scholarly journals Ins and outs of AlphaFold2 transmembrane protein structure predictions

2022 ◽  
Vol 79 (1) ◽  
Author(s):  
Tamás Hegedűs ◽  
Markus Geisler ◽  
Gergely László Lukács ◽  
Bianka Farkas
Keyword(s):  
Structure Prediction ◽  
Drug Targets ◽  
Transmembrane Protein ◽  
Protein Structures ◽  
Rational Drug Design ◽  
Abc Protein ◽  
Rational Drug ◽  
Structural Coverage ◽  
Dynamics Simulations

AbstractTransmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind’s AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.

scholarly journals AlphaFold2 transmembrane protein structure prediction shines

2021 ◽  
Author(s):  
Tamas Hegedus ◽  
Markus Geisler ◽  
Gergely Lukacs ◽  
Bianka Farkas
Keyword(s):  
Prescription Drugs ◽  
Protein Function ◽  
Structure Prediction ◽  
Drug Targets ◽  
Transmembrane Protein ◽  
Structural Data ◽  
Data Bank ◽  
Rational Drug Design ◽  
Abc Protein ◽  
Dynamics Simulations

Transmembrane (TM) proteins are major drug targets, indicated by the high percentage of prescription drugs acting on them. For a rational drug design and an understanding of mutational effects on protein function, structural data at atomic resolution are required. However, hydrophobic TM proteins often resist experimental structure determination and in spite of the increasing number of cryo-EM structures, the available TM folds are still limited in the Protein Data Bank. Recently, the DeepMind's AlphaFold2 machine learning method greatly expanded the structural coverage of sequences, with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the validity of the generated TM structures should be assessed. Therefore, we investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds, and also in specific individual cases. We tested template-free structure prediction also with a new TM fold, dimer modeling, and stability in molecular dynamics simulations. Our results strongly suggest that AlphaFold2 performs astoundingly well in the case of TM proteins and that its neural network is not overfitted. We conclude that a careful application of its structural models will advance TM protein associated studies at an unexpected level.

scholarly journals Universal Architectural Concepts Underlying Protein Folding Patterns

2021 ◽  
Vol 7 ◽  
Author(s):  
Arun S. Konagurthu ◽  
Ramanan Subramanian ◽  
Lloyd Allison ◽  
David Abramson ◽  
Peter J. Stuckey ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Data Bank ◽  
Rational Drug Design ◽  
Basis Set ◽  
Sequence Structure ◽  
Information Theoretic ◽  
Protein Architecture ◽  
Structure Correlations ◽  
Rational Drug

What is the architectural “basis set” of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a dictionary of 1,493 substructures—called concepts—typically at a subdomain level, based on an unbiased subset of known protein structures. Each concept represents a topologically conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the Protein Data Bank and completely inventoried all the concept instances. This yields many insights, including correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence–structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click), provides access to and navigation of the entire dictionary of concepts and their usages, and all associated information. This report is part of a continuing programme with the goal of elucidating fundamental principles of protein architecture, in the spirit of the work of Cyrus Chothia.

scholarly journals Universal architectural concepts underlying protein folding patterns

2018 ◽  
Author(s):  
Arthur M. Lesk ◽  
Ramanan Subramanian ◽  
Lloyd Allison ◽  
David Abramson ◽  
Peter J. Stuckey ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Data Bank ◽  
Rational Drug Design ◽  
Basis Set ◽  
Sequence Structure ◽  
Information Theoretic ◽  
Catalytic Activities ◽  
Structure Correlations ◽  
Rational Drug

ABSTRACTWhat is the architectural ‘basis set’ of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a comprehensive dictionary of 1,493 substructural concepts. Each concept represents a topologically-conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the world-wide protein data bank and completely inventoried all concept instances. This yields an unprecedented source of biological insights. These include: correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence–structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click) provides access to and navigation of the entire dictionary of concepts, and all associated information.

