Protein Structure Prediction Research Articles

Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.

Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design.

A panel of multivalent nanobodies broadly neutralizing Omicron subvariants and recombinant.

The emerging Omicron subvariants have a remarkable ability to spread and escape nearly all current monoclonal antibody (mAb) treatments. Although the virulence of SARS-CoV-2 has now diminished, it remains a significant threat to public health due to its high transmissibility and susceptibility to mutation. Therefore, it is urgent to develop broad-acting and potent therapeutics targeting current and emerging Omicron variants. Here, we identified a panel of Omicron BA.1 spike receptor-binding domain (RBD)-targeted nanobodies (Nbs) from a naive alpaca VHH library. This panel of Nbs exhibited high binding affinity to the spike RBD of wild-type, Alpha B.1.1.7, Beta B.1.351, Delta plus, Omicron BA.1, and BA.2. Through multivalent Nb construction, we obtained a subpanel of ultrapotent neutralizing Nbs against Omicron BA.1, BA.2, BF.7 and even emerging XBB.1.5, and XBB.1.16 pseudoviruses. Protein structure prediction and docking analysis showed that Nb trimer 2F2E5 targets two independent RBD epitopes, thus minimizing viral escape. Taken together, we obtained a panel of broad and ultrapotent neutralizing Nbs against Omicron BA.1, Omicron BA.2, BF.7, XBB.1.5, and XBB.1.16. These multivalent Nbs hold great promise for the treatment against SARS-CoV-2 infection and could possess a superwide neutralizing breadth against novel omicron mutants or recombinants.

Computational Prediction of Structure, Function, and Interaction of Myzus persicae (Green Peach Aphid) Salivary Effector Proteins.

Similar to plant pathogens, phloem-feeding insects such as aphids deliver effector proteins inside their hosts that act to promote host susceptibility and enable feeding and infestation. Despite exciting progress toward identifying and characterizing effector proteins from these insects, their functions remain largely unknown. The recent groundbreaking development in protein structure prediction algorithms, combined with the availability of proteomics and transcriptomic datasets for agriculturally important pests, provides new opportunities to explore the structural and functional diversity of effector repertoires. In this study, we sought to gain insight into the infection strategy used by the Myzus persicae (green peach aphid) by predicting and analyzing the structures of a set of 71 effector candidate proteins. We used two protein structure prediction methods, AlphaFold and OmegaFold, that produced mutually consistent results. We observed a wide continuous spectrum of structures among the effector candidates, from disordered proteins to globular enzymes. We made use of the structural information and state-of-the-art computational methods to predict M. persicae effector protein properties, including function and interaction with host plant proteins. Overall, our investigation provides novel insights into prediction of structure, function, and interaction of M. persicae effector proteins and will guide the necessary experimental characterization to address new hypotheses. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search.

Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by incorporating the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to the nucleotide (nt) database and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI's nt database or 60-fold larger than RNAcentral. The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSAs) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.

Unveiling polymorphism and protein structure prediction insights in diacylglycerol O-acyltransferase 1 and telethonin genes of Egyptian buffalo

BackgroundThe Egyptian buffalo has a sizable impact on Egypt's agricultural sector and food supply. It is regarded as the main dairy animal and an important source of red meat. This study aimed to detect the polymorphisms of the DGAT1 and TCAP genes and assess the potential impact of the discovered nsSNPs on the stability of the tertiary structure polypeptides of selected genes in Egyptian buffalo.MethodsAllele identification was made by the restriction fragment length polymorphism (RFLP), and the single nucleotide polymorphisms (SNPs) were recognized by sequencing the purified PCR products. Protein translation indicated the synonymous and non-synonymous SNPs, and the peptides' 3D tertiary structure of selected genes, as well as the effect of amino acid substitution on the protein structure, was performed using bioinformatics tools.ResultsAnalysis of the data revealed that an nsSNP was detected in a tested region of the DGAT1 gene and caused an amino acid substitution in a polypeptide that was predicted to be neutral and located in the coiled part of the protein. The analysis of the TCAP gene showed four nsSNPs that caused four substitutions located in the α-helix region. Protein prediction analysis showed that the amino acid substitutions in DGAT1 and TCAP were non-conserved with low sensitivity to variation. The non-conservative amino acid substitutions result in amino acids with new properties different from the original amino acid that change the protein's structure and function.ConclusionWe can infer that the DGAT1 and TCAP genes' SNPs may affect meat-related traits and may improve meat quality.

