Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

Duan, Jian‐Xin; Lupyan, Dmitry; Wang, Lingle

doi:10.1016/j.bpj.2020.05.020

Cited by 48 publications

(80 citation statements)

References 82 publications

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“…FoldX and etc. It often precisely predicts the free energy differences between the mutations with a RMSE of about only1.2 kcal/mol [7][8]. It has been also shown that the better sampling approaches lead to much better results and most importantly for more than 90% correct predictions of the effect; i.e.…”

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confidence: 99%

“…Despite the computational demands, the Free energy of binding (FEP) approach is one of the most successful and precise in silico techniques for accurate prediction of both the ligand selectivity [6][7], protein-protein interactions [8][9] and protein stability [10][11]. It outperforms significantly the traditional molecular dynamics based methodologies, such as for example MM/GBSA and empirical solutions like FoldX and etc.…”

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confidence: 99%

“…whether the effect will be positive or negative after certain ligand or protein substitutions [12]. To calculate the differences in the free energy of binding for each complex in this study we employed the Desmond FEP/REST approach described in details previously [5][6][7][8][9][10][11][12]. Initially, the default sampling protocol was applied with the number of lambda (λ) windows either 12 or 24, in dependence of the mutation charge.…”

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confidence: 99%

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The N501Y and K417N mutations in the spike protein of SARS-CoV-2 alter the interactions with both hACE2 and human derived antibody: A Free energy of perturbation study

Fratev

2020

Preprint

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The N501Y and K417N mutations in spike protein of SARS-CoV-2 and their combination arise questions but the data about their mechanism of action at molecular level is limited. Here, we present Free energy perturbation (FEP) calculations for the interactions of the spike S1 receptor binding domain (RBD) with both the ACE2 receptor and an antibody derived from COVID-19 patients. Our results shown that the S1 RBD-ACE2 interactions were significantly increased whereas those with the STE90-C11 antibody dramatically decreased; about over 100 times. The K417N mutation had much more pronounced effect and in a combination with N501Y fully abolished the antibody effect. This may explain the observed in UK and South Africa more spread of the virus but also raise an important question about the possible human immune response and the success of already available vaccines.

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confidence: 99%

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confidence: 99%

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The N501Y and K417N mutations in the spike protein of SARS-CoV-2 alter the interactions with both hACE2 and human derived antibody: A Free energy of perturbation study

Fratev

2020

Preprint

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“…We carried out FEP calculations to calculate the changes of binding affinity between the Streptomyces griseus proteinases B (SGPB) and the turkey ovomucoid third domain (OMTKY3) caused by single mutations 65 . This protein-protein complex has been used to validate the accuracy of FEP calculations in several previous studies 64,66 . FEP calculations were conducted on five selected mutations, which display changes in binding affinity ranging from 3.0 to 8.5 kcal/mol.…”

Section: Rbfe Performance Benchmark For Protein Mutationsmentioning

confidence: 99%

A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery

Zhang¹,

Zou²,

Chen³

et al. 2020

Preprint

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<p>Free energy perturbation (FEP) has become widely used in drug discovery programs for binding affinity prediction between candidate compounds and their biological targets. Simultaneously limitations of FEP applications also exist, including but not limited to, the high cost, long waiting time, limited scalability and application scenarios. To overcome these problems, we have developed a scalable cloud computing platform (XFEP) for both relative and absolute free energy predictions with refined simulation protocols. XFEP enables large-scale FEP calculations in a more efficient, scalable and affordable way, e.g. the evaluation of 5,000 compounds can be performed in one week using 50-100 GPUs with a computing cost approximately corresponding to the cost for one new compound synthesis. Together with artificial intelligence (AI) techniques for goal-directed molecule generation and evaluation, new opportunities can be explored for FEP applications in the drug discovery stages of hit identification, hit-to-lead, and lead optimization with R-group substitutions, scaffold hopping, and completely different molecule evaluation. We anticipate scalable FEP applications will become widely used in more drug discovery projects to speed up the drug discovery process from hit identification to pre-clinical candidate compound nomination. </p>

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“…Proteins being dynamic molecules, substitution mutations impact molecular motions leading to a change in the flexible conformations and vibrational entropy. Most of the tools that predict thermodynamic stability changes assess the change in a static state [15] . Employing molecular dynamics simulations and normal mode analysis aid accurate assessment of stability changes [16] .…”

Section: Introductionmentioning

confidence: 99%

HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae

Vedithi

Malhotra

Skwark

et al. 2020

Computational and Structural Biotechnology Journal

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Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

Cited by 48 publications

References 82 publications

The N501Y and K417N mutations in the spike protein of SARS-CoV-2 alter the interactions with both hACE2 and human derived antibody: A Free energy of perturbation study

The N501Y and K417N mutations in the spike protein of SARS-CoV-2 alter the interactions with both hACE2 and human derived antibody: A Free energy of perturbation study

A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery

HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae

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