AI Binding Site Prediction for Biologic Scaffold Design
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Solution Overview
Problem
The complexity of biologics poses challenges for computational tools in accurately predicting their properties and molecular behavior, hindering the development of new drug candidates, particularly in reducing the time and cost of the preclinical pipeline.
Innovation Solution
The development of systems and methods for predicting amino acid sites on target proteins that are likely to participate in binding interactions, using machine learning models such as graph neural networks and convolutional neural networks to guide the design and testing of custom biologic drugs, both experimentally and in-silico.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computational tools are used to predict properties and molecular behavior of biologics, then the cost and time of the preclinical pipeline are reduced, but the accuracy of predictions deteriorates due to the extraordinary complexity of biologics
Solution Approach 1:
The patent segments the complex prediction problem into two distinct stages: (1) binding site prediction using one or more machine learning models to identify likely binding sites on target proteins, and (2) scaffold-pose evaluation only for combinations involving predicted binding sites. This segmentation allows the system to focus computational resources on relevant regions, improving both speed and accuracy by avoiding unnecessary calculations on non-binding regions.
Solution Approach 2:
The patent applies preliminary action by performing binding site prediction before conducting scaffold-pose evaluation. By pre-identifying binding sites on target proteins using machine learning models, the system prepares a filtered set of relevant regions in advance, which then guides the subsequent docking and evaluation processes. This preliminary filtering step significantly reduces the search space and improves overall prediction accuracy.
2Manufacturing precision
If the search space of scaffold-pose combinations is extensively evaluated, then the quality of identified backbones and poses improves, but the computational time and resources increase significantly
Solution Approach 1:
The patent extracts and removes irrelevant portions of the search space by filtering out scaffold-pose combinations that do not involve predicted binding sites. The system takes out only the relevant combinations—those where the scaffold pose positions the biologic in proximity to predicted binding sites on the target protein—and evaluates these in detail using computationally intensive scoring functions, while dismissing other combinations early in the process.
Solution Approach 2:
The patent applies partial action by evaluating not all possible scaffold-pose combinations, but only a partial subset that involves predicted binding sites. This selective evaluation approach performs fewer calculations than exhaustive search would require, yet still achieves high quality results by concentrating resources on the most promising candidates identified through binding site prediction.
Data Source
AI summary
Presented herein are systems and methods for predicting which amino acid sites of a target proteins of interest will be binding sites—for example, locations and/or identifications of particular amino acid sites—that are amenable or likely to participate in binding interactions with other ligands, such as other proteins. These binding site predictions may, for example, be generated for target proteins that are implicated in disease and, accordingly, be targets for potential new biologic drugs. Binding site prediction technologies described herein may thus be used to guide design and/or testing of new and/or custom biologic drugs, either experimentally or in-silico. In this manner, binding site prediction technologies of the present disclosure can facilitate design and/or testing of new biologic drugs, leading to new and improved candidates and/or improving, among other things, developmental efficiency, success rates of clinical trials, and time to market.