Scientific benchmarks for guiding macromolecular energy function improvement

DIProT: A deep learning based interactive toolkit for efficient and effective Protein design

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design.

Diffusion models in bioinformatics and computational biology

Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.

Exploring Co-occurring POLE Exonuclease and Non-exonuclease Domain Mutations and Their Impact on Tumor Mutagenicity

POLE driver mutations in the exonuclease domain (ExoD driver) are prevalent in several cancers, including colorectal cancer and endometrial cancer, leading to dramatically ultra-high tumor mutation burden (TMB). To understand whether POLE mutations that are not classified as drivers (POLE Variant) contribute to mutagenesis, we assessed TMB in 447 POLE-mutated colorectal cancers, endometrial cancers, and ovarian cancers classified as TMB-high ≥10 mutations/Mb (mut/Mb) or TMB-low <10 mut/Mb. TMB was significantly highest in tumors with "POLE ExoD driver plus POLE Variant" (colorectal cancer and endometrial cancer, P < 0.001; ovarian cancer, P < 0.05). TMB increased with additional POLE variants (P < 0.001), but plateaued at 2, suggesting an association between the presence of these variants and TMB. Integrated analysis of AlphaFold2 POLE models and quantitative stability estimates predicted the impact of multiple POLE variants on POLE functionality. The prevalence of immunogenic neoepitopes was notably higher in the "POLE ExoD driver plus POLE Variant" tumors. Overall, this study reveals a novel correlation between POLE variants in POLE ExoD-driven tumors, and ultra-high TMB. Currently, only select pathogenic ExoD mutations with a reliable association with ultra-high TMB inform clinical practice. Thus, these findings are hypothesis-generating, require functional validation, and could potentially inform tumor classification, treatment responses, and clinical outcomes.

SIGNIFICANCE

Somatic POLE ExoD driver mutations cause proofreading deficiency that induces high TMB. This study suggests a novel modifier role for POLE variants in POLE ExoD-driven tumors, associated with ultra-high TMB. These data, in addition to future functional studies, may inform tumor classification, therapeutic response, and patient outcomes.

Implicit model to capture electrostatic features of membrane environment

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Computational design of N-linked glycans for high throughput epitope profiling

Efficient identification of epitopes is crucial for drug discovery and design as it enables the selection of optimal epitopes, expansion of lead antibody diversity, and verification of binding interface. Although high-resolution low throughput methods like x-ray crystallography can determine epitopes or protein-protein interactions accurately, they are time-consuming and can only be applied to a limited number of complexes. To overcome these limitations, we have developed a rapid computational method that incorporates N-linked glycans to mask epitopes or protein interaction surfaces, thereby providing a mapping of these regions. Using human coagulation factor IXa (fIXa) as a model system, we computationally screened 158 positions and expressed 98 variants to test experimentally for epitope mapping. We were able to delineate epitopes rapidly and reliably through the insertion of N-linked glycans that efficiently disrupted binding in a site-selective manner. To validate the efficacy of our method, we conducted ELISA experiments and high-throughput yeast surface display assays. Furthermore, x-ray crystallography was employed to verify the results, thereby recapitulating through the method of N-linked glycans a coarse-grained mapping of the epitope.

Implicit model to capture electrostatic features of membrane environment

Membrane protein structure prediction and design are challenging due to the complexity of capturing the interactions in the lipid layer, such as those arising from electrostatics. Accurately capturing electrostatic energies in the low-dielectric membrane often requires expensive Poisson-Boltzmann calculations that are not scalable for membrane protein structure prediction and design. In this work, we have developed a fast-to-compute implicit energy function that considers the realistic characteristics of different lipid bilayers, making design calculations tractable. This method captures the impact of the lipid head group using a mean-field-based approach and uses a depth-dependent dielectric constant to characterize the membrane environment. This energy function Franklin2023 (F23) is built upon Franklin2019 (F19), which is based on experimentally derived hydrophobicity scales in the membrane bilayer. We evaluated the performance of F23 on five different tests probing (1) protein orientation in the bilayer, (2) stability, and (3) sequence recovery. Relative to F19, F23 has improved the calculation of the tilt angle of membrane proteins for 90% of WALP peptides, 15% of TM-peptides, and 25% of the adsorbed peptides. The performances for stability and design tests were equivalent for F19 and F23. The speed and calibration of the implicit model will help F23 access biophysical phenomena at long time and length scales and accelerate the membrane protein design pipeline.

