Cipher: 2205
Nomenclature: Structural bioinformatics of proteins and bioactive molecules
Study programme: Molecular biosciences
Module: Bioinformatics
Case holder:

Assoc. Prof. Dr. Sc. Bono Lucic, zn. adviser
Institution of the case holder:

Ruđer Boskovic Institute
Contributors - Contractors:
Subject status: Electoral College
The year in which the case is submitted: Year I
The semester in which the case is submitted: Semester II
Subject objective:

Adopt the general principles of modeling the properties and activities of proteins and bioactive molecules as well as the knowledge necessary for monitoring and critically analyzing scientific results in this field. Independent students to carry out modeling properties or activities of a selected set of bioactive molecules or proteins.
Case contents:

Overview of methods for modeling the properties and stucco of proteins and bioactive molecules. Algorithms for choosing models. Selection of a representative set of poteins and bioactive molecules for analysis, and inclusion of similarities. Describing the structural peculiarities of a set of proteins and bioactive molecules using molecular structural descriptors. Model quality parameters with regard to the implementation of the learning process and the verification of the accuracy of the model's predictions. Parameters of the accuracy of the model in the process of adaptation, cross-checking, and external verification. An overview of the accuracy of existing methods in predicting the pharmacological properties of bioactive molecules and properties and protein stucture. Modeling of secondary structure, protein bending and unfolding constants, stuctural class, secondary structure content, cell protein position, and other global protein properties. Modeling the 3D structure based on similarities. Modeling the structure and topology of membrane proteins. Overview and use of databases and modeling servers in structural bioinformatics. Modeling physicochemical properties (solubility, lipophilicity, transport, absorption), biological activity and toxicity of bioactive molecules.
Learning outcomes: competences, knowledge, skills that the subject develops:

1. Establish the basic principles of modelling the properties and activity of proteins and bioactive molecules.
2. Use the methodology to carry out modelling of the dataset collected by working in the laboratory and/or from the literature.
3. Critically judge the qualities and limitations of theoretical bioinformatic methods and models.
4. Apply in your work the most important methods available over the Internet (in the form of computer programs or servers).
ECTS Credits 6
Lectures 5
Seminars (IS) 5
Exercises (E) 20
Altogether 30
The way of teaching and acquiring knowledge:
Ways of teaching and acquiring knowledge: (notes)

Regular attendance with possible justified absence of up to 3 hours of classes. The student is obliged to hold a smaller seminar based on the literature review. At the end of the lecture, the student should conduct an analysis of the selected set of proteins or bioactive molecules with the help of the subject carrier.
Monitoring and evaluating students (mark in fat printing only relevant categories) Attendance, Mandatory seminar work, Exercise or case study
Rating method: Oral exam, Case Review, Project
Mandatory literature:

1. D. J. Livingstone: "Data Analysis for Chemists – Application to QSAR and Chemical Product Design" Oxford Univerity Press, UK, 1995.
2. D. Juretić: "Bioenergetics – the work of membrane proteins" Informant. Zagreb, 1997.

The most important scientific and papers in the field of stucco bioinformatics and modeling the properties of proteins and bioactive molecules:
3. S. F. Altschul; W. Gish; W. Miller; E.W. Myers; D. J. Lipman, Basic local alignment search tool, J. Mol. Biol. 215 (1990) 403-410.
4. J. Kyte; R. F. Doolittle, A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157 (1982) 105-132.
5. G. von Heijne, Membrane-protein structure prediction – hydrophobicity analysis and the positive-inside rule. J. Mol. Biol. 225 (1992) 487-494.
6.B. Rost; C. Sander, Prediction of protein secondary structure at better than 70-percent accuracy, J. Mol. Biol. 232 (1993) 584-599.
7. A. R. Katritzky; V.S. Lobanov; M. Karelson, QSPR: The Correlation and quantitative prediction of chemical and physical properties from structure, Chem. Soc. Rev. 24 (1995) 279-287.
Supplementary (recommended) literature:

1. 1. C. Gibas; P. Jambeck: "Developing Bioinformatics Computer Skills" O'Reilly and Assoc. Inc., Sebastopol, CA, USA, 2001.
2.K. P. Burnham; D. R. Anderson: "Model Selection and Multi-Model Inference : A Practical Information-Theoretic Approach (2Rev ed)" Springer, Berlin, 2004.
3. W. Kabsch; C. Sander, Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features, Biopolymers 22 (1983) 2577-2637.
4. P. Y. Chou; G. D. Fasman, Prediction of the secondary structure of proteins from their amino acid sequence, Adv. Enzymol. 47 (1978) 45-148.
5. A. Jokes; T. L. Blundell, Comparative protein modeling by satisfaction of spatial restrains, J Mol Biol. 234 (1993) 779-815.
6.C. A. Lipinski; F. Lombardo; B.W. Doming; P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Comrade. It's deliv. 23 (1997) 3-25.
7.C. Hansch; T. J. Fujita, ρ-σ-π Analysis, A method for the correlation of biological activity and chemical structure, J. Am. Soc. 86 (1964) 1616-1626.
8. S.M. Free Jr.; J. W. Wilson, A mathematical contribution to structure activity studies, J. Med. Chem. 7 (1964) 395-399.
How to monitor the quality and performance performance (evaluation):

Questionnaires after 10 hours and at the end of lectures/exercises. Discussion with students and colleagues. Tracking the advances of every student. The success of the course will be evaluated annually by the joint expert committee of the Rudjer Boskovic Institute, the University of Dubrovnik and the University of Osijek.