Eva Smorodina

• Bashour, Habib; Smorodina, Eva; Pariset, Matteo; Zhong, Jahn; Akbar, Rahmad & Chernigovskaia, Maria [Vis alle 15 forfattere av denne artikkelen] (2024). Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability. Communications Biology. 7(1), s. 1–25. doi: 10.1038/s42003-024-06561-3. Fulltekst i vitenarkiv Vis sammendrag

  Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.

• Riccabona, Jakob R.; Spoendlin, Fabian C.; Fischer, Anna-Lena M.; Loeffler, Johannes R.; Quoika, Patrick K. & Jenkins, Timothy P. [Vis alle 14 forfattere av denne artikkelen] (2024). Assessing AF2’s ability to predict structural ensembles of proteins. Structure. ISSN 0969-2126. 32(11), s. 2147–2159. doi: 10.1016/j.str.2024.09.001. Fulltekst i vitenarkiv
• Smorodina, Eva; Tao, Fei; Qing, Rui; Yang, Steve & Zhang, Shuguang (2024). Computational engineering of water-soluble human potassium ion channels through QTY transformation. Scientific Reports. 14:28159(1), s. 1–24. doi: 10.1038/s41598-024-76603-7. Fulltekst i vitenarkiv Vis sammendrag

  Transmembrane potassium ion channels are crucial for ion transport, metabolism, and signaling, and serve as promising targets for anti-cancer therapies. However, their hydrophobic transmembrane nature requires detergents, posing a major bottleneck for experimental handling. In this paper, we present a structural bioinformatics study of six experimentally determined and twelve modeled potassium channel structures, in which hydrophobic amino acids (L, I/V, and F) were systematically replaced with neutral hydrophilic ones (Q, T, and Y), making the proteins more water-soluble. QTY (computationally predicted) and native (experimental and repredicted) variants show remarkable structural similarity (RMSD: ~0.50 Å – ~2.14 Å) despite significant sequence differences. QTY variants, both rigid and refined with MD simulations, maintain comparable to native variants stability, solvent-accessible surface area (SASA), and ionic, aromatic, and van der Waals interactions but differ in the grand average of hydropathy (GRAVY), solubility, and hydrophobic contacts. Overall, our study presents a computational approach for designing hydrophilic potassium ion channels while maintaining the native global structure that could potentially simplify their practical use by eliminating the need for detergents.

• Pan, Emily; Tao, Fei; Smorodina, Eva & Zhang, Shuguang (2024). Structural bioinformatics studies of six human ABC transporters and their AlphaFold2-predicted water-soluble QTY variants. QRB Discovery. 5(e1), s. 1–10. doi: 10.1017/qrd.2024.2. Fulltekst i vitenarkiv Vis sammendrag

  Human ATP-binding cassette (ABC) transporters are one of the largest families of membrane proteins and perform diverse functions. Many of them are associated with multidrug resistance that often results in cancer treatment with poor outcomes. Here, we present the structural bioinformatics study of six human ABC membrane transporters with experimentally determined cryo-electron microscopy (CryoEM) structures including ABCB7, ABCC8, ABCD1, ABCD4, ABCG1, ABCG5, and their AlphaFold2-predicted water-soluble QTY variants. In the native structures, there are hydrophobic amino acids such as leucine (L), isoleucine (I), valine (V), and phenylalanine (F) in the transmembrane alpha helices. These hydrophobic amino acids are systematically replaced by hydrophilic amino acids glutamine (Q), threonine (T), and tyrosine (Y). Therefore, these QTY variants become water soluble. We also present the superposed structures of native ABC transporters and their water-soluble QTY variants. The superposed structures show remarkable similarity with root mean square deviations between 1.064 and 3.413 Å despite significant (41.90–54.33%) changes to the protein sequence of the transmembrane domains. We also show the differences in hydrophobicity patches between the native ABC transporters and their QTY variants. We explain the rationale behind why the QTY membrane protein variants become water soluble. Our structural bioinformatics studies provide insight into the differences between the hydrophobic helices and hydrophilic helices and will likely further stimulate designs of water-soluble multispan transmembrane proteins and other aggregated proteins. The water-soluble ABC transporters may be useful as soluble antigens to generate therapeutic monoclonal antibodies for combating multidrug resistance in clinics.

