Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

De novo design of cavity-containing proteins with a backbone-centered neural network energy function

The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones without predefined architectures by introducing tailored restraints into the backbone sampling driven by SCUBA (Side Chain-Unknown Backbone Arrangement), a neural network statistical energy function. For 521 out of 5816 designs, the root-mean-square deviations (RMSDs) of the Cα atoms for the AlphaFold2-predicted structures and our designed structures are within 2.0 Å. We experimentally tested 10 designed proteins and determined the crystal structures of two of them. One closely agrees with the designed model, while the other forms a domain-swapped dimer, where the partial structures are in agreement with the designed structures. Our results indicate that data-driven methods such as SCUBA hold great potential for designing de novo proteins with tailored small-molecule-binding function.

Computational Enzyme Redesign Enhances Tolerance to Denaturants for Peptide C-Terminal Amidation

The escalating demand for biocatalysts in pharmaceutical and biochemical applications underscores the critical imperative to enhance enzyme activity and durability under high denaturant concentrations. Nevertheless, the development of a practical computational redesign protocol for improving enzyme tolerance to denaturants is challenging due to the limitations of relying solely on model-driven approaches to adequately capture denaturant-enzyme interactions. In this study, we introduce an enzyme redesign strategy termed GRAPE_DA, which integrates multiple data-driven and model-driven computational methods to mitigate the sampling biases inherent in a single approach and comprehensively predict beneficial mutations on both the protein surface and backbone. To illustrate the methodology's effectiveness, we applied it to engineer a peptidylamidoglycolate lyase, resulting in a variant exhibiting up to a 24-fold increase in peptide C-terminal amidation activity under 2.5 M guanidine hydrochloride. We anticipate that this integrated engineering strategy will facilitate the development of enzymatic peptide synthesis and functionalization under denaturing conditions and highlight the role of engineering surface residues in governing protein stability.

Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading

Biotechnological plastic recycling has emerged as a suitable option for addressing the pollution crisis. A major breakthrough in the biodegradation of poly(ethylene terephthalate) (PET) is achieved by using a LCC variant, which permits 90% conversion at an industrial level. Despite the achievements, its applications have been hampered by the remaining 10% of nonbiodegradable PET. Herein, we address current challenges by employing a computational strategy to engineer a hydrolase from the bacterium HR29. The redesigned variant, TurboPETase, outperforms other well-known PET hydrolases. Nearly complete depolymerization is accomplished in 8 h at a solids loading of 200 g kg-1. Kinetic and structural analysis suggest that the improved performance may be attributed to a more flexible PET-binding groove that facilitates the targeting of more specific attack sites. Collectively, our results constitute a significant advance in understanding and engineering of industrially applicable polyester hydrolases, and provide guidance for further efforts on other polymer types.

Protein sequence design on given backbones with deep learning

Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network

Recent advances in deep learning have significantly improved the ability to infer protein sequences directly from protein structures for the fix-backbone design. The methods have evolved from the early use of multi-layer perceptrons to convolutional neural networks, transformers, and graph neural networks (GNN). However, the conventional approach of constructing K-nearest-neighbors (KNN) graph for GNN has limited the utilization of edge information, which plays a critical role in network performance. Here we introduced SPIN-CGNN based on protein contact maps for nearest neighbors. Together with auxiliary edge updates and selective kernels, we found that SPIN-CGNN provided a comparable performance in refolding ability by AlphaFold2 to the current state-of-the-art techniques but a significant improvement over them in term of sequence recovery, perplexity, deviation from amino-acid compositions of native sequences, conservation of hydrophobic positions, and low complexity regions, according to the test by unseen structures, "hallucinated" structures and diffusion models. Results suggest that low complexity regions in the sequences designed by deep learning, for generated structures in particular, remain to be improved, when compared to the native sequences.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

Increasing protein stability by inferring substitution effects from high-throughput experiments

We apply a computational model, global multi-mutant analysis (GMMA), to inform on effects of most amino acid substitutions from a randomly mutated gene library. Using a high mutation frequency, the method can determine mutations that increase the stability of even very stable proteins for which conventional selection systems have reached their limit. As a demonstration of this, we screened a mutant library of a highly stable and computationally redesigned model protein using an in vivo genetic sensor for folding and assigned a stability effect to 374 of 912 possible single amino acid substitutions. Combining the top 9 substitutions increased the unfolding energy 47 to 69 kJ/mol in a single engineering step. Crystal structures of stabilized variants showed small perturbations in helices 1 and 2, which rendered them closer in structure to the redesign template. This case study illustrates the capability of the method, which is applicable to any screen for protein function.

