How can proteins differ from each other

Briefly, ENNA generates a first random population of networks with the topology of a 2-hidden layers neural networks. This population is formally described as a set of sequences with dichotomic variables each sequence is a vector of zeros - ones values representing the input of each network. Each element of the sequence describes the presence or the absence of a particular structure-related variable.

The topology of these networks, involving different variable compositions, was selected in a random way first generation of networks , and the response of each network was derived with a two classes structure: natural and random proteins.

The process then builds a genetic algorithm to evolve the population of networks in a number of generations to identify a precise classification rule. We evaluated the response of each network deriving a net misclassification rate by fold cross validation procedure: the sequences with smaller values are identified as the more promising solutions.

Then we applied to the network population the classical genetic operators, such as natural selection, crossover and mutation, in order to achieve the next generation of promising sequences. At the end of the evolutionary process we achieved the population of Neural Networks with the smaller misclassification rates.

The analysis of the last population of Neural Networks revealed that only a limited number of structure-related variables were required to correctly classify the two dataset, namely: Volume, Coil, Alpha, and Surface hydrophobicity.

These variables had a probability close to 1 to occur in the last population, thus they can be considered robust in correctly classifying the response i. Using these variables, we built a Neural Network to process the whole data by achieving a rate of correct classification of The analysis of structure-related variables employed by the Neural Network is coherent with the descriptive statistical characteristics of variables distributions.

In particular, alpha helix content Figure 1a and volume Figure 2b follow a bell-like distribution in the Rnd dataset. Conversely, the two structural features have a uniform-like distribution in the Nat ensemble. Two important insight emerged from this classification. First, it is possible to effectively identify the two different classes of proteins with a high degree of confidence.

Second, a number of random proteins, 32 sequences, are erroneously classified as natural ones. This observation prompted an in-depth investigation of the structure of those random proteins misclassified as natural ones.

The fold analysis of random proteins misclassified by the ENNA algorithm showed that random polypeptides can adopt a great variety of conformations spanning from all-alpha to all-beta through complex mixed-folds. This result can be explained assuming that the average length 70 amino acids of the random polypeptides does not suffice to construct a complete all-beta structure.

On the other hand, one could advocate that the structural requirements for a beta-sheet formation such as flatness, rigidity and pairing of beta strands far away from each other along the amino acid sequence poses a number of constrains that cannot be met in completely random sequences, as already suggested in a previous study [16].

We identified 29 random proteins among the 32 misclassified by ENNA, which showed a general fold similarity, if not almost equal, to portion or sub-domains of natural proteins. In some cases the whole proteins were considerably similar to known natural proteins. This value is an estimation of A structural homology and B sequence homology, and in general it strictly depends on the size of the query protein. As a reference point a Z-score value lower than 2 must be considered as a spurious result [19].

The obtained RMSD and Z-score values, in general good, should be perceived as exceptional if we consider the completely random nature of these proteins. In the entire misclassified subset, 22 proteins have a Z-score greater than 2; a value greater than 4 was found for the proteins A and A The protein A is characterized by having the highest Z-score associated, equal to 4.

The superposition Figure 4a reveals a high degree of structural homology on the central beta sheet spanning amino acids WR68 of the random protein and IL84 of the natural one , good confidence was found also for a short alpha-helix present in the model and in the natural protein over amino acids FL21 of the random protein and N3-G13 of the natural one Figure 4b. Due the diversity in the amino acid sequences is reasonable to assume that the synthetic protein A does not show inhibitory activity.

Similar results were obtained for the protein A Figure 5. Also in this case DALI was able to identify a significant structural similarity. In general we can affirm that the biggest differences observed between our models and the natural proteins selected by DALI, could be attributed to the relative short length of the synthetic random polypeptides studied.

As for the A protein, the different amino acid sequence does not allow to conclude that protein A has any endonuclease activitiy. Further investigations are necessary to clarify this aspect.

In order to corroborate these results we also verified that random proteins properly classified as non-natural did not show any significant structural similarity to natural ones. We analyzed 32 random proteins correctly classified as non-natural and analyzed their structural features using the same procedure employed for the misclassified subset. Properly classified random proteins display to lesser extent folds similar to natural proteins with an average Z-score of 1.

All together, these results show that our algorithm is capable of effectively discriminate random protein from natural ones and that random proteins misclassified as natural by the ENNA algorithm display structural features strikingly similar to natural proteins. We address this question for the first time by comparing a set of natural proteins and an equal number of completely random ones on the basis of their structural features.

The first striking results is that random proteins do possess structural features comparable to those of natural proteins. However, the statistical indicators, such as mean and variance, of these structural-related variables significantly differ from those of naturally evolved polypeptides.

In particular, random proteins show a narrower distribution with respect to natural ones. This can be regarded as a general feature of random amino acid polymers and it can be explained considering that random proteins represent statistical copolymers and therefore their structural features are centered around the mean with a variance equal to the one expected by the correspondent probability density function.

Conversely, natural proteins display different mean and variance values, the latter being generally broader than the one of random proteins, due the result of the selective pressure that shaped natural protein structural features, leading to a deviation from expected values typical of statistical copolymers. In this regard, extant proteins cannot be regarded as simple edited random polypeptides, rather they clearly show the signature of selective pressure.