Functional characterization of novel or yet uncharacterized ATP7B missense variants detected in patients with clinical Wilson's disease

Wilson's disease (WD, MIM#277900) is an autosomal recessive disorder resulting in copper excess caused by biallelic variants in the ATP7B gene (MIM#606882) encoding a copper transporting P-type ATPase. ATP7B variants of unknown significance (VUS) are detected frequently, sometimes impeding a clear diagnosis. Functional analyses can help to classify these variants as benign or pathogenic. Additionally, variants already classified as (likely) pathogenic benefit from functional analyses to understand their pathomechanism, thus contribute to the development of personalized treatment approaches in the future. We described clinical features of six WD patients and functionally characterized five ATP7B missense variants (two VUS, three yet uncharacterized likely pathogenic variants), detected in these patients. We determined the protein level, copper export capacity, and cellular localization in an in vitro model and potential structural consequences using an ATP7B protein model based on AlphaFold. Our analyses give insight into the pathomechanism and allowed reclassification for the two VUS to likely pathogenic and for two of the three likely pathogenic variants to pathogenic.

Structural Elucidation of Ivermectin Binding to α7nAChR and the Induced Channel Desensitization

The α7 nicotinic acetylcholine receptor (α7nAChR) mediates signaling in the central nervous system and cholinergic anti-inflammatory pathways. Ivermectin is a positive allosteric modulator of a full-length α7nAChR and an agonist of the α7nAChR construct containing transmembrane (TMD) and intracellular (ICD) domains, but structural insights of the binding have not previously been determined. Here, combining nuclear magnetic resonance as a primary experimental tool with Rosetta comparative modeling and molecular dynamics simulations, we have revealed details of ivermectin binding to the α7nAChR TMD + ICD and corresponding structural changes in an ivermectin-induced desensitized state. Ivermectin binding was stabilized predominantly by hydrophobic interactions from interfacial residues between adjacent subunits near the extracellular end of the TMD, where the inter-subunit gap was substantially expanded in comparison to the apo structure. The ion-permeation pathway showed a profile distinctly different from the resting-state profile but similar to profiles of desensitized α7nAChR. The ICD also exhibited structural changes, including reorientation of the MX and h3 helices relative to the channel axis. The resulting structures of the α7nAChR TMD + ICD in complex with ivermectin provide opportunities for discovering new modulators of therapeutic potential and exploring the structural basis of cytoplasmic signaling under different α7nAChR functional states.

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ , of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ = 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.

A backbone-dependent rotamer library with high (ϕ, ψ) coverage using metadynamics simulations

Backbone-dependent rotamer libraries are commonly used to assign the side chain dihedral angles of amino acids when modeling protein structures. Most rotamer libraries are created by curating protein crystal structure data and using various methods to extrapolate the existing data to cover all possible backbone conformations. However, these rotamer libraries may not be suitable for modeling the structures of cyclic peptides and other constrained peptides because these molecules frequently sample backbone conformations rarely seen in the crystal structures of linear proteins. To provide backbone-dependent side chain information beyond the α-helix, β-sheet, and PPII regions, we used explicit-solvent metadynamics simulations of model dipeptides to create a new rotamer library that has high coverage in the (ϕ, ψ) space. Furthermore, this approach can be applied to build high-coverage rotamer libraries for noncanonical amino acids. The resulting Metadynamics of Dipeptides for Rotamer Distribution (MEDFORD) rotamer library predicts the side chain conformations of high-resolution protein crystal structures with similar accuracy (~80%) to a state-of-the-art rotamer library. Our ability to test the accuracy of MEDFORD at predicting the side chain dihedral angles of amino acids in noncanonical backbone conformation is restricted by the limited structural data available for cyclic peptides. For the cyclic peptide data that are currently available, MEDFORD and the state-of-the-art rotamer library perform comparably. However, the two rotamer libraries indeed make different rotamer predictions in noncanonical (ϕ, ψ) regions. For noncanonical amino acids, the MEDFORD rotamer library predicts the χ1 values with approximately 75% accuracy.

