From raw biophysical data to a confident development decision.
Quantifying how a protein folds, binds, and responds to ligands is often the bottleneck between a promising molecule and a confident decision. Raw data — ITC thermograms, fluorescence unfolding curves, SPR sensorgrams — rarely tells the whole story on its own.
ThermoPrōt brings 40+ years of expertise in protein thermodynamics, kinetics, and solution NMR to turn complex experimental data into clear mechanistic answers — for teams who need more than a curve fit. They need to understand why their protein behaves the way it does.
Thermodynamic & Kinetic Modeling
When your binding data is more complicated than a single Kd.
Multi-state equilibria, cooperativity, linked protonation events, and allosteric coupling are common in drug-relevant systems — and they break simple 1:1 fitting. We build thermodynamically rigorous models that account for the real complexity of your system, then fit them globally across your full dataset to extract parameters you can trust.
Describe your system →- ▸Reliable Ka, Kd, ΔG, ΔH, ΔS, ΔCp from ITC, SPR, BLI, MST, MP, or NMR data
- ▸Mechanistic models for coupled equilibria, allostery, and multi-site binding
- ▸Global fitting across conditions to resolve otherwise inseparable parameters
- ▸Kinetic rate constants (kon, koff) with correct error propagation
- ▸Guidance on experimental design to maximize the information content of your data
- ▸Analysis that holds up in regulatory submissions and peer review
Biophysical Study Design & Interpretation
Get the right answer the first time.
Choosing the wrong technique — or using the right one incorrectly — produces ambiguous data that delays decisions and wastes resources. We design experiments that yield unambiguous mechanistic answers, and integrate results across complementary methods to build a complete picture of your protein system.
Conflicting results between methods? Let's work through it →- Circular Dichroism (CD) — secondary structure, thermal stability, folding transitions
- NMR Spectroscopy — solution structure, dynamics, binding-site mapping, exchange kinetics
- Isothermal Titration Calorimetry (ITC) — complete thermodynamic binding profiles
- BLI & SPR — label-free kinetics and affinity
- Microscale Thermophoresis (MST) — binding affinity in solution, minimal sample
- Mass Photometry (MP) — solution-phase stoichiometry and oligomeric state
- ▸Technique selection matched to your scientific question and sample constraints
- ▸Protocols optimized to avoid common artifacts and ambiguities
- ▸Cross-validated results that build a mechanistically coherent picture
- ▸Interpretation grounded in thermodynamic first principles, not just software output
- ▸Publication- and IND-ready analysis with clear uncertainty quantification
Bridging Computational & Experimental Science
When your teams aren't speaking the same language.
Computational and experimental biophysics have developed separate vocabularies, tools, and success criteria. A modeling team fluent in fitness landscapes and energy functions may struggle to communicate with an experimental team reporting Kd values and melting temperatures — and vice versa. Those barriers cost time and lead to experiments that don't answer the right questions.
After four decades at the intersection of theory and experiment, ThermoPrōt operates fluently in both worlds — evaluating a prediction in the framework it was generated, designing the experiment best positioned to test it, and translating the result back in terms each team recognizes.
Predictions you need to validate? Tell us about your system →- Computational protein design→experimental validation of folding, binding, thermostability
- ML-predicted fitness landscapes→ITC, SPR & CD thermodynamic ground truth
- MD & energy-landscape models→kinetic and equilibrium observables
- Sequence-based binding predictions→affinity and specificity measurements
- Conformational selection / induced fit→flux-based mechanistic analysis
- ▸Protocols designed from the start to test specific computational hypotheses
- ▸Results reported in units and frameworks computational teams compare directly to their models
- ▸Mechanistic interpretation that explains why a prediction succeeded or failed — not just pass/fail
- ▸A single collaborator who works productively with both the modeling team and the wet lab
Ready to get more from your biophysical data?
Tell us about your protein system and what you're trying to understand. We'll respond within one business day.
Contact Us→