Authors:

Maria Victoria Liendro

Date Created:

Not specified

Course Title:

Professor:

Not specified

About Paper:

Proteins are sequences of amino acids that have evolved to carry out diverse functions. For example, proteins can make crops more resistant to pests, enhance biofuels production, or aid in developing targeted drugs. Designing custom proteins for specific applications is promising but challenging: with 20 amino acids and sequences often exceeding 1,000 residues, the possible combinations are countless, but only a fraction are viable, resulting in the vast majority of time being spent on evaluating unviable solutions. To address this, we developed the ``Protein Spam Filter'', integrated into the Evolutionary Algorithms for Simulating Molecular Evolution (EASME) toolkit, to predict whether a new protein is structurally functional, enhancing the efficiency of automated protein design. The Protein Spam Filter comprises two key components: (1) the `correctness' module, which distinguishes real proteins from non-functional sequences using an evolved neural network trained on the UniProt database (~58,000 real proteins), and thousands of artificially created `bad' sequences (including random, reversed, and highly mutated sequences), achieving over 95% accuracy; and (2) the `aggregation' module, which predicts protein aggregation propensity -- a phenomenon linked to diseases such as Alzheimer's and type 2 diabetes, as well as pharmaceutical product quality and functionality. This module was trained on the A3D-MODB database (~500,000 predictions across 11 model species) and achieved up to 86% accuracy. Together, these components help ensure that newly- designed proteins are structurally stable and viable, fostering progress in protein customization with applications in biotechnology and medicine.

Source:

Auburn University / Samuel Ginn College of Engineering / 2025

Topics:

No topics listed

Co-authors:

Maria Victoria Liendro