The landscape of computational biology has shifted dramatically in recent years. By 2026, the integration of artificial intelligence into protein engineering has moved beyond theoretical exercises into robust, commercial-grade applications. This guide explores the state-of-the-art tools transforming structure prediction, de novo design, and antibody engineering.<img src="https://ezycourse.b-cdn.net/2858/cmjzwe1llwpo09z9n1oet6pn1.png" containerstyle="position: relative; width: 663px; height: auto; cursor: pointer;" alt="image"><img src="https://ezycourse.b-cdn.net/2858/cmjzw1g8xwz0o6r9n5cb01pg8.png" containerstyle="position: relative; width: 661px; height: auto; cursor: pointer;" alt="image"><h2 id="2ddc2428-15a4-4e2e-8802-b1515d969ee6" data-toc-id="2ddc2428-15a4-4e2e-8802-b1515d969ee6" style="font-size: 24px; font-weight: 600; line-height: 1.2;">The Evolution of Structure Prediction</h2>While AlphaFold2 remains the progenitor of the explosion in biological machine learning (BioML), 2026 has seen the rise of specialised alternatives, particularly given the commercial restrictions on DeepMind's AlphaFold3.<img src="https://ezycourse.b-cdn.net/2858/cmjzw2oahwljqk18z4ipe1cb1.png" containerstyle="position: relative; width: 650px; height: auto; cursor: pointer;" alt="image">For researchers requiring commercial utility and improved accuracy in specific niches, two key tools have emerged as standard-bearers:<ul><li>Chai-1: This tool introduces restraints that allow for the description of binding sites between proteins. It supports substantially increased docking accuracy, making it particularly useful for immunoglobulins (Igs).</li><li>Boltz-2: Offering specific improvements for the physical accuracy of protein-small molecule complexes, this tool is recommended for enzymes or small molecules targeting proteins. It is also capable of predicting binding affinity.</li></ul>For those working with antibody-antigen complexes, AlphaFold3+Rosetta currently represents the highest quality docking protocol available.<h2 id="7039da01-8cb8-42b0-8134-d4ca4a677289" data-toc-id="7039da01-8cb8-42b0-8134-d4ca4a677289" style="font-size: 24px; font-weight: 600; line-height: 1.2;">1) De Novo Design: From Miniproteins to Enzymes</h2>The generation of proteins from "close to nothing" - typically using only a target molecule as input - has seen an explosion in success rates, moving from one in ten thousand to double-digit hit rates.<img src="https://ezycourse.b-cdn.net/2858/cmjzw63jywxpz6o9n31udfoju.png" containerstyle="position: relative; width: 654px; height: auto; cursor: pointer;" alt="image"><h3 id="d2123895-de84-4312-8a78-9f8b829337dc" data-toc-id="d2123895-de84-4312-8a78-9f8b829337dc" style="font-size: 20px; font-weight: 600; line-height: 1.2;">Miniproteins and Antibodies</h3>Miniproteins (50–200 residues) have proven uniquely successful. Tools like BindCraft allow designs to achieve 10–100% hit rates with high affinity (median KD 1–30 nM) after a single round of expression.For antibodies, tools such as Boltzgen, Germinal, and RFantibody are delivering reliable hit rates of up to 10–20% against diverse targets. Notably, mBER leverages structural templates within the ColabDesign framework to produce high-affinity VHH binders.<h3 id="51053d80-b576-47ad-8420-e9067fbaf24f" data-toc-id="51053d80-b576-47ad-8420-e9067fbaf24f" style="font-size: 20px; font-weight: 600; line-height: 1.2;">Enzyme Engineering</h3>The combination of RFdiffusion, LigandMPNN, and PLACER has set a new standard. By growing protein backbones around specified active sites, researchers have produced crystal-validated enzymes with high catalytic efficiencies without needing directed evolution.<h2 id="cfb18f83-66ea-4aa2-8e84-f5b2975086e7" data-toc-id="cfb18f83-66ea-4aa2-8e84-f5b2975086e7" style="font-size: 24px; font-weight: 600; line-height: 1.2;">2) Inverse Folding and Sequence Optimisation</h2>Once a structure is defined, the challenge shifts to "inverse folding"—predicting a sequence that will fold into that structure. The gold standard in this domain remains ProteinMPNN, which replaces residues to improve solubility and stability.