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Abstract: Designing protein sequences that can fold into a given structure is a well‐known inverse protein‐folding problem. One important characteristic to attain for a protein design program is the ability to recover wild‐type sequences given their native backbone structures. The highest average sequence identity accuracy achieved by current protein‐design programs in this problem is around 30%, achieved by our previous system, SPIN. SPIN is a program that predicts sequences compatible with a provided structure using a neural network with fragment‐based local and energy‐based nonlocal profiles. Our new model, SPIN2, uses a deep neural network and additional structural features to improve on SPIN. SPIN2 achieves over 34% in sequence recovery in 10‐fold cross‐validation and independent tests, a 4% improvement over the previous version. The sequence profiles generated from SPIN2 are expected to be useful for improving existing fold recognition and protein design techniques. SPIN2 is available at http://sparks-lab.org. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 , from the SPARKS Lab at , and from the Battelle Center for Mathematical Medicine at . Proteins 2014; 82:3170-3176. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 . Proteins 2014; 82:2565-2573. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

ABSTRACT Detecting protein-RNA interactions is challenging both experimentally and computationally because RNAs are large in number, diverse in cellular location and function, and flexible in structure. As a result, many RNA-binding proteins (RBPs) remain to be identified. Here, a template-based, function-prediction technique SPOT-Seq for RBPs is applied to human proteome and its result is validated by a recent proteomic experimental discovery of 860 mRNA-binding proteins (mRBPs). The coverage (or sensitivity) is 42.6% for 1217 known RBPs annotated in the Gene Ontology and 43.6% for 860 newly discovered human mRBPs. Consistent sensitivity indicates the robust performance of SPOT-Seq for predicting RBPs. More importantly, SPOT-Seq detects 2418 novel RBPs in human proteome, 291 of which were validated by the newly discovered mRBP set. Among 291 validated novel RBPs, 61 are not homologous to any known RBPs. Successful validation of predicted novel RBPs permits us to further analysis of their phenotypic roles in disease pathways. The dataset of 2418 predicted novel RBPs along with confidence levels and complex structures is available at (in publications) for experimental confirmations and hypothesis generation. Proteins 2014; 82:640-647. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A structure alignment program aligns two structures by optimizing a scoring function that measures structural similarity. It is highly desirable that such scoring function is independent of the sizes of proteins in comparison so that the significance of alignment across different sizes of the protein regions aligned is comparable. Here, we developed a new score called SP-score that fixes the cutoff distance at 4 Å and removed the size dependence using a normalization prefactor. We further built a program called SPalign that optimizes SP-score for structure alignment. SPalign was applied to recognize proteins within the same structure fold and having the same function of DNA or RNA binding. For fold discrimination, SPalign improves sensitivity over TMalign for the chain-level comparison by 12% and over DALI for the domain-level comparison by 13% at the same specificity of 99.6%. The difference between TMalign and SPalign at the chain level is due to the inability of TMalign to detect single domain similarity between multidomain proteins. For recognizing nucleic acid binding proteins, SPalign consistently improves over TMalign by 12% and DALI by 31% in average value of Mathews correlation coefficients for four datasets. SPalign with default setting is 14% faster than TMalign. SPalign is expected to be useful for function prediction and comparing structures with or without domains defined. The source code for SPalign and the server are available at . Proteins 2012;. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Protein molecules exhibit varying degrees of flexibility throughout their three-dimensional structures. Protein structural flexibility is often characterized by fluctuations in the Cartesian coordinate space. On the other hand, the protein backbone can be mostly defined by two torsion angles ϕ and ψ only. We introduce a new flexibility descriptor, backbone torsion-angle fluctuation derived from the variation of backbone torsion angles from different NMR models. The torsion-angle fluctuations correlate with mean-squared spatial fluctuations derived from the same collection of NMR models. We developed a neural-network based real-value predictor based on sequence information only. The predictor achieved ten-fold cross-validated correlation coefficients of 0.59 and 0.60, and mean absolute errors of 22.7° and 24.3° for the angle fluctuation of ϕ and ψ, respectively. This predictor is expected to be useful for function prediction and protein structure prediction when predicted torsion angles are used as restraints. Both sequence- and structure-based prediction of torsion-angle fluctuation will be available at within the SPINE-X package. Proteins 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main-chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five-residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino-acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign-SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild-type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi-blast. More importantly, the sequences designed by RosettaDesign-SR have 2-3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild-type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign-SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions. Proteins 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

