MoFvAb: Modeling the Fv region of antibodies

Knowledge of the 3-dimensional structure of the antigen-binding region of antibodies enables numerous useful applications regarding the design and development of antibody-based drugs. We present a knowledge-based antibody structure prediction methodology that incorporates concepts that have arisen from an applied antibody engineering environment. The protocol exploits the rich and continuously growing supply of experimentally derived antibody structures available to predict CDR loop conformations and the packing of heavy and light chain quickly and without user intervention. The homology models are refined by a novel antibody-specific approach to adapt and rearrange sidechains based on their chemical environment. The method achieves very competitive all-atom root mean square deviation values in the order of 1.5 Å on different evaluation datasets consisting of both known and previously unpublished antibody crystal structures.     

Toward high‐resolution homology modeling of antibody Fv regions and application to antibody–antigen docking

High‐resolution homology models are useful in structure‐based protein engineering applications, especially when a crystallographic structure is unavailable. Here, we report the development and implementation of RosettaAntibody, a protocol for homology modeling of antibody variable regions. The protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of V_L‐V_H rigid‐body orientation and CDR backbone and side‐chain conformations. The protocol was tested on a benchmark of 54 antibody crystal structures. The median root mean square deviation (rmsd) of the antigen binding pocket comprised of all the CDR residues was 1.5 Å with 80% of the targets having an rmsd lower than 2.0 Å. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6, 1.9, 2.4, 3.1, and 6.0 Å for very short (4–6 residues), short (7–9), medium (10–11), long (12–14) and very long (17–22) loops, respectively. When the set of ten top‐scoring antibody homology models are used in local ensemble docking to antigen, a moderate‐to‐high accuracy docking prediction was achieved in seven of fifteen targets. This success in computational docking with high‐resolution homology models is encouraging, but challenges still remain in modeling antibody structures for sequences with long H3 loops. This first large‐scale antibody–antigen docking study using homology models reveals the level of “functional accuracy” of these structural models toward protein engineering applications. Proteins 2009; 74:497–514. © 2008 Wiley‐Liss, Inc.     

Antibodies as a model system for comparative model refinement

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the “scaffold” (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge‐based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5–7 residues) are predicted to an average backbone (N? Cα? C? O) RMSD of 2.7 Å while 11 longer loops (8–9 residues) are predicted to 5.1 Å, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 Å for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near‐native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 Å RMSD and 3.1 Å RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8–10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full‐model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Ab initio structure prediction of the antibody hypervariable H3 loop

Antibodies have the capability of binding a wide range of antigens due to the diversity of the six loops constituting the complementarity determining region (CDR). Among the six loops, the H3 loop is the most diverse in structure, length, and sequence identity. Prediction of the three‐dimensional structures of antibodies, especially the CDR loops, is an important step in the computational design and engineering of novel antibodies for improved affinity and specificity. Although it has been demonstrated that the conformation of the five non‐H3 loops can be accurately predicted by comparing their sequences against databases of canonical loop conformations, no such connection has been established for H3 loops. In this work, we present the results for ab initio structure prediction of the H3 loop using conformational sampling and energy calculations with the program Prime on a dataset of 53 loops ranging in length from 4 to 22 residues. When the prediction is performed in the crystal environment and including symmetry mates, the median backbone root mean square deviation (RMSD) is 0.5 Å to the crystal structure, with 91% of cases having an RMSD of less than 2.0 Å. When the prediction is performed in a noncrystallographic environment, where the scaffold is constructed by swapping the H3 loops between homologous antibodies, 70% of cases have an RMSD below 2.0 Å. These results show promise for ab initio loop predictions applied to modeling of antibodies. © 2012 Wiley Periodicals, Inc.     

ABodyBuilder: Automated antibody structure prediction with data–driven accuracy estimation

Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the ‘confidence’ of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root–mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment–II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub–Angstrom predictions for the ‘canonical’ CDR loops. Its ability to model nanobodies, and rapidly generate models (～30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision–making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder.     