<img src="https://ezycourse.b-cdn.net/2858/cmjzw51n2x0d06r9na36ncch6.png" containerstyle="position: relative; width: 648px; height: auto; cursor: pointer;" alt="image">Key variations of this technology include:<ul><li>SolubleMPNN: Specialised for soluble proteins, yielding interesting results in GPCR solubilisation.</li><li>AntiFold &amp; IgDesign: Optimised for antibody-antigen complexes to replace CDRs for improved binding affinity.</li><li>ThermoMPNN: Scores point mutations for their effect on stability (ddG), correlating well with wet lab results.</li></ul><h2 id="43aa46ff-a52c-42a9-87a6-debccc96fe31" data-toc-id="43aa46ff-a52c-42a9-87a6-debccc96fe31" style="font-size: 24px; font-weight: 600; line-height: 1.2;">3) The Rise of Peptide Design</h2>De novo design for linear and cyclic peptides has become significantly more reliable. BindCraft has demonstrated strong hits on targets like MDM2, and RFpeptides has produced high-affinity binders (including sub-nM binders) starting from sequence alone. Additionally, PepMLM offers a sequence-only method for designing linear peptide binders capable of driving targeted degradation.<img src="https://ezycourse.b-cdn.net/2858/cmjzwcenwxbtm8y9n40t45dmi.png" containerstyle="position: relative; width: 736px; height: auto; cursor: pointer;" alt="image"><h2 id="a3c142c0-e404-4498-ae47-326d1b678ac9" data-toc-id="a3c142c0-e404-4498-ae47-326d1b678ac9" style="font-size: 24px; font-weight: 600; line-height: 1.2;">Assessing Developability and Liabilities</h2>Creating a binder is only half the battle; it must also be developable. Modern workflows integrate machine learning to score physical properties:<ul><li>Therapeutic Antibody Profiler (TAP): Compares physics-based features of a candidate against clinical-stage antibodies to flag developability risks.</li><li>BioPhi: Uses statistical methods to predict regions requiring modification for effective humanisation while maintaining affinity.</li><li>DeepImmuno: Predicts the immunogenicity of peptide/HLA combinations.</li></ul><hr><img src="https://ezycourse.b-cdn.net/2858/cmjzwfk44dysxee8z03pp051i.png" containerstyle="position: relative; width: 660px; height: auto; cursor: pointer;" alt="image"><h2 id="e176b99a-a022-4ecb-9370-3685d7716914" data-toc-id="e176b99a-a022-4ecb-9370-3685d7716914" style="font-size: 24px; font-weight: 600; line-height: 1.2;">Frequently Asked Questions</h2>Q: What is the best alternative to AlphaFold3 for commercial use? A: Since AlphaFold3 does not allow commercial use, groups have released reproductions. Chai-1 is a strong alternative for docking accuracy, and Boltz-2 is recommended for protein-small molecule complexes.Q: What is inverse folding in protein design? A: Inverse folding is the process of going from a protein structure to a novel sequence predicted to fold into that structure. Tools like ProteinMPNN are used to generate sequences that are often more soluble and stable than the native sequence.Q: Can AI design enzymes from scratch? A: Yes. Protocols using RFdiffusion to scaffold around active sites, followed by LigandMPNN for sequencing and PLACER for vetting, have produced catalytically active enzymes with high efficiency without directed evolution.Q: How accurate are current de novo antibody design tools? A: Recent advancements have raised success rates from 1 in 10,000 to double digits. Tools like BoltzGen have achieved 60–70% success rates for nanobody binders against novel targets with limited designs tested.Q: What tools are available for peptide binder design? A: Leading tools for peptide design include BindCraft (structure-based filtering), RFpeptides (diffusion-based), and PepMLM (sequence-only design).

AI protein design, featuring commercial AlphaFold alternatives like Chai-1 and Boltz-2. Explore cutting-edge tools for de novo antibody generation, enzyme engineering, and inverse folding

The 2026 Guide to AI for Proteins: Tools, Trends, and Techniques

1) De Novo Design: From Miniproteins to Enzymes

2) Inverse Folding and Sequence Optimisation

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The 2026 Guide to AI for Proteins: Tools, Trends, and Techniques

The 2026 Guide to AI for Proteins: Tools, Trends, and Techniques

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