IU Indianapolis ScholarWorks :: Browsing by Author "Faraggi, Eshel"

Browsing by Author "Faraggi, Eshel"

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Accurate single-sequence prediction of solvent accessible surface area using local and global features
(Wiley Blackwell (John Wiley & Sons), 2014-11) Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej; Department of Biochemistry & Molecular Biology, IU School of Medicine
We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org.
Comparing NMR and X-ray protein structure: Lindemann-like parameters and NMR disorder
(Taylor & Francis, 2017) Faraggi, Eshel; Dunker, A. Keith; Sussman, Joel L.; Klockowski, Andrzej; Biochemistry and Molecular Biology, School of Medicine
Disordered protein chains and segments are fast becoming a major pathway for our understanding of biological function, especially in more evolved species. However, the standard definition of disordered residues: the inability to constrain them in X-ray derived structures, is not easily applied to NMR derived structures. We carry out a statistical comparison between proteins whose structure was resolved using NMR and using X-ray protocols. We start by establishing a connection between these two protocols for obtaining protein structure. We find a close statistical correspondence between NMR and X-ray structures if fluctuations inherent to the NMR protocol are taken into account. Intuitively this tends to lend support to the validity of both NMR and X-ray protocols in deriving biomolecular models that correspond to in vivo conditions. We then establish Lindemann-like parameters for NMR derived structures and examine what order/disorder cutoffs for these parameters are most consistent with X-ray data and how consistent are they. Finally, we find critical value of for the best correspondence between X-ray and NMR derived order/disorder assignment, judged by maximizing the Matthews correlation, and a critical value if a balance between false positive and false negative prediction is sought. We examine a few non-conforming cases, and examine the origin of the structure derived in X-ray. This study could help in assigning meaningful disorder from NMR experiments.
Computational Ways to Enhance Protein Inhibitor Design
(Frontiers Media, 2021-02-03) Jernigan, Robert L.; Sankar, Kannan; Jia, Kejue; Faraggi, Eshel; Kloczkowski, Andrzej; Physics, School of Science
Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.
DescribePROT: database of amino acid-level protein structure and function predictions
(Oxford University Press, 2021-01-08) Zhao, Bi; Katuwawala, Akila; Oldfield, Christopher J.; Dunker, A. Keith; Faraggi, Eshel; Gsponer, Jörg; Kloczkowski, Andrzej; Malhis, Nawar; Mirdita, Milot; Obradovic, Zoran; Söding, Johannes; Steinegger, Martin; Zhou, Yaoqi; Kurgan, Lukasz; Medicine, School of Medicine
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
Direct prediction of profiles of sequences compatible to a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
(Wiley Online Library, 2014-10) Li, Zhixiu; Yang, Yuedong; Faraggi, Eshel; Zhou, Jian; Zhou, Yaoqi; Department of BioHealth Informatics, IU School of Informatics and Computing
Locating sequences compatible with a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org.
Entropy, Fluctuations, and Disordered Proteins
(MDPI, 2019-08) Faraggi, Eshel; Dunker, A. Keith; Jernigan, Robert L.; Kloczkowski, Andrzej; Physics, School of Science
Entropy should directly reflect the extent of disorder in proteins. By clustering structurally related proteins and studying the multiple-sequence-alignment of the sequences of these clusters, we were able to link between sequence, structure, and disorder information. We introduced several parameters as measures of fluctuations at a given MSA site and used these as representative of the sequence and structure entropy at that site. In general, we found a tendency for negative correlations between disorder and structure, and significant positive correlations between disorder and the fluctuations in the system. We also found evidence for residue-type conservation for those residues proximate to potentially disordered sites. Mutation at the disorder site itself appear to be allowed. In addition, we found positive correlation for disorder and accessible surface area, validating that disordered residues occur in exposed regions of proteins. Finally, we also found that fluctuations in the dihedral angles at the original mutated residue and disorder are positively correlated while dihedral angle fluctuations in spatially proximal residues are negatively correlated with disorder. Our results seem to indicate permissible variability in the disordered site, but greater rigidity in the parts of the protein with which the disordered site interacts. This is another indication that disordered residues are involved in protein function.
GENN: A GEneral Neural Network for Learning Tabulated Data with Examples from Protein Structure Prediction
(Springer, 2015) Faraggi, Eshel; Kloczkowski, Andrzej; Biochemistry and Molecular Biology, School of Medicine