• Karagöl, Alper; Karagöl, Taner; Smorodina, Eva & Zhang, Shuguang (2024). Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F. PLOS ONE. 19:e0289644(4), s. 1–23. doi: 10.1371/journal.pone.0289644. Fulltekst i vitenarkiv Vis sammendrag

  Glutamate transporters play key roles in nervous physiology by modulating excitatory neurotransmitter levels, when malfunctioning, involving in a wide range of neurological and physiological disorders. However, integral transmembrane proteins including the glutamate transporters remain notoriously difficult to study, due to their localization within the cell membrane. Here we present the structural bioinformatics studies of glutamate transporters and their water-soluble variants generated through QTY-code, a protein design strategy based on systematic amino acid substitutions. These include 2 structures determined by X-ray crystallography, cryo-EM, and 6 predicted by AlphaFold2, and their predicted water-soluble QTY variants. In the native structures of glutamate transporters, transmembrane helices contain hydrophobic amino acids such as leucine (L), isoleucine (I), and phenylalanine (F). To design water-soluble variants, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, namely glutamine (Q), threonine (T) and tyrosine (Y). The QTY variants exhibited water-solubility, with four having identical isoelectric focusing points (pI) and the other four having very similar pI. We present the superposed structures of the native glutamate transporters and their water-soluble QTY variants. The superposed structures displayed remarkable similarity with RMSD 0.528Å-2.456Å, despite significant protein transmembrane sequence differences (41.1%—>53.8%). Additionally, we examined the differences of hydrophobicity patches between the native glutamate transporters and their QTY variants. Upon closer inspection, we discovered multiple natural variations of L->Q, I->T, F->Y and Q->L, T->I, Y->F in these transporters. Some of these natural variations were benign and the remaining were reported in specific neurological disorders. We further investigated the characteristics of hydrophobic to hydrophilic substitutions in glutamate transporters, utilizing variant analysis and evolutionary profiling. Our structural bioinformatics studies not only provided insight into the differences between the hydrophobic helices and hydrophilic helices in the glutamate transporters, but they are also expected to stimulate further study of other water-soluble transmembrane proteins.

• Sajeev-Sheeja, Akash; Smorodina, Eva & Zhang, Shuguang (2023). Structural bioinformatics studies of bacterial outer membrane beta-barrel transporters and their AlphaFold2 predicted water-soluble QTY variants. PLOS ONE. 18:e0290360(8), s. 1–12. doi: 10.1371/journal.pone.0290360. Fulltekst i vitenarkiv
• Qing, Rui; Xue, Mantian; Zhao, Jiayuan; Wu, Lidong; Breitwieser, Andreas & Smorodina, Eva [Vis alle 13 forfattere av denne artikkelen] (2023). Scalable biomimetic sensing system with membrane receptor dual-monolayer probe and graphene transistor arrays. Science Advances. 9:eadf1402(29), s. 1–12. doi: 10.1126/sciadv.adf1402. Fulltekst i vitenarkiv Vis sammendrag

  Affinity-based biosensing can enable point-of-care diagnostics and continuous health monitoring, which commonly follows bottom-up approaches and is inherently constrained by bioprobes’ intrinsic properties, batch-to-batch consistency, and stability in biofluids. We present a biomimetic top-down platform to circumvent such difficulties by combining a “dual-monolayer” biorecognition construct with graphene-based field-effect-transistor arrays. The construct adopts redesigned water-soluble membrane receptors as specific sensing units, positioned by two-dimensional crystalline S-layer proteins as dense antifouling linkers guiding their orientations. Hundreds of transistors provide statistical significance from transduced signals. System feasibility was demonstrated with rSbpA-ZZ/CXCR4QTY-Fc combination. Nature-like specific interactions were achieved toward CXCL12 ligand and HIV coat glycoprotein in physiologically relevant concentrations, without notable sensitivity loss in 100% human serum. The construct is regeneratable by acidic buffer, allowing device reuse and functional tuning. The modular and generalizable architecture behaves similarly to natural systems but gives electrical outputs, which enables fabrication of multiplex sensors with tailored receptor panels for designated diagnostic purposes.

