Modeling coding-sequence evolution within the context of residue solvent accessibility

A number of methods for predicting levels of solvent accessibility or accessible surface area (ASA) of amino acid residues in proteins have been developed. These methods either predict regularly spaced states of relative solvent accessibility or an analogue real value indicating relative solvent accessibility. While discrete states of exposure can be easily obtained by post prediction assignment of thresholds to the predicted or computed real values of ASA, the reverse, that is, obtaining a real value from quantized states of predicted ASA, is not straightforward as a two-state prediction in such cases would give a large real valued errors. However, prediction of ASA into larger number of ASA states and then finding a corresponding scheme for real value prediction may be helpful in integrating the two approaches of ASA prediction. We report a novel method of obtaining numerical real values of solvent accessibility, using accumulation cutoff set and support vector machine. This so-called SVM-Cabins method first predicts discrete states of ASA of amino acid residues from their evolutionary profile and then maps the predicted states onto a real valued linear space by simple algebraic methods. Resulting performance of such a rigorous approach using 13-state ASA prediction is at least comparable with the best methods of ASA prediction reported so far. The mean absolute error in this method reaches the best performance of 15.1% on the tested data set of 502 proteins with a coefficient of correlation equal to 0.66. Since, the method starts with the prediction of discrete states of ASA and leads to real value predictions, performance of prediction in binary states and real values are simultaneously optimized.

SummaryClassification of missense variants is challenging. Lacking compelling clinical and/or functional data, ACMG/AMP lines of evidence are restricted to PM2 (rarity code applied at supporting level) and PP3/BP4 (computational evidence based mostly on multiple-sequence-alignment conservation tools). Currently, the ClinGen ENIGMA BRCA1/2 Variant Curation Expert Panel uses BayesDel to apply PP3/BP4 to missense variants located in the BRCA1 RING/BRCT domains. The ACMG/AMP framework does not refer explicitly to protein structure as a putative source of pathogenic/benign evidence. Here, we tested the value of incorporating structure-based evidence such as relative solvent accessibility (RSA), folding stability (ΔΔG), and/or AlphaMissense pathogenicity to the classification of BRCA1 missense variants. We used MAVE functional scores as proxies for pathogenicity/benignity. We computed RSA and FoldX5.0 ΔΔG predictions using as alternative input templates for either PDB files or AlphaFold2 models, and we retrieved pre-computed AlphaMissense and BayesDel scores. We calculated likelihood ratios toward pathogenicity/benignity provided by the tools (individually or combined). We performed a clinical validation of major findings using the large-scale BRIDGES case-control dataset. AlphaMissense outperforms ΔΔG and BayesDel, providing similar PP3/BP4 evidence strengths with lower rate of variants in the uninformative score range. AlphaMissense combined with ΔΔG increases evidence strength granularity. AlphaFold2 models perform well as input templates for ΔΔG predictions. Regardless of the tool, BP4 (but not PP3) is highly dependent on RSA, with benignity evidence provided only to variants targeting buried or partially buried residues (RSA ≤ 60%). Stratification by functional domain did not reveal major differences. In brief, structure-based analysis improves PP3/BP4 assessment, uncovering a relevant role for RSA.

Protein structure prediction is often assisted by predicting one-dimensional structural properties including relative solvent accessibility (RSA) surface and backbone torsion angles (BTA) of residues, and these two properties are continuously varying variables because proteins can move freely in a three-dimensional space. Instead of subdividing them into a few arbitrarily defined states that many popular approaches used, this paper proposes an integrated system for realvalue prediction of protein structural motifs’ two local properties, based on the modified Hidden Markov Model that we previously presented. The model was used to capture the relevance of RSA and the dependency of BTA between adjacent residues along the local protein chain in motifs with definite probabilities. These two properties were predicted according to their own probability distribution. The method was applied to a protein fragment library. For nine different classes of motifs, real values of RSA were predicted with mean absolute error (MAE) of 0.122-0.175 and Pearson’s correlation coefficient (PCC) of 0.623-0.714 between predicted and actual RSA. Meanwhile, real values of BTA were obtained with MAE of 8.50-29.40 for Φ angles, 11.20-38.50 for ψ angles and PCC of 0.601-0.716 for Φ, 0.597-0.713 for ψ. The results were compared with well-known Real-SPINE Server, and indicate the proposed method may at least serve as the foundation to obtain better local properties from structural motifs for protein structure prediction. Keywords: Protein structure predication, structural motifs, hidden Markov model, solvent accessibility surface, backbone torsion angles, directional statistics distribution

BackgroundProtein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying protein folds and domains. Although some methods have been presented to the protein solvent accessibility prediction in recent years, the performance is far from satisfactory. In this work, we propose PredRSA, a computational method that can accurately predict relative solvent accessible surface area (RSA) of residues by exploring various local and global sequence features which have been observed to be associated with solvent accessibility. Based on these features, a novel and efficient approach, Gradient Boosted Regression Trees (GBRT), is first adopted to predict RSA.ResultsExperimental results obtained from 5-fold cross-validation based on the Manesh-215 dataset show that the mean absolute error (MAE) and the Pearson correlation coefficient (PCC) of PredRSA are 9.0 % and 0.75, respectively, which are better than that of the existing methods. Moreover, we evaluate the performance of PredRSA using an independent test set of 68 proteins. Compared with the state-of-the-art approaches (SPINE-X and ASAquick), PredRSA achieves a significant improvement on the prediction quality.ConclusionsOur experimental results show that the Gradient Boosted Regression Trees algorithm and the novel feature combination are quite effective in relative solvent accessibility prediction. The proposed PredRSA method could be useful in assisting the prediction of protein structures by applying the predicted RSA as useful restraints.

C1, the complex that triggers the classic pathway of complement, is a 790-kDa assembly resulting from association of a recognition protein C1q with a Ca(2+)-dependent tetramer comprising two copies of the proteases C1r and C1s. Early structural investigations have shown that the extended C1s-C1r-C1r-C1s tetramer folds into a compact conformation in C1. Recent site-directed mutagenesis studies have identified the C1q-binding sites in C1r and C1s and led to a three-dimensional model of the C1 complex (Bally, I., Rossi, V., Lunardi, T., Thielens, N. M., Gaboriaud, C., and Arlaud, G. J. (2009) J. Biol. Chem. 284, 19340-19348). In this study, we have used a mass spectrometry-based strategy involving a label-free semi-quantitative analysis of protein samples to gain new structural insights into C1 assembly. Using a stable chemical modification, we have compared the accessibility of the lysine residues in the isolated tetramer and in C1. The labeling data account for 51 of the 73 lysine residues of C1r and C1s. They strongly support the hypothesis that both C1s CUB(1)-EGF-CUB(2) interaction domains, which are distant in the free tetramer, associate with each other in the C1 complex. This analysis also provides the first experimental evidence that, in the proenzyme form of C1, the C1s serine protease domain is partly positioned inside the C1q cone and yields precise information about its orientation in the complex. These results provide further structural insights into the architecture of the C1 complex, allowing significant improvement of our current C1 model.

How ambiguity codes specify molecular descriptors and information flow in Code Biology

Zn2+ modulates in vitro phase separation of TDP-432C and mutant TDP-432C-A315T C-terminal fragments of TDP-43 protein implicated in ALS and FTLD-TDP diseases

The elastic network models (ENMs)are known as representative coarse-grained models to capture essential dynamics of proteins. Due to simple designs of the force constants as a decay with spatial distances of residue pairs in many previous studies, there is still much room for the improvement of ENMs. In this article, we directly computed the force constants with the inverse covariance estimation using a ridge-type operater for the precision matrix estimation (ROPE)on a large-scale set of NMR ensembles. Distance-dependent statistical analyses on the force constants were further comprehensively performed in terms of several paired types of sequence and structural information, including secondary structure, relative solvent accessibility, sequence distance and terminal. Various distinguished distributions of the mean force constants highlight the structural and sequential characteristics coupled with the inter-residue cooperativity beyond the spatial distances. We finally integrated these structural and sequential characteristics to build novel ENM variations using the particle swarm optimization for the parameter estimation. The considerable improvements on the correlation coefficient of the mean-square fluctuation and the mode overlap were achieved by the proposed variations when compared with traditional ENMs. This study opens a novel way to develop more accurate elastic network models for protein dynamics.

Identification and analyses of natural compounds as potential inhibitors of TRAF6-Basigin interactions in melanoma using structure-based virtual screening and molecular dynamics simulations

Model quality assessment is an important task in protein structure prediction, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction methods. A new prediction category that evaluates the quality of protein models has been implemented since CASP7. In this study, a machine learning-based method for protein global model quality assessment (GMQA) is presented and the web server can be assessed at http://www.iipl.fudan.edu.cn/gmqa/index.html. The GMQA method takes a protein model as input and outputs the predicted MaxSub score that indicates the absolute quality of the protein model. The proposed method extracts the structural features from the 3D coordinates and assigns an absolute quality score to a model by support vector regression. Three types of features are extracted, including secondary structure, relative solvent accessibility, and contact. The closed test is performed on the CASP7 data set using cross validation. An open testing is performed on the LKF data set. In both tests, good correlations between the predicted and true scores are observed. Furthermore, our method is able to discriminate the native or near-native structures from a set of decoys. We also demonstrate that the GMQA method outperforms two existing methods, i.e. ProQ and Victor/FRST. Our results show that GMQA is a useful tool for model quality assessment and ranking.

