scholarly journals Assessing and enhancing foldability in designed proteins

2021 ◽  
Author(s):  
Sarel Jacob Fleishman ◽  
Dina Listov ◽  
Rosalie Lipsh-Sokolik ◽  
Che Yang ◽  
Bruno E Correia
Keyword(s):  
Success Rate ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Prediction Method ◽  
Catalytic Efficiency ◽  
Mutation Scanning ◽  
Designed Proteins ◽  
Low Efficiency ◽  
Design Calculations

Recent advances in protein-design methodology have led to a dramatic increase in its reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major cause of design failure is inaccuracy and misfolding relative to the design model. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs; second, we demonstrate that the AlphaFold2 ab initio structure prediction method flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme, thereby improving its catalytic efficiency by 330 fold. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.

scholarly journals Protein structure prediction and design in a biologically-realistic implicit membrane

2019 ◽  
Author(s):  
Rebecca F. Alford ◽  
Patrick J. Fleming ◽  
Karen G. Fleming ◽  
Jeffrey J. Gray
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Computational Design ◽  
Amino Acid Distribution

ABSTRACTProtein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.Significance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

scholarly journals Designed for life: biocompatible de novo designed proteins and components

2018 ◽  
Vol 15 (145) ◽  
pp. 20180472 ◽  
Author(s):  
Katie J. Grayson ◽  
J. L. Ross Anderson
Keyword(s):  
Protein Design ◽  
De Novo ◽  
Building Blocks ◽  
De Novo Design ◽  
Biochemical Processes ◽  
Designed Proteins ◽  
De Novo Protein Design ◽  
Protein Molecules ◽  
And Function

A principal goal of synthetic biology is the de novo design or redesign of biomolecular components. In addition to revealing fundamentally important information regarding natural biomolecular engineering and biochemistry, functional building blocks will ultimately be provided for applications including the manufacture of valuable products and therapeutics. To fully realize this ambitious goal, the designed components must be biocompatible, working in concert with natural biochemical processes and pathways, while not adversely affecting cellular function. For example, de novo protein design has provided us with a wide repertoire of structures and functions, including those that can be assembled and function in vivo . Here we discuss such biocompatible designs, as well as others that have the potential to become biocompatible, including non-protein molecules, and routes to achieving full biological integration.

scholarly journals Rosetta and the Journey to Predict Proteins’ Structures, 20 Years on

2020 ◽  
Vol 15 (6) ◽  
pp. 611-628
Author(s):  
Jad Abbass ◽  
Jean-Christophe Nebel
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Docking ◽  
Ligand Docking ◽  
Ancient Egyptian ◽  
Docking Protein ◽  
Prediction Approach

For two decades, Rosetta has consistently been at the forefront of protein structure prediction. While it has become a very large package comprising programs, scripts, and tools, for different types of macromolecular modelling such as ligand docking, protein-protein docking, protein design, and loop modelling, it started as the implementation of an algorithm for ab initio protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the literature to describe that algorithm and its contribution to the third edition of the community wide Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers have been contributing to deciphering ’the second half of the genetic code’. Although the focus of Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is associated with its fragment-assembly protein structure prediction approach. Following a presentation of the main concepts underpinning its foundation, especially sequence-structure correlation and usage of fragments, we review the main stages of its developments and highlight the milestones it has achieved in terms of protein structure prediction, particularly in CASP.

scholarly journals Improved protein structure prediction using predicted inter-residue orientations

2019 ◽  
Author(s):  
Jianyi Yang ◽  
Ivan Anishchenko ◽  
Hahnbeom Park ◽  
Zhenling Peng ◽  
Sergey Ovchinnikov ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Quantitative Measure ◽  
Design Problems ◽  
Native Proteins ◽  
Benchmark Tests ◽  
Designed Proteins ◽  
Residue Contacts

AbstractThe prediction of inter-residue contacts and distances from co-evolutionary data using deep learning has considerably advanced protein structure prediction. Here we build on these advances by developing a deep residual network for predicting inter-residue orientations in addition to distances, and a Rosetta constrained energy minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on CASP13 and CAMEO derived sets, the method outperforms all previously described structure prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo designed proteins, identifying the key fold determining residues and providing an independent quantitative measure of the “ideality” of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.

scholarly journals Improved protein structure prediction by deep learning irrespective of co-evolution information

2020 ◽  
Author(s):  
Jinbo Xu ◽  
Matthew Mcpartlon ◽  
Jin Li
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
Model Building ◽  
Evolutionary Information ◽  
Designed Proteins ◽  
Structure Relationship ◽  
Over The Top