• Smorodina, Eva; Kav, Batuhan; Fatafta, Hebah & Strodel, Birgit (2023). Effects of ion type and concentration on the structure and aggregation of the amyloid peptide Aβ<inf>16−22</inf>. Proteins: Structure, Function, and Bioinformatics. ISSN 0887-3585. 0, s. 1–14. doi: 10.1002/prot.26635. Fulltekst i vitenarkiv
• Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke & Gainullin, Murat [Vis alle 13 forfattere av denne artikkelen] (2023). Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Frontiers in Immunology. 14:1265044, s. 1–14. doi: 10.3389/fimmu.2023.1265044. Fulltekst i vitenarkiv Vis sammendrag

  During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8 + T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.

• Khan, Asif; Cowen-Rivers, Alexander I.; Grosnit, Antoine; Deik, Derrick-Goh-Xin; Robert, Philippe A. & Greiff, Victor [Vis alle 15 forfattere av denne artikkelen] (2023). Toward real-world automated antibody design with combinatorial Bayesian optimization. Cell Reports Methods. 3:100374(1), s. 1–21. doi: 10.1016/j.crmeth.2022.100374. Fulltekst i vitenarkiv Vis sammendrag

  Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge.

• Robert, Philippe Paul Auguste; Akbar, Rahmad; Frank, Robert; Pavlović, Milena; Widrich, Michael & Snapkov, Igor [Vis alle 25 forfattere av denne artikkelen] (2022). Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction. Nature Computational Science. 2(12), s. 845–865. doi: 10.1038/s43588-022-00372-4. Fulltekst i vitenarkiv
• Qing, Rui; Hao, Shilei; Smorodina, Eva; Jin, David; Zalevsky, Arthur & Zhang, Shuguang (2022). Protein Design: From the Aspect of Water Solubility and Stability. Chemical Reviews. ISSN 0009-2665. 122(18), s. 14085–14179. doi: 10.1021/acs.chemrev.1c00757. Fulltekst i vitenarkiv Vis sammendrag

  Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

• Akbar, Rahmad; Bashour, Habib; Rawat, Puneet; Robert, Philippe Paul Auguste; Smorodina, Eva & Cotet, Tudor-Stefan [Vis alle 15 forfattere av denne artikkelen] (2022). Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies. mAbs. ISSN 1942-0862. 14:e2008790(1), s. 1–35. doi: 10.1080/19420862.2021.2008790. Fulltekst i vitenarkiv Vis sammendrag

  Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

• Smorodina, Eva; Diankin, Igor; Tao, Fei; Qing, Rui; Yang, Steve & Zhang, Shuguang (2022). Structural informatic study of determined and AlphaFold2 predicted molecular structures of 13 human solute carrier transporters and their water-soluble QTY variants. Scientific Reports. 12:20103(1), s. 1–12. doi: 10.1038/s41598-022-23764-y. Fulltekst i vitenarkiv Vis sammendrag

