Protein Informatics Research Articles

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research.

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search.

Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.

TripletProt: Deep Representation Learning of Proteins Based On Siamese Networks.

Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Herein we introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. Representation learning of biological entities which capture essential features can alleviate many of the challenges associated with supervised learning in bioinformatics. The most important distinction of our proposed method is relying on the protein-protein interaction (PPI) network. The computational cost of the generated representations for any potential application is significantly lower than comparable methods since the length of the representations is significantly smaller than that in other approaches. TripletProt offers great potentials for the protein informatics tasks and can be widely applied to similar tasks. We evaluate TripletProt comprehensively in protein functional annotation tasks including sub-cellular localization (14 categories) and gene ontology prediction (more than 2000 classes), which are both challenging multi-class, multi-label classification machine learning problems. We compare the performance of TripletProt with the state-of-the-art approaches including a recurrent language model-based approach (i.e., UniRep), as well as a protein-protein interaction (PPI) network and sequence-based method (i.e., DeepGO). Our TripletProt showed an overall improvement of F1 score in the above mentioned comprehensive functional annotation tasks, solely relying on the PPI network. Availability: The source code and datasets are available at https://github.com/EsmaeilNourani/TripletProt.

Protein Informatics and Vaccine Development: Cancer Case Study.

Clinical translation is a challenging step in the development of cancer vaccines and is found to be related to the complex nature of cancer immunology. Vaccine-based therapeutic strategies for cancer have gained consideration with the advent of vaccine technology as well as an understanding of cancer immunology. Immunotherapy has been widely used in the treatment of cancer. Some promising candidates have been identified to engineer cancer vaccines like Glycoprotein, Mucin 1, MHC protein, etc. It has benefited from the availability of advanced techniques for rapid identification and selection of proteins for precision engineering. Simultaneously, nanovaccines have been focused on target delivery and artificial intelligence-based approaches for personalized vaccine development. The manuscript summarizes the advances in the development of structurebased cancer vaccines along with the status of clinical studies and applications.

Genomic Observations of a Rare/Pathogenic SMAD3 Variant in Loeys⁻Dietz Syndrome 3 Confirmed by Protein Informatics and Structural Investigations.

Background and objectives: Loeys–Dietz syndrome 3, also known as aneurysms-–osteoarthritis syndrome, is an autosomal dominant genetic connective tissue disease caused by pathogenic variants in SMAD3, a transcription factor involved in TGF-β signaling. This disorder is characterized by early-onset osteoarthritis and arterial aneurysms. Common features include scoliosis, uvula abnormalities, striae, and velvety skin. Materials and Methods: The pathogenicity of a variant of uncertain significance in the SMAD3 gene was evaluated (variant c.220C > T) through personalized protein informatics and molecular studies. Results: The case of a 44-year-old male, who was originally presumed to have Marfan syndrome, is presented. An expanded gene panel determined the probable cause to be a variant in SMAD3, c.220C > T (p.R74W). His case was complicated by a history of stroke, but his phenotype was otherwise characteristic for Loeys–Dietz syndrome 3. Conclusion: This case emphasizes the importance of comprehensive genetic testing to evaluate patients for connective tissue disorders, as well as the potential benefit of utilizing a protein informatics platform for the assessment of variant pathogenicity.

Integrative data fusion for comprehensive assessment of a novel CHEK2 variant using combined genomics, imaging, and functional-structural assessments via protein informatics.

The CHEK2 gene and its encoded protein Chk2 have a well-known role in cancers, especially those related to breast cancer mediated through the BRCA1 gene. Additionally Chk2 has a crucial role in DNA repair, apoptosis and the cell cycle, which is why classification of variants of uncertain significance (VUS) is an area highly sought for a better elucidation of the "genomic effect" that results. Because it can often take years before enough clinical data is accumulated, and the costly and expensive functional analysis for individual variants presents a significant hurdle, it is important to identify other tools to help aid in clarifying the impact of specific variants on a protein's function and eventually the patient's health outcome. Here we describe a newly identified CHEK2 variant and analyze with an integrated approach combining genomics (whole exome analysis), clinical study, radiographic imaging, and protein informatics to identify and predict the functional impact of the VUS on the protein's behavior and predicted impact on the related pathways. The observed and analyzed defects in the protein were consistent with the expected clinical effect. Here, we support the use of personalized protein modeling and informatics and further our goal of developing a large-scale protein deposition archive for all protein-level VUS.

