Protein-RNA　docking　is　still　an　open　question.　One　of　the　main　challenges　is　to　develop　an　effective　scoring　function　that　can　discriminate　near-native　structures　from　the　incorrect　ones.　To　solve　the　problem,　we　have　constructed　a　knowledge-based　residue-nucleotide　pairwise　potential　with　secondary　structure　information　considered　for　nonribosomal　protein-RNA　docking.　Here　we　developed　a　weighted　combined　scoring　function　RpveScore　that　consists　of　the　pairwise　potential　and　six　physics-based　energy　terms.　The　weights　were　optimized　using　the　multiple　linear　regression　method　by　fitting　the　scoring　function　to　L_rmsd　for　the　bound　docking　decoys　from　Benchmark　II.　The　scoring　functions　were　tested　on　35　unbound　docking　cases.　The　results　show　that　the　scoring　function　RpveScore　including　all　terms　performs　best.　Also　RpveScore　was　compared　with　the　statistical　mechanics-based　method　derived　potential　ITScore-PR,　and　the　united　atom-based　statistical　potentials　QUASI-RNP　and　DARS-RNP.　The　success　rate　of　RpveScore　is　71.6%　for　the　top　1000　structures　and　the　number　of　cases　where　a　near-native　structure　is　ranked　in　top　30　is　25　out　of　35　cases.　For　32　systems　(91.4%),　RpveScore　can　find　the　binding　mode　in　top　5　that　has　no　lower　than　50%　native　interface　residues　on　protein　and　nucleotides　on　RNA.　Additionally,　it　was　found　that　the　long-range　electrostatic　attractive　energy　plays　an　important　role　in　distinguishing　near-native　structures　from　the　incorrect　ones.　This　work　can　be　helpful　for　the　development　of　protein-RNA　docking　methods　and　for　the　understanding　of　protein-RNA　interactions.　(C)　2017　Wiley　Periodicals,　Inc.