Rotamer-free protein sequence design based on deep learning and self-consistency

Several previously proposed deep learning methods to design amino acid sequences that autonomously fold into a given protein backbone yielded promising results in computational tests but did not outperform conventional energy function-based methods in wet experiments. Here we present the ABACUS-R method, which uses an encoder-decoder network trained using a multitask learning strategy to predict the sidechain type of a central residue from its three-dimensional local environment, which includes, besides other features, the types but not the conformations of the surrounding sidechains. This eliminates the need to reconstruct and optimize sidechain structures, and drastically simplifies the sequence design process. Thus iteratively applying the encoder-decoder to different central residues is able to produce self-consistent overall sequences for a target backbone. Results of wet experiments, including five structures solved by X-ray crystallography, show that ABACUS-R outperforms state-of-the-art energy function-based methods in success rate and design precision.

DEPACT and PACMatch: A Workflow of Designing De Novo Protein Pockets to Bind Small Molecules

Engineering of new functional proteins such as enzymes and biosensors involves the design of new protein pockets for the specific binding of small molecules. Here, we report a workflow composed of two new computational methods to execute this task. The DEPACT (Design Pocket as a Cluster based on Templates) method is a data-driven approach to design and evaluate small-molecule-binding pockets as isolated clusters, while the PACMatch method is a computational approach to match pocket residues in a cluster model to positions on given protein scaffolds. Using DEPACT and its scoring function, pocket clusters of natural-pocket-like chemical compositions and protein-ligand interaction strength can be designed. DEPACT can design pocket clusters containing water- or metal-ion-mediated protein-ligand interactions. While being able to efficiently treat relatively large pocket cluster models (e.g., of around 10 pocket residues), PACMatch outperforms previous methods in test cases of recovering the native positions of pocket residues in natural enzyme-substrate complexes.

A backbone-centred energy function of neural networks for protein design

A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it^1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions^3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo design.

Knowledge-Based Unfolded State Model for Protein Design

The design of proteins and miniproteins is an important challenge. Designed variants should be stable, meaning the folded/unfolded free energy difference should be large enough. Thus, the unfolded state plays a central role. An extended peptide model is often used, where side chains interact with solvent and nearby backbone, but not each other. The unfolded energy is then a function of sequence composition only and can be empirically parametrized. If the space of sequences is explored with a Monte Carlo procedure, protein variants will be sampled according to a well-defined Boltzmann probability distribution. We can then choose unfolded model parameters to maximize the probability of sampling native-like sequences. This leads to a well-defined maximum likelihood framework. We present an iterative algorithm that follows the likelihood gradient. The method is presented in the context of our Proteus software, as a detailed downloadable tutorial. The unfolded model is combined with a folded model that uses molecular mechanics and a Generalized Born solvent. It was optimized for three PDZ domains and then used to redesign them. The sequences sampled are native-like and similar to a recent PDZ design study that was experimentally validated.

De novo protein design by an energy function based on series expansion in distance and orientation dependence

MOTIVATION

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing.

RESULTS

The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design.

AVAILABILITY AND IMPLEMENTATION

The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

Discovering the Ultimate Limits of Protein Secondary Structure Prediction

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81-86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4-5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84-87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

Computational and Experimental Assessment of Backbone Templates for Computational Redesign of the Thioredoxin Fold

Computational protein design has taken big strides in recent years; however, the tools available are still not at a state where a sequence can be designed to fold into a given protein structure at will and with high probability. We have applied here a recent release of Rosetta Design to redesign a set of structurally very similar proteins belonging to the thioredoxin fold. We used a genetic screening tool to estimate solubility/folding of the designed proteins in E. coli and to select the best hits from this for further biochemical characterization. We have previously used this set of template proteins for redesign and found that success was highly dependent on template structure, a trait which was also found in this study. Nevertheless, state-of-the-art design software is now able to predict the best template, most likely due to the introduction of an energy term that reports on stress in covalent bond lengths and angles. The template that led to the greatest fraction of successful designs was the same (a thioredoxin from spinach) as that identified in our previous study. Our previously described redesign of thioredoxin, which also used the spinach protein as a template, however also performed well as a template. In the present study, both of these templates yielded proteins with compact folded structures and enforced the conclusion that any design project must carefully consider different design templates. Fortunately, selecting designs based on energies appears to correctly identify such templates.