How to make an accurate representation of protein-DNA interaction by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a statistical potential based on the distance-scaled, finite ideal-gas reference state so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcription-factor binding profiles. The results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at . The server version was trained by the entire 212 protein-DNA complexes. Proteins 2009. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

How to refine a near-native structure to make it closer to its native conformation is an unsolved problem in protein-structure and protein-protein complex-structure prediction. In this article, we first test several scoring functions for selecting locally resampled near-native protein-protein docking conformations and then propose a computationally efficient protocol for structure refinement via local resampling and energy minimization. The proposed method employs a statistical energy function based on a Distance-scaled Ideal-gas REference state (DFIRE) as an initial filter and an empirical energy function EMPIRE (EMpirical Protein-InteRaction Energy) for optimization and re-ranking. Significant improvement of final top-1 ranked structures over initial near-native structures is observed in the ZDOCK 2.3 decoy set for Benchmark 1.0 (74% whose global rmsd reduced by 0.5 Å or more and only 7% increased by 0.5 Å or more). Less significant improvement is observed for Benchmark 2.0 (38% versus 33%). Possible reasons are discussed. Proteins 2009. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

A neural network method (SPINE-2D) is introduced to provide a sequence-based prediction of residue-residue contact maps. This method is built on the success of SPINE in predicting secondary structure, residue solvent accessibility, and backbone torsion angles via large-scale training with overfit protection and a two-layer neural network. SPINE-2D achieved a 10-fold cross-validated accuracy of 47% (±2%) for top L/5 predicted contacts between two residues with sequence separation of six or more and an accuracy of 24 ± 1% for nonlocal contacts with sequence separation of 24 residues or more. The accuracies of 23% and 26% for nonlocal contact predictions are achieved for two independent datasets of 500 proteins and 82 CASP 7 targets, respectively. A comparison with other methods indicates that SPINE-2D is among the most accurate methods for contact-map prediction. SPINE-2D is available as a webserver at . Proteins 2009. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The identification of near native protein-protein complexes among a set of decoys remains highly challenging. A strategy for improving the success rate of near native detection is to enrich near native docking decoys in a small number of top ranked decoys. Recently, we found that a combination of three scoring functions (energy, conservation, and interface propensity) can predict the location of binding interface regions with reasonable accuracy. Here, these three scoring functions are modified and combined into a consensus scoring function called ENDES for enriching near native docking decoys. We found that all individual scores result in enrichment for the majority of 28 targets in ZDOCK2.3 decoy set and the 22 targets in Benchmark 2.0. Among the three scores, the interface propensity score yields the highest enrichment in both sets of protein complexes. When these scores are combined into the ENDES consensus score, a significant increase in enrichment of near-native structures is found. For example, when 2000 dock decoys are reduced to 200 decoys by ENDES, the fraction of near-native structures in docking decoys increases by a factor of about six in average. ENDES was implemented into a computer program that is available for download at . Proteins 2009. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