Structure of a shark IgNAR antibody variable domain and modeling of an early-developmental isotype

The new antigen receptor (IgNAR) antibodies from sharks are disulphide bonded dimers of two protein chains, each containing one variable and five constant domains. Three types of IgNAR variable domains have been discovered, with Type 3 appearing early in shark development and being overtaken by the antigen-driven affinity-matured Type 1 and 2 response. Here, we have determined the first structure of a naturally occurring Type 2 IgNAR variable domain, and identified the disulphide bond that links and stabilizes the CDR1 and CDR3 loops. This disulphide bridge locks the CDR3 loop in an "upright" conformation in contrast to other shark antibody structures, where a more lateral configuration is observed. Further, we sought to model the Type 3 isotype based on the crystallographic structure reported here. This modeling indicates (1) that internal Type 3-specific residues combine to pack into a compact immunoglobulin core that supports the CDR loop regions, and (2) that despite apparent low-sequence variability, there is sufficient plasticity in the CDR3 loop to form a conformationally diverse antigen-binding surface.     

Does conformational free energy distinguish loop conformations in proteins?

Limitations in protein homology modeling often arise from the inability to adequately model loops. In this paper we focus on the selection of loop conformations. We present a complete computational treatment that allows the screening of loop conformations to identify those that best fit a molecular model. The stability of a loop in a protein is evaluated via computations of conformational free energies in solution, i.e., the free energy difference between the reference structure and the modeled one. A thermodynamic cycle is used for calculation of the conformational free energy, in which the total free energy of the reference state (i.e., gas phase) is the CHARMm potential energy. The electrostatic contribution of the solvation free energy is obtained from solving the finite-difference Poisson-Boltzmann equation. The nonpolar contribution is based on a surface area-based expression. We applied this computational scheme to a simple but well-characterized system, the antibody hypervariable loop (complementarity-determining region, CDR). Instead of creating loop conformations, we generated a database of loops extracted from high-resolution crystal structures of proteins, which display geometrical similarities with antibody CDRs. We inserted loops from our database into a framework of an antibody; then we calculated the conformational free energies of each loop. Results show that we successfully identified loops with a "reference-like" CDR geometry, with the lowest conformational free energy in gas phase only. Surprisingly, the solvation energy term plays a confusing role, sometimes discriminating "reference-like" CDR geometry and many times allowing "non-reference-like" conformations to have the lowest conformational free energies (for short loops). Most "reference-like" loop conformations are separated from others by a gap in the gas phase conformational free energy scale. Naturally, loops from antibody molecules are found to be the best models for long CDRs (> or = 6 residues), mainly because of a better packing of backbone atoms into the framework of the antibody model.     

CDR-H3 loop ensemble in solution – conformational selection upon antibody binding

ABSTRACT

We analyzed pairs of protein-binding, peptide-binding and hapten-binding antibodies crystallized as complex and in the absence of the antigen with and without conformational differences upon binding in the complementarity-determining region (CDR)-H3 loop. Here, we introduce a molecular dynamics-based approach to capture a diverse conformational ensemble of the CDR-H3 loop in solution. The results clearly indicate that the inherently flexible CDR-H3 loop indeed needs to be characterized as a conformational ensemble. The conformational changes of the CDR-H3 loop in all antibodies investigated follow the paradigm of conformation selection, because we observe the experimentally determined binding competent conformation without the presence of the antigen within the ensemble of pre-existing conformational states in solution before binding. We also demonstrate for several examples that the conformation observed in the antibody crystal structure without antigen present is actually selected to bind the carboxyterminal tail region of the antigen-binding fragment (Fab). Thus, special care must be taken when characterizing antibody CDR-H3 loops by Fab X-ray structures, and the possibility that pre-existing conformations are present should always be considered.     

Predicting antibody complementarity determining region structures without classification

Antibodies are used extensively in medical and biological research. Their complementarity determining regions (CDRs) define the majority of their antigen binding functionality. CDR structures have been intensively studied and classified (canonical structures). Here we show that CDR structure prediction is no different from the standard loop structure prediction problem and predict them without classification. FREAD, a successful database loop prediction technique, is able to produce accurate predictions for all CDR loops (0.81, 0.42, 0.96, 0.98, 0.88 and 2.25 ? RMSD for CDR-L1 to CDR-H3). In order to overcome the relatively poor predictions of CDR-H3, we developed two variants of FREAD, one focused on sequence similarity (FREAD-S) and another which includes contact information (ConFREAD). Both of the methods improve accuracy for CDR-H3 to 1.34 ? and 1.23 ? respectively. The FREAD variants are also tested on homology models and compared to RosettaAntibody (CDR-H3 prediction on models: 1.98 and 2.62 ? for ConFREAD and RosettaAntibody respectively). CDRs are known to change their structural conformations upon binding the antigen. Traditional CDR classifications are based on sequence similarity and do not account for such environment changes. Using a set of antigen-free and antigen-bound structures, we compared our FREAD variants. ConFREAD which includes contact information successfully discriminates the bound and unbound CDR structures and achieves an accuracy of 1.35 ? for bound structures of CDR-H3.     

