Biology subjects

Kepp, K. P.

Publications and source records attributed to Kepp, K. P..

8 recordsLinked to original sources

bioRxiv2023

Computational fitness estimates of SARS-CoV-2 mutations affecting spike protein binding to antibodies and ACE2

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogen. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein ([~]3,80,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

bioinformatics↗

bioRxiv2022

Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding

The binding affinity of the SARS-CoV-2 spike (S)-protein {Delta}{Delta}Gbind to the human membrane protein ACE2 is critical for virus function and evolution. Computational structure-based screening of new S-protein mutations for ACE2 binding lends promise to rationalize virus function directly from protein structure and ideally aid early detection of potentially concerning variants. We used a computational protocol based on cryo-electron microscopy structures of the S-protein to estimate the ACE2-binding that gave good trend agreement with experimental ACE2 affinities. We then expanded predictions to all possible S-protein mutations in 21 different S-protein-ACE2 complexes (400,000 {Delta}{Delta}Gbind data points in total), using mutation group comparisons to reduce systematic errors. We show that mutations that have arisen in major variants as a group maintain ACE2 affinity significantly more than random mutations in the total protein, at the interface, and at evolvable sites, with differences between variant mutations being small relative to these effects. Omicron mutations as a group had a modest change in binding affinity compared to mutations in other major variants. The single-mutation effects are consistent with ACE2 binding being optimized and maintained in omicron, despite increased importance of other selection pressures (antigenic drift). As epistasis, glycosylation and in vivo conditions will modulate these effects, computational predictive SARS-CoV-2 evolution remains far from achieved, but the feasibility of large-scale computation is substantially aided by using many structures and comparison of mutation groups rather than single mutation effects, which are very uncertain. Our results demonstrate substantial challenges but indicate ways to improve the quality of computer models for assessing SARS-CoV-2 mutation effects.

biophysics↗

bioRxiv2022

Stability and expression of SARS-CoV-2 spike-protein mutations

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen for ACE2 binding. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

biophysics↗

bioRxiv2022

Assessment of AlphaFold2 residue conformations for human proteins

MotivationAs only 35% of human proteins feature (often partial) PDB structures, the protein structure prediction tool AlphaFold2 (AF2) could have massive impact on human biology and medicine fields, making independent benchmarks of interest. We studied AF2s ability to describe the backbone solvent exposure as an easily interpretable "natural coordinate" of protein conformation, using human proteins as test case. ResultsAfter screening for appropriate comparative sets, we matched 1818 human proteins predicted by AF2 against 7585 unique experimental PDBs, and after curation for sequence overlap, we assessed 1264 comparative pairs comprising 115 unique AF2-structures and 652 unique experimental structures. AF2 performed markedly worse for multimers, whereas ligands, cofactors and experimental resolution were interestingly not very important for performance. AF2 performed excellently for monomer proteins. Challenges relating to specific groups of residues and multimers were analyzed. We identify larger errors for lower-confidence scores (pLDDT) and exposed residues, and polar residues (Asp, Glu, Asn e.g.) being less accurately described than hydrophobic residues. Proline conformations were the hardest to predict, probably due to common location in dynamic solvent-accessible parts. In summary, using solvent exposure as a natural metric of local conformation, we quantify the performance of AF2 for human proteins and provide estimates of the expected error as a function of ligand presence, multimer/monomer status, resolution, local residue solvent exposure, pLDDT, and amino acid type. Overall performance was found to be excellent. Availability and ImplementationScripts used to perform benchmarking are available at https://github.com/ktbaek/AlphaFold.

biophysics↗

bioRxiv2020

Force-Field Benchmarking by Alternatives: A Systematic Study of Ten Small α- and β-Proteins

Predicting protein structure from sequence is a central challenge of biochemistry, yet different force fields feature distinct structural biases that are hard to quantify, preventing clear assessment of results. Since structural transitions occur on milliseconds to seconds, sampling is out of reach in almost all routine studies, we inherently rely on local sampled structures, and benchmarks have emphasized the ability to reproduce these local structures. Here we approach the force field bias problem in a different way, via alternatives, by revisiting the old question: How unique is the sequence-structure relationship when studied computationally? To circumvent the sampling problem, the system-bias (specific structure choices affect apparent force field structural preference) and the complexity of tertiary structure, we studied ten small - and {beta}-proteins (20-35 amino acids) with one helix or sheet. For each of the ten sequences, we then designed alternative {beta}- or -structures and subjected all 20 proteins to molecular dynamics simulations. We apply this "alternative structure" benchmark to five of the best modern force fields: Amber ff99SB-ILDN, Amber ff99SB*-ILDN, CHARMM22*, CHARMM36, and GROMOS54A8. Surprisingly, we find that all sequences with reported {beta}-structures also feature stable native-like -structures with all five force fields. In contrast, only the alternative {beta}-1T5Q and to some extent {beta}-1CQ0 and {beta}-1V1D resembled native {beta}-proteins. With full phase space sampling being impossible in almost all cases, our benchmark by alternatives, which samples another local part of phase space in direct comparison, is a useful complement to millisecond benchmarks when these become more common.

biophysics↗

bioRxiv2019

Benchmarking Computational Methods for Estimating the Pathogenicity of Wilson’s Disease Mutations