Robust deep learning-based protein sequence design using ProteinMPNN

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. We demonstrate the broad utility and high accuracy of ProteinMPNN using x-ray crystallography, cryo-electron microscopy, and functional studies by rescuing previously failed designs, which were made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target-binding proteins.

A topological refactoring design strategy yields highly stable granulopoietic proteins

Protein therapeutics frequently face major challenges, including complicated production, instability, poor solubility, and aggregation. De novo protein design can readily address these challenges. Here, we demonstrate the utility of a topological refactoring strategy to design novel granulopoietic proteins starting from the granulocyte-colony stimulating factor (G-CSF) structure. We change a protein fold by rearranging the sequence and optimising it towards the new fold. Testing four designs, we obtain two that possess nanomolar activity, the most active of which is highly thermostable and protease-resistant, and matches its designed structure to atomic accuracy. While the designs possess starkly different sequence and structure from the native G-CSF, they show specific activity in differentiating primary human haematopoietic stem cells into mature neutrophils. The designs also show significant and specific activity in vivo. Our topological refactoring approach is largely independent of sequence or structural context, and is therefore applicable to a wide range of protein targets.

Skokowa et al. reconstruct the fold of a granulopoietic cytokine, resulting in de novo, hyperstable, highly active proteins with therapeutic potential for treating several neutropenia disorders.

Viral Mimicry of Interleukin-17A by SARS-CoV-2 ORF8

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection triggers cytokine-mediated inflammation, leading to a myriad of clinical presentations in COVID-19. The SARS-CoV-2 open reading frame 8 (ORF8) is a secreted and rapidly evolving glycoprotein. Patients infected with SARS-CoV-2 variants with ORF8 deleted are associated with mild disease outcomes, but the molecular mechanism behind this is unknown. Here, we report that SARS-CoV-2 ORF8 is a viral cytokine that is similar to but distinct from interleukin 17A (IL-17A) as it induces stronger and broader human IL-17 receptor (hIL-17R) signaling than IL-17A. ORF8 primarily targeted blood monocytes and induced the heterodimerization of hIL-17RA and hIL-17RC, triggering a robust inflammatory response. Transcriptome analysis revealed that besides its activation of the hIL-17R pathway, ORF8 upregulated gene expression for fibrosis signaling and coagulation dysregulation. A naturally occurring ORF8 L84S variant that was highly associated with mild COVID-19 showed reduced hIL-17RA binding and attenuated inflammatory responses. This study reveals how SARS-CoV-2 ORF8 by a viral mimicry of the IL-17 cytokine contributes to COVID-19 severe inflammation. IMPORTANCE Patients infected with SARS-CoV-2 variants lacking open reading frame 8 (ORF8) have been associated with milder infection and disease outcome, but the molecular mechanism behind how this viral accessory protein mediates disease pathogenesis is not yet known. In our study, we revealed that secreted ORF8 protein mimics host IL-17 to activate IL-17 receptors A and C (IL-17RA/C) and induces a significantly stronger inflammatory response than host IL-17A, providing molecular insights into the role of ORF8 in COVID-19 pathogenesis and serving as a potential therapeutic target.

