NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

Background  

The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event.     

An artificial neural network for estimating haplotype frequencies

The problem of estimating haplotype frequencies from population data has been considered by numerous investigators, resulting in a wide variety of possible algorithmic and statistical solutions. We propose a relatively unique approach that employs an artificial neural network (ANN) to predict the most likely haplotype frequencies from a sample of population genotype data. Through an innovative ANN design for mapping genotype patterns to diplotypes, we have produced a prototype that demonstrates the feasibility of this approach, with provisional results that correlate well with estimates produced by the expectation maximization algorithm for haplotype frequency estimation. Given the computational demands of estimating haplotype frequencies for 20 or more single-nucleotide polymorphisms, the ANN approach is promising because its design fits well with parallel computing architectures.     

Artificial neural network model for the generation of muscle activation patterns for human locomotion.

Skilled locomotor behaviour requires information from various levels within the central nervous system (CNS). Mathematical models have permitted researchers to simulate various mechanisms in order to understand the organization of the locomotor control system. While it is difficult to adequately characterize the numerous inputs to the locomotor control system, an alternative strategy may be to use a kinematic movement plan to represent the complex inputs to the locomotor control system based on the possibility that the CNS may plan movements at a kinematic level. We propose the use of artificial neural network (ANN) models to represent the transformation of a kinematic plan into the necessary motor patterns. Essentially, kinematic representation of the actual limb movement was used as the input to an ANN model which generated the EMG activity of 8 muscles of the lower limb and trunk. Data from a wide variety of gait conditions was necessary to develop a robust model that could accommodate various environmental conditions encountered during everyday activity. A total of 120 walking strides representing normal walking and ten conditions where the normal gait was modified in terms of cadence, stride length, stance width or required foot clearance. The final network was assessed on its ability to predict the EMG activity on individual walking trials as well as its ability to represent the general activation pattern of a particular gait condition. The predicted EMG patterns closely matched those recorded experimentally, exhibiting the appropriate magnitude and temporal phasing required for each modification. Only 2 of the 96 muscle/gait conditions had RMS errors above 0.10, only 5 muscle/gait conditions exhibited correlations below 0.80 (most were above 0.90) and only 25 muscle/gait conditions deviated outside the normal range of muscle activity for more than 25% of the gait cycle. These results indicate the ability of single network ANNs to represent the transformation between a kinematic movement plan and the necessary muscle activations for normal steady state locomotion but they were also able to generate muscle activation patterns for conditions requiring changes in walking speed, foot placement and foot clearance. The abilities of this type of network have implications towards both the fundamental understanding of the control of locomotion and practical realizations of artificial control systems for use in rehabilitation medicine.     

An artificial neural network approach for studying phytoplankton succession

Artificial neural networks are used to model phytoplankton succession and gain insight into the relative strengths of bottom-up and top-down forces shaping seasonal patterns in phytoplankton biomass and community composition. Model comparisons indicate that patterns in chlorophyll aconcentrations response instantaneously to patterns in nutrient concentrations (phosphorous (P), nitrite and nitrate (NO₂/NO₃–N) and ammonium (NH₄–H) concentrations) and zooplankton biomass (daphnid cladocera and copepoda biomass); whereas lagged responses in an index of algal community composition are evident. A randomization approach to neural networks is employed to reveal individual and interacting contributions of nutrient concentrations and zooplankton biomass to predictions of phytoplankton biomass and community composition. The results show that patterns in chlorophyll aconcentrations are directly associated with P, NO₂/NO₃–N and daphnid cladocera biomass, as well as related to interactions between daphnid cladocera biomass, and NO₂/NO₃–N and P. Similarly, patterns in phytoplankton community composition are associated with NO₂/NO₃–N and daphnid cladocera biomass; however show contrasting patterns in nutrient– zooplankton and zooplankton–zooplankton interactions. Together, the results provide correlative evidence for the importance of nutrient limitation, zooplankton grazing and nutrient regeneration in shaping phytoplankton community dynamics. This study shows that artificial neural networks can provide a powerful tool for studying phytoplankton succession by aiding in the quantification and interpretation of the individual and interacting contributions of nutrient limitation and zooplankton herbivory on phytoplankton biomass and community composition under natural conditions.     

