Diversity → design → mutations → application → manufacturing

Design proteins for useful chemistry.

Protein Design Space is an independent scientific webspace for enzyme discovery, protein engineering, biocatalysis, green chemistry, computational design, patent intelligence and scalable biomanufacturing.

Explore the workflow Green chemistry examples Open tool directory
PDS
General workflow

From biological diversity to industrial application

A practical protein-engineering workflow connects biological diversity, sequence and structure analysis, variant design, expression, screening, process development and application validation. The aim is to move systematically from discovery to an industrially relevant solution while considering performance, scalability, sustainability and intellectual-property space.

01Diversity & discoveryGenomes, metagenomes, homologues, phylogeny and functional annotation. Search beyond familiar scaffolds before mutating the same protein repeatedly.
02Sequence, structure & mechanismConnect sequence motifs, predicted or experimental structures, active sites, reaction mechanism, channels and conformational constraints.
03Mutation & library designRational substitutions, directed evolution, saturation libraries, second-shell positions, stability networks, interfaces, tunnels and active-learning selection.
04Construct & host selectionChoose expression architecture and host around folding, cofactor needs, soluble expression, secretion, enzyme titre and production economics.
05Expression, production & screeningProduce variants efficiently, purify or use whole-cell/lysate formats, then screen activity, selectivity, stability, solvent tolerance and substrate loading.
06Reaction & catalyst engineeringOptimise pH, temperature, cosolvent, cofactor, donor/acceptor, substrate feed, enzyme loading, immobilisation and catalyst recycling.
07Application & process validationDemonstrate target conversion, product quality, isolation and analytics at proof-of-concept scale, then benchmark gram, kilo and manufacturing-relevant performance.
08TEA, LCA & patent intelligenceTrack cost-of-use, yield, productivity, PMI, E-factor, energy and waste alongside patent claims, sequence scope, legal status and design-around opportunities.
Design principle — “You get what you screen for.” In protein engineering, an evolved protein improves the traits that the assay or selection directly measures and rewards. Design the screen around the property you actually want.
Best protein engineering strategy for your business needs

Choose the engineering mode that fits the problem

The right programme depends on available time and resources, how crowded the technical or IP space is, and whether you already have a workable starting scaffold.

Protein Engineering Strategy quadrant showing Fast & Focused, Targeted Improvement, Smart Exploration and Frontier Discovery across resources/time and known-to-novel design space
A simple strategic view: match the engineering approach to the available resources, maturity of the design space and level of novelty.
Engineering modes: Rational = hypothesis-driven mutations · Semi-rational = focused libraries around informed hotspots · Random / “irrational” = broad mutagenesis where the screen identifies winners.
Let’s collaborate to understand your protein-engineering needs
Broader process redesign

Green chemistry is larger than biocatalysis

The specific pharmaceutical process-redesign examples. These are useful benchmarks for waste reduction even when the redesigned route is not necessarily enzyme-based.

Sitagliptin

Engineered transaminase

Sitagliptin enzyme conversion example

Chiral amine formation through an engineered biocatalytic route.

Montelukast

Engineered ketoreductase

Montelukast enzyme conversion example

Selective reduction as a cleaner route to a key pharmaceutical intermediate.

Pregabalin

Lipase-enabled resolution

Pregabalin enzyme conversion example

Biocatalytic kinetic resolution for a valuable chiral building block.

Protein engineering journey across ten engineering rounds reaching 200-fold improvement
Iterative protein engineering: design, test, learn and improve across multiple rounds.
Founder profile

Dr Vivek Srivastava

Industrial Biotechnology & Biomanufacturing Scientist

PhD molecular and cellular biologist with 19+ years of industrial biotechnology R&D experience spanning industrial enzymes, protein engineering, recombinant proteins and biologics, precision fermentation, downstream processing, analytics, scale-up and commercial translation.

19+years industrial biotechnology R&D
5inventor · international PCT patent families in enzyme engineering
1–100 Lpilot-scale process experience
vivek@proteindesignspace.com LinkedIn ORCID
Selected technical impact

Discovery to manufacturing

Industrial enzyme engineering

Protein and enzyme variant discovery, rational design, directed evolution, screening and global application programs.

Precision biomanufacturing

Fermentation, downstream processing, analytics, process troubleshooting and manufacturability.

Alternative proteins

Built an integrated 1–100 L mycoprotein pilot platform and developed a commercial-scale manufacturing concept.

Biologics & recombinant proteins

Multidisciplinary development across therapeutic proteins, peptides, vaccines, antibodies and microbial enzyme programs.

Open scientific toolbox

Resources organised by the protein-design workflow

A compact, task-based set of resources for discovery, structure, mutation design, visualisation and IP.