  Abstract Solute carrier transporters are integral membrane proteins, and are important for diverse cellular nutrient transports, metabolism, energy demand, and other vital biological activities. They have recently been implicated in pancreatic cancer and other cancer metastasis, angiogenesis, programmed cell death and proliferation, cell metabolism and chemo-sensitivity. Here we report the study of 13 human solute carrier membrane transporters using the highly accurate AlphaFold2 predictions of 3D protein structures. In the native structures, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F) in the transmembrane alpha-helices. These hydrophobic amino acids L, I, V, F are systematically replaced by hydrophilic amino acids glutamine (Q), threonine (T) and tyrosine (Y), thus the QTY code. Therefore, these QTY variant transporters become water-soluble without requiring detergents. We present the superposed structures of these native solute carrier transporters and their water-soluble QTY variants. The superposed structures show remarkable similarity with RMSD ~ 1 Å–&lt; 3 Å despite &gt; 46% protein sequence substitutions in transmembrane alpha-helices. We also show the differences of surface hydrophobicity between the native solute carrier transporters and their QTY variants. Our study may further stimulate designs of water-soluble transmembrane proteins and other aggregated proteins for drug discovery and biotechnological applications.

• Smorodina, Eva; Tao, Fei; Qing, Rui; Jin, David; Yang, Steve & Zhang, Shuguang (2022). Comparing 2 crystal structures and 12 AlphaFold2-predicted human membrane glucose transporters and their water-soluble glutamine, threonine and tyrosine variants. QRB Discovery. 3:e5, s. 1–11. doi: 10.1017/qrd.2022.6. Fulltekst i vitenarkiv Vis sammendrag

  Membrane transporters including glucose transporters (GLUTs) are involved in cellular energy supplies, cell metabolism and other vital biological activities. They have also been implicated in cancer proliferation and metastasis, thus they represent an important target in combatting cancer. However, membrane transporters are very difficult to study due to their multispan transmembrane properties. The new computational tool, AlphaFold2, offers highly accurate predictions of three-dimensional protein structures. The glutamine, threonine and tyrosine (QTY) code provides a systematic method of rendering hydrophobic sequences into hydrophilic ones. Here, we present computational studies of native integral membrane GLUTs with 12 transmembrane helical segments determined by X-ray crystallography and CryoEM, comparing the AlphaFold2-predicted native structure to their water-soluble QTY variants predicted by AlphaFold2. In the native structures of the transmembrane helices, there are hydrophobic amino acids leucine (L), isoleucine (I), valine (V) and phenylalanine (F). Applying the QTY code, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, glutamine (Q), threonine (T) and tyrosine (Y) rendering them water-soluble. We present the superposed structures of native GLUTs and their water-soluble QTY variants. The superposed structures show remarkable similar residue mean square distance values between 0.47 and 3.6 Å (most about 1–2 Å) despite >44% transmembrane amino acid differences. We also show the differences of hydrophobicity patches between the native membrane transporters and their QTY variants. We explain the rationale why the membrane protein QTY variants become water-soluble. Our study provides insight into the differences between the hydrophobic helices and hydrophilic helices, and offers confirmation of the QTY method for studying multispan transmembrane proteins and other aggregated proteins through their water-soluble variants.

• Dorraji, Seyed Esmaeil; Borgen, Elin; Segura-Pena, Dario; Rawat, Puneet; Smorodina, Eva & Dunn, Claire [Vis alle 10 forfattere av denne artikkelen] (2022). Development of a High-Affinity Antibody against the Tumor-Specific and Hyperactive 611-p95HER2 Isoform. Cancers. 14:4859(19), s. 1–24. doi: 10.3390/cancers14194859. Fulltekst i vitenarkiv Vis sammendrag