IntroductionTNIK (Traf2- and Nck-interacting kinase) is a serine/threonine kinase that plays a crucial role in cytoskeletal organization, Wnt pathway activation, and cancer progression. Recent studies have implicated the role of TNIK in oncogenic signaling pathways and neuropsychiatric regulation. However, the phosphosignaling dynamics of TNIK remain largely unknown.MethodsTo explore TNIK phosphoregulation, we systematically assembled and integrated global human phosphoproteomic datasets. We identified the predominant phosphosites based on the frequency. Relative solvent accessibility (RSA) and Phosphosite accessibility index (PAI) were calculated to determine the solvent exposure and structural flexibility of TNIK predominant phosphosites. To assess the functional significance of TNIK, we examined proteins that were differentially co-regulated with its predominant phosphosite, along with the corresponding upstream kinases, downstream substrates, and interacting proteins.ResultsAnalysis of the global human cellular phosphoproteome datasets revealed phosphosites S640, S680, S707, and S769 of TNIK to be the most frequently perturbed phosphosites across diverse experimental conditions. The results of the RSA and PAI analysis revealed that the predominant sites are located within highly solvent-exposed and structurally flexible regions. Notably, we obtained a large number of co-regulated proteins that were associated with cell growth, carcinogenesis, and apoptosis. The interactors identified were primarily enriched towards carcinogenesis. Our analysis revealed PRKAA1 and RPS6KB2 as robust upstream kinases of TNIK_S640 and TNIK_S707. We also identified many proteins involved in RNA splicing, cytoskeletal organisation, and cell migration as potential downstream substrates of TNIK.DiscussionConsidering the challenges in targeted experimental analysis of these sites, a global co-regulation analysis approach was employed. Our results show that these phosphorylation sites in TNIK can influence carcinogenesis and related biological functions. It offers new insights into TNIK-mediated cellular functions, deepening our comprehension of its involvement in carcinogenesis and RNA splicing.

We previously developed the first method to predict real valued accessible surface area in proteins, which was shown to contain more information than 100% correct predictions in a binary space of exposure states. Neural networks used for solvent accessibility predictions, however work as good predictors with invisible modeling of knowledge they learn. In view of this a direct statistical analysis of solvent accessibility of residues was desired, in terms of the contribution of residue neighbors in making them buried or exposed to solvent. Present work aims at compiling singlet to quintets of residue fragments and examining, how the solvent accessibility of residues is affected by the change in a neighboring residue in C-terminal, N-terminal or other positions of these residues. In addition, a graphical method of representing solvent accessibility was developed. Online graphical plots for all proteins in the entire Protein Data Bank (PDB) were made available on the web at www.netasa.org/ asa-view/.

Simulating multiple sequence alignments (MSAs) using probabilistic models of sequence evolution plays an important role in the evaluation of phylogenetic inference tools and is crucial to the development of novel learning-based approaches for phylogenetic reconstruction, for instance, neural networks. These models and the resulting simulated data need to be as realistic as possible to be indicative of the performance of the developed tools on empirical data and to ensure that neural networks trained on simulations perform well on empirical data. Over the years, numerous models of evolution have been published with the goal to represent as faithfully as possible the sequence evolution process and thus simulate empirical-like data. In this study, we simulated DNA and protein MSAs under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how accurately supervised learning methods are able to predict whether a given MSA is simulated or empirical. Our results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate several aspects of empirical MSAs, including site-wise rates as well as amino acid and nucleotide composition.

In Bayesian phylogenetics, confidence in evolutionary relationships is expressed as posterior probability--the probability that a tree or clade is true given the data, evolutionary model, and prior assumptions about model parameters. Model parameters, such as branch lengths, are never known in advance; Bayesian methods incorporate this uncertainty by integrating over a range of plausible values given an assumed prior probability distribution for each parameter. Little is known about the effects of integrating over branch length uncertainty on posterior probabilities when different priors are assumed. Here, we show that integrating over uncertainty using a wide range of typical prior assumptions strongly affects posterior probabilities, causing them to deviate from those that would be inferred if branch lengths were known in advance; only when there is no uncertainty to integrate over does the average posterior probability of a group of trees accurately predict the proportion of correct trees in the group. The pattern of branch lengths on the true tree determines whether integrating over uncertainty pushes posterior probabilities upward or downward. The magnitude of the effect depends on the specific prior distributions used and the length of the sequences analyzed. Under realistic conditions, however, even extraordinarily long sequences are not enough to prevent frequent inference of incorrect clades with strong support. We found that across a range of conditions, diffuse priors--either flat or exponential distributions with moderate to large means--provide more reliable inferences than small-mean exponential priors. An empirical Bayes approach that fixes branch lengths at their maximum likelihood estimates yields posterior probabilities that more closely match those that would be inferred if the true branch lengths were known in advance and reduces the rate of strongly supported false inferences compared with fully Bayesian integration.

PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).