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An accurate binding interaction model in de novo computational protein design of interactions: If you build it, they will bind

An accurate binding interaction model in de novo computational protein design of interactions: If you build it, they will bind | Computational approaches for protein engineering and design | Scoop.it

Computational protein design efforts aim to create novel proteins and functions in an automated manner and, in the process, these efforts shed light on the factors shaping natural proteins. The focus of these efforts has progressed from the interior of proteins to their surface and the design of functions, such as binding or catalysis. Here we examine progress in the development of robust methods for the computational design of non-natural interactions between proteins and molecular targets such as other proteins or small molecules. This problem is referred to as the de novo computational design of interactions. Recent successful efforts inde novo enzyme design and the de novo design of protein–protein interactions open a path towards solving this problem. We examine the common themes in these efforts, and review recent studies aimed at understanding the nature of successes and failures in the de novo computational design of interactions. While several approaches culminated in success, the use of a well-defined structural model for a specific binding interaction in particular has emerged as a key strategy for a successful design, and is therefore reviewed with special consideration.

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A very nice review on protein design of binding interfaces...

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Computational approaches for protein engineering and design
Interesting papers on computation (in-silico) approaches on protein engineering and design.
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Negative Example Selection for Protein Function Prediction: The NoGO Database

Negative Example Selection for Protein Function Prediction: The NoGO Database | Computational approaches for protein engineering and design | Scoop.it

Abstract


Negative examples – genes that are known not to carry out a given protein function – are rarely recorded in genome and proteome annotation databases, such as the Gene Ontology database. Negative examples are required, however, for several of the most powerful machine learning methods for integrative protein function prediction. Most protein function prediction efforts have relied on a variety of heuristics for the choice of negative examples. Determining the accuracy of methods for negative example prediction is itself a non-trivial task, given that the Open World Assumption as applied to gene annotations rules out many traditional validation metrics. We present a rigorous comparison of these heuristics, utilizing a temporal holdout, and a novel evaluation strategy for negative examples. We add to this comparison several algorithms adapted from Positive-Unlabeled learning scenarios in text-classification, which are the current state of the art methods for generating negative examples in low-density annotation contexts. Lastly, we present two novel algorithms of our own construction, one based on empirical conditional probability, and the other using topic modeling applied to genes and annotations. We demonstrate that our algorithms achieve significantly fewer incorrect negative example predictions than the current state of the art, using multiple benchmarks covering multiple organisms. Our methods may be applied to generate negative examples for any type of method that deals with protein function, and to this end we provide a database of negative examples in several well-studied organisms, for general use (The NoGO database, available at: bonneaulab.bio.nyu.edu/nogo.html).

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Dynamics and hydration explain failed functional transformation in dehalogenase design

Dynamics and hydration explain failed functional transformation in dehalogenase design | Computational approaches for protein engineering and design | Scoop.it

Authors : Jan Sykora, Jan Brezovsky, Tana Koudelakova, Maryna Lahoda, Andrea Fortova, Tatsiana Chernovets, Radka Chaloupkova, Veronika Stepankova, Zbynek Prokop, Ivana Kuta Smatanova, Martin Hof & Jiri Damborsky

 

The transplantation of residues from a selective to a nonselective haloalkane dehalogenase yields the correct active site geometry but not function. Computational and biophysical results explain this disparity, showing that the dynamics and hydration of the engineered protein match its parent, not its target.

 

Abstract 

 

We emphasize the importance of dynamics and hydration for enzymatic catalysis and protein design by transplanting the active site from a haloalkane dehalogenase with high enantioselectivity to nonselective dehalogenase. Protein crystallography confirms that the active site geometry of the redesigned dehalogenase matches that of the target, but its enantioselectivity remains low. Time-dependent fluorescence shifts and computer simulations revealed that dynamics and hydration at the tunnel mouth differ substantially between the redesigned and target dehalogenase.

Bernard Offmann's insight:

Another interesting paper from Jiri Damborsky group where they applied horizontal amino acid substitution strategy to design a dehalogenase... It is interesting in the sense that horizontal transfer strategy used here was not efficient to design the desired function though the conformation was as expected.

