· Ravit Netzer
Predicting Thermostability Directly from Protein Sequence
How Scala's stability model moves from amino acid sequence to a predicted melting temperature shift without requiring solved structure.
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Writing from the Scala Biodesign team on stability prediction, machine learning for proteins, and practical computational biology.
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· Oren Katz
Mutation Landscape Scanning for Enzyme Engineering
Why scanning the full combinatorial variant space first saves weeks of back-and-forth at the synthesis stage.
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· Tamar Levy
How Protein Stability Data Gets Better with More Pilot Cohorts
A look at how prediction accuracy in our early-access program shifted as we incorporated feedback from more diverse protein families.
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· Ravit Netzer
Computational Stability Screening Versus Experimental Screening
Breaking down where computational pre-screening saves the most time and where wet-lab validation is still non-negotiable.
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· Oren Katz
Balancing Solubility and Thermal Stability in Industrial Enzymes
The tension between solubility and thermal stability is one of the trickiest engineering tradeoffs. Here is how our model handles it.
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· Tamar Levy
Epistatic Interactions in Protein Mutation Panels
Single-point mutation predictions miss epistatic effects. We describe how Scala accounts for pairwise and higher-order interactions in variant scoring.
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· Oren Katz
Evaluating Energy Function Models for Stability Prediction
A technical comparison of physics-based energy function approaches and where each one gains or loses accuracy on real stability benchmarks.
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· Ravit Netzer
Case Study: Lipase Stability Engineering with Scala's Early Model
Results from one of our first extended pilot runs: a lipase variant panel where the top-ranked computational candidates matched the wet-lab outcomes.
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· Tamar Levy
Fitness Landscape Methods for Protein Optimization
An overview of how fitness landscape representations help protein engineers navigate large variant spaces toward higher-stability solutions.
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· Oren Katz
Designing a Sequence-to-Stability Prediction Pipeline
The architectural decisions behind Scala's core pipeline: from raw sequence input through feature extraction to a stability delta score output.
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· Ravit Netzer
Introducing the Scala Mutation Scanner
The mutation scanner lets you map stability predictions across your full single-point and combinatorial variant space in a single run.
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· Tamar Levy
Structural Biology Meets Machine Learning at Scala
Why we built Scala's models on a hybrid foundation of physics-informed energy functions and sequence-fitness learned features, and why that matters for accuracy.
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· Ravit Netzer
Why Protein Stability Engineering Matters for Biotech Pipelines
Stability failures are the leading cause of attrition in early enzyme and antibody engineering campaigns. Here is how computational tools change the economics.
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· Oren Katz
Building AI Tools Designed for Protein Engineers
Reflecting on the product decisions that shaped Scala's interface: why we optimized for wet-lab scientists who are not machine learning experts.
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