We describe our latest study of the deep convolutional residual neural networks (ResNet) for protein structure prediction, including deeper and wider ResNets, the efficacy of different input features, and improved 3D model building methods. Our ResNet can predict correct folds (TMscore>0.5) for 26 out of 32 CASP13 FM (template-free-modeling) targets and L/5 long-range contacts for these targets with precision over 80%, a significant improvement over the CASP13 results. Although co-evolution analysis plays an important role in the most successful structure prediction methods, we show that when co-evolution is not used, our ResNet can still predict correct folds for 18 of the 32 CASP13 FM targets including several large ones. This marks a significant improvement over the top co-evolution-based, non-deep learning methods at CASP13, and other non-coevolution-based deep learning models, such as the popular recurrent geometric network (RGN). With only primary sequence, our ResNet can also predict correct folds for all 21 human-designed proteins we tested. In contrast, RGN predicts correct folds for only 3 human-designed proteins and zero CASP13 FM target. In addition, we find that ResNet may fare better for the human-designed proteins when trained without co-evolution information than with co-evolution. These results suggest that ResNet does not simply denoise co-evolution signals, but instead is able to learn important sequence-structure relationship from experimental structures. This has important implications on protein design and engineering especially when evolutionary information is not available.

scholarly journals De novo protein design by deep network hallucination

2020 ◽  
Author(s):  
Ivan Anishchenko ◽  
Tamuka M. Chidyausiku ◽  
Sergey Ovchinnikov ◽  
Samuel J. Pellock ◽  
David Baker
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Structures ◽  
Monte Carlo Sampling ◽  
Amino Acid Sequences ◽  
Wide Range ◽  
Physically Based ◽  
Folded Proteins

AbstractThere has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1–3. We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures. We obtained synthetic genes encoding 129 of these network hallucinated sequences, expressed and purified the proteins in E coli, and found that 27 folded to monomeric stable structures with circular dichroism spectra consistent with the hallucinated structures. Thus deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute, alongside traditional physically based models, to the de novo design of proteins with new functions.

scholarly journals AlphaDesign: A de novo protein design framework based on AlphaFold

2021 ◽  
Author(s):  
Michael Jendrusch ◽  
Jan O. Korbel ◽  
S. Kashif Sadiq
Keyword(s):  
Protein Design ◽  
Structure Prediction ◽  
Structural Integrity ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Structures ◽  
Higher Order ◽  
Design Framework ◽  
Oligomeric State ◽  
Dynamics Simulations

De novo protein design is a longstanding fundamental goal of synthetic biology, but has been hindered by the difficulty in reliable prediction of accurate high-resolution protein structures from sequence. Recent advances in the accuracy of protein structure prediction methods, such as AlphaFold (AF), have facilitated proteome scale structural predictions of monomeric proteins. Here we develop AlphaDesign, a computational framework for de novo protein design that embeds AF as an oracle within an optimisable design process. Our framework enables rapid prediction of completely novel protein monomers starting from random sequences. These are shown to adopt a diverse array of folds within the known protein space. A recent and unexpected utility of AF to predict the structure of protein complexes, further allows our framework to design higher-order complexes. Subsequently a range of predictions are made for monomers, homodimers, heterodimers as well as higher-order homo-oligomers -trimers to hexamers. Our analyses also show potential for designing proteins that bind to a pre-specified target protein. Structural integrity of predicted structures is validated and confirmed by standard ab initio folding and structural analysis methods as well as more extensively by performing rigorous all-atom molecular dynamics simulations and analysing the corresponding structural flexibility, intramonomer and interfacial amino-acid contacts. These analyses demonstrate widespread maintenance of structural integrity and suggests that our framework allows for fairly accurate protein design. Strikingly, our approach also reveals the capacity of AF to predict proteins that switch conformation upon complex formation, such as involving switches from α-helices to β-sheets during amyloid filament formation. Correspondingly, when integrated into our design framework, our approach reveals de novo design of a subset of proteins that switch conformation between monomeric and oligomeric state.

scholarly journals De novo design of a homo-trimeric amantadine-binding protein

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jooyoung Park ◽  
Brinda Selvaraj ◽  
Andrew C McShan ◽  
Scott E Boyken ◽  
Kathy Y Wei ◽  
...  
Keyword(s):  
Small Molecule ◽  
Protein Design ◽  
Metal Ion ◽  
Structural Changes ◽  
De Novo ◽  
Computational Design ◽  
Solution Nmr ◽  
Hydrogen Bond Networks ◽  
De Novo Protein Design ◽  
Design Calculations

The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the C3 symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems.

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