Decoding Tomorrows Biotechnology: AI and Machine Learning Unveiled

Abstract: This review paper explores the impact of AI and ML on biotechnology, focusing on drug discovery, protein prediction, automation, and ethics.AI and ML are transforming drug discovery by automating tasks, identifying new drug targets, and designing more effective drugs. AI-powered languages and functional genomics are being used to accelerate drug discovery processes. AI tools such as AlphaFold are being used to predict protein structures and functions. Biotechnology automation is being used to improve efficiency in laboratory settings. The use of AI and ML in biotechnology raises ethical and regulatory concerns. It is important to find the potential risks and benefits of this technology before it is widely adopted

ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2.

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.

Genome-wide profiling of CBL interacting protein kinases (CIPKs) in banana unveils their role in abiotic stress signaling and stress tolerance enhancement

Calcineurin B-like (CBL)-interacting protein kinases (CIPKs) play a crucial role in the complex molecular systems of plants, acting as coordinators in different plant development processes, manage hormone signaling, and respond to environmental stress. Despite their well-established importance in various physiological process, the CIPK gene family in banana has remained an unexplored, creating a knowledge gap that this research aims to address. Through comprehensive analysis, we identified and characterized 34 CIPK genes in the banana genome. Structural diversity analysis revealed distinct clades characterized by homologs with varying intron-rich and intron-poor members. Particularly, evolutionary conservation was observed in intron-less members. Exploring conserved motifs and physicochemical properties provided insights into the detailed structure of specific homologs. Subcellular localization predictions indicated that most CIPK proteins primarily reside in the cytoplasm and nucleus, indicating their involvement in essential cellular activities. The predicted chromosomal distribution pattern suggests that the CIPK gene family largely expanded through segmental duplications in the banana genome. The phylogenetic analysis has uncovered two monophyletic clades for intron-less and intron-containing homologs, providing insights into two separate evolutionary history and expansion of the CIPK gene family. The clustering pattern in the phylogeny suggest that sequence of CIPKs members in the different genomes are largely conserved during course of evolution after evolution of higher plants. However, a few members having diverse sequences that these homologs are evolving with higher rate of evolution. Diverse physiological roles of banana CIPKs members are further supported by the presence of numerous cis-acting elements associated with growth, development, hormone responsiveness, and stress, with certain MaCIPK promoters exhibiting multiple hormone-sensitive elements. The protein-protein interaction network sheds light on the functional associations between MaCIPKs and MaCBLs, highlighting specific clusters of interactions. 3D protein structure predictions provide additional insights, revealing variations among the MaCIPK proteins. Real-time expression analysis across various tissues and developmental stages emphasizes the significant roles played by CIPKs in plant development. Under stress conditions, 16 in leaves and 22 in root MaCIPK gene transcript levels were up –regulated in response to drought and cold stress. These insights into the dynamic expression and functional roles of CIPK genes provide valuable information for understanding the mechanisms underlying banana's resilience to stress, with direct implications for the development of stress-resistant banana varieties. Overall this research offers promising prospects for enhancing global food security by ensuring the sustainability of banana production in the face of changing environmental conditions.

Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms.

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of ~1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of "new" canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst withholding all antibody structures of a specific CDR loop length or canonical form. These "starved" models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the ~1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181.

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers.

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here we introduce a modular strategy to generate conformations based on "backmapping" from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction

Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary structure prediction (PSSP) is the foundation for three-dimensional PSP. An Advanced hybrid ensemble deep predictor is utilized for predicting the structure of a protein using Long-Short Term Memory (LSTM), in which the performance of the predictor is improved for obtaining the features through the Salp-J Colony Optimization, which is developed by integrating the features of three optimizations the exploration behavior of Ulmaris, the immune system of virus colony and the teamwork of salp for solution update that helps to predict the accurate protein structure. The proposed method achieved the value of 99.1% accuracy, 99.5% sensitivity, 98.85% specificity, and 0.9% error at the 80% of training percentage 90 using CullPDB. Similarly, in Protein Net, the attained value of accuracy is 97.27%, sensitivity is 98.13%, specificity is 97%, and error is 2.7% concerning training percentage 90%. Communicated by Ramaswamy H. Sarma

Protein structure accuracy estimation using geometry-complete perceptron networks.

Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally-predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry-complete perceptron network for protein structure EMA (GCPNet-EMA), where we demonstrate through rigorous computational benchmarks that GCPNet-EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground-truth measures of per-residue (per-target) structural accuracy compared to baseline state-of-the-art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet-EMA are available on GitHub, and a public web server implementation is freely available.

Protein structure prediction based on deep learning: HER2 in complex with a covalent inhibitor

HER2 protein overexpression is associated with the malignant degree and poor prognosis of breast cancer. HER2 levels are elevated in 20% of breast tumors. Several covalent tyrosine kinase inhibitors have been found to reduce tumor cell survival and proliferation in vitro and inhibit downstream HER2 signaling. In the field of protein structure prediction, AlphaFold2, which achieved excellent results in CASP14, can periodically predict protein structures with atomic precision in the absence of similar protein structures. In this study, AlphaFold2 was used to predict the monomeric structure of the HER2 protein. This predicted structure was compared to the conformation of HER2 in complex with a covalent inhibitor, allowing for an examination of the conformational changes induced by the inhibitor. By combining the conformational changes of HER2 protein with the docking results of Protein-Ligand Interaction Profiler, other potential binding sites were identified, which could further reveal the mechanism of drug discovery.

Artificial Intelligence with Great Potential in Medical Informatics: A Brief Review

In the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering the 21st century, the completion of the Human Genome Project marks the arrival of the era of biomedical big data, providing a large amount of data for the application of artificial intelligence in this field. Especially in recent years, the continuous accumulation of medical data has pushed the application of artificial intelligence in the medical field to a broader and more practical level. This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, and other fields. I hope this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and also promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems.

A multi-faceted approach to unravel coding and non-coding gene fusions and target chimeric proteins in ataxia

Ataxia represents a heterogeneous group of neurodegenerative disorders characterized by a loss of balance and coordination, often resulting from mutations in genes vital for cerebellar function and maintenance. Recent advances in genomics have identified gene fusion events as critical contributors to various cancers and neurodegenerative diseases. However, their role in ataxia pathogenesis remains largely unexplored. Our study Hdelved into this possibility by analyzing RNA sequencing data from 1443 diverse samples, including cell and mouse models, patient samples, and healthy controls. We identified 7067 novel gene fusions, potentially pivotal in disease onset. These fusions, notably in-frame, could produce chimeric proteins, disrupt gene regulation, or introduce new functions. We observed conservation of specific amino acids at fusion breakpoints and identified potential aggregate formations in fusion proteins, known to contribute to ataxia. Through AI-based protein structure prediction, we identified topological changes in three high-confidence fusion proteins—TEN1-ACOX1, PEX14-NMNAT1, and ITPR1-GRID2—which could potentially alter their functions. Subsequent virtual drug screening identified several molecules and peptides with high-affinity binding to fusion sites. Molecular dynamics simulations confirmed the stability of these protein-ligand complexes at fusion breakpoints. Additionally, we explored the role of non-coding RNA fusions as miRNA sponges. One such fusion, RP11-547P4-FLJ33910, showed strong interaction with hsa-miR-504-5p, potentially acting as its sponge. This interaction correlated with the upregulation of hsa-miR-504-5p target genes, some previously linked to ataxia. In conclusion, our study unveils new aspects of gene fusions in ataxia, suggesting their significant role in pathogenesis and opening avenues for targeted therapeutic interventions. Communicated by Ramaswamy H. Sarma

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges.

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Evaluation of a plant salinity-stress B2-protein gene in wild barley (Hordeum spontaneum) and cultivated barley (Hordeum vulgare L.)

