COMPUTATIONAL BIOMOLECULAR DESIGN

This course is an advanced educational programme that integrates computational physics, mathematical modelling and the biological sciences, in line with the objectives of the Master’s degree programme in Engineering Physics. Specifically, the course equips students with the physical and engineering tools necessary to understand and simulate complex systems at the nanometric and molecular scales. The rigorous approach based on classical, statistical and quantum mechanics is applied here to the in silico design and modelling of macromolecules. This programme prepares future engineering physicists to work in the emerging fields of quantitative biotechnology, nanomedicine, soft matter and the development of biosensors or bio-inspired materials.

In accordance with the Dublin Descriptors, upon completion of the course, the student will be able to:
• Knowledge and understanding: Describe the physical, geometric and energetic principles governing the structure and interactions of proteins. Understand the algorithms underlying molecular mechanics, molecular dynamics and energy minimisation methods.
• Ability to apply knowledge and understanding: Use advanced computational tools and structural databases to visualise, analyse and characterise protein-protein interfaces and complexes. Apply predictive methods and structural design protocols for the in silico development of protein ligands.
• Independence of judgement: Critically evaluate the quality, reliability and limitations of protein structural models generated using predictive methods or computational simulations.
• Communication skills: Present the results of an analysis or a molecular modelling project clearly, rigorously and using the correct technical and scientific terminology, linking microscopic physical properties to macromolecular biological function.
• Learning skills: Demonstrate the methodological skills required to independently explore the relevant scientific literature and to master the use of new software or algorithms in the field of computational biophysics.

To benefit fully from the course, the following basic skills are required:
• Physics and Mathematics: Fundamental concepts of classical mechanics, thermodynamics, statistical mechanics and mathematical or numerical methods for physics.
• Computer Science: No prerequisites are required, but some familiarity with scientific computing environments or basic knowledge of programming/scripting for data processing is recommended.
• Chemistry/Biology: Basic knowledge of general chemistry (chemical bonds, intermolecular interactions) and a basic understanding of biochemistry and protein structure.

For this course, it is strongly recommended to have a mobile computer for use during the ‘hands-on’ sessions.

1. Structural Properties of Proteins.
- Amino Acids: Beads in the String of Proteins.
- Structural Hierarchy of Proteins
- Major Driving Forces for Protein Structural Stability
- Protein Folding

2. Methods for the Determination of Protein Structure.
- X-ray Crystallography
- Nuclear Magnetic Resonance (NMR) Spectroscopy
- Cryo-Electron Microscopy (Cryo-EM)
- Predicting the Protein Structure: a Historical Overview.
- Homology Modeling.
- AlphaFold

3. Introduction to Molecular Modelling.
- What is Molecular Modeling?
- Units of Length and Energy
- Available Databases
- Molecular Graphics

(A) The Protein Database: a Hands-on Approach by Using Molecular Graphics.

(B) Hands-on Training in Computational Tools for Protein Structural Prediction.

4. Structural Aspects of Protein-Protein Interactions.
- Classification of Protein–Protein Interactions
- Principles of Molecular Recognition
- Cooperativity
- Flexibility
- Self-assembly
- Functional Relevance of Protein-Protein Interactions.

5. Energetic Aspects of Protein-Protein Interactions.
- Overview of Protein-Protein Interaction Energetics
- Experimental Methods to Estimate the Binding Affinity
- Impotant Features Influencing the Binding Affinity
- Effects of Mutations on Binding Affinity
- In silico Prediction of Protein–Protein Interaction Affinity and Stability.
- Protein-protein structure prediction: Molecular Docking.

(C) Hands-on Training in Computational Tools for Characterizing the Protein-Protein Interface.

(D) Hands-on Training in Computational Tools for Predicting the Protein-Protein Structural Conformation.

6. Molecular Mechanics.
- Quantum Mechanics (QM) Foundations of Molecular Mechanics (MM)
- Underlying Principles of Molecular Mechanics.
- Force Fields.
- Approaches for Reducing Computational Cost

7. Basics in Energy Minimization Methods
- Optimization Fundamentals
- Basic Algorithmic Components
- First Order Methods.
- Second Order Methods.
- Choice of Method

8. Molecular Dynamics Simulations: Basics
- Why Molecular Dynamics?
- The Verlet Algorithm
- Statistical Ensembles.
- Monitoring Equilibrium.
- Properties as Statistical Averages.
- Analysis of Molecular Dynamics Trajectories.

(E) Hands-on training on Molecular Dynamics Analysis.

(F) Current Methods for In Silico Design of Protein Binders.

Molecular Modelling:
Schlick, Tamar. Molecular Modeling and Simulation: An Interdisciplinary Guide. 2nd ed., Springer, 2010.
Leach, Andrew R. Molecular Modelling: Principles and Applications. 2nd ed., Prentice Hall, 2001.
Protein-Protein Interaction:
Poluri, Krishna Mohan, Khushboo Gulati, and Sharanya Sarkar. Protein-protein interactions. Singapore:: Springer, 2021.
Gromiha, M. Michael, editor. Protein Interactions: Computational Methods, Analysis and Applications. World Scientific Publishing, 2020.

Additional materials – such as peer-reviewed articles and digital resources (software tutorials) – will be shared by the lecturer to support the hands-on sessions and the journal club.

Option 1 (attending students):
The final mark is determined by the following components:
· Practical portfolio (70%): assesses consistent participation and includes the computational projects carried out during the hands-on sessions and a presentation on a scientific article.
· Oral examination (30%): consists of at least three open-ended questions on the computational projects included in the portfolio and/or on the topics covered in class. The following will be assessed equally: the use of appropriate language, the depth of analysis of the topics, and the accuracy of the description.

Option 2 (non-attending students):
Development of an independent computational project (e.g. the design of a stabilised protein variant or a binding peptide), documented in a written report and presented orally with the aid of slides. Following this, the lecturer will ask a few questions relating to the presentation (20% of the final mark). The following will be assessed: the use of appropriate language (maximum 10 marks), the depth of analysis of the topics (maximum 10 marks), and the accuracy of the description (maximum 10 marks).

oral

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

The mark is expressed out of thirty.
The grading scale (the system by which marks are awarded) is generally defined as follows:
- satisfactory knowledge and applied understanding of the syllabus (scores between 18 and 22 points);
- reasonable knowledge and applied understanding of the syllabus (scores between 23 and 26 points);
- good or very good knowledge and applied understanding of the syllabus (scores between 27 and 30);
Honours will be awarded where there is excellent knowledge and applied understanding of the syllabus.

Contents will be presented via lectures.
Hands-on experiences of different computational tools applied to the modelling and design of biomolecules will be carried out.
Practical cases based on different scientific articles will be discussed in class, with the possibility for the students to prepare and to train introductory oral presentations for each case report.
Recordings of lectures will be available for the current academic year only, from the start of teaching activities until the end of the final exam session (fourth sitting); once this period has ended, the recordings will no longer be accessible.

Accessibility, Disability and Inclusion

Accommodation and support services for students with disabilities and students with specific learning impairments: Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). In the case of disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development