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

Multiobjective heuristic algorithm for de novo protein design in a quantified continuous sequence space

Protein design usually involves sequence search process and evaluation criteria. Commonly used methods primarily implement the Monte Carlo or simulated annealing algorithm with a single-energy function to obtain ideal solutions, which is often highly time-consuming and limited by the accuracy of the energy function. In this report, we introduce a multiobjective algorithm named Hydra for protein design, which employs two different energy functions to optimize solutions simultaneously and makes use of the latent quantitative relationship between different amino acid types to facilitate the search process. The framework uses two kinds of prior information to transform the original disordered discrete sequence space into a relatively ordered space, and decoy sequences are searched in this ordered space through a multiobjective swarm intelligence algorithm. This algorithm features high accuracy and a high-speed search process. Our method was tested on 40 targets covering different fold classes, which were computationally verified to be well folded, and it experimentally solved the 1UBQ fold by NMR in excellent agreement with the native structure with a backbone RMSD deviation of 1.074 Å. The Hydra software package can be downloaded from: http://www.csbio.sjtu.edu.cn/bioinf/HYDRA/ for academic use.

Rational Design of Adenylate Kinase Thermostability through Coevolution and Sequence Divergence Analysis

Protein engineering is actively pursued in industrial and laboratory settings for high thermostability. Among the many protein engineering methods, rational design by bioinformatics provides theoretical guidance without time-consuming experimental screenings. However, most rational design methods either rely on protein tertiary structure information or have limited accuracies. We proposed a primary-sequence-based algorithm for increasing the heat resistance of a protein while maintaining its functions. Using adenylate kinase (ADK) family as a model system, this method identified a series of amino acid sites closely related to thermostability. Single- and double-point mutants constructed based on this method increase the thermal denaturation temperature of the mesophilic Escherichia coli (E. coli) ADK by 5.5 and 8.3 °C, respectively, while preserving most of the catalytic function at ambient temperatures. Additionally, the constructed mutants have improved enzymatic activity at higher temperature.

De novo sequence redesign of a functional Ras-binding domain globally inverted the surface charge distribution and led to extreme thermostability

To acquire extremely thermostable proteins of given functions is challenging for conventional protein engineering. Here we applied ABACUS, a statistical energy function we developed for de novo amino acid sequence design, to globally redesign a Ras-binding domain (RBD), and obtained an extremely thermostable RBD that unfolds reversibly at above 110°C, the redesigned RBD experimentally confirmed to have expected structure and Ras-binding interface. Directed evolution of the redesigned RBD improved its Ras-binding affinity to the native protein level without excessive loss of thermostability. The designed amino acid substitutions were mostly at the protein surface. For many substitutions, strong epistasis or significantly differentiated effects on thermostability in the native sequence context relative to the redesigned sequence context were observed, suggesting the globally redesigned sequence to be unreachable through combining beneficial mutations of the native sequence. Further analyses revealed that by replacing 38 of a total of 48 non-interfacial surface residues at once, ABACUS redesign was able to globally "invert" the protein's charge distribution pattern in an optimized way. Our study demonstrates that computational protein design provides powerful new tools to solve challenging protein engineering problems.

GRAPE, a greedy accumulated strategy for computational protein engineering

Nature harbors fascinating enzymatic catalysts with high efficiency, chemo-, regio- and stereoselectivity. However, the insufficient stability of the enzymes often prevents their widespread utilization for industrial processes. Not content with the finite repertoire of naturally occurring enzymes, protein engineering holds promises to extend the applications of the improved enzymes with desired physical and catalytic properties. Herein, we devised a computational strategy (greedy accumulated strategy for protein engineering, GRAPE) to enhance the thermostability of enzymes. Through scanning of all point mutations of the structural and evolutionary consensus analysis, a library containing fewer than 100 mutations was established for characterization. After preliminary experimental verification, effective mutations are clustered in a multidimensional physical property space and then accumulated via the greedy algorithm to produce the final designed enzyme. Using the recently reported IsPETase from Ideonella sakaiensis that decomposes PET under ambient temperatures as a starting point, we adopted the GRAPE strategy to come up with a DuraPETase (T_M=77°C, raised by 31°C) which showed drastically enhanced degradation performance (300-fold) on semicrystalline PET films at 40°C.