scholarly journals NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Homa MohammadiPeyhani ◽  
Anush Chiappino-Pepe ◽  
Kiandokht Haddadi ◽  
Jasmin Hafner ◽  
Noushin Hadadi ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Rational Drug Design ◽  
Bioactive Molecules ◽  
Lead Discovery ◽  
Minimal Impact ◽  
Practical Applications ◽  
Rational Drug ◽  
Golden Standard ◽  
And Performance

The discovery of a drug requires over a decade of intensive research and financial investments – and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug–drug and drug–metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

scholarly journals Computational investigation of marine bioactive compounds reveals frigocyclinone as a potent inhibitor of Kaposi’s Sarcoma Associated Herpesvirus (KSHV) targets

2019 ◽  
Vol 12 (3) ◽  
pp. 1289-1302
Author(s):  
Nirmaladevi Ponnusamy ◽  
Rajasree Odumpatta ◽  
Pavithra Damodharan ◽  
Mohanapriya Arumugam
Keyword(s):  
Bioactive Compounds ◽  
Drug Targets ◽  
Kaposi's Sarcoma ◽  
Protein Structures ◽  
In Silico Analysis ◽  
Kaposi’S Sarcoma ◽  
The Stability ◽  
Salicylihalamide A ◽  
Experimental Structure

In the present study, in silico analysis was employed to identify the action of marine bioactive compounds against KSHV targets. Virulence factor analysis of KSHV from literature review, three proteins LANA1, vIRF3/LANA2 and PF-8 were identified as putative drug targets. The quality of protein structures play a significant role in the experimental structure validation and prediction, where the predicted structures may contain considerable errors was checked by SAVES v5.0 servers. By virtual screening four potential bioactive compounds Ascorbic acid, Salicylihalamide A, Salicylihalamide B and Frigocyclinone were predicted. One of the potential compounds of Frigocyclinone has acting against KSHV proteins. Hence, determined as the good lead molecule against KSHV. Molecular dynamic simulation studies revealed the stability of LANA1- Frigocyclinone complex and it could be a futuristic perspective chemical compound for Kaposi’s sarcoma.

scholarly journals A Deep-Learning Proteomic-Scale Approach for Drug Design

2021 ◽  
Vol 14 (12) ◽  
pp. 1277
Author(s):  
Brennan Overhoff ◽  
Zackary Falls ◽  
William Mangione ◽  
Ram Samudrala
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Protein Structures ◽  
Holistic Approach ◽  
Rational Drug Design ◽  
P Value ◽  
Experimental Drugs ◽  
Rational Drug ◽  
Drug Compound ◽  
Novel Drug

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

scholarly journals Naegleria fowleri: Protein structures to facilitate drug discovery for the deadly, pathogenic free-living amoeba

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0241738
Author(s):  
Logan Tillery ◽  
Kayleigh Barrett ◽  
Jenna Goldstein ◽  
Jared W. Lassner ◽  
Bram Osterhout ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Protein Structures ◽  
Naegleria Fowleri ◽  
Combination Drug ◽  
Free Living ◽  
Rational Drug ◽  
Free Living Amoeba ◽  
The Face ◽  
Nægleria Fowleri

Naegleria fowleri is a pathogenic, thermophilic, free-living amoeba which causes primary amebic meningoencephalitis (PAM). Penetrating the olfactory mucosa, the brain-eating amoeba travels along the olfactory nerves, burrowing through the cribriform plate to its destination: the brain’s frontal lobes. The amoeba thrives in warm, freshwater environments, with peak infection rates in the summer months and has a mortality rate of approximately 97%. A major contributor to the pathogen’s high mortality is the lack of sensitivity of N. fowleri to current drug therapies, even in the face of combination-drug therapy. To enable rational drug discovery and design efforts we have pursued protein production and crystallography-based structure determination efforts for likely drug targets from N. fowleri. The genes were selected if they had homology to drug targets listed in Drug Bank or were nominated by primary investigators engaged in N. fowleri research. In 2017, 178 N. fowleri protein targets were queued to the Seattle Structural Genomics Center of Infectious Disease (SSGCID) pipeline, and to date 89 soluble recombinant proteins and 19 unique target structures have been produced. Many of the new protein structures are potential drug targets and contain structural differences compared to their human homologs, which could allow for the development of pathogen-specific inhibitors. Five of the structures were analyzed in more detail, and four of five show promise that selective inhibitors of the active site could be found. The 19 solved crystal structures build a foundation for future work in combating this devastating disease by encouraging further investigation to stimulate drug discovery for this neglected pathogen.

