Building a BioChemformatics Database

Declarative technologies have made great strides in expressivity between SQL and SBVR. SBVR models are more expressive that SQL schemas, but not as imminently executable yet. In this paper, we complete the architecture of a system that can execute SBVR models. We do this by describing how SBVR rules can be transformed into SQL DML so that they can be automatically checked against the database using a standard SQL query. In particular, we describe a formalization of the basic structure of an SQL query which includes aggregate functions, arithmetic operations, grouping, and grouping on condition. We do this while staying within a predicate calculus semantics which can be related to the standard SBVR-LF specification and equip it with a concrete semantics for expressing business rules formally. Our approach to transforming SBVR rules into standard SQL queries is thus generic, and the resulting queries can be readily executed on a relational schema generated from the SBVR model.

Rich information on mutations and their impacts is scattered across scientific texts and literature. Reuse of mutation impact annotations requires grounding mutations to the correct positions on sequences extracted from protein databases as a critical step. This paper presents a generic method for grounding textual mentions of mutation entities to protein sequences, that is based on an OWL-DL ontology driven workflow that integrates text and sequence information in a semantically consistent way. Mutation mentions mined from texts are iteratively mapped onto candidate proteins, and an ontology mining algorithm facilitates their correct grounding to a protein sequence. Using a gold standard corpus of full text articles and corresponding protein sequences we show the proposed method is promising compared to existing approaches.

Referential integrity quality metrics

The paper presents concept and structure of relational database for description of noise sources investigated within the framework of the project “Development of methodologies and means for protection from environmental noise.” The database is used for collecting and storing experimental results of industrial noise measurements that were carried out for the purpose of noise mapping and noise protection planning. The database provides information about equivalent noise level and one-third octave noise spectrum in the near field of sound sources. The access to the database is possible through standard SQL queries, which is suitable for software tools used for noise reduction. The rights for submitting data are granted only to the developers, while all collected data are publicly available via the Internet.

Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized implementation. In this paper we present an evaluation of parallel SQL based data mining on large scale PC cluster. The performance achieved by parallelizing SQL query for mining association rule using 4 processing nodes is even with C based program.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Percolation of annotation errors through hierarchically structured protein sequence databases

MotivationProtein function prediction is important for drug development and disease treatment. Recently, deep learning methods have leveraged protein sequence and structural information, achieving remarkable progress in the field of protein function prediction. However, existing methods ignore the complex multimodal interaction information between sequence and structural features. Since protein sequence and structural information reveal the functional characteristics of proteins from different perspectives, it is challenging to effectively fuse the information from these two modalities to portray protein functions more comprehensively. In addition, current methods have difficulty in effectively capturing long-range dependencies and global contextual information in protein sequences during feature extraction, thus limiting the ability of the model to recognize critical functional residues.ResultsIn this study, we propose a novel framework termed Multi-stage Attention-based Extraction and Fusion model for GO prediction (MAEF-GO) based on a multistage attention mechanism to predict protein functions. MAEF-GO innovatively integrates the graph convolutional network and the graph attention network to extract protein structural features. To address the issue of modeling long-range dependencies within protein sequences, we introduce a frequency-domain attention mechanism capable of extracting global contextual relationships. Additionally, a cross-attention module is implemented to facilitate interactive fusion between protein sequence and structural modalities. Experimental evaluations demonstrate that MAEF-GO achieves superior performance compared to several state-of-the-art baseline models across standard benchmarks. Furthermore, analysis of the cross-attention weight distributions demonstrates MAEF-GO’s interpretability. It can effectively identify critical functional residues of proteins.Availability and implementationThe MAEF-GO source code can be found at https://github.com/nebstudio/MAEF-GO, an archived snapshot of the code used in this study is also available via Zenodo at https://doi.org/10.5281/zenodo.15422392.

Proteins belong to the most important molecules in living organisms. They function as messengers, transporters and catalysts, and provide cells and tissues with structure. The expression profile of proteins is rich in information, which can be used, for example, in diagnosing diseases. Therefore proteomics, the large scale study of proteins, can give us valuable information on molecular pathways and state of health. As a result, proteomics has the potential to transform personalized medicine. Recent advances in mass spectrometry have led to a draft of the human proteome. With current mass spectrometry based techniques, these types of large scale studies remain an enormous effort. Therefore, there is a great need for breakthrough technologies to push proteomics from fundamental research into the clinic. Genomics has benefitted from fast and inexpensive emerging single-molecule techniques. We envision similar effects for single-molecule protein sequencing. In this thesis we present our technology that will allow us to analyze protein expression profiles of samples as small as a single cell with large dynamic range. Back in 2011, when this project was initiated, there was hardly any literature available on this topic. However, the past years more research groups openly shifted their focus to single-molecule protein sequencing. In Chapter 1, we give an overview of recent efforts to establish single-molecule protein sequencing. The foremost reason for the absence of highly sensitive and high-throughput protein sequencing techniques is the complexity of primary protein structures compared to DNA/RNA molecules. Where DNA and RNA consist of four unique building blocks, proteins are built from 20 distinctive amino acids. Independent of the read out method of choice, this requires the detection of 20 distinguishable signals. A non-trivial challenge. Fortunately, a limited number of proteins occur compared to the theoretical number that could be created using 20 unique building blocks. While the exact number of protein coding genes in the human genome is still under debate, the number is believed to be roughly 20,000, resulting in a number of protein products that is finite. This, together with protein databases such as UniProt, allows for an alternative way of identifying protein sequences. Rather than detecting every single element, as is essential for DNA sequencing, we choose to focus on detecting the sequence of a subset of elements.