Halophilic to mesophilic adaptation of ubiquitin-like proteins

Elucidating how proteins adapt from halophilic to mesophilic environments will enable a better understanding of protein evolution and folding. In this study, by directed evolution and site-directed mutagenesis of the halophilic ubiquitin-like protein (ULP) Samp2, we find that substitution of the prebiotic amino acid Asp31 by Gly is uniquely effective in the mesophilic adaptation of ULP. Sequence analysis shows that substitution of Asp/Glu in halophilic ULPs by Gly in mesophilic ULPs has higher occurrence than other substitutions, supporting the unique role of the substitution in the mesophilic adaptation of ULP. Molecular dynamic simulations indicate that the mesophilic adaptation might result from the effect of the substitution on the conformational flexibility of ULP.

Testing amino acid-codon affinity hypothesis using molecular docking

Genetic code refers to a set of rules that assign trinucleotides called codons to amino acids in the process of protein synthesis. Investigating the genetic code's logic and its evolutionary origin has always been both intriguing and challenging. While the correspondence rules between codons and amino acids in the genetic code are well-known, it is still unclear whether those assignments can be explained based on energetic or/and entropic arguments. As an attempt at deciphering basic thermodynamic rules governing DNA translation, we used molecular docking to investigate the ability of amino acids to bind to their corresponding anticodon compared to other codons. The total number of 1280 direct docking interactions have been performed for each amino acid-codon/anti-codon case to find whether the amino acids have a preference to bind to their cognate anticodons or codons. Based on docking scores which are expected to correlate with binding affinity, no correlation with genetic correspondence rules was observed suggesting a more subtle process, other than direct binding, to explain codon-amino-acid specificity.

IPRO+/-: Computational Protein Design Tool Allowing for Insertions and Deletions

Insertions and deletions (indels) in protein sequences alter the residue spacing along the polypeptide backbone and consequently open up possibilities for tuning protein function in a way that is inaccessible by amino acid substitution alone. We describe an optimization-based computational protein redesign approach centered around predicting beneficial combinations of indels along with substitutions and also obtain putative substrate-docked structures for these protein variants. This modified algorithmic capability would be of interest for enzyme engineering and broadly inform other protein design tasks. We highlight this capability by (1) identifying active variants of a bacterial thioesterase enzyme ('TesA) with experimental corroboration, (2) recapitulating existing active TEM-1 β-Lactamase sequences of different sizes, and (3) identifying shorter 4-Coumarate:CoA ligases with enhanced in vitro activities toward non-native substrates. A separate PyRosetta-based open-source tool, Indel-Maker (http://www.maranasgroup.com/software.htm), has also been created to construct computational models of user-defined protein variants with specific indels and substitutions.

A physics-based energy function allows the computational redesign of a PDZ domain

Computational protein design (CPD) can address the inverse folding problem, exploring a large space of sequences and selecting ones predicted to fold. CPD was used previously to redesign several proteins, employing a knowledge-based energy function for both the folded and unfolded states. We show that a PDZ domain can be entirely redesigned using a "physics-based" energy for the folded state and a knowledge-based energy for the unfolded state. Thousands of sequences were generated by Monte Carlo simulation. Three were chosen for experimental testing, based on their low energies and several empirical criteria. All three could be overexpressed and had native-like circular dichroism spectra and 1D-NMR spectra typical of folded structures. Two had upshifted thermal denaturation curves when a peptide ligand was present, indicating binding and suggesting folding to a correct, PDZ structure. Evidently, the physical principles that govern folded proteins, with a dash of empirical post-filtering, can allow successful whole-protein redesign.

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

Selection and screening strategies in directed evolution to improve protein stability

Protein stability is not only fundamental for experimental, industrial, and therapeutic applications, but is also the baseline for evolving novel protein functions. For decades, stability engineering armed with directed evolution has continued its rapid development and inevitably poses challenges. Generally, in directed evolution, establishing a reliable link between a genotype and any interpretable phenotype is more challenging than diversifying genetic libraries. Consequently, we set forth in a small picture to emphasize the screening or selection techniques in protein stability-directed evolution to secure the link. For a more systematic review, two main branches of these techniques, namely cellular or cell-free display and stability biosensors, are expounded with informative examples.