A systematic approach for evaluating the role of surface-exposed loops in trypsin-like serine proteases applied to the 170 loop in coagulation factor VIIa

Proteases play a major role in many vital physiological processes. Trypsin-like serine proteases (TLPs), in particular, are paramount in proteolytic cascade systems such as blood coagulation and complement activation. The structural topology of TLPs is highly conserved, with the trypsin fold comprising two β-barrels connected by a number of variable surface-exposed loops that provide a surprising capacity for functional diversity and substrate specificity. To expand our understanding of the roles these loops play in substrate and co-factor interactions, we employ a systematic methodology akin to the natural truncations and insertions observed through evolution of TLPs. The approach explores a larger deletion space than classical random or directed mutagenesis. Using FVIIa as a model system, deletions of 1–7 amino acids through the surface exposed 170 loop, a vital allosteric regulator, was introduced. All variants were extensively evaluated by established functional assays and computational loop modelling with Rosetta. The approach revealed detailed structural and functional insights recapitulation and expanding on the main findings in relation to 170 loop functions elucidated over several decades using more cumbersome crystallization and single deletion/mutation methodologies. The larger deletion space was key in capturing the most active variant, which unexpectedly had a six-amino acid truncation. This variant would have remained undiscovered if only 2–3 deletions were considered, supporting the usefulness of the methodology in general protease engineering approaches. Our findings shed further light on the complex role that surface-exposed loops play in TLP function and supports the important role of loop length in the regulation and fine-tunning of enzymatic function throughout evolution.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.

Structures of highly flexible intracellular domain of human α7 nicotinic acetylcholine receptor

The intracellular domain (ICD) of Cys-loop receptors mediates diverse functions. To date, no structure of a full-length ICD is available due to challenges stemming from its dynamic nature. Here, combining nuclear magnetic resonance (NMR) and electron spin resonance experiments with Rosetta computations, we determine full-length ICD structures of the human α7 nicotinic acetylcholine receptor in a resting state. We show that ~57% of the ICD residues are in highly flexible regions, primarily in a large loop (loop L) with the most mobile segment spanning ~50 Å from the central channel axis. Loop L is anchored onto the MA helix and virtually forms two smaller loops, thereby increasing its stability. Previously known motifs for cytoplasmic binding, regulation, and signaling are found in both the helices and disordered flexible regions, supporting the essential role of the ICD conformational plasticity in orchestrating a broad range of biological processes.

The intracellular domain (ICD) of Cys-loop receptors mediates many of their functions, but no complete structure of a Cys-loop receptor ICD is available to date. Here, the authors combine NMR and ESR spectroscopy to determine the full-length ICD structures of the human α7 nicotinic acetylcholine receptor (α7nAChR).

Protein sequence design with a learned potential

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues

We have curated a high-quality, "best-parts" reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting prefiltered data typically contain the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 prefiltered datasets have been released on the Zenodo online web service and are freely available for all uses under a Creative Commons license. Currently, one dataset is residue filtered on main chain plus Cβ atoms, and a second dataset is full-residue filtered; each is available at four different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore, the open distribution of these very large, prefiltered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Epistasis shapes the fitness landscape of an allosteric specificity switch

Epistasis is a major determinant in the emergence of novel protein function. In allosteric proteins, direct interactions between inducer-binding mutations propagate through the allosteric network, manifesting as epistasis at the level of biological function. Elucidating this relationship between local interactions and their global effects is essential to understanding evolution of allosteric proteins. We integrate computational design, structural and biophysical analysis to characterize the emergence of novel inducer specificity in an allosteric transcription factor. Adaptive landscapes of different inducers of the designed mutant show that a few strong epistatic interactions constrain the number of viable sequence pathways, revealing ridges in the fitness landscape leading to new specificity. The structure of the designed mutant shows that a striking change in inducer orientation still retains allosteric function. Comparing biophysical and functional properties suggests a nonlinear relationship between inducer binding affinity and allostery. Our results highlight the functional and evolutionary complexity of allosteric proteins.

Epistasis plays an important role in the evolution of novel protein functions because it determines the mutational path a protein takes. Here, the authors combine functional, structural and biophysical analyses to characterize epistasis in a computationally redesigned ligand-inducible allosteric transcription factor and found that epistasis creates distinct biophysical and biological functional landscapes.