The differences are so remarkable that we were able to build a classification algorithm which effectively distinguishes natural proteins from random ones with an accuracy of In addition, random proteins misclassified as natural ones are characterized by structural similarity to natural proteins.

In particular, misclassified random proteins exhibit a significant fold similarity to portions or subdomains of extant proteins at atomic resolution. These results support the idea that random polypeptides do possess intrinsic structural features that render them particularly suitable for natural selection.

In particular, secondary structure elements and well-defined folds are readily detected among completely random proteins. These intrinsic structural characteristics are then systematically tuned and shaped by the action of evolutionary optimization. This scenario is consistent with experimental results which show that compact and thermodynamically stable proteins can be easily found screening small libraries of completely random sequences by phage display [20] and functional proteins can be selected in vitro [21] , [22] or in vivo [23] from random sequences libraries in relatively few evolutionary cycles.

A similar scenario has been proposed also for other biopolymers such as single-stranded RNA [24] , [25]. Our results suggest that random proteins are significantly different from extant ones, yet they display inherent conformational order which derives from chemico-physical constrains rather than from natural selection.

Random sequences employed for this study were generated using the RandomBlast algorithm described elsewhere [26]. The RandomBlast algorithm consists of two main modules: a pseudo random sequence generation module and a Blast software interface module. The first module uses the Mersenne Twister pseudo-random number generation algorithm [27] to generate pseudo-random numbers between 0 and To each amino acid is assigned a fixed number and single amino acids are then concatenated to reach the sequence length of 70 amino acids used in this work.

Each generated sequence is then given in input to the second RandomBlast module, an interface to the Blast blastall program which invokes the following command:. In our case we regard as valid only the protein sequences that do not display significant similarity to any natural protein present in the database.

In other words, contrary to the normal Blast usage, Randomblast consider as valid only completely random sequences. The sequence length of 70 amino acids was chosen as a good compromise between the computational requirements and the scientific investigation.

The three-dimensional model structures of random proteins were predicted using Rosetta Abinitio, an ab initio protein structure prediction software based on the assumption that in a polypeptide chain local interactions bias the conformation of sequence fragments, while global interactions determine the three-dimensional structure with minimal energy [30]. For each sequence The decoys were clustered using the Rosetta clustering integrated module.

Only the first model proposed for each sequence was taken into consideration. Detailed fold analysis was conducted only for the 32 proteins misclassified by ENNA. The DALI protein structure database searching web server was used [31]. Sarcopenia is the reason why falls and fractures are so common among the elderly.

Increasing your protein intake, as well as continuing to exercise, can help preserve muscle and fight off sarcopenia. While consuming sufficient protein is important, the truly important metric is not how much we consume , but how much of the protein our muscles actually absorb. Research shows that the average person can absorb about 10g of protein per hour, and maxes out at about 30g per meal.

There are certain things we can do to bump up absorption and make sure we get the most out of our protein intake. Space out Meals. Intuitively, the first step you can take is to space out your meals and consume appx. Try a Protein Complex. A protein complex is a combination of different protein types that have different digestion periods.

For example, combine whey a fast digesting protein with casein a slow digesting protein allows the body to continuously process the protein over a longer period of time than with just whey alone.

Another option is to increase the absorption rate so that your muscles utilize more of the protein you consume. Research shows that digestive enzymes like protease and papain help your body break down protein more efficiently to allow for easier absorption. An article from the Journal of the International Society of Sports Nutrition compared people who drank protein shakes with additional digestive enzymes to a control group that consumed only the protein shakes.

HMB also has shown to reduce muscle catabolism, meaning you lose less and hold onto more muscle. Protein is incredibly important to our health, not only to build bigger muscles, but to heal better from injury, age better, and prevent certain disease conditions.

That is why MEND Regenerate contains a blend of three high quality proteins, selected specifically to aid digestion, increase protein synthesis, and to prevent protein breakdown while providing a prolonged delivery of amino acids to the skeletal muscles.

But protein is not one size fits all, and it is important to understand the different types of protein, how much you need, and how your body processes protein so that you can set yourself up to properly heal and live your healthiest. We hope you found this article helpful to better understand protein and how to incorporate it into being the healthiest you.

Share on email. Animal protein. Found in: meat, poultry, eggs, dairy, fish Cramer: The human body needs 20 different amino acids. Plant protein. Found in: beans, legumes, nuts, seeds, quinoa, leafy greens such as broccoli and kale, whole grains Cramer: Certain plants can be excellent sources of protein, often with fewer calories and fewer potentially harmful effects than animal products.

Whey protein. Found in: dairy products such as milk, cheese, and yogurt, whey protein supplements, hydrolyzed-whey infant formula Cramer: Whey protein is a popular dietary protein supplement and one of the main proteins found in dairy products; it's a byproduct of cheese manufacturing.

Soy protein. Found in: soybeans edamame , miso, soy sauce, tofu, tempeh Cramer: Soy protein is found in soybeans, a legume that does not contain cholesterol and is low in saturated fat. Tags: Expert Advice. Food and Nutrition. Popular Categories. Popular Topics. Women's Health. Expert Advice. Patient Stories. Make an Appointment.

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