From molecular signal activation to locomotion: an integrated, multiscale analysis of cell motility on defined matrices

The adhesion, mechanics, and motility of eukaryotic cells are highly sensitive to the ligand density and stiffness of the extracellular matrix (ECM). This relationship bears profound implications for stem cell engineering, tumor invasion and metastasis. Yet, our quantitative understanding of how ECM biophysical properties, mechanotransductive signals, and assembly of contractile and adhesive structures collude to control these cell behaviors remains extremely limited. Here we present a novel multiscale model of cell migration on ECMs of defined biophysical properties that integrates local activation of biochemical signals with adhesion and force generation at the cell-ECM interface. We capture the mechanosensitivity of individual cellular components by dynamically coupling ECM properties to the activation of Rho and Rac GTPases in specific portions of the cell with actomyosin contractility, cell-ECM adhesion bond formation and rupture, and process extension and retraction. We show that our framework is capable of recreating key experimentally-observed features of the relationship between cell migration and ECM biophysical properties. In particular, our model predicts for the first time recently reported transitions from filopodial to "stick-slip" to gliding motility on ECMs of increasing stiffness, previously observed dependences of migration speed on ECM stiffness and ligand density, and high-resolution measurements of mechanosensitive protrusion dynamics during cell motility we newly obtained for this study. It also relates the biphasic dependence of cell migration speed on ECM stiffness to the tendency of the cell to polarize. By enabling the investigation of experimentally-inaccessible microscale relationships between mechanotransductive signaling, adhesion, and motility, our model offers new insight into how these factors interact with one another to produce complex migration patterns across a variety of ECM conditions.     

RESCUE: An artificial neural network tool for the NMR spectral assignment of proteins

The assignment of the ¹H spectrum of a protein or a polypeptide is the prerequisite for advanced NMR studies. We present here an assignment tool based on the artificial neural network technology, which determines the type of the amino acid from the chemical shift values observed in the ¹ H spectrum. Two artificial neural networks have been trained and extensively tested against a non-redundant subset of the BMRB chemical shift data bank [Seavey, B.R. et al. (1991) J. Biomol. NMR, 1, 217–236]. The most promising of the two accomplishes the analysis in two steps, grouping related amino acids together. It presents a mean rate of success above 80% on the test set. The second network tested separates down to the single amino acid; it presents a mean rate of success of 63%. This tool has been used to assist the manual assignment of peptides and proteins and can also be used as a block in an automated approach to assignment. The program has been called RESCUE and is made publicly available at the following URL: http://www.infobiosud.univ-montp1.fr/rescue.     

Application of an unsupervised artificial neural network technique to multivariant surface water quality data

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the Kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies–Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, K, Cl, NH₄-N, NO₂-N, o-PO₄, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.     

An artificial neural network for biomass estimation from automatic pH control signal

This study developed an artificial neural network (ANN) to estimate the growth of microorganisms during a fermentation process. The ANN relies solely on the cumulative consumption of alkali and the buffer capacity, which were measured on-line from the on/off control signal and pH values through automatic pH control. The two input variables were monitored on-line from a series of different batch cultivations and used to train the ANN to estimate biomass. The ANN was refined by optimizing the network structure and by adopting various algorithms for its training. The software estimator successfully generated growth profiles that showed good agreement with the measured biomass of separate batch cultures carried out between at 25 and 35_C.     

From artificial kidneys to artificial hearts and beyond: new developments offer great hope

Artificial organs have already become an important part of medical care, and with the advent of new devices, materials, and approaches, either in development or already on the market, they will become even more commonplace in the future.     

An hierarchical artificial neural network system for the classification of transmembrane proteins.