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

The complete amino acid sequence of the CNBr fragment comprising residues 229-284 of the murine major histocompatibility complex antigen H-2Db has been determined using radiochemical methodology. The sequence was determined by N-terminal sequence analysis of the intact CNBr fragment and by sequence determinations of peptides derived from this fragment by trypsin and staphylococcal V8 protease cleavage. In addition to the amino acid assignments for H-2Db, it was possible to assign the linkage position of the third N-linked glycosyl unit to the asparagine at residue 256. Additional amino acid sequence assignments have also been made for three other CNBr fragments that span residues 99-138, 139-228, and 308-331 of the H-2Db molecule. The total protein sequence information available (222 of 338 residues) agrees in every comparable position with the protein sequence derived from the cDNA clone (pH203) isolated by Reyes and co-workers (1982b), which strongly suggests that this clone encodes H-2Db. Combination of the protein sequence with that deduced from the cDNA clone provides the complete H-2Db protein sequence. Comparison of this sequence with other available protein sequence information for murine class I molecules has revealed protein sequences that may be unique to either K or D region molecules.

BackgroundThe fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.ResultsIn this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.ConclusionsIncorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

This text is addressed to biologists working with DNA or protein data bases, but without enthusiasm for mathematical detail. It is intended to expound the principles of significance theory of homology studies and pattern search without the details of formalism. Complete avoidance is impossible: One cannot explain mathematics without any mathematics.

BackgroundDNA recognition by proteins is one of the most important processes in living systems. Therefore, understanding the recognition process in general, and identifying mutual recognition sites in proteins and DNA in particular, carries great significance. The sequence and structural dependence of DNA-binding sites in proteins has led to the development of successful machine learning methods for their prediction. However, all existing machine learning methods predict DNA-binding sites, irrespective of their target sequence and hence, none of them is helpful in identifying specific protein-DNA contacts. In this work, we formulate the problem of predicting specific DNA-binding sites in terms of contacts between the residue environments of proteins and the identity of a mononucleotide or a dinucleotide step in DNA. The aim of this work is to take a protein sequence or structural features as inputs and predict for each amino acid residue if it binds to DNA at locations identified by one of the four possible mononucleotides or one of the 10 unique dinucleotide steps. Contact predictions are made at various levels of resolution viz. in terms of side chain, backbone and major or minor groove atoms of DNA.ResultsSignificant differences in residue preferences for specific contacts are observed, which combined with other features, lead to promising levels of prediction. In general, PSSM-based predictions, supported by secondary structure and solvent accessibility, achieve a good predictability of ~70–80%, measured by the area under the curve (AUC) of ROC graphs. The major and minor groove contact predictions stood out in terms of their poor predictability from sequences or PSSM, which was very strongly (>20 percentage points) compensated by the addition of secondary structure and solvent accessibility information, revealing a predominant role of local protein structure in the major/minor groove DNA-recognition. Following a detailed analysis of results, a web server to predict mononucleotide and dinucleotide-step contacts using PSSM was developed and made available at or .ConclusionMost residue-nucleotide contacts can be predicted with high accuracy using only sequence and evolutionary information. Major and minor groove contacts, however, depend profoundly on the local structure. Overall, this study takes us a step closer to the ultimate goal of predicting mutual recognition sites in protein and DNA sequences.

Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved. We begin with a review of the basics of machine learning with a focus on applications to protein engineering and protein sequence-function datasets (Chapter 1). We used the entire machine-learning guided engineering paradigm to engineer the algal-derived light-gated channel channelrhodopsin (ChR), which can be used to modulate neuronal activity with light. We build models that discover ChRs with strong plasma membrane localization in mammalian cells (Chapter 2) and unprecedented light sensitivity and photocurrents for optogenetic applications (Chapter 3). Machine learning-guided evolution requires a machine-learning model that learns the relationship between sequence and function. For machine-learning models to learn about protein sequences, protein sequences must be represented as vectors or matrices of numbers. How each protein sequence is represented determines what can be learned. We learn continuous vector encodings of sequences from patterns in unlabeled sequences (Chapter 4). Learned encodings are low-dimensional, do not require alignments, and may improve performance by transferring information in unlabeled sequences to specific prediction tasks. Alternately, we demonstrate an interpretable Gaussian process kernel tailored to biological sequences (Chapter 6). In addition to a model to predict function from sequence, engineering requires a method to use the model to choose sequences for the next round of evolution. Most machine-learning guided engineering strategies assume that selected sequences can be queried directly. However, in directed evolution it is common to design a library of sequences and then sample stochastic batches from that library. We propose a batched stochastic Bayesian optimization algorithm for iteratively designing and screening site-saturation mutagenesis libraries (Chapter 5).