A common NH53K mutation in the combining site of antibodies raised against chlamydial LPS glycoconjugates significantly increases avidity

The crystal structures of the antigen-binding fragment of the murine monoclonal antibody (mAb) S25-39 in the presence of several antigens representing chlamydial lipopolysaccharide (LPS) epitopes based on the bacterial sugar 3-deoxy-α-D-manno-oct-2-ulosonic acid (Kdo) have been determined at resolutions from 2.4 to 1.8 ?. The antigen-binding site of this antibody differs from the well-characterized antibody S25-2 by a single mutation away from the germline of asparagine H53 to lysine, yet this one mutation results in a significant increase in avidity across a range of antigens. A comparison of the two antibody structures reveals that the mutated Lys H53 forms additional hydrogen bonds and/or charged-residue interactions with the second Kdo residue of every antigen having two or more carbohydrate residues. Significantly, the NH53K mutation results from a single nucleotide substitution in the germline sequence common among a panel of antibodies raised against glycoconjugates containing carbohydrate epitopes of chlamydial LPS. Like S25-2, S25-39 displays significant induced fit of complementarity determining region (CDR) H3 upon antigen binding, with the unliganded structure possessing a conformation distinct from those reported earlier for S25-2. The four different observed conformations for CDR H3 suggest that this CDR has evolved to exploit the recognition potential of a flexible loop while minimizing the associated entropic penalties of binding by adopting a limited number of ordered conformations in the unliganded state. These observations reveal strategies evolved to balance adaptability and specificity in the germline antibody response to carbohydrate antigens.     

Modeling the anti-CEA antibody combining site by homology and conformational search.

A model for an antibody specific for the carcinoembryonic antigen (CEA) has been constructed using a method which combines the concept of canonical structures with conformational search. A conformational search technique is introduced which couples random generation of backbone loop conformations to a simulated annealing method for assigning side chain conformations. This technique was used both to verify conformations selected from the set of known canonical structures and to explore conformations available to the H3 loop in CEA ab initio. Canonical structures are not available for H3 due to its variability in length, sequence, and observed conformation in known antibody structures. Analysis of the results of conformational search resulted in three equally probable conformations for H3 loop in CEA. Force field energies, solvation free energies, exposure of charged residues and burial of hydrophobic residues, and packing of hydrophobic residues at the base of the loop were used as selection criteria. The existence of three equally plausible structures may reflect the high degree of flexibility expected for an exposed loop of this length. The nature of the combining site and features which could be important to interaction with antigen are discussed.     

Predicting antibody hypervariable loop conformations. II: Minimization and molecular dynamics studies of MCPC603 from many randomly generated loop conformations

We describe a method for predicting the conformations of loops in proteins and its application to four of the complementarity determining regions [CDRs] in the crystallographically determined structure of MCPC603. The method is based on the generation of a large number of randomly generated conformations for the backbone of the loop being studied, followed by either minimization or molecular dynamics followed by minimization starting from these random structures. The details of the algorithm for the generation of the loops are presented in the first paper in this series (Shenkin et al. [submitted]). The results of minimization and molecular dynamics applied to these loops is presented here. For the two shortest CDRs studied (H1 and L2, which are five and seven amino acids long), minimizations and dynamics simulations which ignore interactions of the loop amino acids beyond the carbon beta replicate the conformation of the crystal structure closely. This suggests that these loops fold independently of sequence variation. For the third CDR (L3, which is nine amino acids), those portions of the CDR near its base which are hydrogen bonded to framework are well replicated by our procedures, but the top of the loop shows significant conformational variability. This variability persists when side chain interactions for the MCPC603 sequence are included. For a fourth CDR (H3, which is 11 amino acids long), new low-energy backbone conformations are found; however, only those which are close to the crystal are compatible with the sequence when side chain interactions are taken into account. Results from minimization and dynamics on single CDRs with all other CDRs removed are presented. These allow us to explore the extent to which individual CDR conformations are determined by interactions with framework only.     

