5,111 research outputs found
Rigidity and flexibility of biological networks
The network approach became a widely used tool to understand the behaviour of
complex systems in the last decade. We start from a short description of
structural rigidity theory. A detailed account on the combinatorial rigidity
analysis of protein structures, as well as local flexibility measures of
proteins and their applications in explaining allostery and thermostability is
given. We also briefly discuss the network aspects of cytoskeletal tensegrity.
Finally, we show the importance of the balance between functional flexibility
and rigidity in protein-protein interaction, metabolic, gene regulatory and
neuronal networks. Our summary raises the possibility that the concepts of
flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl
Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems
A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a
predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the
Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in
Computational Biology.Peer ReviewedPostprint (author's final draft
Frustration in Biomolecules
Biomolecules are the prime information processing elements of living matter.
Most of these inanimate systems are polymers that compute their structures and
dynamics using as input seemingly random character strings of their sequence,
following which they coalesce and perform integrated cellular functions. In
large computational systems with a finite interaction-codes, the appearance of
conflicting goals is inevitable. Simple conflicting forces can lead to quite
complex structures and behaviors, leading to the concept of "frustration" in
condensed matter. We present here some basic ideas about frustration in
biomolecules and how the frustration concept leads to a better appreciation of
many aspects of the architecture of biomolecules, and how structure connects to
function. These ideas are simultaneously both seductively simple and perilously
subtle to grasp completely. The energy landscape theory of protein folding
provides a framework for quantifying frustration in large systems and has been
implemented at many levels of description. We first review the notion of
frustration from the areas of abstract logic and its uses in simple condensed
matter systems. We discuss then how the frustration concept applies
specifically to heteropolymers, testing folding landscape theory in computer
simulations of protein models and in experimentally accessible systems.
Studying the aspects of frustration averaged over many proteins provides ways
to infer energy functions useful for reliable structure prediction. We discuss
how frustration affects folding, how a large part of the biological functions
of proteins are related to subtle local frustration effects and how frustration
influences the appearance of metastable states, the nature of binding
processes, catalysis and allosteric transitions. We hope to illustrate how
Frustration is a fundamental concept in relating function to structural
biology.Comment: 97 pages, 30 figure
Tradeoff Between Stability and Multispecificity in the Design of Promiscuous Proteins
Natural proteins often partake in several highly specific protein-protein interactions. They are thus subject to multiple opposing forces during evolutionary selection. To be functional, such multispecific proteins need to be stable in complex with each interaction partner, and, at the same time, to maintain affinity toward all partners. How is this multispecificity acquired through natural evolution? To answer this compelling question, we study a prototypical multispecific protein, calmodulin (CaM), which has evolved to interact with hundreds of target proteins. Starting from high-resolution structures of sixteen CaM-target complexes, we employ state-of-the-art computational methods to predict a hundred CaM sequences best suited for interaction with each individual CaM target. Then, we design CaM sequences most compatible with each possible combination of two, three, and all sixteen targets simultaneously, producing almost 70,000 low energy CaM sequences. By comparing these sequences and their energies, we gain insight into how nature has managed to find the compromise between the need for favorable interaction energies and the need for multispecificity. We observe that designing for more partners simultaneously yields CaM sequences that better match natural sequence profiles, thus emphasizing the importance of such strategies in nature. Furthermore, we show that the CaM binding interface can be nicely partitioned into positions that are critical for the affinity of all CaM-target complexes and those that are molded to provide interaction specificity. We reveal several basic categories of sequence-level tradeoffs that enable the compromise necessary for the promiscuity of this protein. We also thoroughly quantify the tradeoff between interaction energetics and multispecificity and find that facilitating seemingly competing interactions requires only a small deviation from optimal energies. We conclude that multispecific proteins have been subjected to a rigorous optimization process that has fine-tuned their sequences for interactions with a precise set of targets, thus conferring their multiple cellular functions
A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins
<p>Abstract</p> <p>Background</p> <p>Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research.</p> <p>Results</p> <p>In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the <it>high-sensitivity </it>mode and the <it>high-specificity </it>mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the <it>high-sensitivity </it>mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the <it>high-specificity </it>mode.</p> <p>Conclusion</p> <p>Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.</p
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