CDD is a protein annotation resource that consists of a collection of annotated multiple sequence alignment models for ancient domains and full-length proteins. These are available as position-specific score matrices (PSSMs) for fast identification of conserved domains in protein sequences via RPS-BLAST. CDD content includes NCBI-curated domain models, which use 3D-structure information to explicitly define domain boundaries and provide insights into sequence/structure/function relationships, as well as domain models imported from a number of external source databases.

Protein Sequence Analysis Tool Market Overview: The global protein sequence analysis tool market is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The increasing demand for advanced tools for protein analysis in academic research, clinical diagnosis, and biopharmaceutical applications is driving the market growth. Additionally, advancements in next-generation sequencing technologies and the growing adoption of artificial intelligence and machine learning techniques in protein analysis are further contributing to market expansion. Key Market Drivers and Trends: The key drivers of the protein sequence analysis tool market include the rising prevalence of chronic diseases, the need for personalized medicine, and the increasing use of high-throughput sequencing technologies. Trends such as the adoption of cloud-based analysis platforms, the integration of bioinformatics, and the emergence of novel methods for protein identification and characterization are also influencing market growth. However, factors such as limited software and hardware accessibility, data privacy concerns, and regulatory challenges may restrain the market to some extent. The global protein sequence analysis tool market is poised for substantial growth in the coming years, driven by the advancements in proteomics and genomics research. This tool enables researchers to analyze protein sequences and uncover their structure, function, and interactions.

The CATH-Gene3D database describes protein families and domain architectures in complete genomes. Protein families are formed using a Markov clustering algorithm, followed by multi-linkage clustering according to sequence identity. Mapping of predicted structure and sequence domains is undertaken using hidden Markov models libraries representing CATH and Pfam domains. CATH-Gene3D is based at University College, London, UK.

The global Protein Sequence Analysis Tool market is experiencing robust growth, driven by the increasing demand for advanced biopharmaceutical research and clinical diagnostics. The market, estimated at $2.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. Firstly, the burgeoning biopharmaceutical industry relies heavily on protein sequence analysis for drug discovery and development, leading to a substantial demand for sophisticated software and services. Secondly, advancements in next-generation sequencing technologies are generating massive amounts of protein sequence data, requiring robust analytical tools for efficient processing and interpretation. Thirdly, the growing prevalence of chronic diseases is driving increased investment in clinical diagnostics, creating a significant market opportunity for protein sequence analysis tools that enhance disease understanding and facilitate personalized medicine. The market is segmented by application (Academic Research, Clinical Diagnosis, Biopharmaceuticals, Others) and type (Software, Services), with the biopharmaceutical application segment and software segment currently dominating. However, several restraining factors are also at play. The high cost of sophisticated software and services can limit accessibility, particularly for smaller research institutions and laboratories in developing countries. Furthermore, the complexity of analyzing large datasets and the need for specialized expertise can pose challenges for some users. Despite these limitations, ongoing technological advancements, including the development of user-friendly interfaces and cloud-based solutions, are expected to mitigate these challenges and further stimulate market growth. The competitive landscape is marked by the presence of established players like Waters Corp., Agilent Technologies, and Thermo Fisher Scientific, as well as emerging innovative companies offering specialized solutions. Geographical distribution of the market is broad, with North America and Europe currently holding the largest market shares, followed by Asia-Pacific which is expected to witness rapid growth driven by increasing investments in life sciences research across countries like China and India.

PIRSF protein classification system is a network with multiple levels of sequence diversity from superfamilies to subfamilies that reflects the evolutionary relationship of full-length proteins and domains. PIRSF is based at the Protein Information Resource, Georgetown University Medical Centre, Washington DC, US.

PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family a new sequence belongs. PROSITE is based at the Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland.

Code based on molecular structure of amino acid side chains by Chaudhuri et al. [18].

Molecular phylogenetic research has relied on the analysis of the coding sequences by genes or of the amino acid sequences by the encoded proteins. Enumerating the numbers of mismatches, being indicators of mutation, has been central to pertinent algorithms. However, the constraining forces of selection and self-organization have been unaccounted for in conventional approaches, possibly causing available models to fall short of representing the actual evolutionary history. Specific amino acids possess quantifiable characteristics that enable the conversion from “words” (strings of letters denoting amino acids or bases) to “waves” (strings of quantitative values representing the physico-chemical properties) or to matrices (coordinates representing the positions in a comprehensive property space). The application of such numerical representations to evolutionary analysis takes into account not only mutation but also selection/self-organization as influences that drive speciation, because selective pressures favor certain mutations over others, and this predilection is represented in the characteristics of the incorporated amino acids (it is not born out solely by the mismatches). Besides being more discriminating sources for treegenerating algorithms than match/mismatch, the number strings can be examined for overall similarity with average mutual information, autocorrelation, and fractal dimension. Bivariate wavelet analysis aids in distinguishing hypermutable versus conserved domains of the protein. Further, the matrix depiction is readily subjected to comparisons of distances (Euclidean distance, Frobenius distance), and it allows the generation of heat maps or graphs. These analytical algorithms have been automated in R and are applicable to various processes that are describable in matrix format.