Structural diversity in a human antibody germline library

To support antibody therapeutic development, the crystal structures of a set of 16 germline variants composed of 4 different kappa light chains paired with 4 different heavy chains have been determined. All four heavy chains of the antigen-binding fragments (Fabs) have the same complementarity-determining region (CDR) H3 that was reported in an earlier Fab structure. The structure analyses include comparisons of the overall structures, canonical structures of the CDRs and the VH:VL packing interactions. The CDR conformations for the most part are tightly clustered, especially for the ones with shorter lengths. The longer CDRs with tandem glycines or serines have more conformational diversity than the others. CDR H3, despite having the same amino acid sequence, exhibits the largest conformational diversity. About half of the structures have CDR H3 conformations similar to that of the parent; the others diverge significantly. One conclusion is that the CDR H3 conformations are influenced by both their amino acid sequence and their structural environment determined by the heavy and light chain pairing. The stem regions of 14 of the variant pairs are in the ‘kinked’ conformation, and only 2 are in the extended conformation. The packing of the VH and VL domains is consistent with our knowledge of antibody structure, and the tilt angles between these domains cover a range of 11 degrees. Two of 16 structures showed particularly large variations in the tilt angles when compared with the other pairings. The structures and their analyses provide a rich foundation for future antibody modeling and engineering efforts.     

Antibody side chain conformations are position‐dependent

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone‐dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody‐specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position‐dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ₁ and accuracy (78.7% and 64.8%) compared to three leading backbone‐dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain–side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears .     

The structure of an antitumor C(H)2-domain-deleted humanized antibody

C(H)2-domain-deleted CC49 (HuCC49DeltaCH2), a recombinant humanized antibody that recognizes the TAG-72 antigen expressed on a variety of human carcinomas, is secreted from cultured cells as a mixture of two homodimeric isoforms. Isoform A contains two covalent interchain disulfide bonds at heavy chain positions 239 and 242, while isoform B fails to develop any interchain disulfide bonds but has 239-242 intrachain disulfide bonds instead. Form A is currently in preclinical development as a therapeutic agent for treating colorectal carcinoma, though form B shows equal efficacy. HuCC49DeltaCH2 form B can be crystallized from sodium formate only in the presence of detergents. X-ray diffraction data were collected on a single cryo-cooled crystal grown with Triton X-100 and the structure was solved by molecular replacement. The model has refined to R=0.246 (R(free)=0.297) for 2.8A data. The antibodies pack in the crystal around crystallographic 2-fold axes as tetramers with approximate 222 symmetry. Atomic force microscopy studies show that this tetrameric structure is the crystal building block and also exists free in the mother liquor. The tetramer is composed of two rings, back-to-back, with a thickness of approximately 83A. Each ring is composed of two antibodies with the complementarity-determining regions (CDR) of the two Fabs of one antibody interacting with the CDR regions of the second antibody in a head-to-head fashion. These rings are approximately 167A long and 112A wide. The C(H)3 domain is inverted with respect to the Fabs when compared to the usual orientation found in conventional antibodies. The polypeptides joining the C(H)3 domains to the Fab portions of the antibody are not seen and are almost certainly disordered. The antigen combining site of HuCC49DeltaCH2 is very similar, but not identical, in topology and charge distribution to that of antibody B72.3, which binds a similar epitope on TAG-72. The combining site consists of a deep cleft, heavily lined with aromatic amino acid side-chains but bounded by numerous charged groups.     

The structure of the anti-c-myc antibody 9E10 Fab fragment/epitope peptide complex reveals a novel binding mode dominated by the heavy chain hypervariable loops

The X-ray structure of the Fab fragment from the anti-c-myc antibody 9E10 was determined both as complex with its epitope peptide and for the free Fab. In the complex, two Fab molecules adopt an unusual head to head orientation with the epitope peptide arranged between them. In contrast, the free Fab forms a dimer with different orientation. In the Fab/peptide complex the peptide is bound to one of the two Fabs at the "back" of its extended CDR H3, in a cleft with CDR H1, thus forming a short, three-stranded antiparallel beta-sheet. The N- and C-terminal parts of the peptide are also in contact with the neighboring Fab fragment. Comparison between the CDR H3s of the two Fab molecules in complex with the peptide and those from the free Fab reveals high flexibility of this loop. This structural feature is in line with thermodynamic data from isothermic titration calorimetry.     