The Allosteric Activation of α7 nAChR by α-Conotoxin MrIC Is Modified by Mutations at the Vestibular Site

α-conotoxins are 13–19 amino acid toxin peptides that bind various nicotinic acetylcholine receptor (nAChR) subtypes. α-conotoxin Mr1.7c (MrIC) is a 17 amino acid peptide that targets α7 nAChR. Although MrIC has no activating effect on α7 nAChR when applied by itself, it evokes a large response when co-applied with the type II positive allosteric modulator PNU-120596, which potentiates the α7 nAChR response by recovering it from a desensitized state. A lack of standalone activity, despite activation upon co-application with a positive allosteric modulator, was previously observed for molecules that bind to an extracellular domain allosteric activation (AA) site at the vestibule of the receptor. We hypothesized that MrIC may activate α7 nAChR allosterically through this site. We ran voltage-clamp electrophysiology experiments and in silico peptide docking calculations in order to gather evidence in support of α7 nAChR activation by MrIC through the AA site. The experiments with the wild-type α7 nAChR supported an allosteric mode of action, which was confirmed by the significantly increased MrIC + PNU-120596 responses of three α7 nAChR AA site mutants that were designed in silico to improve MrIC binding. Overall, our results shed light on the allosteric activation of α7 nAChR by MrIC and suggest the involvement of the AA site.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Diverse Scientific Benchmarks for Implicit Membrane Energy Functions

Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).

Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here, we leverage our expertise in computational antibody engineering to rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV-2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we suggest application of NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

De Novo Protein Design Using the Blueprint Builder in Rosetta

While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner. In the Baker laboratory we have been applying a design pipeline that uses the blueprint builder to design different folds of de novo proteins, and have successfully obtained libraries of de novo proteins with improved stability and engineering potential. In this article, we will use the design of de novo β-barrel proteins as an example to describe the principles and basic procedures of the blueprint builder-based design pipeline. © 2020 Wiley Periodicals LLC. Basic Protocol 1: The construction of blueprints Alternate Protocol: Build blueprints based on existing protein .pdb files Basic Protocol 2: De novo protein design pipeline using the blueprint builder.

Membrane Protein Engineering with Rosetta

Protein engineering can yield new molecular tools for nanotechnology and therapeutic applications through modulating physiochemical and biological properties. Engineering membrane proteins is especially attractive because they perform key cellular processes including transport, nutrient uptake, removal of toxins, respiration, motility, and signaling. In this chapter, we describe two protocols for membrane protein engineering with the Rosetta software: (1) ΔΔG calculations for single point mutations and (2) sequence optimization in different membrane lipid compositions. These modular protocols are easily adaptable for more complex problems and serve as a foundation for efficient membrane protein engineering calculations.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

Design of novel granulopoietic proteins by topological rescaffolding

Computational protein design is rapidly becoming more powerful, and improving the accuracy of computational methods would greatly streamline protein engineering by eliminating the need for empirical optimization in the laboratory. In this work, we set out to design novel granulopoietic agents using a rescaffolding strategy with the goal of achieving simpler and more stable proteins. All of the 4 experimentally tested designs were folded, monomeric, and stable, while the 2 determined structures agreed with the design models within less than 2.5 Å. Despite the lack of significant topological or sequence similarity to their natural granulopoietic counterpart, 2 designs bound to the granulocyte colony-stimulating factor (G-CSF) receptor and exhibited potent, but delayed, in vitro proliferative activity in a G-CSF-dependent cell line. Interestingly, the designs also induced proliferation and differentiation of primary human hematopoietic stem cells into mature granulocytes, highlighting the utility of our approach to develop highly active therapeutic leads purely based on computational design.

De novo designed cytokines that activate the G-CSF receptor show that the receptor-binding information can be encoded onto stable, miniaturised protein scaffolds that possess potent granulopoietic activity; such novel proteins provide for ideal candidates for protein-based therapeutics.