InCoB2014: bioinformatics to tackle the data to knowledge challenge. Introduction.

Since 2006, the International Conference on Bioinformatics (InCoB) has been publishing selected papers in BMC Bioinformatics. Papers within the scope of the journal from the 13th InCoB July 31-2 August, 2014 in Sydney, Australia have been compiled in this supplement. These span protein and proteome informatics, structural bioinformatics, software development and bioimaging to pharmacoinformatics and disease informatics, representing the breadth of bioinformatics research in the Asia-Pacific.

Pyridones as NNRTIs against HIV-1 mutants: 3D-QSAR and protein informatics

CoMFA and CoMSIA based 3D-QSAR of HIV-1 RT wild and mutant (K103, Y181C, and Y188L) inhibitory activities of 4-benzyl/benzoyl pyridin-2-ones followed by protein informatics of corresponding non-nucleoside inhibitors' binding pockets from pdbs 2BAN, 3MED, 1JKH, and 2YNF were analysed to discover consensus features of the compounds for broad-spectrum activity. The CoMFA/CoMSIA models indicated that compounds with groups which lend steric-cum-electropositive fields in the vicinity of C5, hydrophobic field in the vicinity of C3 of pyridone region and steric field in aryl region produce broad-spectrum anti-HIV-1 RT activity. Also, a linker rendering electronegative field between pyridone and aryl moieties is common requirement for the activities. The protein informatics showed considerable alteration in residues 181 and 188 characteristics on mutation. Also, mutants' isoelectric points shifted in acidic direction. The study offered fresh avenues for broad-spectrum anti-HIV-1 agents through designing new molecules seeded with groups satisfying common molecular fields and concerns of mutating residues.

Humoral Response Profiling Reveals Pathways to Prostate Cancer Progression

There is considerable evidence for an association between prostate cancer development and inflammation, which results in autoantibody generation against tumor proteins. This immune system-driven amplification of the autoantibody response to intracellular antigens can serve as a sensitive tool to detect low abundance serum proteomic tumor markers for prostate cancer as well as provide insight into biological processes perturbed during cancer development. Here we examine serum humoral responses in a cohort of 34 patients with either benign prostatic hyperplasia or clinically localized prostate cancer (PCa). The experimental strategy couples multidimensional liquid-phase protein fractionation of localized and metastatic prostate cancer tissue lysates to protein microarrays and subsequent mass spectrometry. A supervised learning analysis of the humoral response arrays generated a parsimonious predictor having 78% sensitivity and 75% specificity in distinguishing PCa from benign prostatic hyperplasia in a cohort of American males with elevated prostate-specific antigen. Enrichment analysis of the PCa-specific humoral signature revealed large scale immune reprogramming mediated by STAT transcription factors and the generation of autoantibodies to enzymes involved in nitrogen metabolism. Meta-analysis of independent prostate cancer gene expression data validated the presence of STAT-induced immunomodulation. Concomitant validation of elevated levels of the nitrogen metabolism pathway was obtained by direct measurement of metabolic levels of glutamate and aspartate in prostate cancer tissues. Thus, in addition to functioning as markers in prostate cancer detection, humoral response profiles can serve as powerful tools revealing pathway dysregulation that might otherwise be suppressed by the complexity of the cancer proteome.

プロテインインフォマティクスによるタンパク質の機能推定

According to the progress of structural genomics projects, we have a large number of protein structures without any annotations. For the further understandings of biological systems from the view point of genome science, methods are now required to get some clues to identify the function of these“hypothetical”proteins. Here we would like to introduce several recent approaches based on protein informatics for the problem.

PIRSF: family classification system at the Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The system adopts a network structure for protein classification from superfamily to subfamily levels. Protein family members are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture). The PIRSF database consists of two data sets, preliminary clusters and curated families. The curated families include family name, protein membership, parent-child relationship, domain architecture, and optional description and bibliography. PIRSF is accessible from the website at http://pir.georgetown.edu/pirsf/ for report retrieval and sequence classification. The report presents family annotation, membership statistics, cross-references to other databases, graphical display of domain architecture, and links to multiple sequence alignments and phylogenetic trees for curated families. PIRSF can be utilized to analyze phylogenetic profiles, to reveal functional convergence and divergence, and to identify interesting relationships between homeomorphic families, domains and structural classes.