Improving folding properties of computationally designed proteins

While the field of computational protein design has witnessed amazing progression in recent years, folding properties still constitute a significant barrier towards designing new and larger proteins. In order to assess and improve folding properties of designed proteins, we have developed a genetics-based folding assay and selection system based on the essential enzyme, orotate phosphoribosyl transferase from Escherichia coli. This system allows for both screening of candidate designs with good folding properties and genetic selection of improved designs. Thus, we identified single amino acid substitutions in two failed designs that rescued poorly folding and unstable proteins. Furthermore, when these substitutions were transferred into a well-structured design featuring a complex folding profile, the resulting protein exhibited native-like cooperative folding with significantly improved stability. In protein design, a single amino acid can make the difference between folding and misfolding, and this approach provides a useful new platform to identify and improve candidate designs.

Increasing the efficiency and accuracy of the ABACUS protein sequence design method

Motivation

The ABACUS (a backbone-based amino acid usage survey) method uses unique statistical energy functions to carry out protein sequence design. Although some of its results have been experimentally verified, its accuracy remains improvable because several important components of the method have not been specifically optimized for sequence design or in contexts of other parts of the method. The computational efficiency also needs to be improved to support interactive online applications or the consideration of a large number of alternative backbone structures.

Results

We derived a model to measure solvent accessibility with larger mutual information with residue types than previous models, optimized a set of rotamers which can approximate the sidechain atomic positions more accurately, and devised an empirical function to treat inter-atomic packing with parameters fitted to native structures and optimized in consistence with the rotamer set. Energy calculations have been accelerated by interpolation between pre-determined representative points in high-dimensional structural feature spaces. Sidechain repacking tests showed that ABACUS2 can accurately reproduce the conformation of native sidechains. In sequence design tests, the native residue type recovery rate reached 37.7%, exceeding the value of 32.7% for ABACUS1. Applying ABACUS2 to designed sequences on three native backbones produced proteins shown to be well-folded by experiments.

Availability and implementation

The ABACUS2 sequence design server can be visited at http://biocomp.ustc.edu.cn/servers/abacus-design.php.

Supplementary information

Supplementary data are available at Bioinformatics online.

Selection and analyses of variants of a designed protein suggest importance of hydrophobicity of partially buried sidechains for protein stability at high temperatures

Computationally designed proteins of high stability provide specimen in addition to natural proteins for the study of sequence-structure stability relationships at the very high end of protein stability spectrum. The melting temperature of E_1r26, a protein we previously designed using the A Backbone-based Amino aCid Usage Survey (ABACUS) sequence design program, is above 110 °C, more than 50 °C higher than that of the natural thioredoxin protein whose backbone (PDB ID 1R26) has been used as the design target. Using an experimental selection approach, we obtained variants of E_1r26 that remain folded but are of reduced stability, including one whose unfolding temperature and denaturing guanidine concentration are similar to those of 1r26. The mutant unfolds with a certain degree of cooperativity. Its structure solved by X-ray crystallography agrees with that of 1r26 by a root mean square deviation of 1.3 Å, adding supports to the accuracy of the ABACUS method. Analyses of intermediate mutants indicate that the substitution of two partially buried hydrophobic residues (isoleucine and leucine) by polar residues (threonine and serine, respectively) are responsible for the dramatic change in the unfolding temperature. It is suggested that the effects of mutations located in rigid secondary structure regions, but not those in loops, may be well predicted through ABACUS mutation energy analysis. The results also suggest that hydrophobic effects involving intermediately buried sidechains can be critically important for protein stability at high temperatures.

Biochemical engineering in China

Chinese biochemical engineering is committed to supporting the chemical and food industries, to advance science and technology frontiers, and to meet major demands of Chinese society and national economic development. This paper reviews the development of biochemical engineering, strategic deployment of these technologies by the government, industrial demand, research progress, and breakthroughs in key technologies in China. Furthermore, the outlook for future developments in biochemical engineering in China is also discussed.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

Electrostatic control of calcineurin's intrinsically-disordered regulatory domain binding to calmodulin