Dehydration-inducing conditions are major factors affecting plants geographic distribution worldwide. Salinity is a major factor affecting crop productivity and survival. Advances in molecular studies enabled scientists to explore salinity and genetic factors that control and contribute to salinity tolerance in plants. We previously identified clones from barley substractive library “from plants growing under high salinity regime” that was similar to previously identified B2-protein gene. In the current study, the relative expression of the B2-protein gene is assessed in wild barley (Hordeum spontaneum) and cultivated barley (Hordeum vulgare L) varieties. Our results indicated that the relative expression of the B2 gene displayed 8.00, 10.34, 12.12 and 11.37 folds at 100 mM NaCl and 4.28, 3.62, 2.70 and 1.58 folds at 200 mM NaCl compared to the non-salt stressed control in wild barley, Giza 129, Giza 132 and Giza 123, respectively. The full-length sequence of the B2 gene (1449 bp) was successfully assembled “accession number MT249004” from wild barley. Comparison studies of the wild barley B2 sequence to its published homologue in cultivated barley indicated the presence of an extra 23 bp long sequence, but its deduced amino acids sequence is five a.a. residues shorter (354 vs. 359 a.a.) with 96.1 % identity. Computational analysis at the protein level indicated the presence of differences in the physicochemical properties between the two proteins. Also, the subcellular localization prediction of the protein revealed the detection of the former protein in the nucleus and the later in the chloroplast. However, the protein modelling and structure prediction do not reveal differences since around 60 % of the protein is disordered. On the other hand, B2-protein interaction predicted networks composed of proteins directly or indirectly associated with the stress responses. In conclusion, the B2- protein gene is a promising candidate for dehydration stress tolerance.

EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

Intrinsically disordered Pseudomonas chaperone FapA slows down the fibrillation of major biofilm-forming functional amyloid FapC.

Functional bacterial amyloids play a crucial role in the formation of biofilms, which mediate chronic infections and contribute to antimicrobial resistance. This study focuses on the FapC amyloid fibrillar protein from Pseudomonas, a major contributor to biofilm formation. We investigate the initial steps of FapC amyloid formation and the impact of the chaperone-like protein FapA on this process. Using solution nuclear magnetic resonance (NMR), we recently showed that both FapC and FapA are intrinsically disordered proteins (IDPs). Here, the secondary structure propensities (SSPs) are compared to alphafold (DeepMind, protein structure prediction tool/algorithm: https://alphafold.ebi.ac.uk/) models. We further demonstrate that the FapA chaperone interacts with FapC and significantly slows down the formation of FapC fibrils. Our NMR titrations reveal ~ 18% of the resonances show FapA-induced chemical shift perturbations (CSPs), which has not been previously observed, the largest being for A82, N201, C237, C240, A241, and G245. These sites may suggest a specific interaction site and/or hotspots of fibrillation inhibition/control interface at the repeat-1 (R1)/loop-2 (L2) and L2/R3 transition areas and at the C-terminus of FapC. Remarkably, ~ 90% of FapA NMR signals exhibit substantial CSPs upon titration with FapC, the largest being for S63, A69, A80, and I92. A temperature-dependent effect of FapA was observed on FapC by thioflavin T (ThT) and NMR experiments. This study provides a detailed understanding of the interaction between the FapA and FapC, shedding light on the regulation and slowing down of amyloid formation, and has important implications for the development of therapeutic strategies targeting biofilms and associated infections.

Using quantum annealing to design lattice proteins

Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice model, the HP model, in which amino acids are classified into two broad groups: hydrophobic (H) and polar (P). Using a set of 22 HP sequences with up to 64 amino acids, we demonstrated the fast and consistent identification of the correct HP model ground states using the D-Wave hybrid quantum-classical solver. An equally relevant biophysical challenge, called the protein design problem, is the inverse of the above, where the task is to predict protein sequences that fold to a given structure. Here, we approach the design problem by a two-step procedure implemented and executed on a D-Wave machine. In the first step, we perform a pure sequence-space search by varying the type of amino acid at each sequence position, and seek sequences which minimize the HP-model energy of the target structure. After mapping this task onto an Ising spin-glass representation, we employ a hybrid quantum-classical solver to deliver energy-optimal sequences for structures with 30–64 amino acids, with a 100% success rate. In the second step, we filter the optimized sequences from the first step according to their ability to fold to the intended structure. In addition, we try solving the sequence optimization problem using only the quantum processing unit (QPU), which confines us to sizes ≤20, due to exponentially decreasing success rates. To shed light on the pure QPU results, we investigate the effects of control errors caused by an imperfect implementation of the intended Hamiltonian on the QPU, by numerically analyzing the Schrödinger equation. We find that the simulated success rates in the presence of control noise semiquantitatively reproduce the modest pure QPU results for larger chains. Published by the American Physical Society 2024