Harnessing protein folding neural networks for peptide–protein docking

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Янв. 10, 2022

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology beyond. Here, we show that, although these learning approaches originally been developed for the in silico folding of monomers, also enables quick modeling peptide-protein interactions. Our simple implementation generates complex models without requiring multiple sequence alignment information peptide partner, can handle binding-induced conformational changes receptor. We explore what has memorized learned, describe specific examples that highlight differences compared to state-of-the-art docking protocol PIPER-FlexPepDock. These results holds great promise providing insight into a wide range complexes, serving starting point detailed characterization manipulation

Язык: Английский

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Computational approaches streamlining drug discovery

Nature, Год журнала: 2023, Номер 616(7958), С. 673 - 685

Опубликована: Апрель 26, 2023

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This is largely defined by flood of data on ligand properties binding to therapeutic targets their 3D structures, abundant computing capacities advent on-demand virtual libraries drug-like small molecules billions. Taking full advantage these resources requires fast methods effective screening. includes structure-based screening gigascale chemical spaces, further facilitated iterative approaches. Highly synergistic are developments deep learning predictions target activities lieu receptor structure. Here we review recent advances technologies, potential reshaping whole process development, as well challenges they encounter. We also discuss how rapid identification highly diverse, potent, target-selective ligands protein can democratize process, presenting new opportunities cost-effective development safer more small-molecule treatments. Recent approaches application streamlining discussed.

Язык: Английский

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Critical assessment of methods of protein structure prediction (CASP)—Round XIV

Proteins Structure Function and Bioinformatics, Год журнала: 2021, Номер 89(12), С. 1607 - 1617

Опубликована: Сен. 17, 2021

Critical assessment of structure prediction (CASP) is a community experiment to advance methods computing three-dimensional protein from amino acid sequence. Core components are rigorous blind testing and evaluation the results by independent assessors. In most recent (CASP14), deep-learning one research group consistently delivered computed structures rivaling corresponding experimental ones in accuracy. this sense, represent solution classical protein-folding problem, at least for single proteins. The models have already been shown be capable providing solutions problematic crystal structures, there broad implications rest structural biology. Other groups also substantially improved performance. Here, we describe these outline some many implications. related areas CASP, including modeling complexes, refinement, estimation model accuracy, inter-residue contacts distances, described.

Язык: Английский

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Sampling alternative conformational states of transporters and receptors with AlphaFold2

eLife, Год журнала: 2022, Номер 11

Опубликована: Март 3, 2022

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate passage molecules across cell membranes by alternating between inward- outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although plasticity these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, recent success AlphaFold2 (AF2) in CASP14 culminated modeling transporter multiple conformations to high accuracy. Given AF2 was designed predict static structures proteins, it remains unclear if this result represents an underexplored capability accurately and/or heterogeneity. Here, we present approach drive sample alternative topologically diverse G-protein-coupled are absent from training set. Whereas models most generated using default pipeline conformationally homogeneous nearly identical one another, reducing depth input sequence alignments stochastic subsampling led generation accurate conformations. In our benchmark, spanned range two experimental interest, with at extremes distributions observed be among (average template score 0.94). These results suggest straightforward identifying native-like also highlighting need next deep learning algorithms ensembles biophysically relevant states.

Язык: Английский

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Applying and improving AlphaFold at CASP14

Proteins Structure Function and Bioinformatics, Год журнала: 2021, Номер 89(12), С. 1711 - 1721

Опубликована: Окт. 4, 2021

We describe the operation and improvement of AlphaFold, system that was entered by team AlphaFold2 to "human" category in 14th Critical Assessment Protein Structure Prediction (CASP14). The AlphaFold CASP14 is entirely different one CASP13. It used a novel end-to-end deep neural network trained produce protein structures from amino acid sequence, multiple sequence alignments, homologous proteins. In assessors' ranking summed z scores (>2.0), scored 244.0 compared 90.8 next best group. predictions made had median domain GDT_TS 92.4; this first time level average accuracy has been achieved during CASP, especially on more difficult Free Modeling targets, represents significant state art structure prediction. reported how run as human improved such it now achieves an equivalent performance without intervention, opening door highly accurate large-scale

Язык: Английский

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BenchmarkingAlphaFoldfor protein complex modeling reveals accuracy determinants

Protein Science, Год журнала: 2022, Номер 31(8)

Опубликована: Июль 13, 2022

High-resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due the massive number and limitations there is a need for computational methods that can accurately model their structures. Here we explore use recently developed deep learning method, AlphaFold, predict structures protein complexes from sequence. With benchmark 152 diverse heterodimeric complexes, multiple implementations parameters AlphaFold were tested accuracy. Remarkably, many cases (43%) had near-native models (medium or high critical assessment predicted accuracy) generated as top-ranked predictions by greatly surpassing performance unbound docking (9% success rate models), however modeling antibody-antigen within our set was unsuccessful. We identified sequence features associated with lack success, also investigated impact alignment input. Benchmarking multimer-optimized version (AlphaFold-Multimer) released confirmed low (11% success), found T cell receptor-antigen are likewise not modeled algorithm, showing adaptive immune recognition poses challenge current algorithm model. Overall, study demonstrates end-to-end transient highlights areas improvement future developments reliably any interaction interest.