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

Service providing functional analysis of proteins by classifying them into families and predicting domains and important sites. They combine protein signatures from a number of member databases into a single searchable resource, capitalizing on their individual strengths to produce a powerful integrated database and diagnostic tool. This integrated database of predictive protein signatures is used for the classification and automatic annotation of proteins and genomes. InterPro classifies sequences at superfamily, family and subfamily levels, predicting the occurrence of functional domains, repeats and important sites. InterPro adds in-depth annotation, including GO terms, to the protein signatures. You can access the data programmatically, via Web Services. The member databases use a number of approaches: # ProDom: provider of sequence-clusters built from UniProtKB using PSI-BLAST. # PROSITE patterns: provider of simple regular expressions. # PROSITE and HAMAP profiles: provide sequence matrices. # PRINTS provider of fingerprints, which are groups of aligned, un-weighted Position Specific Sequence Matrices (PSSMs). # PANTHER, PIRSF, Pfam, SMART, TIGRFAMs, Gene3D and SUPERFAMILY: are providers of hidden Markov models (HMMs). Your contributions are welcome. You are encouraged to use the ''''Add your annotation'''' button on InterPro entry pages to suggest updated or improved annotation for individual InterPro entries.

GTOP is a database consists of data analyses of proteins identified by various genome projects. This database mainly uses sequence homology analyses and features extensive utilization of information on three-dimensional structures. GTOP is built by the Laboratory of Gene-Product Informatics at the National Institute of Genetics. This research is supported by the Japan Science and Technology Corporation and Grants-in-Aid for Scientific Research (Genomes in category C) from the Ministry of Education, Science, Sports and Culture of Japan. We use the following methods: Prediction of 3D structure Sequence homology search of PDB, using REVERSE PSI-BLAST. Functional predictions (family classifications) Sequence homology search of Swiss-Prot, a well-annotated sequence database, with the use of BLAST. Other analytical methods We are also carrying out the following analyses: Motif Analysis(PROSITE) Family classification(Pfam) Prediction of transmembrane helix domains(SOSUI) Prediction of coiled-coil regions(Multicoil) Repetitive sequence analysis(RepAlign)

ProRepeat is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats' corresponding codons.

The size of the Protein Sequencing Market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 3.60% during the forecast period.Protein sequencing is the backbone of biochemical and molecular biology, which determines the sequence of amino acids in a protein. It is thus paramount in understanding protein structure, function, as well as interactions with other molecules. Protein sequencing is applied in almost every field of science. This helps one to understand the genetic basis of the diseases, identify disease-causing mutations, and its targeted therapies in medical research. Through this method, by sequencing proteins which contribute to such disease processes, one could find vital clues on how they operate and what drugs might target them. This is the reason why protein sequencing becomes more significant in drug discovery, not only in the pursuit of new drugs but in the optimization of existing ones as well. Indeed, knowledge of the structure and function of the protein becomes crucial for the rational design of molecules that are to act on a target by virtue of their interaction with that target, culminating in better drugs that are much more selective. In biotechnology, it is crucial for the sequencing of proteins in the characterization and engineering of proteins having specific functional properties. Amino acid sequences can thus be modified to improve stability, increase activity or specificity at the protein level. This technology has applications in enzyme engineering, antibody production, and the development of biomaterials. Recent developments include: December 2022: Quantum-Si launched the 'Platinum' tech for benchtop protein sequencing. Platinum provides next-generation, single-molecule protein sequencing. The technology can be used for proteomic research to advance drug discovery and health diagnostics., January 2022: Seer launched a next-generation proteomics research platform. Seer has launched its system for categorizing the tens of thousands of proteins within the human body that drive the biological functions of life and disease. The hardware is likely to aim to do for proteomics what next-generation sequencing has done for the field of DNA research by offering deep and rapid analyses on a much wider scale.. Key drivers for this market are: Rising Focus on Target based Drug Development, Increasing Funding for Proteomic Research. Potential restraints include: High Cost of Protein Sequencing Equipment. Notable trends are: Protein Engineering Studies are Expected to Witness a Growth in the Protein Sequencing Market Over the Forecast Period.

Inference of deep phylogenies has almost exclusively used protein rather than DNA sequences, based on the perception that protein sequences are less prone to homoplasy and saturation or to issues of compositional heterogeneity than DNA sequences. Here we analyze a model of codon evolution under an idealized genetic code and demonstrate that those perceptions may be misconceptions. We conduct a simulation study to assess the utility of protein versus DNA sequences for inferring deep phylogenies, with protein-coding data generated under models of heterogeneous substitution processes across sites in the sequence and among lineages on the tree, and then analyzed using nucleotide, amino acid, and codon models. Analysis of DNA sequences under nucleotide-substitution models (possibly with the third codon positions excluded) recovered the correct tree at least as often as analysis of the corresponding protein sequences under modern amino acid models. We also applied the different data-analysis ...

This Excel workbook allows the analysis of sample or imported protein sequences. The model can analyze protein sequences up to 500 amino acids long.

Introduction into computational approaches in phylogeny and protein modeling based on coronavirus SARS-CoV-2 (caused COVID-19 pandemic). Two self-guided tutorials for standard lab classes of 2.5 hours. Level: undergraduate students majoring in biology.

Protein Sequencing Market valuation is estimated to reach USD 2.39 Bn in 2025 and is anticipated to grow to USD 5.32 Bn by 2032 with steady CAGR of 12.1%.

Multiple sequence alignment used in Fig. 1a. (TXT 43 kb)

Identifies the conserved domains present in a protein sequence. CD-Search uses RPS-BLAST (Reverse Position-Specific BLAST) to compare a query sequence against position-specific score matrices that have been prepared from conserved domain alignments present in the Conserved Domain Database (CDD).