Calcineurin (CaN) is a serine/threonine phosphatase that regulates a variety of physiological and pathophysiological processes in mammalian tissue. The calcineurin (CaN) regulatory domain (RD) is responsible for regulating the enzyme's phosphatase activity, and is believed to be highly-disordered when inhibiting CaN, but undergoes a disorder-to-order transition upon diffusion-limited binding with the regulatory protein calmodulin (CaM). The prevalence of polar and charged amino acids in the regulatory domain (RD) suggests electrostatic interactions are involved in mediating calmodulin (CaM) binding, yet the lack of atomistic-resolution data for the bound complex has stymied efforts to probe how the RD sequence controls its conformational ensemble and long-range attractions contribute to target protein binding. In the present study, we investigated via computational modeling the extent to which electrostatics and structural disorder facilitate CaM/CaN association kinetics. Specifically, we examined several RD constructs that contain the CaM binding region (CAMBR) to characterize the roles of electrostatics versus conformational diversity in controlling diffusion-limited association rates, via microsecond-scale molecular dynamics (MD) and Brownian dynamic (BD) simulations. Our results indicate that the RD amino acid composition and sequence length influence both the dynamic availability of conformations amenable to CaM binding, as well as long-range electrostatic interactions to steer association. These findings provide intriguing insight into the interplay between conformational diversity and electrostatically-driven protein-protein association involving CaN, which are likely to extend to wide-ranging diffusion-limited processes regulated by intrinsically-disordered proteins.

Net Evolutionary Loss of Residue Polarity in Drosophilid Protein Cores Indicates Ongoing Optimization of Amino Acid Composition

Amino acid frequencies in proteins may not be at equilibrium. We consider two possible explanations for the nonzero net residue fluxes in drosophilid proteins. First, protein interiors may have a suboptimal residue composition and be under a selective pressure favoring stability, that is, leading to the loss of polar (and the gain of large) amino acids. One would then expect stronger net fluxes on the protein interior than at the exposed sites. Alternatively, if most of the polarity loss occurs at the exposed sites and the selective constraint on amino acid composition at such sites decreases over time, net loss of polarity may be neutral and caused by disproportionally high occurrence of polar residues at exposed, least constrained sites. We estimated net evolutionary fluxes of residue polarity and volume at sites with different solvent accessibility in conserved protein families from 12 species of Drosophila. Net loss of polarity, miniscule in magnitude, but consistent across all lineages, occurred at all sites except the most exposed ones, where net flux of polarity was close to zero or, in membrane proteins, even positive. At the intermediate solvent accessibility the net fluxes of polarity and volume were similar to neutral predictions, whereas much of the polarity loss not attributable to neutral expectations occurred at the buried sites. These observations are consistent with the hypothesis that residue composition in many proteins is structurally suboptimal and continues to evolve toward lower polarity in the protein interior, in particular in proteins with intracellular localization. The magnitude of polarity and volume changes was independent from the protein’s evolutionary age, indicating that the approach to equilibrium has been slow or that no such single equilibrium exists.

Computational Protein Design with Deep Learning Neural Networks

Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy of 38.3%. Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta. Moreover, the predictions from our network show ~3% higher sequence identity than a previous method. Results from this study may benefit further development of computational protein design methods.

TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones

To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.

Directed evolution to improve protein folding in vivo

Recently, several innovative approaches have been developed that allow one to directly screen or select for improved protein folding in the cellular context. These methods have the potential of not just leading to a better understanding of the in vivo folding process, they may also allow for improved production of proteins of biotechnological interest.

Rational Design of Membrane-Pore-Forming Peptides

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

A pair-conformation-dependent scoring function for evaluating 3D RNA-protein complex structures

Computational prediction of RNA-protein complex 3D structures includes two basic steps: one is sampling possible structures and another is scoring the sampled structures to pick out the correct one. At present, constructing accurate scoring functions is still not well solved and the performances of the scoring functions usually depend on used benchmarks. Here we propose a pair-conformation-dependent scoring function, 3dRPC-Score, for 3D RNA-protein complex structure prediction by considering the nucleotide-residue pairs having the same energy if their conformations are similar, instead of the distance-only dependence of the most existing scoring functions. Benchmarking shows that 3dRPC-Score has a consistent performance in three test sets.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Computational Protein Design Under a Given Backbone Structure with the ABACUS Statistical Energy Function

An important objective of computational protein design is to identify amino acid sequences that stably fold into a given backbone structure. A general approach to this problem is to minimize an energy function in the sequence space. We have previously reported a method to derive statistical energies for fixed-backbone protein design and showed that it led to de novo proteins that fold as expected. Here, we present the usage of the program that implements this method, which we now name as ABACUS (A Backbone-based Amino aCid Usage Survey).