This article attempts to increase the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins through improved learning. Most methods developed for improving the backpropagation algorithm of artificial neural networks are limited to small neural networks. Here, we introduce a guided-learning method suitable for networks of any size. The method employs a part of the weights for guiding and the other part for training and optimization. We demonstrate this technique by predicting residue solvent accessibility and real-value backbone torsion angles of proteins. In this application, the guiding factor is designed to satisfy the intuitive condition that for most residues, the contribution of a residue to the structural properties of another residue is smaller for greater separation in the protein-sequence distance between the two residues. We show that the guided-learning method makes a 2-4% reduction in 10-fold cross-validated mean absolute errors (MAE) for predicting residue solvent accessibility and backbone torsion angles, regardless of the size of database, the number of hidden layers and the size of input windows. This together with introduction of two-layer neural network with a bipolar activation function leads to a new method that has a MAE of 0.11 for residue solvent accessibility, 36° for ψ, and 22° for ϕ. The method is available as a Real-SPINE 3.0 server in . Proteins 2009. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Proteins fold into unique three-dimensional structures by specific, orientation-dependent interactions between amino acid residues. Here, we extract orientation-dependent interactions from protein structures by treating each polar atom as a dipole with a direction. The resulting statistical energy function successfully refolds 13 out of 16 fully unfolded secondary-structure terminal regions of 10-23 amino acid residues in 15 small proteins. Dissecting the orientation-dependent energy function reveals that the orientation preference between hydrogen-bonded atoms is not enough to account for the structural specificity of proteins. The result has significant implications on the theoretical and experimental searches for specific interactions involved in protein folding and molecular recognition between proteins and other biologically active molecules. Proteins 2008. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The backbone structure of a protein is largely determined by the ϕ and ψ torsion angles. Thus, knowing these angles, even if approximately, will be very useful for protein-structure prediction. However, in a previous work, a sequence-based, real-value prediction of ψ angle could only achieve a mean absolute error of 54° (83°, 35°, 33° for coil, strand, and helix residues, respectively) between predicted and actual angles. Moreover, a real-value prediction of ϕ angle is not yet available. This article employs a neural-network based approach to improve ψ prediction by taking advantage of angle periodicity and apply the new method to the prediction to ϕ angles. The 10-fold-cross-validated mean absolute error for the new method is 38° (58°, 33°, 22° for coil, strand, and helix, respectively) for ψ and 25° (35°, 22°, 16° for coil, strand, and helix, respectively) for ϕ. The accuracy of real-value prediction is comparable to or more accurate than the predictions based on multistate classification of the ϕ−ψ map. More accurate prediction of real-value angles will likely be useful for improving the accuracy of fold recognition and ab initio protein-structure prediction. The Real-SPINE 2.0 server is available on the website . Proteins 2008. © 2008 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

How to make an objective assignment of secondary structures based on a protein structure is an unsolved problem. Defining the boundaries between helix, sheet, and coil structures is arbitrary, and commonly accepted standard assignments do not exist. Here, we propose a criterion that assesses secondary structure assignment based on the similarity of the secondary structures assigned to pairwise sequence-alignment benchmarks, where these benchmarks are determined by prior structural alignments of the protein pairs. This criterion is used to rank six secondary structure assignment methods: STRIDE, DSSP, SECSTR, KAKSI, P-SEA, and SEGNO with three established sequence-alignment benchmarks (PREFAB, SABmark, and SALIGN). STRIDE and KAKSI achieve comparable success rates in assigning the same secondary structure elements to structurally aligned residues in the three benchmarks. Their success rates are between 1-4% higher than those of the other four methods. The consensus of STRIDE, KAKSI, SECSTR, and P-SEA, called SKSP, improves assignments over the best single method in each benchmark by an additional 1%. These results support the usefulness of the sequence-alignment benchmarks as a means to evaluate secondary structure assignment. The SKSP server and the benchmarks can be accessed at Proteins 2008. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Near-native selections from docking decoys have proved challenging especially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked structures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein-protein docking selection by introducing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys. Proteins 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

