Linking protein structural and functional change to mutation using amino acid networks.

The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures.

Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.

k-core genes underpin structural features of breast cancer.

Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.

Eight years of homicide evolution in Monterrey, Mexico: a network approach.

Homicide is without doubt one of Mexico's most important security problems, with data showing that this dismal kind of violence sky-rocketed shortly after the war on drugs was declared in 2007. Since then, violent war-like zones have appeared and disappeared throughout Mexico, causing unfathomable human, social and economic losses. One of the most emblematic of these zones is the Monterrey metropolitan area (MMA), a central scenario in the narco-war. Being an important metropolitan area in Mexico and a business hub, MMA has counted hundreds to thousands of casualties. In spite of several approaches being developed to understand and analyze crime in general, and homicide in particular, the lack of accurate spatio-temporal homicide data results in incomplete descriptions. In order to describe the manner in which violence has evolved and spread in time and space through the city, here we propose a network-based approach. For this purpose, we define a homicide network where nodes are geographical entities that are connected through spatial and temporal relationships. We analyzed the time series of homicides in different municipalities and neighborhoods of the MMA, to observe whether or not a global correlation appeared. We studied the spatial correlation between neighborhoods where homicides took place, to observe whether distance is a factor of influence in the frequency of homicides. We constructed yearly co-occurrence networks, by correlating neighborhoods with homicides happening within a same week, and counting the co-occurrences of these neighborhood pairs in 1 year. We also constructed a crime network by aggregating all data of homicides, eliminating the temporal correlation, in order to observe whether homicide clusters appeared, and what those clusters were distributed geographically. Finally, we correlated the location and frequency of homicides with roads, freeways and highways, to observe if a trend in the homicidal violence appeared. Our network approach in the homicide evolution of MMA allows us to identify that (1) analyzing the whole 86-month period, we observed a correlation between close cities, which decreases in distant places. (2) at neighborhood level, correlations are not distance-dependent, on the contrary, highest co-occurrences appeared between distant neighborhoods and a polygon formed by close neighborhoods in downtown Monterrey. Moreover, (3) An elevated number of homicides occur close to the 85th freeway, which connects MMA with the US border. (4) Some socioeconomic barriers determine the presence of homicide violence. Finally, (5) we show a relation between homicidal crime and the urban landscape by studying the distance of safe and violent neighborhoods to the closest highway and by studying the evolution of highway and crime distance over the cartel-related years and the following period. With this approach, we are able to describe the spatial and temporal evolution of homicidal crime in a metropolitan area.

In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few.

A disease has distinct genetic and molecular hallmarks such as sequence variants that are likely to produce the alternative protein structures accountable for individual responses to drugs and disease development. Thus, to set up customized therapies, the structural influences of amino acids on one another need to be tracked down. Using network-based models and classical analysis of amino acid and atomic packing in protein structures, the influence of first shell neighbors on the structural fate of a position upon mutation, is revisited. Regardless of the type and position in a structure, amino acids satisfy on average over their neighbors, a low and similar number of atomic interactions, the average called the neighborhood watch (Nw). The structural tolerance of a position to mutation depends on the modulation of the composition and/or proximity of neighbors to maintain the same Nw, before and after mutation, at every position. Changes, upon mutation of the number of atomic interactions at the level of individual pairs (wij) are structurally tolerated but influence structural dynamics. Robust, fragile and rescue interactions can be identified with Nw and wij, offering a framework to classify sequence variants according to position-dependent structural changes.

Protein structural robustness to mutations: an in silico investigation.

Proteins possess qualities of robustness and adaptability to perturbations such as mutations, but occasionally fail to withstand them, resulting in loss of function. Herein, the structural impact of mutations is investigated independently of the functional impact. Primarily, we aim at understanding the mechanisms of structural robustness pre-requisite for functional integrity. The structural changes due to mutations propagate from the site of mutation to residues much more distant than typical scales of chemical interactions, following a cascade mechanism. This can trigger dramatic changes or subtle ones, consistent with a loss of function and disease or the emergence of new functions. Robustness is enhanced by changes producing alternative structures, in good agreement with the view that proteins are dynamic objects fulfilling their functions from a set of conformations. This result, robust alternative structures, is also coherent with epistasis or rescue mutations, or more generally, with non-additive mutational effects and compensatory mutations. To achieve this study, we developed the first algorithm, referred to as Amino Acid Rank (AAR), which follows the structural changes associated with mutations from the site of the mutation to the entire protein structure and quantifies the changes so that mutations can be ranked accordingly. Assessing the paths of changes opens the possibility of assuming secondary mutations for compensatory mechanisms.