We present a GEneral Neural Network (GENN) for learning trends from existing data and making predictions of unknown information. The main novelty of GENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs–output pairs or window-based data using data structures to efficiently represent input–output pairs. The program was tested on predicting the accessible surface area of globular proteins, scoring proteins according to similarity to native, predicting protein disorder, and has performed remarkably well. In this paper we describe the program and its use. Specifically, we give as an example the construction of a similarity to native protein scoring function that was constructed using GENN. The source code and Linux executables for GENN are available from Research and Information Systems at http://mamiris.com and from the Battelle Center for Mathematical Medicine at http://mathmed.org. Bugs and problems with the GENN program should be reported to EF.
Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation
(Springer, 2013) Zhang, Tuo; Faraggi, Eshel; Li, Zhixiu; Zhou, Yaoqi; Biochemistry and Molecular Biology, School of Medicine
Intrinsically disordered proteins (IDPs) refer to those proteins without fixed three-dimensional structures under physiological conditions. Although experiments suggest that the conformations of IDPs can vary from random coils, semi-compact globules, to compact globules with different contents of secondary structures, computational efforts to separate IDPs into different states are not yet successful. Recently, we developed a neural-network-based disorder prediction technique SPINE-D that was ranked as one of the top performing techniques for disorder prediction in the biannual meeting of critical assessment of structure prediction techniques (CASP 9, 2010). Here, we further analyze the results from SPINE-D prediction by defining a semi-disordered state that has about 50% predicted probability to be disordered or ordered. This semi-disordered state is partially collapsed with intermediate levels of predicted solvent accessibility and secondary structure content. The relative difference in compositions between semi-disordered and fully disordered regions is highly correlated with amyloid aggregation propensity (a correlation coefficient of 0.86 if excluding four charged residues and proline, 0.73 if not). In addition, we observed that some semi-disordered regions participate in induced folding, and others play key roles in protein aggregation. More specifically, a semi-disordered region is amyloidogenic in fully unstructured proteins (such as alpha-synuclein and Sup35) but prone to local unfolding that exposes the hydrophobic core to aggregation in structured globular proteins (such as SOD1 and lysozyme). A transition from full disorder to semi-disorder at about 30-40 Qs is observed in the poly-Q (poly-glutamine) tract of huntingtin. The accuracy of using semi-disorder to predict binding-induced folding and aggregation is compared with several methods trained for the purpose. These results indicate the usefulness of three-state classification (order, semi-disorder, and full-disorder) in distinguishing nonfolding from induced-folding and aggregation-resistant from aggregation-prone IDPs and in locating weakly stable, locally unfolding, and potentially aggregation regions in structured proteins. A comparison with five representative disorder-prediction methods showed that SPINE-D is the only method with a clear separation of semi-disorder from ordered and fully disordered states.
Many-to-one binding by intrinsically disordered protein regions
(WORLD SCIENTIFIC, 2019-11-02) Alterovitz, Wei-Lun; Faraggi, Eshel; Oldfield, Christopher J.; Meng, Jingwei; Xue, Bin; Huang, Fei; Romero, Pedro; Kloczkowski, Andrzej; Uversky, Vladimir N.; Dunker, A. Keith; Biochemistry and Molecular Biology, School of Medicine
Disordered binding regions (DBRs), which are embedded within intrinsically disordered proteins or regions (IDPs or IDRs), enable IDPs or IDRs to mediate multiple protein-protein interactions. DBR-protein complexes were collected from the Protein Data Bank for which two or more DBRs having different amino acid sequences bind to the same (100% sequence identical) globular protein partner, a type of interaction herein called many-to-one binding. Two distinct binding profiles were identified: independent and overlapping. For the overlapping binding profiles, the distinct DBRs interact by means of almost identical binding sites (herein called “similar”), or the binding sites contain both common and divergent interaction residues (herein called “intersecting”). Further analysis of the sequence and structural differences among these three groups indicate how IDP flexibility allows different segments to adjust to similar, intersecting, and independent binding pockets.
Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment
(Springer Nature, 2024-08-27) Faraggi, Eshel; Jernigan, Robert L.; Kloczkowski, Andrzej; Physics, School of Science
We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence alignments or structural information. Instead, we used global characterizations of the protein sequence. Training and testing data for human gene mutations was obtained from ClinVar (ncbi.nlm.nih.gov/pub/ClinVar/), and for non-human gene mutations from Uniprot (www.uniprot.org). Testing was done on post-training data from ClinVar. This testing yielded high AUC and Matthews correlation coefficient (MCC) for well trained examples but low generalizability. For genes with either sparse or unbalanced training data, the prediction accuracy is poor. The resulting prediction server is available online at http://www.mamiris.com/Shoni.cagi6.

Browsing by Author "Faraggi, Eshel"

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