In this article, we perform a dynamic Monte Carlo simulation study of the helix-coil transition by using a bond-fluctuation lattice model. The results of the simulations are compared with those predicted by the Zimm-Bragg statistical thermodynamic theory with propagation and nucleation parameters determined from simulation data. The Zimm-Bragg theory provides a satisfactory description of the helix-coil transition of a homopolypeptide chain of 32 residues ( N = 32). For such a medium-length chain, however, the analytical equation based on a widely-used large- N approximation to the Zimm-Bragg theory is not suitable to predict the average length of helical blocks at low temperatures when helicity is high. We propose an analytical large-eigenvalue (λ) approximation. The new equation yields a significantly improved agreement on the average helix-block length with the original Zimm-Bragg theory for both medium and long chain lengths in the entire temperature range. Nevertheless, even the original Zimm-Bragg theory does not provide an accurate description of helix-coil transition for longer chains. We assume that the single-residue nucleation of helix formation as suggested in the original Zimm-Bragg model might be responsible for this deviation. A mechanism of nucleation by a short helical block is proposed by us and provides a significantly improved agreement with our simulation data. Proteins 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Recognizing the structural similarity without significant sequence identity (called fold recognition) is the key for bridging the gap between the number of known protein sequences and the number of structures solved. Previously, we developed a fold-recognition method called SP3 which combines sequence-derived sequence profiles, secondary-structure profiles and residue-depth dependent, structure-derived sequence profiles. The use of residue-depth-dependent profiles makes SP3 one of the best automatic predictors in CASP 6. Because residue depth (RD) and solvent accessible surface area (solvent accessibility) are complementary in describing the exposure of a residue to solvent, we test whether or not incorporation of solvent-accessibility profiles into SP3 could further increase the accuracy of fold recognition. The resulting method, called SP4, was tested in SALIGN benchmark for alignment accuracy and Lindahl, LiveBench 8 and CASP7 blind prediction for fold recognition sensitivity and model-structure accuracy. For remote homologs, SP4 is found to consistently improve over SP3 in the accuracy of sequence alignment and predicted structural models as well as in the sensitivity of fold recognition. Our result suggests that RD and solvent accessibility can be used concurrently for improving the accuracy and sensitivity of fold recognition. The SP4 server and its local usage package are available on . Proteins 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Proteins can move freely in three-dimensional space. As a result, their structural properties, such as solvent accessible surface area, backbone dihedral angles, and atomic distances, are continuous variables. However, these properties are often arbitrarily divided into a few classes to facilitate prediction by statistical learning techniques. In this work, we establish an integrated system of neural networks (called Real-SPINE) for real-value prediction and apply the method to predict residue-solvent accessibility and backbone ψ dihedral angles of proteins based on information derived from sequences only. Real-SPINE is trained with a large data set of 2640 protein chains, sequence profiles generated from multiple sequence alignment, representative amino-acid properties, a slow learning rate, overfitting protection, and predicted secondary structures. The method optimizes more than 200,000 weights and yields a 10-fold cross-validated Pearson's correlation coefficient (PCC) of 0.74 between predicted and actual solvent accessible surface areas and 0.62 between predicted and actual ψ angles. In particular, 90% of 2640 proteins have a PCC value greater than 0.6 between predicted and actual solvent-accessible surface areas. The results of Real-SPINE can be compared with the best reported correlation coefficients of 0.64-0.67 for solvent-accessible surface areas and 0.47 for ψ angles. The real-SPINE server, executable programs, and datasets are freely available on . Proteins 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

An integrated system of neural networks, called SPINE, is established and optimized for predicting structural properties of proteins. SPINE is applied to three-state secondary-structure and residue-solvent-accessibility (RSA) prediction in this paper. The integrated neural networks are carefully trained with a large dataset of 2640 chains, sequence profiles generated from multiple sequence alignment, representative amino acid properties, a slow learning rate, overfitting protection, and an optimized sliding-widow size. More than 200,000 weights in SPINE are optimized by maximizing the accuracy measured by Q3 (the percentage of correctly classified residues). SPINE yields a 10-fold cross-validated accuracy of 79.5% (80.0% for chains of length between 50 and 300) in secondary-structure prediction after one-month (CPU time) training on 22 processors. An accuracy of 87.5% is achieved for exposed residues (RSA >95%). The latter approaches the theoretical upper limit of 88-90% accuracy in assigning secondary structures. An accuracy of 73% for three-state solvent-accessibility prediction (25%/75% cutoff) and 79.3% for two-state prediction (25% cutoff) is also obtained. Proteins 2007. © 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Solvent accessibility, one of the key properties of amino acid residues in proteins, can be used to assist protein structure prediction. Various approaches such as neural network, support vector machines, probability profiles, information theory, Bayesian theory, logistic function, and multiple linear regression have been developed for solvent accessibility prediction. In this article, a much simpler quadratic programming method based on the buriability parameter set of amino acid residues is developed. The new method, called QBES (Quadratic programming and Buriability Energy function for Solvent accessibility prediction), is reasonably accurate for predicting the real value of solvent accessibility. By using a dataset of 30 proteins to optimize three parameters, the average correlation coefficients between the predicted and actual solvent accessibility are about 0.5 for all four independent test sets ranging from 126 to 513 proteins. The method is efficient. It takes only 20 min for a regular PC to obtain results of 30 proteins with an average length of 263 amino acids. Although the proposed method is less accurate than a few more sophisticated methods based on neural network or support vector machines, this is the first attempt to predict solvent accessibility by energy optimization with constraints. Possible improvements and other applications of the method are discussed. Proteins 2006. © 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

