protein RNA DNA Predicting Protein Function

Biochemical function (molecular function) What does it do? Kinase??? Ligase??? Page 245

Function based on ligand binding specificity What (who) does it bind ?? Page 245

Function based on biological process What is it good for ?? Amino acid metabolism? Page 245

Function based on cellular location DNARNA Page 245 Where is it active?? Nucleolus ?? Cytoplasm??

Function based on cellular location DNARNA Page 245 Where is the Protein Expressed ?? Brain? Testis? Where it is under expressed??

GO (gene ontology) The GO project is aimed to develop three structured, controlled vocabularies (ontologies) that describe gene products in terms of their associated molecular functions (F) biological processes (P) cellular components (C) Ontology is a description of the concepts and relationships that can exist for an agent or a community of agents

Inferring protein function Bioinformatics approach Based on homology Based on functional characteristics “protein signature”

Homologous proteins  Rule of thumb: Proteins are homologous if 25% identical (length >100)

Proteins with a common evolutionary origin Paralogs - Proteins encoded within a given species that arose from one or more gene duplication events. Orthologs - Proteins from different species that evolved by speciation. Hemoglobin human vs Hemoglobin mouse Hemoglobin human vs Myoglobin human Homologous proteins

COGs Clusters of Orthologous Groups of proteins > Each COG consists of individual orthologous proteins or orthologous sets of paralogs. > Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. DATABASE Refence: Classification of conserved genes according to their homologous relationships. (Koonin et al., NAR)

Inferring protein function based on the protein signature

The Protein Signature Expression Pattern Where it is expressed ? Motif (or fingerprint): a short, conserved region of a protein typically 10 to 20 contiguous amino acid residues Domain: A region of a protein that can adopt a 3 dimensional structure

1 50 ecblc MRLLPLVAAA TAAFLVVACS SPTPPRGVTV VNNFDAKRYL GTWYEIARFD vc MRAIFLILCS V...LLNGCL G..MPESVKP VSDFELNNYL GKWYEVARLD hsrbp ~~~MKWVWAL LLLAAWAAAE RDCRVSSFRV KENFDKARFS GTWYAMAKKD GTWYEI K AV M GXW[YF][EA][IVLM] Protein Motifs Protein motifs can be represented as a consensus or a profile

Searching for Protein Motifs - ProSite a database of protein patterns that can be searched by either regular expression patterns or sequence profiles. - PHI BLAST Searching a specific protein sequence pattern with local alignments surrounding the match. -MEME searching for a common motifs in unaligned sequences

Protein Domains Domains can be considered as building blocks of proteins. Some domains can be found in many proteins with different functions, while others are only found in proteins with a certain function.

DNA Binding domain Zinc-Finger

Varieties of protein domains Page 228 Extending along the length of a protein Occupying a subset of a protein sequence Occurring one or more times

Example of a protein with 2 domains: Methyl CpG binding protein 2 (MeCP2) MBDTRD The protein includes a Methylated DNA Binding Domain (MBD) and a Transcriptional Repression Domain (TRD). MeCP2 is a transcriptional repressor.

Result of an MeCP2 blastp search: A methyl-binding domain shared by several proteins

Are proteins that share only a domain homologous?

Pfam > Database that contains a large collection of multiple sequence alignments of protein domains Based on Profile hidden Markov Models (HMMs).

Profile HMM (Hidden Markov Model) D16D17D18 D19 M16M17M18M19 I16I19I18I17 100% D 0.8 S 0.2 P 0.4 R 0.6 T 1.0 R 0.4 S 0.6 XXXX 50% D R T R D R T S S - - S S P T R D R T R D P T S D - - S D - - R HMM is a probabilistic model of the MSA consisting of a number of interconnected states Match delete insert

Pfam > Database that contains a large collection of multiple sequence alignments of protein domains Based on Profile Hidden Markov Models (HMMs). > The Pfam database is based on two distinct classes of alignments – Seed alignments which are deemed to be accurate and used to produce Pfam A -Alignments derived by automatic clustering of SwissProt, which are less reliable and give rise to Pfam B

Physical properties of proteins

DNA binding domains have relatively high frequency of basic (positive) amino acids M K D P A A L K R A R N T E A A R R S S R A R K L Q R M GCN4 zif268 M E R P Y A C P V E S C D R R F S R S D E L T R H I R I H T myoD S K V N E A F E T L K R C T S S N P N Q R L P K V E I L R N A I R

Transmembrane proteins have a unique hydrophobicity pattern

Knowledge Based Approach IDEA Find the common properties of a protein family (or any group of proteins of interest) which are unique to the group and different from all the other proteins. Generate a model for the group and predict new members of the family which have similar properties.

Knowledge Based Approach Generate a dataset of proteins with a common function (DNA binding protein) Generate a control dataset Calculate the different properties which are characteristic of the protein family you are interested for all the proteins in the data (DNA binding proteins and the non-DNA binding proteins Represent each protein in a set by a vector of calculated features and build a statistical model to split the groups Basic Steps 1. Building a Model

Calculate the properties for a new protein And represent them in a vector Predict whether the tested protein belongs to the family Basic Steps 2. Predicting the function of a new protein

TEST CASE Y14 – A protein sequence translated from an ORF (Open Reading Frame) Obtained from the Drosophila complete Genome >Y14 PQRSVGWILFVTSIHEEAQEDEIQEKFCDYGEIKNIHL NLDRRTGFSKGYALVEYETHKQALAAKEALNGAEIM GQTIQVDWCFVKG G

>Y14 PQRSVGWILFVTSIHEEAQEDEIQEKFCDYGEIKNI HLNLDRRTGFSKGYALVEYETHKQALAAKEALN GAEIMGQTIQVDWCFVKG G Y14 DOES NOT BIND RNA

Projects

Key dates lists of suggested projects published * *You are highly encouraged to choose a project yourself or find a relevant project which can help in your research 29.1 Submission project overview (power point presentation Max 5 slides) -Title -Main question -Major Tools you are planning to use to answer the questions 30.1/31.1 Presentation of project overview 7.3 Poster submission 14.3 Poster presentation Instructions for the final project Introduction to Bioinformatics

2. Planning your research After you have described the main question or questions of your project, you should carefully plan your next steps A. Make sure you understand the problem and read the necessary background to proceed B. formulate your working plan, step by step C. After you have a plan, start from extracting the necessary data and decide on the relevant tools to use at the first step. When running a tool make sure to summarize the results and extract the relevant information you need to answer your question, it is recommended to save the raw data for your records, don't present raw data in your final written project. Your initial results should guide you towards your next steps. D. When you feel you explored all tools you can apply to answer your question you should summarize and get to conclusions. Remember NO is also an answer as long as you are sure it is NO. Also remember this is a course project not only a HW exercise..

3.Summarizing final project in a poster (in pairs) Prepare in PPT poster size cm Title of the project Names and affiliation of the students presenting The poster should include 5 sections : Background should include description of your question (can add figure) Goal and Research Plan: Describe the main objective and the research plan Results (main section) : Present your results in 3-4 figures, describe each figure (figure legends) and give a title to each result Conclusions : summarized in points the conclusions of your project References : List the references of paper/databases/tools used for your project Examples of posters will be presented in class