Computational and Pharmacogenomic Insights on Hypertension Treatment: Rational Drug Design and Optimization Strategies

2019 ◽  
Vol 21 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Lakshmanan Loganathan ◽  
Krishnasamy Gopinath ◽  
Vadivel Murugan Sankaranarayanan ◽  
Ritushree Kukreti ◽  
Kannan Rajendran ◽  
...  
Keyword(s):  
Drug Design ◽  
Drug Targets ◽  
Antihypertensive Drugs ◽  
Rational Drug Design ◽  
Hypertension Treatment ◽  
Genetic Traits ◽  
Drug Responses ◽  
Computational Screening ◽  
Rational Drug ◽  
Multiple Drugs

Background:: Hypertension is a prevalent cardiovascular complication caused by genetic and nongenetic factors. Blood pressure (BP) management is difficult because most patients become resistant to monotherapy soon after treatment initiation. Although many antihypertensive drugs are available, some patients do not respond to multiple drugs. Identification of personalized antihypertensive treatments is a key for better BP management. Objective:: This review aimed to elucidate aspects of rational drug design and other methods to develop better hypertension management. Results:: Among hypertension-related signaling mechanisms, the renin-angiotensin-aldosterone system is the leading genetic target for hypertension treatment. Identifying a single drug that acts on multiple targets is an emerging strategy for hypertension treatment, and could be achieved by discovering new drug targets with less mutated and highly conserved regions. Extending pharmacogenomics research to include patients with hypertension receiving multiple antihypertensive drugs could help identify the genetic markers of hypertension. However, available evidence on the role of pharmacogenomics in hypertension is limited and primarily focused on candidate genes. Studies on hypertension pharmacogenomics aim to identify the genetic causes of response variations to antihypertensive drugs. Genetic association studies have identified single nucleotide polymorphisms affecting drug responses. To understand how genetic traits alter drug responses, computational screening of mutagenesis can be utilized to observe drug response variations at the protein level, which can help identify new inhibitors and drug targets to manage hypertension. Conclusions:: Rational drug design facilitates the discovery and design of potent inhibitors. However, further research and clinical validation are required before novel inhibitors can be clinically used as antihypertensive therapies.

scholarly journals A pharmacological master key mechanism that unlocks the selectivity filter gate in K+channels

Science ◽  
2019 ◽  
Vol 363 (6429) ◽  
pp. 875-880 ◽  
Author(s):  
Marcus Schewe ◽  
Han Sun ◽  
Ümit Mert ◽  
Alexandra Mackenzie ◽  
Ashley C. W. Pike ◽  
...  
Keyword(s):  
Selectivity Filter ◽  
Rational Drug Design ◽  
Related Gene ◽  
K Channels ◽  
X Ray ◽  
Voltage Gated ◽  
X Ray Crystallography ◽  
Rational Drug ◽  
Dynamics Simulations ◽  
Pore Domain

Potassium (K+) channels have been evolutionarily tuned for activation by diverse biological stimuli, and pharmacological activation is thought to target these specific gating mechanisms. Here we report a class of negatively charged activators (NCAs) that bypass the specific mechanisms but act as master keys to open K+channels gated at their selectivity filter (SF), including many two-pore domain K+(K2P) channels, voltage-gated hERG (human ether-à-go-go–related gene) channels and calcium (Ca2+)–activated big-conductance potassium (BK)–type channels. Functional analysis, x-ray crystallography, and molecular dynamics simulations revealed that the NCAs bind to similar sites below the SF, increase pore and SF K+occupancy, and open the filter gate. These results uncover an unrecognized polypharmacology among K+channel activators and highlight a filter gating machinery that is conserved across different families of K+channels with implications for rational drug design.

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