  Simple Summary In the present study, we addressed the unmet need for a molecular antibody (mAb) with high affinity and specificity against a truncated hyperactive isoform of human epidermal growth factor receptor 2 (HER2), called 611-carboxy terminal fragment (CTF)-p95HER2. Patients with p95HER2+ breast cancer are at risk of developing metastatic breast cancer with a poor prognosis and resistance to therapies targeting full-length HER2. We have generated a mAb named Oslo-2, which react specifically with 611-CTF-p95HER2 and has a high affinity. We also characterized the antigenic determinant (epitope) on the p95HER2 protein and the antigen-binding site (paratope) on the Oslo-2 mAb. The antibody can be used to develop antibody- or cell-based therapies targeting p95HER2, as well as a diagnostic assay to identify p95HER2+ disease. Abstract The expression of human epidermal growth factor receptor 2 (HER2) is a key classification factor in breast cancer. Many breast cancers express isoforms of HER2 with truncated carboxy-terminal fragments (CTF), collectively known as p95HER2. A common p95HER2 isoform, 611-CTF, is a biomarker for aggressive disease and confers resistance to therapy. Contrary to full-length HER2, 611-p95HER2 has negligible normal tissue expression. There is currently no approved diagnostic assay to identify this subgroup and no therapy targeting this mechanism of tumor escape. The purpose of this study was to develop a monoclonal antibody (mAb) against 611-CTF-p95HER2. Hybridomas were generated from rats immunized with cells expressing 611-CTF. A hybridoma producing a highly specific Ab was identified and cloned further as a mAb. This mAb, called Oslo-2, gave strong staining for 611-CTF and no binding to full-length HER2, as assessed in cell lines and tissues by flow cytometry, immunohistochemistry and immunofluorescence. No cross-reactivity against HER2 negative controls was detected. Surface plasmon resonance analysis demonstrated a high binding affinity (equilibrium dissociation constant 2 nM). The target epitope was identified at the N-terminal end, using experimental alanine scanning. Further, the mAb paratope was identified and characterized with hydrogen-deuterium-exchange, and a molecular model for the (Oslo-2 mAb:611-CTF-p95HER2) complex was generated by an experimental-information-driven docking approach. We conclude that the Oslo-2 mAb has a high affinity and is highly specific for 611-CTF-p95HER2. The Ab may be used to develop potent and safe therapies, overcoming p95HER2-mediated tumor escape, as well as for developing diagnostic assays. Keywords: HER2; p95HER2; antibody; tumor-specific; breast cancer; cancer stem cells; HDX-MS; epitope and paratope mapping; docking

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• Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke & Gainullin, Murat [Vis alle 13 forfattere av denne artikkelen] (2024). Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence (Frontiers in Immunology, (2023), 14, (1265044), 10.3389/fimmu.2023.1265044). Frontiers in Immunology. 15. doi: 10.3389/fimmu.2024.1377041. Fulltekst i vitenarkiv
• Smorodina, Eva & Greiff, Victor (2026). Structural Rules of Antibody Function. Universitetet i Oslo. ISSN 9788234808647. Fulltekst i vitenarkiv Vis sammendrag

  Antibodies are highly specific biomolecules composed of two heavy and two light chains, whose complementarity-determining regions (CDRs) form the antigen-binding site. The remarkable ability of antibodies to recognize almost any target molecule has led to their central role in diagnostics and therapeutic development. Despite significant experimental advances, therapeutic antibody discovery remains a time-consuming and low-throughput process, heavily dependent on iterative laboratory screening and animal testing. Computational design promises to accelerate this process, yet its success remains limited due to the complexity and flexibility of antibody-antigen interactions. Traditional physics-based methods have provided valuable structural insights but rely heavily on empirical estimations and require significant resources. Novel data-driven methods provide practical solutions, but their black-box nature limits mechanistic understanding. Both currently achieve low predictive success rates, highlighting the need for alternative approaches. This thesis argues that this limited success stems from an incomplete understanding of the three-dimensional and biochemical rules governing antibody-antigen binding. Rather than introducing another predictive model, it focuses on uncovering the mechanistic principles that define effective binding, integrating experimental and computational approaches across three scales: structural properties of unbound antibodies, structural determinants of affinity and specificity at binding interfaces, and molecular dynamics simulations establishing quantitative structure-based rules validated against experimental data. This work produced the first quantitative set of structural rules capable of differentiating high- from low-affinity antibody binders in agreement with wet lab observations, even when applied to modeled rather than experimental structures. It contributes to advancing the next generation of computational antibody design, guided by deeper biological understanding rather than empirical approximation.

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