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Towards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins

BMC Genomics. 2014;15 Suppl 1:S4

(Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): Genomics)

 

Authors: Folkman L, Stantic B, Sattar A

 

Reliable prediction of stability changes induced by a single amino acid substitution is an important aspect of computational protein design. Several machine learning methods capable of predicting stability changes from the protein sequence alone have been introduced. Prediction performance of these methods is evaluated on mutations unseen during training. Nevertheless, different mutations of the same protein, and even the same residue, as encountered during training are commonly used for evaluation. We argue that a faithful evaluation can be achieved only when a method is tested on previously unseen proteins with low sequence similarity to the training set.

 

Abstract


BACKGROUND: Reliable prediction of stability changes induced by a single amino acid substitution is an important aspect of computational protein design. Several machine learning methods capable of predicting stability changes from the protein sequence alone have been introduced. Prediction performance of these methods is evaluated on mutations unseen during training. Nevertheless, different mutations of the same protein, and even the same residue, as encountered during training are commonly used for evaluation. We argue that a faithful evaluation can be achieved only when a method is tested on previously unseen proteins with low sequence similarity to the training set.
RESULTS: We provided experimental evidence of the limitations of the evaluation commonly used for assessing the prediction performance. Furthermore, we demonstrated that the prediction of stability changes in previously unseen non-homologous proteins is a challenging task for currently available methods. To improve the prediction performance of our previously proposed method, we identified features which led to over-fitting and further extended the model with new features. The new method employs Evolutionary And Structural Encodings with Amino Acid parameters (EASE-AA). Evaluated with an independent test set of more than 600 mutations, EASE-AA yielded a Matthews correlation coefficient of 0.36 and was able to classify correctly 66% of the stabilising and 74% of the destabilising mutations. For real-value prediction, EASE-AA achieved the correlation of predicted and experimentally measured stability changes of 0.51.
CONCLUSIONS: Commonly adopted evaluation with mutations in the same protein, and even the same residue, randomly divided between the training and test sets lead to an overestimation of prediction performance. Therefore, stability changes prediction methods should be evaluated only on mutations in previously unseen non-homologous proteins. Under such an evaluation, EASE-AA predicts stability changes more reliably than currently available methods.

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Expanding Anfinsen’s Principle: Contributions of Synonymous Codon Selection to Rational Protein Design

Expanding Anfinsen’s Principle: Contributions of Synonymous Codon Selection to Rational Protein Design | Computational approaches for protein engineering and design | Scoop.it

Abstract

Anfinsen’s principle asserts that all information required to specify the structure of a protein is encoded in its amino acid sequence. However, during protein synthesis by the ribosome, the N-terminus of the nascent chain can begin to fold before the C-terminus is available. We tested whether this cotranslational folding can alter the folded structure of an encoded protein in vivo, versus the structure formed when refolded in vitro. We designed a fluorescent protein consisting of three half-domains, where the N- and C-terminal half-domains compete with each other to interact with the central half-domain. The outcome of this competition determines the fluorescence properties of the resulting folded structure. Upon refolding after chemical denaturation, this protein produced equimolar amounts of the N- and C-terminal folded structures, respectively. In contrast, translation in Escherichia coli resulted in a 2-fold enhancement in the formation of the N-terminal folded structure. Rare synonymous codon substitutions at the 5′ end of the C-terminal half-domain further increased selection for the N-terminal folded structure. These results demonstrate that the rate at which a nascent protein emerges from the ribosome can specify the folded structure of a protein.

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Bernard Offmann's curator insight, February 23, 9:48 AM

This very nice paper was quoted by Catherine Goodman in Nature Chemical Biology (Protein Folding; The Inside Scoop).(http://www.nature.com/nchembio/journal/v10/n3/full/nchembio.1465.html)

It shows elegantly how codon usage can affect folding.

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Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization

Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization | Computational approaches for protein engineering and design | Scoop.it

Weitzner BD, Kuroda D, Marze N, Xu J, Gray JJ.

 

Abstract

 

Antibody Modeling Assessment II (AMA-II) provided an opportunity to benchmark RosettaAntibody on a set of eleven unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non-H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the eleven targets, two of which were predicted with sub-Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo CDR H3 modeling remains challenging at long loop lengths, but constraining the C-terminal end of H3 to a kinked conformation allows near-native conformations to be sampled more frequently. We also found that incorrect VL–VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL–VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody.

Bernard Offmann's insight:

This is a very interesting assessment of Rosetta Antibody 3.0 for antibody modeling. Definitely a must read paper. 

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Antibody Structure Determination Using a Combination of Homology Modeling, Energy-Based Refinement and Loop Prediction

Antibody Structure Determination Using a Combination of Homology Modeling, Energy-Based Refinement and Loop Prediction | Computational approaches for protein engineering and design | Scoop.it

Zhu K, Day T, Warshaviak D, Murrett C, Friesner R, Pearlman D.

 

ABSTRACT

We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets. 

 

Bernard Offmann's insight:

This is a nice methodological paper from Shrodinger's team that tackles on of the hardest problem in protein loop design.

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Incorporating Replacement Free Energy of Binding-site Waters in Molecular Docking

Incorporating Replacement Free Energy of Binding-site Waters in Molecular Docking | Computational approaches for protein engineering and design | Scoop.it
ABSTRACT

Binding-site water molecules play a crucial role in protein-ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding-site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding-site waters into molecular docking. We firstly developed a Solvent Property Analysis (SPA) program to compute the replacement free energies of binding-site water molecules by post-processing molecular dynamics trajectories obtained from ligand-free protein structure simulation in explicit water. Next, we implemented a distance-dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding-site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein-ligand-water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available upon request) may be applied in identifying hot-spot binding-site residues and structure-based lead optimization.

 

Keywords:binding-site water;molecular dynamics;molecular docking;receptor desolvation energy;binding pose;docking enrichment
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This might be of interest for people working on docking carbohydrates to proteins...

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Coarse Grained Model for Biological Simulations: Recent Refinements and Validation - Vicatos - Proteins: Structure, Function, and Bioinformatics - Wiley Online Library

Coarse Grained Model for Biological Simulations: Recent Refinements and Validation - Vicatos - Proteins: Structure, Function, and Bioinformatics - Wiley Online Library | Computational approaches for protein engineering and design | Scoop.it
Abstract

Exploring the free energy landscape of proteins and modeling the corresponding functional aspects presents a major challenge for computer simulation approaches. This challenge is due to the complexity of the landscape and the enormous computer time needed for converging simulations. The use of various simplified coarse grained (CG) models offers an effective way of sampling the landscape, but most current models are not expected to give a reliable description of protein stability and functional aspects. The main problem is associated with insufficient focus on the electrostatic features of the model. In this respect our recent CG model offers significant advantage as it has been refined while focusing on its electrostatic free energy. Here we review the current state of our model, describing recent refinement, extensions and validation studies while focusing on demonstrating key applications. These include studies of protein stability, extending the model to include membranes and electrolytes and electrodes as well as studies of voltage activated proteins, protein insertion trough the translocon, the action of molecular motors and even the coupling of the stalled ribosome and the translocon. Our example illustrates the general potential of our approach in overcoming major challenges in studies of structure function correlation in proteins and large macromolecular complexes. 

Keywords:Coarse Grained model;free energy calculations;dielectric constants;proton transfer;protein residue interactions;simulated protein unfolding
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Nice paper by Nobel Prize Arieh Warshel & co.

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Computational Protein Design Quantifies Structural Constraints on Amino Acid Covariation

Computational Protein Design Quantifies Structural Constraints on Amino Acid Covariation | Computational approaches for protein engineering and design | Scoop.it
Abstract Amino acid covariation, where the identities of amino acids at different sequence positions are correlated, is a hallmark of naturally occurring proteins. This covariation can arise from multiple factors, including selective pressures for maintaining protein structure, requirements imposed by a specific function, or from phylogenetic sampling bias. Here we employed flexible backbone computational protein design to quantify the extent to which protein structure has constrained amino acid covariation for 40 diverse protein domains. We find significant similarities between the amino acid covariation in alignments of natural protein sequences and sequences optimized for their structures by computational protein design methods. These results indicate that the structural constraints imposed by protein architecture play a dominant role in shaping amino acid covariation and that computational protein design methods can capture these effects. We also find that the similarity between natural and designed covariation is sensitive to the magnitude and mechanism of backbone flexibility used in computational protein design. Our results thus highlight the necessity of including backbone flexibility to correctly model precise details of correlated amino acid changes and give insights into the pressures underlying these correlations. Author Summary Proteins generally fold into specific three-dimensional structures to perform their cellular functions, and the presence of misfolded proteins is often deleterious for cellular and organismal fitness. For these reasons, maintenance of protein structure is thought to be one of the major fitness pressures acting on proteins. Consequently, the sequences of today's naturally occurring proteins contain signatures reflecting the constraints imposed by protein structure. Here we test the ability of computational protein design methods to recapitulate and explain these signatures. We focus on the physical basis of evolutionary pressures that act on interactions between amino acids in folded proteins, which are critical in determining protein structure and function. Such pressures can be observed from the appearance of amino acid covariation, where the amino acids at certain positions in protein sequences are correlated with each other. We find similar patterns of amino acid covariation in natural sequences and sequences optimized for their structures using computational protein design, demonstrating the importance of structural constraints in protein molecular evolution and providing insights into the structural mechanisms leading to covariation. In addition, these results characterize the ability of computational methods to model the precise details of correlated amino acid changes, which is critical for engineering new proteins with useful functions beyond those seen in nature.
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Innovation by homologous recombination

Devin L Trudeau, Matthew A Smith, Frances H Arnold


Swapping fragments among protein homologs can produce chimeric proteins with a wide range of properties, including properties not exhibited by the parents. Computational methods that use information from structures and sequence alignments have been used to design highly functional chimeras and chimera libraries. Recombination has generated proteins with diverse thermostability and mechanical stability, enzyme substrate specificity, and optogenetic properties. Linear regression, Gaussian processes, and support vector machine learning have been used to model sequence-function relationships and predict useful chimeras. These approaches enable engineering of protein chimeras with desired functions, as well as elucidation of the structural basis for these functions.

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Incorporating metals into de novo proteins

Anna FA Peacock


Abstract


The de novo design of artificial metalloproteins from first-principles is a powerful strategy with which to establish the minimum structure required for function, as well as to identify the important design features for tuning the chemistry of the coordinated metal ion. Herein we describe recent contributions to this field, covering metallo-porphyrin, mononuclear and multinuclear metal ion sites engineered into de novo proteins. Using miniature artificial scaffolds these examples demonstrate that complex natural protein folds are not required to mimic naturally occurring metal ion sites in proteins. More importantly progress is being made to engineer de novo metalloproteins capable of performing functions not in the repertoire of biology.

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Precision is essential for efficient catalysis in an evolved Kemp eliminase

Rebecca Blomberg, Hajo Kries, Daniel M. Pinkas, Peer R. E. Mittl, Markus G. Grütter, Heidi K. Privett, Stephen L. Mayo & Donald Hilvert 

 

Linus Pauling established the conceptual framework for understanding and mimicking enzymes more than six decades ago1. The notion that enzymes selectively stabilize the rate-limiting transition state of the catalysed reaction relative to the bound ground state reduces the problem of design to one of molecular recognition. Nevertheless, past attempts to capitalize on this idea, for example by using transition state analogues to elicit antibodies with catalytic activities2, have generally failed to deliver true enzymatic rates. The advent of computational design approaches, combined with directed evolution, has provided an opportunity to revisit this problem. Starting from a computationally designed catalyst for the Kemp elimination3—a well-studied model system for proton transfer from carbon—we show that an artificial enzyme can be evolved that accelerates an elementary chemical reaction 6 × 108-fold, approaching the exceptional efficiency of highly optimized natural enzymes such as triosephosphate isomerase. A 1.09 Å resolution crystal structure of the evolved enzyme indicates that familiar catalytic strategies such as shape complementarity and precisely placed catalytic groups can be successfully harnessed to afford such high rate accelerations, making us optimistic about the prospects of designing more sophisticated catalysts.

Bernard Offmann's insight:

Very nice protein design paper from Donald Hilvert and Steve Mayo.

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Free energy computations identify the mutations required to confer trans-sialidase activity into Trypanosoma rangeli sialidase

Free energy computations identify the mutations required to confer trans-sialidase activity into Trypanosoma rangeli sialidase | Computational approaches for protein engineering and design | Scoop.it

Abstract

Trypanosoma rangeli‘s sialidase (TrSA) and Trypanosoma cruzi‘s trans-sialidase (TcTS) are members of the glycoside hydrolase family 33 (GH-33). They share 70% of sequence identity and their crystallographic Cα-rmsd is 0.59 Å. In spite of these similarities they catalyze different reactions. TcTS transfers sialic acid between glycoconjugates while TrSA can only cleave sialic acid from sialyl-glyconjugates. Significant effort has been invested into unraveling the differences between TrSA and TcTS, and into conferring TrSA with trans-sialidase activity through appropriate point mutations. Recently, we calculated the free energy change for the formation of the covalent intermediate (CI) in TcTS and performed an energy decomposition analysis of that process. In this article we present a similar study for the formation of the CI in TrSA, as well as in a quintuple mutant (TrSA5mut), which has faint trans-sialidase activity. The comparison of these new results with those previously obtained for TcTS allowed identifying five extra mutations to be introduced in TrSA5mut that should create a mutant (TrSA10mut) with high trans-sialidase activity. © Proteins 2013;. © 2013 Wiley Periodicals, Inc.

Bernard Offmann's insight:

Very interesting paper. In a preliminary study, 5 mutations were identified by the authors that confered slight trans-sialidase activity to a sialidase from Trypanosoma rangeli (TrSA). In this paper, the authors identified 5 additional mutations that is expected to confer trans-sialidase to this TrSA. They seem not to provide experimental evidence for this claim of functional switch.

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Accurate design of co-assembling multi-component protein nanomaterials : Nature : Nature Publishing Group

Accurate design of co-assembling multi-component protein nanomaterials : Nature : Nature Publishing Group | Computational approaches for protein engineering and design | Scoop.it

The self-assembly of proteins into highly ordered nanoscale architectures is a hallmark of biological systems. The sophisticated functions of these molecular machines have inspired the development of methods to engineer self-assembling protein nanostructures; however, the design of multi-component protein nanomaterials with high accuracy remains an outstanding challenge. Here we report a computational method for designing protein nanomaterials in which multiple copies of two distinct subunits co-assemble into a specific architecture. We use the method to design five 24-subunit cage-like protein nanomaterials in two distinct symmetric architectures and experimentally demonstrate that their structures are in close agreement with the computational design models. The accuracy of the method and the number and variety of two-component materials that it makes accessible suggest a route to the construction of functional protein nanomaterials tailored to specific applications.

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Design of activated serine–containing catalytic triads with atomic-level accuracy : Nature Chemical Biology : Nature Publishing Group

Design of activated serine–containing catalytic triads with atomic-level accuracy : Nature Chemical Biology : Nature Publishing Group | Computational approaches for protein engineering and design | Scoop.it

Authors : Sridharan Rajagopalan, Chu Wang, Kai Yu, Alexandre P Kuzin, Florian Richter, Scott Lew, Aleksandr E Miklos, Megan L Matthews, Jayaraman Seetharaman, Min Su, John F Hunt, Benjamin F Cravatt & David Baker


Abstract


A challenge in the computational design of enzymes is that multiple properties, including substrate binding, transition state stabilization and product release, must be simultaneously optimized, and this has limited the absolute activity of successful designs. Here, we focus on a single critical property of many enzymes: the nucleophilicity of an active site residue that initiates catalysis. We design proteins with idealized serine-containing catalytic triads and assess their nucleophilicity directly in native biological systems using activity-based organophosphate probes. Crystal structures of the most successful designs show unprecedented agreement with computational models, including extensive hydrogen bonding networks between the catalytic triad (or quartet) residues, and mutagenesis experiments demonstrate that these networks are critical for serineactivation and organophosphate reactivity. Following optimization by yeast display, the designs react with organophosphate probes at rates comparable to natural serine hydrolases. Co-crystal structures with diisopropyl fluorophosphate bound to the serine nucleophile suggest that the designs could provide the basis for a new class of organophosphate capture agents.

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Automating human intuition for protein design

ABSTRACT

 

In the design of new enzymes and binding proteins, human intuition is often used to modify computationally designed amino acid sequences prior to experimental characterization. The manual sequence changes involve both reversions of amino acid mutations back to the identity present in the parent scaffold and the introduction of residues making additional interactions with the binding partner or backing up first shell interactions. Automation of this manual sequence refinement process would allow more systematic evaluation and considerably reduce the amount of human designer effort involved. Here we introduce a benchmark for evaluating the ability of automated methods to recapitulate the sequence changes made to computer-generated models by human designers, and use it to assess alternative computational methods. We find the best performance for a greedy one-position-at-a-time optimization protocol that utilizes metrics (such as shape complementarity) and local refinement methods too computationally expensive for global Monte Carlo (MC) sequence optimization. This protocol should be broadly useful for improving the stability and function of designed binding proteins. Proteins 2014; 82:858–866.

Bernard Offmann's insight:

The benchmark provided is interesting and contains many unpublished data...

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An integrated approach for thermal stabilization of a mesophilic adenylate kinase

An integrated approach for thermal stabilization of a mesophilic adenylate kinase | Computational approaches for protein engineering and design | Scoop.it

Sojin Moon, Du-kyo Jung, George N. Phillips, Euiyoung Bae

 

ABSTRACT

 

Thermally stable proteins are desirable for research and industrial purposes, but redesigning proteins for higher thermal stability can be challenging. A number of different techniques have been used to improve the thermal stability of proteins, but the extents of stability enhancement were sometimes unpredictable and not significant. Here, we systematically tested the effects of multiple stabilization techniques including a bioinformatic method and structure-guided mutagenesis on a single protein, thereby providing an integrated approach to protein thermal stabilization. Using a mesophilic adenylate kinase as a model, we identified stabilizing mutations based on various stabilization techniques, and generated a series of adenylate kinase variants by introducing mutations both individually and collectively. The redesigned proteins displayed a range of increased thermal stabilities, the most stable of which was comparable to a naturally evolved thermophilic homologue with more than a 25 degree increase in its thermal denaturation midpoint. We also solved crystal structures of three representative variants including the most stable variant, to confirm the structural basis for their increased stabilities. These results provide a unique opportunity for systematically analyzing the effectiveness and additivity of various stabilization mechanisms, and they represent a useful approach for improving protein stability by integrating the reduction of local structural entropy and the optimization of global non-covalent interactions such as hydrophobic contact and ion pairs.

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Antibody modeling assessment II: Structures and models

Antibody modeling assessment II: Structures and models | Computational approaches for protein engineering and design | Scoop.it

Alexey Teplyakov, Jinquan Luo, Galina Obmolova, Thomas J. Malia, Raymond Sweet, Robyn L. Stanfield, Sreekumar Kodangattil, Juan Carlos Almagro, Gary L. Gilliland

 

Abstract

 

To assess the state of the art in antibody structure modeling, a blinded study was conducted. Eleven unpublished Fab crystal structures were used as a benchmark to compare Fv models generated by 7 structure prediction methodologies. In the first round, each participant submitted 3 non-ranked complete Fv models for each target. In the second round, CDR-H3 modeling was performed in the context of the correct environment provided by the crystal structures with CDR-H3 removed. In this report we describe the reference structures and present our assessment of the models. Some of the essential sources of errors in the predictions were traced to the selection of the structure template, both in terms of the CDR canonical structures and VL/VH packing. On top of this, the errors present in the PDB structures were sometimes propagated in the current models, which emphasized the need for the curated structural database devoid of errors. Modeling non-canonical structures, including CDR-H3, remains the biggest challenge for antibody structure prediction.

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Combinatorial Engineering of Dextransucrase Specificity

Combinatorial Engineering of Dextransucrase Specificity | Computational approaches for protein engineering and design | Scoop.it

Romain Irague, Laurence Tarquis, Isabelle André, Claire Moulis, Sandrine Morel, Pierre Monsan, Gabrielle Potocki-Véronèse, Magali Remaud-Siméon.

 

We used combinatorial engineering to investigate the relationships between structure and linkage specificity of the dextransucrase DSR-S from Leuconostoc mesenteroides NRRL B-512F, and to generate variants with altered specificity. Sequence and structural analysis of glycoside-hydrolase family 70 enzymes led to eight amino acids (D306, F353, N404, W440, D460, H463, T464 and S512) being targeted, randomized by saturation mutagenesis and simultaneously recombined. Screening of two libraries totaling 3.6.104 clones allowed the isolation of a toolbox comprising 81 variants which synthesize high molecular weight α-glucans with different proportions of α(1→3) linkages ranging from 3 to 20 %. Mutant sequence analysis, biochemical characterization and molecular modelling studies revealed the previously unknown role of peptide 460DYVHT464 in DSR-S linkage specificity. This peptide sequence together with residue S512 contribute to defining +2 subsite topology, which may be critical for the enzyme regiospecificity.


Via Anna Nikulina
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A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. (Scientific Reports : Nature Publishing Group)

A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction. (Scientific Reports : Nature Publishing Group) | Computational approaches for protein engineering and design | Scoop.it

Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions.

Bernard Offmann's insight:

Interesting paper on fold recognition that assesses the performance of template identification methods based on sequence search for protein structure prediction.

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Exploring Fold Space Preferences of New-born an...

Exploring Fold Space Preferences of New-born an... | Computational approaches for protein engineering and design | Scoop.it
Abstract The evolution of proteins is one of the fundamental processes that has delivered the diversity and complexity of life we see around ourselves today.
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Membrane proteins by accident or design

John Simms, Paula J Booth


Protein design is a valuable tool to create bespoke proteins with desired properties as well as for investigating sequence, structure and function relationships. Membrane protein design is a burgeoning field that is hampered by the lack of high-resolution structural information. In spite of these shortcomings, computational methods have offered a route towards blueprints for these hydrophobic proteins. Advances in structural scoring and sampling methods are enabling more accurate predictions of a folded structure from the primary amino acid sequence. This review highlights a number of novel studies focusing on the methods and information used to successfully design membrane proteins.

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Computational protein design of ligand binding and catalysis

Kaspar Feldmeier, Birte Höcker


Highlights
• Computational methods hold great potential for custom-made protein design.
• Designs were constructed for many catalytic reaction types and substrates.
• Experimental characterization after or during the design provides valuable feedback.
• Combination with directed evolution is powerful in improving efficiency and affinity.
• Ligand recognition is still a bottleneck for computational enzyme design.


The vision of custom-made proteins by computation appears closer than ever. Computational methods have advanced rapidly in recent years and proteins have been designed to catalyze new reactions. A number of second-generation enzyme designs analyzed possible bottlenecks and started tackling emergent problems. Detailed experimental analysis combined with structure determination and molecular dynamics simulations as well as design optimization with directed evolution techniques have led to important insights. While ligand recognition seems to be particularly problematic, new approaches focus on this design aspect and promising improvements have been made.

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New designed protein assemblies

Sabina Božič, Tibor Doles, Helena Gradišar, Roman Jerala


Abstract

 

Self-assembly is an essential concept of all organisms. Polypeptides self-assemble either within a single polypeptide chain or through assembly of protein domains. Recent advances in designed protein assemblies were achieved by genetic or chemical linkage of oligomerization domains and by engineering new interaction interfaces, which resulted in formation of lattices and cage-like protein assemblies. The absence of new experimentally determined protein folds in the last few years underlines the challenge of designing new folds. Recently a new strategy for designing self-assembly of a polypeptide fold, based on the topological arrangement of coiled-coil modules as the protein origami, has been proposed. The polypeptide tetrahedron was designed from a single chain concatenating of coiled-coil forming building modules interspersed with flexible hinges. In this strategy the order of coiled-coil segments defines the fold of the polypeptide nanostructure.

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Construction of proteins with molecular recognition capabilities using α3β3 de novo protein scaffolds

The molecular recognition ability of proteins is essential in biological systems, and therefore a considerable amount of effort has been devoted to constructing desired target-binding proteins using a variety of naturally occurring proteins as scaffolds. However, since generating a binding site in a native protein can often affect its structural properties, highly stablede novo protein scaffolds may be more amenable than the native proteins. We previously reported the generation of de novo proteins comprising three α-helices and three β-strands (α3β3) from a genetic library coding simplified amino acid sets. Two α3β3 de novo proteins, vTAJ13 and vTAJ36, fold into a native-like stable and molten globule-like structures, respectively, even though the proteins have similar amino acid compositions. Here, we attempted to create binding sites for the vTAJ13 and vTAJ36 proteins to prove the utility of de novo designed artificial proteins as a molecular recognition tool. Randomization of six amino acids at two linker sites of vTAJ13 and vTAJ36 followed by biopanning generated binding proteins that recognize the target molecules, fluorescein and green fluorescent protein, with affinities of 10−7–10−8 M. Of note, the selected proteins from the vTAJ13-based library tended to recognize the target molecules with high specificity, probably due to the native-like stable structure of vTAJ13. Our studies provide an example of the potential of de novo protein scaffolds, which are composed of a simplified amino acid set, to recognize a variety of target compounds.

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