment problems, genome rearrangement problems, string selection and comparison problems, and protein structure prediction and recognition. This paper provides a detailed description of some among the most interesting molecular biology problems that can be formulated as combinatorial optimization problems and proposes a new heuristic to find improved solutions for a particular class of them, known as the far from most string problem.

Three-dimensional threading approach to protein structure recognition

We describe a gapped structural threading method starting from aligning the query protein sequence to the dominant eigenvector of the structure contact-matrix. A mathematically straightforward iteration scheme provides a self-consistent optimum global sequence-structure alignment. The computational efficiency of this method makes it possible to search whole protein structure databases for structural homology without relying on sequence similarity. The sensitivity and specificity of this method are discussed, along with a case of blind test prediction. This method will provide a versatile tool for protein structure prediction and protein domain recognition complementary to existing tools that rely on sequence homology.

Improved recognition of native-like protein structures using a family of designed sequences.

The goal of the inverse protein folding problem is to identify amino acid sequences that stabilize a given target protein conformation. Methods that attempt to solve this problem have proven useful for protein sequence design. Here we show that the same methods can provide valuable information for protein fold recognition and for ab initio protein structure prediction. We present a measure of the compatibility of a test sequence with a target model structure, based on computational protein design. The model structure is used as input to design a family of low free energy sequences, and these sequences are compared with the test sequence by using a metric in sequence space based on nearest-neighbor connectivity. We find that this measure is able to recognize the native fold of a myoglobin sequence among different globin folds. It is also powerful enough to recognize near-native protein structures among non-native models.

Homogeneous glycopeptides and glycoproteins for biological investigation.

Protein glycosylation is widely recognized as a modulator of protein structure, localization, and cell-cell recognition in multicellular systems. Glycoproteins are typically expressed as mixtures of glycoforms, their oligosaccharides being generated by a template-independent biosynthetic process. Investigation of their function has been greatly assisted by sources of homogeneous material. This review summarizes current efforts to obtain homogeneous glycopeptide and glycoprotein materials by a variety of methods that draw from the techniques of recombinant expression, chemical synthesis, enzymatic transformation, and chemoselective ligation. Some of these techniques remove obstacles to glycoprotein synthesis by installing nonnative linkages and other modifications for facilitated assembly. The end purpose of the described approaches is the production of glycosylated materials for experiments relevant to the biological investigation of glycoproteins, although the strategies presented apply to other posttranslational modifications as well.