Ranking Docking Models by Coevolution Analysis

ObjectiveWe aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability.MethodsWe utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT).ResultsAmong four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80).ConclusionThis is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age.Critical relevance statementMutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals’ brain age.Key PointsMutual information (MI) interprets estimated brain age with morphometric features.Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age.Cerebrospinal fluid volume in the cingulate has the highest MI value.Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value.The value of mutual information underscores the key brain regions related to brain age.Graphical

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

Measures of relevance between features play an important role in classification and regression analysis. Mutual information has been proved to be an effective measure for categorical features. However, there is a limitation in computing relevance between numerical features with mutual information. In this work, we generalize Shannon's information entropy to neighborhood information entropy and propose a measure of neighborhood mutual information. It is shown that the new measure is a natural extension of classical mutual information which reduces to the classical one if features are discrete; thus the new measure can also be used to compute the relevance between discrete variables. In experiment, we show that neighborhood mutual information produces the nearly same outputs as mutual information. However, unlike mutual information, no discretization is required in computing relevance when used the proposed algorithm.

In this work we apply the network physiology paradigm to retrieve information from central and autonomic nervous systems before focal epileptic seizure, represented respectively by electroencephalogram (EEG) signals and R-R intervals (RRI), and investigate on the presence and strength of brain-heart interactions by computing mutual information (MI) measures. Statistical significance of MI values was tested through surrogate time series generated with the random shuffle approach. Our results suggest that the proposed method for aligning signals representing brain and heart activity measured with different sampling rates, is capable of revealing coupling between RRI representing heart system, and aligned averaged power spectrum of brain processes, measured with EEG, resulting in significant MI. For electrodes C3, Fp2, Cz, and T4 in correspondingly α, β, γ, and total frequency bands, we obtain significantly smaller values of MI in the pre-ictal period in comparison with baseline period, as well as general decrease of significant and all estimated MI values before the focal seizure can be observed.

Enzymes are highly efficient bio-catalysts interesting for industries and medicine. Therefore, a goal of utmost importance in biochemical research is to understand how an enzyme catalyzes a chemical reaction. Here, the computational identification of functionally or structurally important residue positions can be of tremendous help. The datasets that are most informative for the algorithms are the 3D structure of a protein and a multiple sequence alignment (MSA) composed of homologous sequences. For example, an MSA allows for the quantification of residue conservation. Residue conservation at a given position indicates that only one type of amino acid fulfills all constraints imposed by protein structure or function. Furthermore, a detailed analysis of less strictly conserved residue positions may identify pairs, whose orchestration is mutually dependent and induces correlated mutations. Both of these conservation signals are indicative of functionally or structurally important positions. In the first part of this thesis, methods of machine learning were used to identify and classify these residue positions. It was the aim to predict in a mutually exclusively manner a role in catalysis, ligand-binding or protein stability for each residue position of a protein. Unfortunately, for many proteins the 3D structure is unknown. For other proteins, the number of known homologs is not sufficient to compile a meaningful MSA. Therefore, three variants of a classifier were designed and implemented, named CLIPS-1D, CLIPS-3D, and CLIPS-4D. These multi-class support vector machines allow for a classification based on an MSA (CLIPS-1D), a 3D structure (CLIPS-3D), and a combination of both (CLIPS-4D). CLIPS-1D exploits seven sequence-based features, whereas CLIPS-3D utilizes seven structure-based features. CLIPS-4D combines the seven sequence-based features of CLIPS-1D with those two structure-based features that increased its classification performance. A comparison with existing methods and a detailed analysis on a well-studied enzyme confirmed state-of-the-art prediction quality for CLIPS-1D and CLIPS-4D. In the second part of this thesis an algorithm for the identification of correlated mutations was improved. A common method for the identification of correlated mutations is to deduce the mutual information (MI) of a pair of residue positions from an MSA. The classical MI is based on Shannon’s information theory that utilizes probabilities only. Consequently, these approaches do not consider the similarity of residue pairs, which is a severe limitation. In order to improve these algorithms, H2rs was developed for this thesis. Thus, the MIvalues originate from the von Neumann entropy (vNE), which takes into account amino acid similarities modeled by means of a substitution matrix. To further improve the specificity of H2rs, the significance of MIvNE-values was assessed with a bootstrapping approach. The analysis of a large in silico testbed and the detailed assessment of five well-studied enzymes demonstrated state-of-the-art performance.

Chaos feature criteria tree has many fruits with geometric maps and calculated values. To easily understand how to study chaos identification, we propose one new learning combination solution based on Hilbert transform (HT) and mutual information (MI). The most important coding probe (x-axis) used the HT of locked uniformly distributed random number (LUSRN), so far the y-axis in contrast standard map points to LUDRN. The measure between contrast standard map and unknown map (normalized test data, HT of LUSRN) used MI values. The test cases cover five chaos equations and eighteen quasi-period functions. The results show in statistics that our new maps and corresponding MI values can classify three kinds of signals with periodic, chaotic and random states. And this work may promote rapid growth of an apple embedded in novel chaos criteria.

Mutual information map as a new way for exploring the independence of chemically meaningful solutions in two-component analytical data

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.