Perturbing the energy landscape for improved packing during computational protein design

The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.

Predicting antibody affinity changes upon mutations by combining multiple predictors

Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding. Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.

Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors

Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.

Assessing multiple score functions in Rosetta for drug discovery

Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.

EvoEF2: accurate and fast energy function for computational protein design

MOTIVATION

The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs.

RESULTS

We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein-protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data.

AVAILABILITY AND IMPLEMENTATION

The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Machine learning classification can reduce false positives in structure-based virtual screening

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC₅₀ better than 50 μM. Without any medicinal chemistry optimization, the most potent hit has IC₅₀ 280 nM, corresponding to K_i of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Computationally Designed Cyclic Peptides Derived from an Antibody Loop Increase Breadth of Binding for Influenza Variants

The influenza hemagglutinin (HA) glycoprotein is the target of many broadly neutralizing antibodies. However, influenza viruses can rapidly escape antibody recognition by mutation of hypervariable regions of HA that overlap with the binding epitope. We hypothesized that by designing peptides to mimic antibody loops, we could enhance breadth of binding to HA antigenic variants by reducing contact with hypervariable residues on HA that mediate escape. We designed cyclic peptides that mimic the heavy-chain complementarity-determining region 3 (CDRH3) of anti-influenza broadly neutralizing antibody C05 and show that these peptides bound to HA molecules with <100 nM affinity, comparable with that of the full-length parental C05 IgG. In addition, these peptides exhibited increased breadth of recognition to influenza H4 and H7 subtypes by eliminating clashes between the hypervariable antigenic regions and the antibody CDRH1 loop. This approach can be used to generate antibody-derived peptides against a wide variety of targets.

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

Computational stabilization of T cell receptors allows pairing with antibodies to form bispecifics

Recombinant T cell receptors (TCRs) can be used to redirect naïve T cells to eliminate virally infected or cancerous cells; however, they are plagued by low stability and uneven expression. Here, we use molecular modeling to identify mutations in the TCR constant domains (Cα/Cβ) that increase the unfolding temperature of Cα/Cβ by 20 °C, improve the expression of four separate α/β TCRs by 3- to 10-fold, and improve the assembly and stability of TCRs with poor intrinsic stability. The stabilizing mutations rescue the expression of TCRs destabilized through variable domain mutation. The improved stability and folding of the TCRs reduces glycosylation, perhaps through conformational stabilization that restricts access to N-linked glycosylation enzymes. The Cα/Cβ mutations enables antibody-like expression and assembly of well-behaved bispecific molecules that combine an anti-CD3 antibody with the stabilized TCR. These TCR/CD3 bispecifics can redirect T cells to kill tumor cells with target HLA/peptide on their surfaces in vitro.

Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane

Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. Although soluble protein design has advanced, membrane protein design remains challenging because of difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational benchmarks against experimental targets, including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure discrimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Furthermore, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.

NMR "Crystallography" for Uniformly (13 C, 15 N)-Labeled Oriented Membrane Proteins

In oriented-sample (OS) solid-state NMR of membrane proteins, the angular-dependent dipolar couplings and chemical shifts provide a direct input for structure calculations. However, so far only ¹ H-¹⁵ N dipolar couplings and ¹⁵ N chemical shifts have been routinely assessed in oriented ¹⁵ N-labeled samples. The main obstacle for extending this technique to membrane proteins of arbitrary topology has remained in the lack of additional experimental restraints. We have developed a new experimental triple-resonance NMR technique, which was applied to uniformly doubly (¹⁵ N, ¹³ C)-labeled Pf1 coat protein in magnetically aligned DMPC/DHPC bicelles. The previously inaccessible ¹ H_α -¹³ C_α dipolar couplings have been measured, which make it possible to determine the torsion angles between the peptide planes without assuming α-helical structure a priori. The fitting of three angular restraints per peptide plane and filtering by Rosetta scoring functions has yielded a consensus α-helical transmembrane structure for Pf1 protein.