Genetic variations in the gene encoding the copper-transport protein ATP7B are the primary cause of Wilsons disease. Controversially, clinical prevalence seems much smaller than prevalence estimated by genetic screening tools, causing fear that many people are undiagnosed although early diagnosis and treatment is essential. To address this issue, we benchmarked 16 state-of-the-art computational disease-prediction methods against established data of missense ATP7B mutations. Our results show that the quality of the methods vary widely. We show the importance of optimizing the threshold of the methods used to distinguish pathogenic from non-pathogenic mutations against data of clinically confirmed pathogenic and non-pathogenic mutations. We find that most methods use thresholds that predict too many ATP7B mutations to be pathogenic. Thus, our findings explain the current controversy on Wilsons disease prevalence, because meta analysis and text search methods include many computational estimates that lead to higher disease prevalence than clinically observed. Since proteins differ widely, a one-size-fits-all threshold for all proteins cannot distinguish efficiently pathogenic and non-pathogenic mutations, as shown here. We also show that amino acid changes with small evolutionary substitution probability, mainly due to amino acid volume, are more associated with disease, implying a pathological effect on the conformational state of the protein, which could affect copper transport or ATP recognition and hydrolysis. These findings may be a first step towards a more quantitative genotype-phenotype relationship of Wilsons disease.

biochemistry↗

bioRxiv2019

Membrane dynamics of Notch-bound γ-secretase produces two distinct Notch conformations

Cleavage of Notch by the major intramembrane aspartyl protease complex {gamma}-secretase is a central event in cell regulation and is also important to Alzheimers disease, with more than 200 mutations in the catalytic subunit of {gamma}-secretase (PS1) causing severe early-onset forms of the disease. Recently, cryogenic electron microscopy (cryo-EM) has revealed the electron density of the protein-Notch complex in frozen solution, indicating major changes upon substrate binding and a possible helix unwinding to expose peptide bonds. In order understand the all-atom dynamics that cause this process, and to test the Notch binding in a membrane protein rather than solution, we developed an all-atom model of mature wild-type {gamma}-secretase bound to Notch in a complete membrane-water system and studied the system using three independent 500-nanosecond molecular dynamics simulations. Our ensembles are in essential agreement with known cryo-EM data. As in previous simulations we find unusual {beta}-strand transitions in exposed parts of PS1. We also observe the atomic helix motions that cause loss of helicity in bound Notch by direct comparison to corresponding 500 ns simulations of free Notch, in particular five residues to the N-terminal site of the primary cleavage site. Most importantly, we identify three conformation states, with two of them differing in the Notch-bound catalytic site. These dynamics produce a ping-pong relationship of positioning the S3 cleavage sites of Notch relative to the aspartates. These conformation states are not visible in the cryo-EM data; probably the density is an average snapshot of the two states. Our identified conformation states rationalize how Notch cleavage can be imprecise and yield multiple products. Our identified conformation states may aid efforts to develop conformation-selective drugs that target C99 and Notch cleavage differently.\n\nStatement of SignificanceThe atomic dynamics underlying cleavage of Notch by {gamma}-secretase in the membrane is of major biological importance. Electron microscopy has revealed the protein-Notch complex in frozen solution, showing major changes upon substrate binding and helix unwinding to expose peptide bonds, but does not explain why substrate cleavage is imprecise and produces several products. Our model of wild-type {gamma}-secretase bound to Notch in a complete membrane-water system equilibrated by 3 x 500 nanoseconds of molecular dynamics strongly complements the electron microscopy data: We identify the specific loop and helix motions that cause the {beta}-strand transitions in PS1 and the loss of helicity in specific residues of bound Notch. We identify different conformations of Notch, which importantly affect the S3 cleavage site; the open state may cause the imprecise cleavage with earlier release of products. Our identified states can aid development of conformation-selective drugs that target C99 and Notch cleavage differently.

biophysics↗

bioRxiv2019

A quantitative model of human neurodegenerative diseases involving protein aggregation

Human neurodegenerative diseases such as Alzheimers disease, Parkinsons disease, and Amyotrophic lateral sclerosis involve protein aggregation and share many other similarities. It is widely assumed that the protein aggregates exhibit a specific molecular mode of toxic action. This paper presents a simple mathematical model arguing that clinical cognitive status relates to the energy available after subtracting cell maintenance due to general turnover of the misfolded proteins, rather than a specific toxic molecular action per se. Proteomic cost minimization can explain why highly expressed proteins changed less during evolution, leaving more energy for reproducing microorganisms on longer evolutionary timescales. In higher organisms, the excess energy instead defines cognitive capability, and the same equations remarkably apply. Proteomic cost minimization can explain why late-onset neurodegenerative diseases involve protein aggregation. The model rationalizes clinical ages of symptom onset for patients carrying pathogenic protein mutations: Unstable or aggregation-prone mutations confer a direct energy cost of turnover, but other risk modifiers also change the available cellular energy as ultimately defining clinical outcome. Proteomic cost minimization is consistent with current views on biomarker histories, explains conflicting data on overexpression models, and is supported by specific experiments showing that proteasome activity is required to confer toxicity to pathogenic mutants. The mechanism and model lend promise to a quantitative personalized medicine of neurodegenerative disease.

neuroscience↗