Foundations & key reading

Protein engineering concepts from specialist and primary sources

Research overview

Nature — Protein engineering

Current protein-engineering research.

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Biocatalysis scale-up

Integrating protein engineering into biocatalytic process scale-up

Protein engineering for process scale-up.

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Directed evolution

Protein Design by Directed Evolution

Directed-evolution principles.

Open DOI ↗
Semi-rational design

Beyond directed evolution

Semi-rational design strategies.

Open DOI ↗
Enzyme engineering & biocatalysis

Kazlauskas Lab

Enzyme engineering and biocatalysis.

Open resource ↗
Sequence discovery & protein properties

Find diversity before designing mutations

Find homologues and sequence patterns.

Multiple sequence alignment

MAFFT

Fast large-family alignment.

Open MAFFT ↗
Multiple sequence alignment

MUSCLE

Alignment for conservation analysis.

Open MUSCLE ↗
Structure prediction & structural references

Move from sequence to three-dimensional hypotheses

Retrieve or build 3D models.

Predicted structures

AlphaFold Protein Structure Database

Predicted protein structures.

Open AlphaFold DB ↗
Experimental structures

RCSB Protein Data Bank

Experimental protein structures.

Open RCSB PDB ↗
Structure prediction & design

RosettaCommons

Protein modelling and design.

Open Rosetta ↗
Remote homology / templates

HHpred

Remote homology and template search.

Open HHpred ↗
Mutation design & stability engineering

Prioritise positions and mutations

Choose hotspots and stabilising variants.

Tunnels, channels & access pathways

Engineer how substrates and products reach the active site

Analyse substrate and product access.

Molecular visualisation & dynamics

Inspect, compare and communicate structural hypotheses

Inspect structures and trajectories.

Visualisation & MD analysis

VMD

Molecular-dynamics visualisation.

Open VMD ↗
Patent landscape & biological-sequence IP

Connect design space with intellectual property space

Map claims and patented sequences.

Patent & scholarly landscape

Lens.org

Patent and scholarly search.

Open Lens ↗
For beginners · AI-enabled protein engineering

Use AI to narrow the search — experiments still decide

A simple workflow for moving from large sequence and mutation spaces to a small, testable set.

Goal → Define the property you want to improve.
Diversity → Collect homologues or metagenomic candidates.
Consensus → Align sequences and identify conserved or variable sites.
Structure → Map active sites, pockets, interfaces and tunnels.
Hotspots → Prioritise positions most worth mutating.
AI scoring → Rank plausible substitutions before experiments.
AI design → Generate a focused set of plausible variants.
Experiment → Test a small, information-rich library.
Iterate → Use measured results to guide the next round.
Beginner mental model:
Evolutionshows what nature conserves or varies.
Structureshows where residues sit and interact.
AIhelps rank or generate plausible choices.
Experimentsdecide what actually works.
Beginner worked example

How could you choose one mutation from hundreds of possibilities?

This is a simple hypothetical example to show the logic. Imagine your enzyme has Methionine at position 148 (M148), and you want to know whether this position is worth engineering.

1 · Look at diversity You collect about 500 related sequences and align them.
2 · Check consensus At position 148 you find: Leu 61%, Ile 22%, Met 8%, Val 6%. Your protein's Met is relatively uncommon.
3 · Check the structure M148 is not catalytic, but sits near a hydrophobic pocket and does not make an essential interaction.
4 · Ask AI / design tools A sequence model and a structure-aware design tool both rank Leu and Ile as plausible substitutions.
5 · Test, do not assume You build M148L and M148I, then measure activity, stability and expression against the original enzyme.
What did AI actually do? It did not prove that M148L would be better. It helped reduce a huge mutation space to a small, testable set supported by evolution + structure + model predictions. The experiment still decides whether the variant is useful.
For beginners: if any part of the workflow is confusing, or you want help mapping your first protein-engineering project, connect with me.
Tools HotSpot Wizard AlphaFold DB
Design principles

What makes an enzyme industrially useful?

Catalytic activity alone is not enough. A useful industrial enzyme must combine performance, robustness, manufacturability and process compatibility.

catalytic activity selectivity broad substrate scope thermostability pH stability protease resistance solvent tolerance substrate loading cofactor economy expression titre reusability downstream compatibility PMI / E-factor
Patent-aware engineering

Map claims before building libraries

A strong protein-design programme can combine sequence and structure analysis with patent intelligence: identify claimed sequence families and mutation positions, then explore genuinely distinct scaffolds, underused structural regions and alternative process architectures.

Lens patent search PatSeq Finder
Contact

Scientific discussion & collaboration

Protein engineering, enzyme discovery, biocatalysis, green chemistry, patent landscapes, technical reviews and industrial biotechnology.

vivek@proteindesignspace.com