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.

Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries

Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.

Designing protein structures and complexes with the molecular modeling program Rosetta

Proteins perform an amazingly diverse set of functions in all aspects of life. Critical to the function of many proteins are the highly specific three-dimensional structures they adopt. For this reason, there is strong interest in learning how to rationally design proteins that adopt user-defined structures. Over the last 25 years, there has been significant progress in the field of computational protein design as rotamer-based sequence optimization protocols have enabled accurate design of protein tertiary and quaternary structure. In this award article, I will summarize how the molecular modeling program Rosetta is used to design new protein structures and describe how we have taken advantage of this capability to create proteins that have important applications in research and medicine. I will highlight three protein design stories: the use of protein interface design to create therapeutic bispecific antibodies, the engineering of light-inducible proteins that can be used to recruit proteins to specific locations in the cell, and the de novo design of new protein structures from pieces of naturally occurring proteins.

Computational design of Bacillus licheniformis RN-01 levansucrase for control of the chain length of levan-type fructooligosaccharides

Levansucrase (LS) from Gram-positive bacteria generally produces a large quantity of levan polymer, a polyfructose with glucose at the end (GF_n) but a small quantity of levan-type fructooligosaccharides (LFOs). The properties of levan and LFOs depend on their chain lengths, thereby determining their potential applications in food and pharmaceutical industries such as prebiotics and anti-tumor agents. Therefore, an ability to redesign and engineer the active site of levansucrase for synthesis of products with desired degree of polymerization (DP) is very beneficial. We employed computational protein design, docking and molecular dynamics to redesign and engineer the active site of Bacillus licheniformis RN-01 levansucrase for production of LFOs with DP up to five (GF₄), using two approaches: 1) blocking oligosaccharide binding track of GF₃-LS complex with large aromatic residues and 2) eliminating hydrogen bond interactions between terminal glucose of GF₄ and side chains of binding residues of GF₄-LS complex. The designed enzymes and their product patterns from these two approaches were experimentally characterized. The experimental results show that the first approach was successful in creating N251W and N251W/K372Y mutants that synthesized LFOs with DP up to five. This work illustrates how computer-aided approaches can offer novel opportunities to engineer enzymes for desired products.

Artificial intelligence-based multi-objective optimization protocol for protein structure refinement

MOTIVATION

Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures.

RESULTS

We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising.

AVAILABILITY AND IMPLEMENTATION

The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A benchmark study of loop modeling methods applied to G protein-coupled receptors

G protein-coupled receptors (GPCR) are important drug discovery targets. Despite progress, many GPCR structures have not yet been solved. For these targets, comparative modeling is used in virtual ligand screening to prioritize experimental efforts. However, the structure of extracellular loop 2 (ECL2) is often poorly predicted. This is significant due to involvement of ECL2 in ligand binding for many Class A GPCR. Here we examine the performance of loop modeling protocols available in the Rosetta (cyclic coordinate descent [CCD], KIC with fragments [KICF] and next generation KIC [NGK]) and Molecular Operating Environment (MOE) software suites (de novo search). ECL2 from GPCR crystal structures served as the structure prediction targets and were divided into four sets depending on loop length. Results suggest that KICF and NGK sampled and scored more loop models with sub-angstrom and near-atomic accuracy than CCD or de novo search for loops of 24 or fewer residues. None of the methods were able to sample loop conformations with near-atomic accuracy for the longest targets ranging from 25 to 32 residues based on 1000 models generated. For these long loop targets, increased conformational sampling is necessary. The strongly conserved disulfide bond between Cys3.25 and Cys45.50 in ECL2 proved an effective filter. Setting an upper limit of 5.1 Å on the S-S distance improved the lowest RMSD model included in the top 10 scored structures in Groups 1-4 on average between 0.33 and 1.27 Å. Disulfide bond formation and geometry optimization of ECL2 provided an additional incremental benefit in structure quality.