High‐accuracy modeling of antibody structures by a search for minimum‐energy recombination of backbone fragments

Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo‐chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low‐energy conformations built from backbone segments and rigid‐body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo‐chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved <1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30–38. © 2016 Wiley Periodicals, Inc.     

Modeling the antigen combining site of an anti-dinitrophenyl antibody, ANO2.

A model structure has been constructed for a monoclonal anti-dinitrophenyl antibody. The antibody, ANO2, has been sequenced and cloned (Anglister, J., Frey, T., & McConnell, H.M., 1984, Biochemistry 23, 1138-1142). Its amino acid sequence shows striking homology with the anti-lysozyme Fab fragments HyHel5 (83%) and HyHel10 (73%). Based on this homology, a model for the ANO2 variable heavy and variable light chain framework was constructed using a hybrid of the HyHel5 light chain and the HyHel10 heavy chain backbone, omitting the hypervariable loops. These coordinates were used as scaffolds for the model building of ANO2. The CONGEN conformational sampling algorithm (Bruccoleri, R.E. & Karplus, M., 1987, Biopolymers 26, 127-196) was used to model the six hypervariable loops that contain the antigen-combining site. All the possible conformations of the loop backbones were constructed and the best loop structures were selected using a combination of the CHARMM potential energy function and evaluation of the solvent-accessible surface area of the conformers. The order in which the loops were searched was carried out based on the relative locations of the loops with reference to the framework of the beta-barrel, namely, L2-H1-L3-H2-H3-L1. The model structures thus obtained were compared to the high resolution X-ray structure (Brünger, A.T., Leahy, D.J., Hynes, T.R., & Fox, R.O., 1991, J. Mol. Biol. 221, 239-256).     

Discrimination of native loop conformations in membrane proteins: decoy library design and evaluation of effective energy scoring functions

The recent determination of crystal structures for several important membrane proteins opens the way for comparative modeling of their membrane-spanning regions. However, the ability to predict correctly the structures of loop regions, which may be critical, for example, in ligand binding, remains a considerable challenge. To meet this challenge, accurate scoring methods have to discriminate between candidate conformations of an unknown loop structure. Some success in loop prediction has been reported for globular proteins; however, the proximity of membrane protein loops to the lipid bilayer casts doubt on the applicability of the same scoring methods to this problem. In this work, we develop "decoy libraries" of non-native folds generated, using the structures of two membrane proteins, with molecular dynamics and Monte Carlo techniques over a range of temperatures. We introduce a new approach for decoy library generation by constructing a flat distribution of conformations covering a wide range of Calpha-root-mean-square deviation (RMSD) from the native structure; this removes possible bias in subsequent scoring stages. We then score these decoy conformations with effective energy functions, using increasingly more cpu-intensive implicit solvent models, including (1) simple Coulombic electrostatics with constant or distance-dependent dielectrics; (2) atomic solvation parameters; (3) the effective energy function (EEF1) of Lazaridis and Karplus; (4) generalized Born/Analytical Continuum Solvent; and (5) finite-difference Poisson-Boltzmann energy functions. We show that distinction of native-like membrane protein loops may be achieved using effective energies with the assumption of a homogenous environment; thus, the absence of the adjacent lipid bilayer does not affect the scoring ability. In particular, the Analytical Continuum Solvent and finite-difference Poisson-Boltzmann energy functions are seen to be the most powerful scoring functions. Interestingly, the use of the uncharged states of ionizable sidechains is shown to aid prediction, particularly for the simplest energy functions.     

Crystal packing effects on protein loops

The effects of crystal packing on protein loop structures are examined by (1) a comparison of loops in proteins that have been crystallized in alternate packing arrangements, and (2) theoretical prediction of loops both with and without the inclusion of the crystal environment. Results show that in a minority of cases, loop geometries are dependent on crystal packing effects. Explicit representation of the crystal environment in a loop prediction algorithm can be used to model these effects and to reconstruct the structures, and relative energies, of a loop in alternative packing environments. By comparing prediction results with and without the inclusion of the crystal environment, the loop prediction algorithm can further be used to identify cases in which a crystal structure does not represent the most stable state of a loop in solution. We anticipate that this capability has implications for structural biology.