Epigenetic communication through histone and cytosine modifications is essential for gene regulation and cell identity. Here, we propose a framework that is based on a chromatin communication model to get insight on the function of epigenetic modifications in ESCs. The epigenetic communication network was inferred from genome-wide location data plus extensive manual annotation. Notably, we found that 5-hydroxymethylcytosine (5hmC) is the most-influential hub of this network, connecting DNA demethylation to nucleosome remodeling complexes and to key transcription factors of pluripotency. Moreover, an evolutionary analysis revealed a central role of 5hmC in the co-evolution of chromatin-related proteins. Further analysis of regions where 5hmC co-localizes with specific interactors shows that each interaction points to chromatin remodeling, stemness, differentiation, or metabolism. Our results highlight the importance of cytosine modifications in the epigenetic communication of ESCs.

We present a massively-parallel implementation of the computation of (co)evolutionary signals from biomolecular sequence alignments based on mutual information (MI) and a normalization procedure to neutral evolution. The MI is computed for two- and three-point correlations within any multiple-sequence alignment. The high computational demand in the normalization procedure is efficiently met by an implementation on Graphics Processing Units (GPUs) using NVIDIA's CUDA framework. GPU computation serves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time. In particular, the normalization of the MI for three-point 'cliques' of amino acids or nucleotides requires large sampling numbers in the normalization, that can only be achieved using GPUs.We illustrate a) the computational efficiency and b) the biological usefulness of two- and three-point MI by an application to the well-known protein calmodulin. Here, we find striking coevolutionary patterns and distinct information on the molecular evolution of this molecule.

A multi-model fusion strategy for multivariate calibration using near and mid-infrared spectra of samples from brewing industry

In normal free-breathing PET/CT imaging protocol, a single CT image of the patient is obtained before the PET scan, which is inadequate for attenuation correction of the free-breathing PET data. The aim of this study is to design an automatic workflow to correct mismatch and reduce motion blurring of free-breathing PET and single-scan CT data. First, the PET data was divided into four equal-count respiratory frames using data-driven respiratory gating method. Modified attenuation map was used in during reconstruction to obtain mismatch-free gated PET images. Second, the respiratory phase of the original CT image was identified automatically using mutual information (MI) as the similarity metric. Two regions of interests (ROIs) near the lungs were segmented from CT image. The mutual information (MI) inside the ROIs of each gated PET reconstruction and the CT image was measured for all gates and the phase with highest MI value is identified as the correct respiratory phase of CT image. Third, using the respiratory phase of the CT image as the reference gate, MVFs from every other gate to the reference gate was estimated using a B-spline based multi-resolution image registration algorithm. Fourth, the original CT image with identified respiratory phase was transformed to every other frame using estimated MVFs to obtain attenuation maps for all respiratory frames. Finally, image reconstruction of gated PET data was repeated using phase-matched gated PET-CT data pairs. MVFs estimated in step 3 were applied to this set of gated PET images to obtain motion compensated PET image. This method was applied to clinical datasets using both F18-FDG and Ga68 -DOTA-NOC tracer. The proposed method successfully identified the respiratory phase of the CT image and generated phase-matched CT images for all respiratory phases of PET data. Final reconstructed PET data using the proposed method showed much-reduced mismatch artifacts and motion blurring, as well as improved statistics.

Inspired by mutual information (MI) based feature selection in SVMs and logistic regression, in this paper, we propose MI-based layer-wise pruning: for each layer of a multi-layer neural network, neurons with higher values of MI with respect to preserved neurons in the upper layer are preserved. Starting from the top softmax layer, layer-wise pruning proceeds in a top-down fashion until reaching the bottom word embedding layer. The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into their smaller but dense counterparts, leading to greater speedup; (2) in a manner of top-down pruning, the proposed method operates from a more global perspective based on training signals in the top layer, and prunes each layer by propagating the effect of global signals through layers, leading to better performances at the same sparsity level. Extensive experiments show that at the same sparsity level, the proposed strategy offers both greater speedup and higher performances than weight-based pruning methods (e.g., magnitude pruning, movement pruning).

Autonomous robots that work in the same environment as humans must operate safely and adapt to handle various tools and deal with partial malfunctions. We propose an approach for estimating the robot structure and apply this approach for building a controller of dynamic motions. The robot structure is estimated by evaluating the mutual information (MI) among the sensor variables. Variables with high values of MI are edge-connected and the controller is automatically constructed based on the estimated structure. The proposed approach can accommodate changes in the robot parameters and dynamic motions. We verify the proposed method by using a simulator of a musculoskeletal arm driven that is driven by artificial muscle for mechanical safety.

Calculations of entropy of a signal or mutual information between two variables are valuable analytical tools in the field of neuroscience. They can be applied to all types of data, capture non-linear interactions and are model independent. Yet the limited size and number of recordings one can collect in a series of experiments makes their calculation highly prone to sampling bias. Mathematical methods to overcome this so-called “sampling disaster” exist, but require significant expertise, great time and computational costs. As such, there is a need for a simple, unbiased and computationally efficient tool for estimating the level of entropy and mutual information. In this article, we propose that application of entropy-encoding compression algorithms widely used in text and image compression fulfill these requirements. By simply saving the signal in PNG picture format and measuring the size of the file on the hard drive, we can estimate entropy changes through different conditions. Furthermore, with some simple modifications of the PNG file, we can also estimate the evolution of mutual information between a stimulus and the observed responses through different conditions. We first demonstrate the applicability of this method using white-noise-like signals. Then, while this method can be used in all kind of experimental conditions, we provide examples of its application in patch-clamp recordings, detection of place cells and histological data. Although this method does not give an absolute value of entropy or mutual information, it is mathematically correct, and its simplicity and broad use make it a powerful tool for their estimation through experiments.