This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class. This may be important in the functional assignment and analysis of open reading frames (ORF's) identified in complete genomes and, especially, those ORF's that correspond to proteins with unknown function. The network described here has a simple hierarchical feed-forward topology and a limited number of neurons which makes it very fast. By using only information contained in 11 protein sequences, the method was able to identify, with 100% accuracy, all membrane proteins with reliable topologies collected from several papers in the literature. Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment). The method was also applied to the complete SWISS-PROT database with considerable success and on ORF's of several complete genomes. The neural network developed was associated with the PRED-TMR algorithm (Pasquier,C., Promponas,V.J., Palaios,G.A., Hamodrakas,J.S. and Hamodrakas,S.J., 1999) in a new application package called PRED-TMR2. A WWW server running the PRED-TMR2 software is available at http://o2.db.uoa.gr/PRED-TMR2     

HTP: a neural network-based method for predicting the topology of helical transmembrane domains in proteins

In this paper we describe a microcomputer program (HTP) forpredicting the location and orientation of -helical transmemhranesegments in integral membrane proteins. HTP is a neural network-basedtool which gives as output the protein membrane topology basedon the statistical propensity of residues to be located in externaland internal loops. This method, which uses single protein sequencesas input to the network system, correctly predicts the topologyof 71 out of 92 membrane proteins of putative membrane orientation,independently of the protein source.     

A walking robot called human: lessons to be learned from neural control of locomotion

From what we know at present with respect to the neural control of walking, it can be concluded that an optimal biologically inspired robot could have the following features. The limbs should include several joints in which position changes can be obtained by actuators across the joints. The control of mono- and biarticular actuators should occur at least at three levels: one at direct control of the actuators (equivalent to motoneuron level), the second at indirect control acting at a level which controls whole limb movement (flexion or extension) and the third at a still higher level controlling the interlimb coordination. The limb level circuits should be able to produce alternating flexion and extension movements in the limb by means of coupled oscillator flexor and extensor parts which are mutually inhibitory. The interlimb control level should be able to command the various limb control centers. All three control levels should have some basic feedback circuits but the most essential one is needed at the limb control level and concerns the decision to either flex or extend a given limb. The decision to activate the extensor part of the limb oscillator has to be based on feedback signalling the onset of loading of the limb involved. This should be signalled by means of load sensors in the limb. The decision to activate the flexor part of the limb oscillator has to depend on various types of feedback. The most important requirement is that flexion should only occur when the limb concerned is no longer loaded above a given threshold. The rule for the initiation of limb flexion can be made more robust by adding the requirement that position at the base of the limb ("hip") should be within a normal end of stance phase range. Hence, human locomotion is thought to use a number of principles which simplify control, just as in other species such as the cat. It is suggested that cat and human locomotion are good models to learn from when designing efficient walking robots.     

Guidelines: From artificial evolution to computational evolution: a research agenda

Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.     

Clustering of nicotinic acetylcholine receptors: From the neuromuscular junction to interneuronal synapses

Fast and accurate synaptic transmission requires high-density accumulation of neurotransmitter receptors in the postsynaptic membrane. During development of the neuromuscular junction, clustering of acetylcholine receptors (AChR) is one of the first signs of postsynaptic specialization and is induced by nerve-released agrin. Recent studies have revealed that different mechanisms regulate assembly vs stabilization of AChR clusters and of the postsynaptic apparatus. MuSK, a receptor tyrosine kinase and component of the agrin receptor, and rapsyn, an AChR-associated anchoring protein, play crucial roles in the postsynaptic assembly. Once formed, AChR clusters and the postsynaptic membrane are stabilized by components of the dystrophin/utrophin glycoprotein complex, some of which also direct aspects of synaptic maturation such as formation of postjunctional folds. Nicotinic receptors are also expressed across the peripheral and central nervous system (PNS/CNS). These receptors are localized not only at the pre- but also at the postsynaptic sites where they carry out major synaptic transmission. In neurons, they are found as clusters at synaptic or extrasynaptic sites, suggesting that different mechanisms might underlie this specific localization of nicotinic receptors. This review summarizes the current knowledge about formation and stabilization of the postsynaptic apparatus at the neuromuscular junction and extends this to explore the synaptic structures of interneuronal cholinergic synapses.     

Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work

Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdöven Dam Reservoir. The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA.