Protein Design & Directed Evolution (In Silico) — Hands-on

Learn how to design, optimize and evolve proteins using purely computational workflows. This module connects fitness landscapes, in-silico mutagenesis, library design and multi-parameter scoring to support directed evolution style campaigns for enzymes, antibodies and other protein products.

Protein Design & Directed Evolution (In Silico)

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Session Index

Session 1 — Protein Design Concepts & Fitness Landscapes Session 2 — In-Silico Mutagenesis & Virtual Libraries Session 3 — Computational Directed Evolution Campaigns Session 4 — Multi-Objective Design, Reporting & Case Studies

Session 1

Fee: Rs 21800

Protein Design Concepts & Fitness Landscapes

Foundations of protein design and directed evolution

design vs selection paradigms sequence space and search local vs global optimization

Fitness landscapes and design objectives

activity, stability, specificity solubility and expression developability style constraints

Structural and sequence contexts for design

active site vs scaffold regions loops, surface and core positions conservation and covariation signals

Session 2

Fee: Rs 25800

In-Silico Mutagenesis & Virtual Libraries

In-silico mutagenesis strategies

single site scans and saturation maps combinatorial positions and libraries restricting to allowed amino acid sets

Building structure and sequence based libraries

focused libraries around hotspots codon based design considerations reducing library size with heuristics

Scoring variants with simple predictors

stability and aggregation scores sequence based functional predictors filtering libraries before heavy calculations

Session 3

Fee: Rs 29800

Computational Directed Evolution Campaigns

Emulating directed evolution cycles in silico

design → screen → select → iterate exploration vs exploitation balance capturing epistatic interactions

Using structural and docking scores in campaigns

docking and interaction metrics stability and biophysical constraints integrating multiple score components

Basic ML assisted directed evolution ideas

surrogate models for fitness prediction iterative model update with new data uncertainty and exploration candidates

Session 4

Fee: Rs 32800

Multi-Objective Design, Reporting & Case Studies

Multi-objective ranking and Pareto style views

activity vs stability trade offs solubility and developability filters shortlisting balanced variants

Case studies: enzymes, binders and industrial proteins

improving catalytic efficiency or selectivity tuning binding affinity and specificity engineering robustness for process conditions

Deliverables: design report, variant panel and documentation

ranked variant tables and scores structure figures and interaction views experiment ready design summary

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Protein Design & Directed Evolution (In Silico) Training | Libraries, Fitness Landscapes & Design Campaigns

Protein Design & Directed Evolution (In Silico) — Hands-on