Collection, mapping, and annotation of over 28,000 cDNA clones from japonica rice.

We collected and completely sequenced 28,469 full-length complementary DNA clones from Oryza sativa L. ssp. japonica cv. Nipponbare. Through homology searches of publicly available sequence data, we assigned tentative protein functions to 21,596 clones (75.86%). Mapping of the cDNA clones to genomic DNA revealed that there are 19,000 to 20,500 transcription units in the rice genome. Protein informatics analysis against the InterPro database revealed the existence of proteins presented in rice but not in Arabidopsis. Sixty-four percent of our cDNAs are homologous to Arabidopsis proteins.

Protein family classification and functional annotation

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.

The Protein Information Resource.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.

The Human Proteome Organization: a mission to advance proteome knowledge.

With the completion of the genome project, it has been viewed that the proteome is the next major challenge. With heightened expectations comes the need for organizing public effort. The Human Proteome Organization (HUPO) was founded to regroup scientists in the public and private sectors engaged worldwide in various aspects of proteomics. Over the past year, HUPO has strived to develop a mission for itself and to begin the process of organizing its activities. The mission of HUPO as it is currently defined is to: 1) consolidate national and regional proteome organizations into a worldwide organization; 2) engage in scientific and educational activities to encourage the spread of proteomics technologies and disseminate knowledge pertaining to the human proteome and that of model organisms; and 3) assist in the coordination of public proteome initiatives. HUPO consists of three major geographic spheres that include Europe, Asia/ Oceania, and the Americas. In addition to current national proteome organizations affiliated with HUPO, plans are underway to assist in the formation of additional national proteome organizations and to expand affiliations with HUPO of other organizations that have a significant proteomics component such as biochemistry and cell biology societies.

An integrated approach to the analysis and modeling of protein sequences and structures. I. Protein structural alignment and a quantitative measure for protein structural distance

We have devised and implemented in PrISM (protein informatics system for modeling) a new measure of protein structural relationships, the protein structural distance (PSD). The PSD is designed to describe relationships between protein structures in quantitative rather than descriptive terms and is applicable both when two structures are very similar, and when they are very different. It is calculated with a structural alignment procedure that uses double dynamic programming to align secondary structure elements and an iterative rigid body superposition that minimizes the root-mean-square deviation of Cα atoms. The alignment algorithm, as implemented on a modest workstation, is computationally efficient, allowing for large-scale structural comparisons. PSD scores for more than one and a half million pairs of proteins were calculated and compared to the discrete classification of proteins in the SCOP database. The PSD scores, which were obtained automatically, are in large part consistent with the manually derived classifications in SCOP. Discrepancies do arise, however, due, in part, to the fact that SCOP uses criteria other than structural similarity to derive classifications while the PrISM procedure is exclusively structure based. Analysis of PSD scores suggests that there is a continuous aspect of protein conformation space, even though various classification schemes are extremely useful. The use of a continuous measure for structural distance between all pairs of proteins allows us, as described in the two accompanying papers to derive sequence/structure relationships in a more quantitative way than has previously been possible. An important strength of the approach implemented in PrISM is its ability to address many different kinds of queries interactively, making its structural comparison procedure a convenient computational tool that complements structural classification databases such as SCOP and CATH.

Sequence to structure alignment in comparative modeling using PrISM

PrISM (Protein Informatics System for Modeling) is a protein analysis and modeling system in which informatics, alignment, modeling, and assessment modules are integrated in a computational environment where protein analysis and modeling protocols can be designed and assessed interactively. It can then be used automatically and repetitively in response to a variety of protein analysis and modeling problems. PrISM was used to predict a single model for each of the 43 targets in the CASP3 experiment. In this paper, we present results for 13 target sequences, which we consider to be comparative modeling targets with clearly related structural templates. We emphasize the problem of aligning a target sequence to a template structure with various alignment methods. When more than one alignment method and/or parameter set are applied, the final alignment is chosen on the basis of a model ranking system also used in PrISM's fold recognition module. Advanced sequence–template alignment procedures in PrISM are useful in some cases when standard pairwise dynamic programming algorithm fail to make any reasonable global alignment. The same procedures, however, failed in other cases, corresponding to remotely related query-template pairs that involved extensive insertions and deletions. Proteins Suppl 1999;3:66–72. © 1999 Wiley-Liss, Inc.