Язык: Английский

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AI-based structure prediction empowers integrative structural analysis of human nuclear pores

Science, Год журнала: 2022, Номер 376(6598)

Опубликована: Июнь 9, 2022

INTRODUCTION The eukaryotic nucleus pro-tects the genome and is enclosed by two membranes of nuclear envelope. Nuclear pore complexes (NPCs) perforate envelope to facilitate nucleocytoplasmic transport. With a molecular weight ∼120 MDa, human NPC one larg-est protein complexes. Its ~1000 proteins are taken in multiple copies from set about 30 distinct nucleoporins (NUPs). They can be roughly categorized into classes. Scaf-fold NUPs contain folded domains form cylindrical scaffold architecture around central channel. Intrinsically disordered line extend channel, where they interact with cargo highly dynamic. It responds changes tension conforma-tional breathing that manifests dilation constriction movements. Elucidating architecture, ultimately at atomic resolution, will important for gaining more precise understanding function dynamics but imposes substantial chal-lenge structural biologists. RATIONALE Considerable progress has been made toward this goal joint effort field. A synergistic combination complementary approaches turned out critical. In situ biology techniques were used reveal overall layout defines spatial reference modeling. High-resolution structures many determined vitro. Proteomic analysis extensive biochemical work unraveled interaction network NUPs. Integra-tive modeling combine different types data, resulting rough outline scaffold. Previous struc-tural models NPC, however, patchy limited accuracy owing several challenges: (i) Many high-resolution individual have solved distantly related species and, consequently, do not comprehensively cover their counterparts. (ii) scaf-fold interconnected intrinsically linker straight-forwardly accessible common techniques. (iii) intimately embraces fused inner outer distinctive topol-ogy cannot studied isolation. (iv) conformational limits resolution achievable structure determination. RESULTS study, we artificial intelligence (AI)-based prediction generate an exten-sive repertoire subcomplexes. various interfaces so far remained structurally uncharac-terized. Benchmarking against previous unpublished x-ray cryo-electron micros-copy revealed unprecedented accu-racy. We obtained well-resolved tomographic maps both constricted dilated states hu-man NPC. Using integrative modeling, fit-ted microscopy maps. explicitly included traced trajectory through scaf-fold. elucidated great detail how mem-brane-associated transmembrane distributed across fusion topology membranes. architectural model increases coverage twofold. extensively validated our earlier new experimental data. completeness enabled microsecond-long coarse-grained simulations within explicit membrane en-vironment solvent. These prevents otherwise stable double-membrane small diameters absence tension. CONCLUSION Our 70-MDa atomically re-solved covers >90% captures occur during constriction. also reveals anchoring sites NUPs, identification which prerequisite complete dy-namic study exempli-fies AI-based may accelerate elucidation subcellular ar-chitecture resolution. [Figure: see text].

Язык: Английский

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AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

Chemical Science, Год журнала: 2023, Номер 14(6), С. 1443 - 1452

Опубликована: Янв. 1, 2023

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for whole human genome, which remarkable breakthrough both AI applications structural biology. Despite varying confidence levels, these could still significantly contribute to structure-based design novel targets, especially ones with no or limited information. this work, we successfully applied our end-to-end AI-powered engines, including biocomputational platform PandaOmics generative chemistry Chemistry42. A hit molecule against target without an experimental structure was identified, starting from selection towards identification, cost- time-efficient manner. provided interest treatment hepatocellular carcinoma (HCC) Chemistry42 generated molecules based on by AlphaFold, selected were synthesized tested biological assays. Through approach, identified small compound cyclin-dependent kinase 20 (CDK20) binding constant Kd value 9.2 ± 0.5 μM (n = 3) within 30 days after only synthesizing 7 compounds. Based available data, second round generation conducted through this, more potent molecule, ISM042-2-048, discovered average 566.7 256.2 nM 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity IC50 33.4 22.6 addition, demonstrated selective anti-proliferation HCC cell line overexpression, Huh7, 208.7 3.3 nM, compared counter screen HEK293 (IC50 1706.7 670.0 nM). This work is first demonstration applying identification process discovery.

Язык: Английский

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Multi‐state modeling of G‐protein coupled receptors at experimental accuracy

Proteins Structure Function and Bioinformatics, Год журнала: 2022, Номер 90(11), С. 1873 - 1885

Опубликована: Май 5, 2022

The family of G-protein coupled receptors (GPCRs) is one the largest protein families in human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts state biased toward either or conformation depending on GPCR class. Here, multi-state prediction protocol introduced that extends predict at very high accuracy using state-annotated templated databases. predicted models accurately capture main structural changes activation atomic level. For most benchmarked (10 out 15), were closer their corresponding structures. Median RMSDs transmembrane 1.12 Å 1.41 for models, respectively. more suitable protein-ligand docking than original template-based models. Finally, our accurate GPCR-peptide complex Dock 2021, blind GPCR-ligand modeling competition. We expect both will promote understanding mechanisms drug discovery GPCRs. At time, new paves way towards capturing dynamics proteins machine-learning methods.

Язык: Английский

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Before and after AlphaFold2: An overview of protein structure prediction

Frontiers in Bioinformatics, Год журнала: 2023, Номер 3

Опубликована: Фев. 28, 2023

Three-dimensional protein structure is directly correlated with its function and determination critical to understanding biological processes addressing human health life science problems in general. Although new structures are experimentally obtained over time, there still a large difference between the number of sequences placed Uniprot those resolved tertiary structure. In this context, studies have emerged predict by methods based on template or free modeling. last years, different been combined overcome their individual limitations, until emergence AlphaFold2, which demonstrated that predicting high accuracy at unprecedented scale possible. Despite current impact field, AlphaFold2 has limitations. Recently, language models promised revolutionize structural biology allowing discovery only from evolutionary patterns present sequence. Even though these do not reach accuracy, they already covered some being able more than 200 million proteins metagenomic databases. mini-review, we provide an overview breakthroughs prediction before after emergence.

Язык: Английский

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