OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

Hallen, Mark A.; Martin, Jeffrey W.; Ojewole, Adegoke; Jou, Jonathan; Lowegard, Anna U.; Frenkel, Marcel S.; Gainza, Pablo; Nisonoff, Hunter; Mukund, Adi; Wang, Siyu; Holt, Graham T.; Dowd, Elizabeth; Donald, Bruce Randall

doi:10.1101/306324

Cited by 11 publications

(18 citation statements)

References 21 publications

(42 reference statements)

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“…The computational design of new biomolecular interactions remains a fundamentally unsolved problem in protein design, despite notable advances. In the future, protein design programs such as Osprey 39 and Rosetta 40 may become fingerprint-aware, optimizing the sequence of de novo -designed proteins to display the interaction fingerprints on the molecular surface necessary to perform a functional task.…”

Section: Discussionmentioning

confidence: 99%

Deciphering interaction fingerprints from protein molecular surfaces

Gainza

Sverrisson

Monti

et al. 2019

Preprint

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Predicting interactions between proteins and other biomolecules purely based on structure is an unsolved problem in biology. A high-level description of protein structure, the molecular surface, displays patterns of chemical and geometric features thatfingerprinta protein’s modes of interactions with other biomolecules. We hypothesize that proteins performing similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We presentMaSIF, a conceptual framework based on a new geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction, and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

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Section: Discussionmentioning

confidence: 99%

Deciphering interaction fingerprints from protein molecular surfaces

Gainza

Sverrisson

Monti

et al. 2019

Preprint

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“…Aggregate values for the ensembles in each state were computed by bounding the energy for each conformation in the ensemble, and combining these energy bounds. For each bound and unbound state, we first computed bounds on the the energy of each conformation in the conformational ensemble defined by that state, as was done in Ref., [2][3][4]42,71,72 Although the K * score exhibits good Spearman's rank correlation with experimental K a values, 3,71 the correlation between K * scores and K a is not yet quantitative. First, most physics-based 9 energy functions are based on small-molecule energetics, which can overestimate van der Waals terms and thereby overestimate internal energy.…”

Section: Entropy Internal Energy and Helmholtz Free Energy Calculationmentioning

confidence: 99%

“…CSPD algorithms search over a user-specified input model (viz., a structural model, allowed side chain and backbone flexibility, allowed mutations, energy function, etc. 3 ). Because proteins exist as thermodynamic ensembles, 39,46 principled algorithms should exploit statistical thermodynamics of non-covalent binding, and therefore require approximation of the partition function.…”

mentioning

confidence: 99%

“…52 Algorithms in the OSPREY software package efficiently solve protein design problems without these simplifications. 3 Recently we developed the MARK * algorithm, which, in addition to provably and efficiently approximating partition functions for input model states (i.e. bound complex, unbound protein and ligand), allows visualization of the entire energy landscape accessible to a protein input model.…”

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confidence: 99%

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Computational Analysis of Energy Landscapes Reveals Dynamic Features that Contribute to Binding of Inhibitors to CFTR-Associated Ligand

Holt

Jou²,

Gill

et al. 2019

Preprint

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PDZ domains are small protein-binding domains that interact with short, mostly C-terminal peptides and play important roles in cellular signaling and the trafficking and localization of ion channels. The CFTR-associated ligand PDZ domain (CALP) binds to the cystic fibrosis transmembrane conductance regulator (CFTR) and mediates degradation of mature CFTR through lysosomal pathways. Inhibition of the CALP:CFTR interaction has been explored as a potential therapeutic avenue for cystic fibrosis (CF). 1 Previously, we reported 2 the ensemblebased computational design of a novel 6-residue peptide inhibitor of CALP, which resulted in the most binding-efficient inhibitor of CALP to date. This inhibitor, kCAL01, was designed using OSPREY 3 and displayed significant biological activity in in vitro cell-based assays. Here, we report a crystal structure of kCAL01 bound to CALP (PDB ID: 6OV7). To elucidate the structural basis for the enhanced binding efficiency of kCAL01, we compare this structure to that of a previously developed inhibitor of CALP, iCAL36 (PDB ID: 4E34). In addition to performing traditional structural analysis, we compute the side-chain energy landscapes for each structure using the recently developed MARK * partition function approximation algorithm. 4Analysis of these energy landscapes not only enables approximation of binding thermodynamics for these structural models of CALP:inhibitor binding, but also foregrounds important structural features and reveals dynamic features, both of which contribute to the comparatively efficient binding of kCAL01. The investigation of energy landscapes complements traditional analysis of the few low-energy conformations found in crystal structures, and provides information about the entire conformational ensemble that is accessible to a protein structure model. Finally, we compare the previously reported NMR-based design model ensemble for kCAL01 vs. the new crystal structure and show that, despite the notable differences between the CALP NMR model and crystal structure, many significant features are successfully captured in the design ensemble. This suggests not only that ensemble-based design captured thermodynamically significant features observed in vitro, but also that a design algorithm eschewing ensembles would likely miss the kCAL01 sequence entirely. 2Interactions between proteins and short linear motif peptides are important in many cellular contexts. 5 One such class of peptide-binding proteins is the PDZ (PSD-95, discs large, ZO-1) domain family, characterized by an 80-90 residue motif 6 that adopts a conserved fold comprised of 2-3 α-helices and 5-6 β-strands and binds C-terminal peptides through β-sheet interactions. 7 These domains commonly modulate protein localization and complex assembly 8-10 and regulate cell signalling, 11,12 thereby playing critical roles in auditory and visual systems; 13,14 epilepsy, pain, and addiction; 10,15 synapse formation; 9 cancer; 11,12,16,17 and cystic fibrosis. [18][19][20][21] Protein-peptide interactions have been imp...

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“…Fraction of RCs unpruned after pruning by iMinDEE (red) or by PLUG followed by iMinDEE (blue), as a function of the pruning interval used for iMinDEE. iMinDEE pruning was performed using the AMBER/EEF1 energy function 47,48 implemented in OSPREY, 49 which is pairwise and thus admits iMinDEE pruning (PLUG pruning is not dependent on the energy function). The green line shows the pruning power of PLUG alone, which is independent of the pruning interval (this is equivalent to the rightmost blue dot, because with an infinite pruning interval, iMinDEE only prunes the most obvious steric clashes A pairwise LUTE matrix was also computed for each of these systems with a rigid backbone but with continuous sidechain entropy.…”

Section: Plug Enables the Highly Efficient Lute Algorithm To Incorpmentioning

confidence: 99%

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Hallen

2018

Proteins

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Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead‐end elimination (DEE). However, the mathematical assumptions of DEE‐based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming‐based method. PLUG's biophysical model consists only of well‐known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy‐based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).

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OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

Cited by 11 publications

References 21 publications

Deciphering interaction fingerprints from protein molecular surfaces

Deciphering interaction fingerprints from protein molecular surfaces

Computational Analysis of Energy Landscapes Reveals Dynamic Features that Contribute to Binding of Inhibitors to CFTR-Associated Ligand

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

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