We entered the CAPRI experiment during the middle of Round 4 and have submitted predictions for all 6 targets released since then. We used the following procedures for docking prediction: (1) the identification of possible binding region(s) of a target based on known biological information, (2) rigid-body sampling around the binding region(s) by using the docking program ZDOCK, (3) ranking of the sampled complex conformations by employing the DFIRE-based statistical energy function, (4) clustering based on pairwise root-mean-square distance and the DFIRE energy, and (5) manual inspection and relaxation of the side-chain conformations of the top-ranked structures by geometric constraint. Reasonable predictions were made for 4 of the 6 targets. The best fraction of native contacts within the top 10 models are 89.1% for Target 12, 54.3% for Target 13, 29.3% for Target 14, and 94.1% for Target 18. The origin of successes and failures is discussed. Proteins 2005;60:314-318. © 2005 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Two single-method servers, SPARKS 2 and SP3, participated in automatic-server predictions in CASP6. The overall results for all as well as detailed performance in comparative modeling targets are presented. It is shown that both SPARKS 2 and SP3 are able to recognize their corresponding best templates for all easy comparative modeling targets. The alignment accuracy, however, is not always the best among all the servers. Possible factors are discussed. SPARKS 2 and SP3 fold recognition servers, as well as their executables, are freely available for all academic users on . Proteins 2005;Suppl 7:152-156. © 2005 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence-based and structure-based methods as well as their combinations have been developed. Here, we propose a fold-recognition method that incorporates structural information without the need of sequence-to-structure threading. This is accomplished by generating sequence profiles from protein structural fragments. The structure-derived sequence profiles allow a simple integration with evolution-derived sequence profiles and secondary-structural information for an optimized alignment by efficient dynamic programming. The resulting method (called SP3) is found to make a statistically significant improvement in both sensitivity of fold recognition and accuracy of alignment over the method based on evolution-derived sequence profiles alone (SP) and the method based on evolution-derived sequence profile and secondary structure profile (SP2). SP3 was tested in SALIGN benchmark for alignment accuracy and Lindahl, PROSPECTOR 3.0, and LiveBench 8.0 benchmarks for remote-homology detection and model accuracy. SP3 is found to be the most sensitive and accurate single-method server in all benchmarks tested where other methods are available for comparison (although its results are statistically indistinguishable from the next best in some cases and the comparison is subjected to the limitation of time-dependent sequence and/or structural library used by different methods.). In LiveBench 8.0, its accuracy rivals some of the consensus methods such as ShotGun-INBGU, Pmodeller3, Pcons4, and ROBETTA. SP3 fold-recognition server is available on . Proteins 2005. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Extracting knowledge-based statistical potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified statistical potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based potential is more accurate than an existing all-atom knowledge-based potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody-antigen and other complexes based on Z score). In addition, the monomer potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the potential provides an accurate prediction of binding free energy of protein-peptide and protein-protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein-protein complexes, may be found at . Proteins 2004. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

An elaborate knowledge-based energy function is designed for fold recognition. It is a residue-level single-body potential so that highly efficient dynamic programming method can be used for alignment optimization. It contains a backbone torsion term, a buried surface term, and a contact-energy term. The energy score combined with sequence profile and secondary structure information leads to an algorithm called SPARKS (Sequence, secondary structure Profiles and Residue-level Knowledge-based energy Score) for fold recognition. Compared with the popular PSI-BLAST, SPARKS is 21% more accurate in sequence-sequence alignment in ProSup benchmark and 10%, 25%, and 20% more sensitive in detecting the family, superfamily, fold similarities in the Lindahl benchmark, respectively. Moreover, it is one of the best methods for sensitivity (the number of correctly recognized proteins), alignment accuracy (based on the MaxSub score), and specificity (the average number of correctly recognized proteins whose scores are higher than the first false positives) in LiveBench 7 among more than twenty servers of non-consensus methods. The simple algorithm used in SPARKS has the potential for further improvement. This highly efficient method can be used for fold recognition on genomic scales. A web server is established for academic users on . Proteins 2004. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

We describe the modeling method for RNA tertiary structures employed by team AIchemy_RNA2 in the 15th Critical Assessment of Structure Prediction (CASP15). The method consists of the following steps. Firstly, secondary structure information was derived from various manually-verified sources. With this information, the full length RNA was fragmented into structural modules. The structures of each module were predicted and then assembled into the full structure. To reduce the searching conformational space, an RNA structure was organized into an optimal base folding tree. And to further improve the sampling efficiency, the energy surface was smoothed at high temperatures during the Monte Carlo sampling to make it easier to move across the energy barrier. The statistical potential energy function BRiQ was employed during Monte Carlo energy optimization., (© 2023 Wiley Periodicals LLC.)

Two single-method servers, SPARKS 2 and SP3, participated in automatic-server predictions in CASP6. The overall results for all as well as detailed performance in comparative modeling targets are presented. It is shown that both SPARKS 2 and SP3 are able to recognize their corresponding best templates for all easy comparative modeling targets. The alignment accuracy, however, is not always the best among all the servers. Possible factors are discussed. SPARKS 2 and SP3 fold recognition servers, as well as their executables, are freely available for all academic users on http://theory.med.buffalo.edu., (2005 Wiley-Liss, Inc.)

The average contribution of individual residue to folding stability and its dependence on buried accessible surface area (ASA) are obtained by two different approaches. One is based on experimental mutation data, and the other uses a new knowledge-based atom-atom potential of mean force. We show that the contribution of a residue has a significant correlation with buried ASA and the regression slopes of 20 amino acid residues (called the buriability) are all positive (pro-burial). The buriability parameter provides a quantitative measure of the driving force for the burial of a residue. The large buriability gap observed between hydrophobic and hydrophilic residues is responsible for the burial of hydrophobic residues in soluble proteins. Possible factors that contribute to the buriability gap are discussed., (Copyright 2003 Wiley-Liss, Inc.)

Predicting the folding mechanism of the second beta-hairpin fragment of the Ig-binding domain B of streptococcal protein G is unexpectedly challenging for simplified reduced models because the models developed so far indicated a different folding mechanism from what was suggested from high-temperature unfolding and equilibrium free-energy surface analysis based on established all-atom empirical force fields in explicit or implicit solvent. This happened despite the use of empirical residue-based interactions, multibody hydrophobic interactions, and inclusions of hydrogen bonding effects in the simplified models. This article employs a recently developed all-atom (except nonpolar hydrogens) model interacting with simple square-well potentials to fold the peptide fragment by molecular dynamics simulation methods. In this study, 193 out of 200 trajectories are folded at two reduced temperatures (3.5 and 3.7) close to the transition temperature T* approximately 4.0. Each simulation takes <7 h of CPU time on a Pentium 800-MHz PC. Folding of the new all-atom model is found to be initiated by collapse before the formation of main-chain hydrogen bonds. This verifies the mechanism proposed from previous all-atom unfolding and equilibrium simulations. The new model further predicts that the collapse is initiated by two nucleation contacts (a hydrophilic contact between D46 and T49 and a hydrophobic contact between Y45 and F52), in agreement with recent NMR measurements. The results suggest that atomic packing and native contact interactions play a dominant role in folding mechanism., (Copyright 2002 Wiley-Liss, Inc.)